feat(ag2): add AG2 framework backend integration (#1143)

feat(ag2): add AG2 framework backend integration

.github/actions/claude-code-action/Dockerfile

@@ -0,0 +1,9 @@
+FROM node:20-alpine
+
+RUN npm install -g @anthropic-ai/claude-code
+
+WORKDIR /app
+COPY entrypoint.sh .
+RUN chmod +x entrypoint.sh
+
+ENTRYPOINT ["/app/entrypoint.sh"] 

.github/actions/claude-code-action/action.yml

@@ -0,0 +1,20 @@
+name: "Claude Code Action"
+description: "Custom Claude Code runner for PraisonAI"
+
+inputs:
+  anthropic_api_key:
+    required: true
+    description: "Anthropic API key"
+  github_token:
+    required: true
+    description: "GitHub token for repo access"
+
+runs:
+  using: "docker"
+  image: "ghcr.io/mervinpraison/praisonai-claudecode:latest"
+  env:
+    ANTHROPIC_API_KEY: ${{ inputs.anthropic_api_key }}
+    GITHUB_TOKEN: ${{ inputs.github_token }}
+  args:
+    - "--anthropic-api-key=${{ inputs.anthropic_api_key }}"
+    - "--github-token=${{ inputs.github_token }}" 

.github/actions/claude-code-action/entrypoint.sh

@@ -0,0 +1,21 @@
+#!/bin/sh
+
+set -e
+
+echo "Running Claude Code in CI mode..."
+
+# Extract GitHub context and create a smart prompt
+PROMPT="Analyse the GitHub issue or PR context and generate a smart response based on the repository context."
+
+# Set environment variables from arguments
+export ANTHROPIC_API_KEY="${1#--anthropic-api-key=}"
+export GITHUB_TOKEN="${2#--github-token=}"
+
+# Verify environment variables
+if [ -z "$ANTHROPIC_API_KEY" ] || [ -z "$GITHUB_TOKEN" ]; then
+    echo "Error: Required environment variables are not set"
+    exit 1
+fi
+
+# Run Claude with the prompt
+claude -p "$PROMPT" 

.github/actions/claude-code/action.yml

@@ -0,0 +1,117 @@
+name: "Claude Code Action"
+description: "Run Claude Code in GitHub Actions workflows"
+
+inputs:
+  github_token:
+    description: "GitHub token with repo and issues permissions"
+    required: true
+  anthropic_api_key:
+    description: "Anthropic API key"
+    required: true
+  prompt:
+    description: "The prompt to send to Claude Code"
+    required: false
+    default: ""
+  prompt_file:
+    description: "Path to a file containing the prompt to send to Claude Code"
+    required: false
+    default: ""
+  allowed_tools:
+    description: "Comma-separated list of allowed tools for Claude Code to use"
+    required: false
+    default: ""
+  output_file:
+    description: "File to save Claude Code output to (optional)"
+    required: false
+    default: ""
+  timeout_minutes:
+    description: "Timeout in minutes for Claude Code execution"
+    required: false
+    default: "10"
+  install_github_mcp:
+    description: "Whether to install the GitHub MCP server"
+    required: false
+    default: "false"
+
+runs:
+  using: "composite"
+  steps:
+    - name: Install Claude Code
+      shell: bash
+      run: npm install -g @anthropic-ai/claude-code
+
+    - name: Install GitHub MCP Server
+      if: inputs.install_github_mcp == 'true'
+      shell: bash
+      run: |
+        claude mcp add-json github '{
+          "command": "docker",
+          "args": [
+            "run",
+            "-i",
+            "--rm",
+            "-e",
+            "GITHUB_PERSONAL_ACCESS_TOKEN",
+            "ghcr.io/github/github-mcp-server:sha-ff3036d"
+          ],
+          "env": {
+            "GITHUB_PERSONAL_ACCESS_TOKEN": "${{ inputs.GITHUB_TOKEN }}"
+          }
+        }'
+
+    - name: Prepare Prompt File
+      shell: bash
+      id: prepare_prompt
+      run: |
+        if [ -z "${{ inputs.prompt }}" ] && [ -z "${{ inputs.prompt_file }}" ]; then
+          echo "::error::Neither 'prompt' nor 'prompt_file' was provided. At least one is required."
+          exit 1
+        fi
+
+        if [ ! -z "${{ inputs.prompt_file }}" ]; then
+          if [ ! -f "${{ inputs.prompt_file }}" ]; then
+            echo "::error::Prompt file '${{ inputs.prompt_file }}' does not exist."
+            exit 1
+          fi
+          PROMPT_PATH="${{ inputs.prompt_file }}"
+        else
+          mkdir -p /tmp/claude-action
+          PROMPT_PATH="/tmp/claude-action/prompt.txt"
+          echo "${{ inputs.prompt }}" > "$PROMPT_PATH"
+        fi
+
+        if [ ! -s "$PROMPT_PATH" ]; then
+          echo "::error::Prompt is empty. Please provide a non-empty prompt."
+          exit 1
+        fi
+
+        echo "PROMPT_PATH=$PROMPT_PATH" >> $GITHUB_ENV
+
+    - name: Run Claude Code
+      shell: bash
+      id: run_claude
+      run: |
+        timeout_seconds=$((${{ inputs.timeout_minutes }} * 60))
+
+        if [ -z "${{ inputs.output_file }}" ]; then
+          timeout $timeout_seconds claude \
+            -p \
+            --verbose \
+            --output-format stream-json \
+            "$(cat ${{ env.PROMPT_PATH }})" \
+            ${{ inputs.allowed_tools != '' && format('--allowedTools "{0}"', inputs.allowed_tools) || '' }}
+        else
+          timeout $timeout_seconds claude \
+            -p \
+            --verbose \
+            --output-format stream-json \
+            "$(cat ${{ env.PROMPT_PATH }})" \
+            ${{ inputs.allowed_tools != '' && format('--allowedTools "{0}"', inputs.allowed_tools) || '' }} | tee output.txt
+
+          jq -s '.' output.txt > output.json
+          jq -r '.[-1].result' output.json > "${{ inputs.output_file }}"
+          echo "Complete output saved to output.json, final response saved to ${{ inputs.output_file }}"
+        fi
+      env:
+        ANTHROPIC_API_KEY: ${{ inputs.anthropic_api_key }}
+        GITHUB_TOKEN: ${{ inputs.github_token }}

.github/workflows/build-image.yml

@@ -0,0 +1,39 @@
+name: Build Claude Code Image
+
+on:
+  workflow_dispatch:
+    inputs:
+      version:
+        description: 'Image version tag'
+        required: true
+        default: 'latest'
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v5
+        with:
+          context: ./.github/actions/claude-code-action
+          push: true
+          tags: ghcr.io/mervinpraison/praisonai-claudecode:${{ inputs.version }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          provenance: false 

.github/workflows/claude.yml

@@ -23,15 +23,60 @@ jobs:
       pull-requests: read
       issues: read
       id-token: write
+      packages: read
+    env:
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
         with:
           fetch-depth: 1
 
+      - name: Debug Environment
+        run: |
+          echo "=== Environment Debug ==="
+          echo "GITHUB_ACTOR: $GITHUB_ACTOR"
+          echo "GITHUB_REPOSITORY: $GITHUB_REPOSITORY"
+          echo "GITHUB_REPOSITORY_OWNER: $GITHUB_REPOSITORY_OWNER"
+          echo "GITHUB_TOKEN exists: $([ -n "$GITHUB_TOKEN" ] && echo "yes" || echo "no")"
+          echo "GITHUB_TOKEN length: ${#GITHUB_TOKEN}"
+
+      - name: Check Package Visibility
+        run: |
+          echo "=== Checking Package Visibility ==="
+          curl -s -H "Authorization: Bearer $GITHUB_TOKEN" \
+               -H "Accept: application/vnd.github.v3+json" \
+               "https://api.github.com/users/MervinPraison/packages/container/praisonai-claudecode" \
+               || echo "Package API call failed"
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Test Docker Authentication
+        run: |
+          echo "=== Testing Docker Authentication ==="
+          echo "Checking ~/.docker/config.json"
+          cat ~/.docker/config.json | jq '.' || echo "No docker config found"
+          echo "=== Testing Registry Access ==="
+          curl -s -H "Authorization: Bearer $GITHUB_TOKEN" \
+               "https://ghcr.io/v2/" || echo "Registry access failed"
+
+      - name: Test Docker Pull
+        run: |
+          echo "=== Testing Docker Pull ==="
+          echo "Attempting to pull image: ghcr.io/mervinpraison/praisonai-claudecode:latest"
+          docker pull ghcr.io/mervinpraison/praisonai-claudecode:latest || echo "Pull failed with exit code: $?"
+          echo "=== Checking Docker Images ==="
+          docker images | grep praisonai-claudecode || echo "No images found"
+
       - name: Run Claude Code
         id: claude
-        uses: anthropics/claude-code-action@beta
+        uses: ./.github/actions/claude-code-action
         with:
           anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+          github_token: ${{ secrets.GITHUB_TOKEN }}
 

.github/workflows/coverage.yml

@@ -0,0 +1,60 @@
+name: Run tests and upload coverage
+
+on: 
+  push:
+    branches: [ main, develop ]
+  pull_request:
+    branches: [ main, develop ]
+
+jobs:
+  test:
+    name: Run tests and collect coverage
+    runs-on: ubuntu-latest
+    
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 2
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.11
+
+      - name: Install UV
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+      - name: Install dependencies
+        run: |
+          cd src/praisonai
+          uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+          uv pip install --system duckduckgo_search
+          uv pip install --system pytest pytest-cov pytest-asyncio
+          # Install knowledge dependencies from praisonai-agents
+          uv pip install --system "praisonaiagents[knowledge]"
+
+      - name: Set environment variables
+        run: |
+          echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+          echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+          echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+          echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
+          echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
+
+      - name: Run tests
+        run: |
+          cd src/praisonai
+          pytest --cov=praisonai --cov-branch --cov-report=xml tests/unit/
+
+      - name: Upload results to Codecov
+        uses: codecov/codecov-action@v5
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          file: src/praisonai/coverage.xml
+          flags: unit-tests
+          name: unit-tests-coverage
+          fail_ci_if_error: false
+          verbose: true 

.github/workflows/python-package.yml

@@ -40,6 +40,8 @@ jobs:
         python -m pip install ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         python -m pip install duckduckgo_search
         python -m pip install pytest-asyncio pytest-cov
+        # Install knowledge dependencies from praisonai-agents
+        python -m pip install "praisonaiagents[knowledge]"
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
         
     - name: Debug API Key Status
@@ -63,4 +65,4 @@ jobs:
     #     flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
-        cd src/praisonai && python -m pytest
+        cd src/praisonai && python tests/test_runner.py --pattern fast

.github/workflows/test-comprehensive.yml

@@ -51,6 +51,8 @@ jobs:
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system duckduckgo_search
         uv pip install --system pytest pytest-asyncio pytest-cov pytest-benchmark
+        # Install knowledge dependencies from praisonai-agents
+        uv pip install --system "praisonaiagents[knowledge]"
 
     - name: Set environment variables
       run: |
@@ -67,10 +69,10 @@ jobs:
         
         case $TEST_TYPE in
           "unit")
-            cd src/praisonai && python tests/test_runner.py --pattern unit
+            cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=xml --cov-branch
             ;;
           "integration")
-            cd src/praisonai && python tests/test_runner.py --pattern integration
+            cd src/praisonai && python -m pytest tests/integration/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=xml --cov-branch
             ;;
           "fast")
             cd src/praisonai && python tests/test_runner.py --pattern fast
@@ -137,6 +139,16 @@ jobs:
         echo "- 🔌 MCP server connections" >> comprehensive_report.md
         echo "- 💬 LLM integrations (OpenAI, Anthropic, etc.)" >> comprehensive_report.md
 
+    - name: Upload coverage reports to Codecov
+      uses: codecov/codecov-action@v5
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: src/praisonai/coverage.xml
+        flags: comprehensive-tests
+        name: comprehensive-tests-coverage
+        fail_ci_if_error: false
+        verbose: true
+
     - name: Upload Comprehensive Test Results
       uses: actions/upload-artifact@v4
       if: always()
@@ -144,9 +156,9 @@ jobs:
         name: comprehensive-test-results-python-${{ matrix.python-version }}
         path: |
           comprehensive_report.md
-          htmlcov/
-          coverage.xml
-          .coverage
+          src/praisonai/htmlcov/
+          src/praisonai/coverage.xml
+          src/praisonai/.coverage
         retention-days: 30
 
   test-matrix-summary:

.github/workflows/test-core.yml

@@ -34,6 +34,8 @@ jobs:
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system duckduckgo_search
         uv pip install --system pytest pytest-asyncio pytest-cov
+        # Install knowledge dependencies from praisonai-agents
+        uv pip install --system "praisonaiagents[knowledge]"
 
     - name: Set environment variables
       run: |
@@ -193,7 +195,7 @@ jobs:
 
     - name: Run Unit Tests
       run: |
-        cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=term-missing
+        cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=term-missing --cov-report=xml --cov-branch
 
     - name: Run Integration Tests  
       run: |
@@ -261,12 +263,24 @@ jobs:
         export OPENAI_MODEL_NAME="$OPENAI_MODEL_NAME"
         cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
 
-    - name: Upload Coverage Reports
+    - name: Upload coverage reports to Codecov
+      if: matrix.python-version == '3.11'
+      uses: codecov/codecov-action@v5
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: src/praisonai/coverage.xml
+        flags: core-tests
+        name: core-tests-coverage
+        fail_ci_if_error: false
+        verbose: true
+
+    - name: Upload Coverage Reports (Artifacts)
       if: matrix.python-version == '3.11'
       uses: actions/upload-artifact@v4
       with:
         name: coverage-reports
         path: |
-          .coverage
-          htmlcov/
+          src/praisonai/.coverage
+          src/praisonai/htmlcov/
+          src/praisonai/coverage.xml
         retention-days: 7

.github/workflows/test-extended.yml

@@ -34,6 +34,8 @@ jobs:
         cd src/praisonai
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system duckduckgo_search
+        # Install knowledge dependencies from praisonai-agents
+        uv pip install --system "praisonaiagents[knowledge]"
 
     - name: Set environment variables
       run: |
@@ -92,6 +94,8 @@ jobs:
         cd src/praisonai
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system pytest pytest-benchmark
+        # Install knowledge dependencies from praisonai-agents
+        uv pip install --system "praisonaiagents[knowledge]"
 
     - name: Run Performance Benchmarks
       run: |

.github/workflows/test-real.yml

@@ -58,6 +58,8 @@ jobs:
         cd src/praisonai
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system pytest pytest-asyncio pytest-cov
+        # Install knowledge dependencies from praisonai-agents
+        uv pip install --system "praisonaiagents[knowledge]"
 
     - name: Set environment variables
       run: |

.github/workflows/unittest.yml

@@ -27,6 +27,8 @@ jobs:
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system duckduckgo_search
         uv pip install --system pytest pytest-asyncio pytest-cov
+        # Install knowledge dependencies from praisonai-agents
+        uv pip install --system "praisonaiagents[knowledge]"
 
     - name: Set environment variables
       run: |
@@ -469,8 +471,8 @@ jobs:
 
     - name: Run Fast Tests
       run: |
-        # Run the fastest, most essential tests
-        cd src/praisonai && python tests/test_runner.py --pattern fast
+        # Run the fastest, most essential tests with coverage
+        cd src/praisonai && python -m pytest tests/unit/test_core_agents.py -v --tb=short --disable-warnings --cov=praisonai --cov-report=xml --cov-branch
 
     - name: Run Real API Tests
       run: |
@@ -489,6 +491,16 @@ jobs:
         cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
       continue-on-error: true
 
+    - name: Upload coverage reports to Codecov
+      uses: codecov/codecov-action@v5
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: src/praisonai/coverage.xml
+        flags: quick-validation
+        name: quick-validation-coverage
+        fail_ci_if_error: false
+        verbose: true
+
     - name: Restore Root Config Files
       run: |
         echo "🔄 Restoring root configuration files..."

.gitignore

@@ -74,4 +74,5 @@ agents/praisonaiagents/praisonaiagents.egg-info
 .praison
 # Local Netlify folder
 .netlify
-.qodo
+.qodo
+CopilotKit*

docker/Dockerfile

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.2.22" \
+    "praisonai>=2.2.33" \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.22" \
+    "praisonai>=2.2.33" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.22" \
+    "praisonai>=2.2.33" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.22" \
+    "praisonai>=2.2.33" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.22`
+- PraisonAI: `>=2.2.33`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`
 
@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.22" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.33" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

docker/docker-compose.dev.yml

@@ -129,12 +129,29 @@ services:
 
   # Standalone database for development
   database:
+    security_opt:
+      - "no-new-privileges:true"
     image: postgres:15-alpine
     environment:
       POSTGRES_DB: praisonai_dev
       POSTGRES_USER: praisonai
       POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-praisonai_dev_pass}
     volumes:
+   database:
+     image: postgres:15-alpine
+     environment:
+       POSTGRES_DB: praisonai_dev
+       POSTGRES_USER: praisonai
+       POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-praisonai_dev_pass}
+     volumes:
+       - postgres_data:/var/lib/postgresql/data
+     read_only: true
+     tmpfs:
+       - /tmp
+       - /var/tmp
+     ports:
+       - "5432:5432"
+     networks:
       - postgres_data:/var/lib/postgresql/data
     ports:
       - "5432:5432"
@@ -181,4 +198,4 @@ volumes:
 
 networks:
   praison_network:
-    driver: bridge
+    driver: bridge

docs/.readthedocs.yml

@@ -1,23 +0,0 @@
-# .readthedocs.yaml
-# Read the Docs configuration file
-# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-# Required
-version: 2
-
-# Set the OS, Python version and other tools you might need
-build:
-  os: ubuntu-22.04
-  tools:
-    python: "3.12"
-  jobs:
-    post_install:
-    - bash docs/generate_docs.sh
-
-mkdocs:
-  configuration: mkdocs.yml
-  fail_on_warning: true
-
-python:
-  install:
-    - requirements: docs/requirements.txt

docs/CNAME

@@ -1 +0,0 @@
-docs.praison.ai

docs/Makefile

@@ -1,20 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = .
-BUILDDIR      = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/agents/agents.mdx

@@ -1,124 +0,0 @@
----
-title: "AI Agents"
-description: "Overview of all available PraisonAI agents and their capabilities"
-icon: "robot"
----
-
-PraisonAI provides a diverse set of specialized agents for various tasks. Each agent is designed with specific capabilities and tools to handle different types of tasks effectively.
-
-```mermaid
-graph LR
-    %% Define the main flow
-    Start([▶ Start]) --> Agent1
-    Agent1 --> Process[⚙ Process]
-    Process --> Agent2
-    Agent2 --> Output([✓ Output])
-    Process -.-> Agent1
-    
-    %% Define subgraphs for agents and their tasks
-    subgraph Agent1[ ]
-        Task1[📋 Task]
-        AgentIcon1[🤖 AI Agent]
-        Tools1[🔧 Tools]
-        
-        Task1 --- AgentIcon1
-        AgentIcon1 --- Tools1
-    end
-    
-    subgraph Agent2[ ]
-        Task2[📋 Task]
-        AgentIcon2[🤖 AI Agent]
-        Tools2[🔧 Tools]
-        
-        Task2 --- AgentIcon2
-        AgentIcon2 --- Tools2
-    end
-
-    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
-    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
-    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
-    classDef transparent fill:none,stroke:none
-
-    class Start,Output,Task1,Task2 input
-    class Process,AgentIcon1,AgentIcon2 process
-    class Tools1,Tools2 tools
-    class Agent1,Agent2 transparent
-```
-
-## Data & Analysis
-
-<CardGroup cols={2}>
-  <Card title="Data Analyst" icon="chart-line" href="/agents/data-analyst">
-    Analyze data from various sources, create visualizations, and generate insights.
-  </Card>
-  <Card title="Finance" icon="money-bill-trend-up" href="/agents/finance">
-    Track stocks, analyze financial data, and provide investment recommendations.
-  </Card>
-  <Card title="Research" icon="magnifying-glass-chart" href="/agents/research">
-    Conduct comprehensive research and analysis across various topics.
-  </Card>
-  <Card title="Wikipedia" icon="book" href="/agents/wikipedia">
-    Search and extract information from Wikipedia articles.
-  </Card>
-</CardGroup>
-
-## Media & Content
-
-<CardGroup cols={2}>
-  <Card title="Image Analysis" icon="image" href="/agents/image">
-    Analyze and understand visual content from images.
-  </Card>
-  <Card title="Image to Text" icon="text" href="/agents/image-to-text">
-    Convert images to textual descriptions and extract text content.
-  </Card>
-  <Card title="Video" icon="video" href="/agents/video">
-    Analyze video content and extract meaningful information.
-  </Card>
-  <Card title="Markdown" icon="markdown" href="/agents/markdown">
-    Generate and format content in Markdown syntax.
-  </Card>
-</CardGroup>
-
-## Search & Recommendations
-
-<CardGroup cols={2}>
-  <Card title="Web Search" icon="globe" href="/agents/websearch">
-    Perform intelligent web searches and gather information.
-  </Card>
-  <Card title="Recommendation" icon="thumbs-up" href="/agents/recommendation">
-    Generate personalized recommendations based on preferences.
-  </Card>
-  <Card title="Shopping" icon="shop" href="/agents/shopping">
-    Compare prices and find the best deals across stores.
-  </Card>
-  <Card title="Planning" icon="calendar" href="/agents/planning">
-    Create travel plans and detailed itineraries.
-  </Card>
-</CardGroup>
-
-## Development
-
-<CardGroup cols={2}>
-  <Card title="Programming" icon="code" href="/agents/programming">
-    Write, analyze, and debug code across multiple languages.
-  </Card>
-  <Card title="Single Agent" icon="circle-1" href="/agents/single">
-    Simple, focused agent for basic tasks without external tools.
-  </Card>
-</CardGroup>
-
-## Getting Started
-
-Each agent can be easily initialized and customized for your specific needs. Here's a basic example:
-
-```python
-from praisonaiagents import Agent
-
-# Create an agent with specific instructions
-agent = Agent(instructions="Your task-specific instructions")
-
-# Start the agent with a task
-response = agent.start("Your task description")
-```
-
-For more detailed information about each agent, click on the respective cards above.

docs/agents/data-analyst.mdx

@@ -1,120 +0,0 @@
----
-title: "Data Analyst Agent"
-sidebarTitle: "Data Analyst"
-description: "Learn how to create AI agents for data analysis and insights generation."
-icon: "chart-line"
----
-
-```mermaid
-flowchart LR
-    In[Data Source] --> Reader[Data Reader]
-    Reader --> Analyzer[Data Analyzer]
-    Analyzer --> Generator[Insights Generator]
-    Generator --> Out[Output]
-    
-    style In fill:#8B0000,color:#fff
-    style Reader fill:#2E8B57,color:#fff
-    style Analyzer fill:#2E8B57,color:#fff
-    style Generator fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Data Analyst Agent can read data from various sources, analyze it, and generate insights.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `data_analysis.py`:
-        ```python
-        from praisonaiagents import Agent, Tools
-        from praisonaiagents.tools import (
-            read_csv, read_excel, write_csv, write_excel,
-            filter_data, get_summary, group_by, pivot_table
-        )
-
-        agent = Agent(
-            instructions="You are a Data Analyst Agent",
-            tools=[
-                read_csv, read_excel, write_csv, write_excel,
-                filter_data, get_summary, group_by, pivot_table
-            ]
-        )
-
-        # Start analysis with a specific task
-        agent.start(f"""
-            Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
-            Analyse the data and give me the insights
-            read_csv to read the file
-        """)
-        ```
-    </Step>
-</Steps>
-
-## Understanding Data Analysis Workflow
-
-The Data Analyst Agent is designed to perform comprehensive data analysis tasks using a suite of specialized tools. Here's how it works:
-
-1. **Data Reading**: The agent can read data from various sources:
-   - CSV files using `read_csv`
-   - Excel files using `read_excel`
-
-2. **Data Analysis**: Multiple analysis tools are available:
-   - `filter_data`: Filter datasets based on conditions
-   - `get_summary`: Generate statistical summaries
-   - `group_by`: Group data by specific columns
-   - `pivot_table`: Create pivot tables for analysis
-
-3. **Data Export**: Results can be exported to:
-   - CSV format using `write_csv`
-   - Excel format using `write_excel`
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Multiple Data Sources" icon="table">
-    Support for both CSV and Excel file formats.
-  </Card>
-  <Card title="Analysis Tools" icon="calculator">
-    Comprehensive suite of analysis tools including filtering, summarization, and pivoting.
-  </Card>
-  <Card title="Data Export" icon="file-export">
-    Export capabilities to various formats.
-  </Card>
-  <Card title="Automated Insights" icon="lightbulb">
-    Automatic generation of data insights and patterns.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Analyzing stock data
-agent.start("""
-    1. Read 'stock_data.csv'
-    2. Filter data for the last 30 days
-    3. Calculate daily returns
-    4. Generate summary statistics
-    5. Export results to 'analysis_results.xlsx'
-""")
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex analysis workflows
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
-- Check out other specialized agents like the [Finance Agent](/agents/finance) for specific use cases

docs/agents/finance.mdx

@@ -1,97 +0,0 @@
----
-title: "Finance Agent"
-sidebarTitle: "Finance"
-description: "Learn how to create AI agents for financial analysis and investment recommendations."
-icon: "money-bill-trend-up"
----
-
-```mermaid
-flowchart LR
-    In[Request] --> Price[Stock Price]
-    Price --> Info[Stock Info]
-    Info --> History[Historical Data]
-    History --> Out[Investment Advice]
-    
-    style In fill:#8B0000,color:#fff
-    style Price fill:#2E8B57,color:#fff
-    style Info fill:#2E8B57,color:#fff
-    style History fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Finance Agent can analyze stock market data and provide investment recommendations.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `finance_analysis.py`:
-        ```python
-        from praisonaiagents import Agent, Tools
-        from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
-
-        agent = Agent(
-            instructions="You are a Research Agent",
-            tools=[get_stock_price, get_stock_info, get_historical_data]
-        )
-        agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")
-        ```
-    </Step>
-</Steps>
-
-## Understanding Financial Analysis
-
-The Finance Agent specializes in stock market analysis and investment recommendations using three key components:
-
-1. **Real-time Price Data**: Uses `get_stock_price` to fetch current market prices
-2. **Company Information**: Retrieves company details using `get_stock_info`
-3. **Historical Analysis**: Analyzes trends with `get_historical_data`
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Real-time Data" icon="clock">
-    Access to current stock market prices and information.
-  </Card>
-  <Card title="Company Analysis" icon="building">
-    Detailed company information and fundamentals.
-  </Card>
-  <Card title="Historical Trends" icon="chart-line">
-    Analysis of historical price movements and patterns.
-  </Card>
-  <Card title="Investment Advice" icon="money-bill">
-    AI-powered investment recommendations.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Analyzing specific stocks
-agent.start("""
-    1. Get current prices for AAPL and GOOGL
-    2. Retrieve company information
-    3. Analyze 6-month historical data
-    4. Provide investment recommendations
-""")
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex financial analysis
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
-- Check out the [Data Analyst Agent](/agents/data-analyst) for detailed data analysis capabilities

docs/agents/image-to-text.mdx

@@ -1,140 +0,0 @@
----
-title: "Image to Text Agent"
-sidebarTitle: "Image to Text"
-description: "Learn how to create AI agents for converting images to textual descriptions and extracting text from images."
-icon: "text"
----
-
-```mermaid
-flowchart LR
-    In[Image Input] --> OCR[Text Extractor]
-    OCR --> Analyzer[Content Analyzer]
-    Analyzer --> Generator[Description Generator]
-    Generator --> Out[Text Output]
-    
-    style In fill:#8B0000,color:#fff
-    style OCR fill:#2E8B57,color:#fff
-    style Analyzer fill:#2E8B57,color:#fff
-    style Generator fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Image-to-Text Agent can extract text from images and generate comprehensive descriptions.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `image_to_text.py`:
-        ```python
-        from praisonaiagents import Agent, Task, PraisonAIAgents
-
-        # Create Image-to-Text Agent
-        image_text_agent = Agent(
-            name="ImageTextConverter",
-            role="Image Text Extraction Specialist",
-            goal="Convert image content to textual descriptions and extract text",
-            backstory="""You are an expert in OCR and image understanding.
-            You excel at extracting text from images and generating detailed descriptions.""",
-            llm="gpt-4o-mini",
-            self_reflect=False
-        )
-
-        # Create text extraction task
-        extraction_task = Task(
-            name="extract_text",
-            description="Extract all text from this image and describe its layout.",
-            expected_output="Extracted text and layout description",
-            agent=image_text_agent,
-            images=["document.jpg"]
-        )
-
-        # Create description task
-        description_task = Task(
-            name="generate_description",
-            description="Generate a detailed description of the image content.",
-            expected_output="Comprehensive description of visual elements",
-            agent=image_text_agent,
-            images=["scene.jpg"]
-        )
-
-        # Create PraisonAIAgents instance
-        agents = PraisonAIAgents(
-            agents=[image_text_agent],
-            tasks=[extraction_task, description_task],
-            process="sequential",
-            verbose=1
-        )
-
-        # Run analysis
-        agents.start()
-        ```
-    </Step>
-</Steps>
-
-## Understanding Image-to-Text Conversion
-
-The Image-to-Text Agent combines multiple capabilities to convert visual content into textual form:
-
-1. **OCR Processing**: Extracts text from images using optical character recognition
-2. **Layout Analysis**: Understands the spatial arrangement of text and visual elements
-3. **Content Description**: Generates natural language descriptions of image content
-4. **Text Formatting**: Preserves text formatting and structure where possible
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Text Extraction" icon="text">
-    Advanced OCR capabilities for text extraction.
-  </Card>
-  <Card title="Layout Understanding" icon="table-layout">
-    Analysis of text and content layout.
-  </Card>
-  <Card title="Content Description" icon="pen-to-square">
-    Detailed descriptions of visual content.
-  </Card>
-  <Card title="Format Preservation" icon="paragraph">
-    Maintains text formatting and structure.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Processing a document image
-document_task = Task(
-    name="process_document",
-    description="Extract text and analyze document layout",
-    expected_output="Extracted text with layout information",
-    agent=image_text_agent,
-    images=["business_document.jpg"]
-)
-
-# Run single task
-agents = PraisonAIAgents(
-    agents=[image_text_agent],
-    tasks=[document_task],
-    process="sequential"
-)
-agents.start()
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex document processing
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving text extraction accuracy
-- Check out the [Image Agent](/agents/image) for pure image analysis capabilities

docs/agents/image.mdx

@@ -1,139 +0,0 @@
----
-title: "Image Analysis Agent"
-sidebarTitle: "Image Analysis"
-description: "Learn how to create AI agents for image analysis and visual content understanding."
-icon: "image"
----
-
-```mermaid
-flowchart LR
-    In[Image Input] --> Analyzer[Image Analyzer]
-    Analyzer --> Detector[Object Detector]
-    Detector --> Generator[Description Generator]
-    Generator --> Out[Analysis Output]
-    
-    style In fill:#8B0000,color:#fff
-    style Analyzer fill:#2E8B57,color:#fff
-    style Detector fill:#2E8B57,color:#fff
-    style Generator fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Image Analysis Agent can process and analyze visual content, detect objects, and generate detailed descriptions.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `image_analysis.py`:
-        ```python
-        from praisonaiagents import Agent, Task, PraisonAIAgents
-
-        # Create Image Analysis Agent
-        image_agent = Agent(
-            name="ImageAnalyst",
-            role="Image Analysis Specialist",
-            goal="Analyze images and videos to extract meaningful information",
-            backstory="""You are an expert in computer vision and image analysis.
-            You excel at describing images, detecting objects, and understanding visual content.""",
-            llm="gpt-4o-mini",
-            self_reflect=False
-        )
-
-        # Create tasks for different types of analysis
-        task1 = Task(
-            name="analyze_landmark",
-            description="Describe this famous landmark and its architectural features.",
-            expected_output="Detailed description of the landmark's architecture and significance",
-            agent=image_agent,
-            images=["https://example.com/landmark.jpg"]
-        )
-
-        task2 = Task(
-            name="analyze_local_image",
-            description="What objects can you see in this image? Describe their arrangement.",
-            expected_output="Detailed description of objects and their spatial relationships",
-            agent=image_agent,
-            images=["local_image.jpg"]
-        )
-
-        # Create PraisonAIAgents instance
-        agents = PraisonAIAgents(
-            agents=[image_agent],
-            tasks=[task1, task2],
-            process="sequential",
-            verbose=1
-        )
-
-        # Run analysis
-        agents.start()
-        ```
-    </Step>
-</Steps>
-
-## Understanding Image Analysis
-
-The Image Analysis Agent is designed to process and understand visual content through several key capabilities:
-
-1. **Image Input**: Supports both URL and local file inputs
-2. **Object Detection**: Identifies and locates objects within images
-3. **Architectural Analysis**: Specialized in analyzing architectural features
-4. **Spatial Understanding**: Describes spatial relationships between objects
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Multiple Input Sources" icon="images">
-    Support for both URL and local image files.
-  </Card>
-  <Card title="Object Detection" icon="magnifying-glass">
-    Advanced object detection and recognition.
-  </Card>
-  <Card title="Architectural Analysis" icon="building">
-    Specialized analysis of architectural features.
-  </Card>
-  <Card title="Detailed Descriptions" icon="pen">
-    Generation of comprehensive visual descriptions.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Analyzing a landmark image
-landmark_task = Task(
-    name="analyze_famous_landmark",
-    description="Analyze this historical landmark",
-    expected_output="Architectural details and historical significance",
-    agent=image_agent,
-    images=["https://example.com/landmark.jpg"]
-)
-
-# Run single task
-agents = PraisonAIAgents(
-    agents=[image_agent],
-    tasks=[landmark_task],
-    process="sequential"
-)
-agents.start()
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex image analysis workflows
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
-- Check out the [Video Agent](/agents/video) for video content analysis

docs/agents/markdown.mdx

@@ -1,120 +0,0 @@
----
-title: "Markdown Agent"
-sidebarTitle: "Markdown"
-description: "Learn how to create AI agents for generating and formatting content in Markdown."
-icon: "markdown"
----
-
-```mermaid
-flowchart LR
-    In[Content Request] --> Generator[Content Generator]
-    Generator --> Formatter[Markdown Formatter]
-    Formatter --> Validator[Format Validator]
-    Validator --> Out[Markdown Output]
-    
-    style In fill:#8B0000,color:#fff
-    style Generator fill:#2E8B57,color:#fff
-    style Formatter fill:#2E8B57,color:#fff
-    style Validator fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Markdown Agent can generate and format content in Markdown syntax.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `markdown_generator.py`:
-        ```python
-        from praisonaiagents import Agent
-
-        # Create Markdown Agent
-        markdown_agent = Agent(
-            name="MarkdownWriter",
-            role="Markdown Content Specialist",
-            goal="Generate well-formatted content in Markdown syntax",
-            instructions="You are a Markdown Agent, output in markdown format",
-            llm="gpt-4o-mini",
-            self_reflect=False
-        )
-
-        # Generate content
-        response = markdown_agent.start(
-            "Write a blog post about artificial intelligence"
-        )
-        
-        # Save to file
-        with open('blog_post.md', 'w') as f:
-            f.write(response)
-        ```
-    </Step>
-</Steps>
-
-## Understanding Markdown Generation
-
-The Markdown Agent specializes in creating properly formatted Markdown content:
-
-1. **Content Generation**: Creates original content based on prompts
-2. **Markdown Formatting**: Applies proper Markdown syntax
-3. **Structure Validation**: Ensures correct formatting
-4. **Document Organization**: Creates well-structured documents
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Content Creation" icon="pen-to-square">
-    Generates original, well-structured content.
-  </Card>
-  <Card title="Markdown Syntax" icon="code">
-    Proper implementation of Markdown formatting.
-  </Card>
-  <Card title="Document Structure" icon="sitemap">
-    Organized document hierarchy and sections.
-  </Card>
-  <Card title="Format Validation" icon="check">
-    Ensures correct Markdown syntax usage.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Generate a technical documentation
-agent = Agent(
-    instructions="You are a Markdown Agent, output in markdown format"
-)
-
-# Generate API documentation
-response = agent.start("""
-    Create technical documentation for a REST API with:
-    - Introduction
-    - Authentication
-    - Endpoints
-    - Examples
-""")
-
-# Save documentation
-with open('api_docs.md', 'w') as f:
-    f.write(response)
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex document generation
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving content quality
-- Check out the [Documentation Agent](/agents/documentation) for specialized documentation generation

docs/agents/planning.mdx

@@ -1,109 +0,0 @@
----
-title: "Planning Agent"
-sidebarTitle: "Planning"
-description: "Learn how to create AI agents for trip planning and itinerary generation."
-icon: "calendar"
----
-
-```mermaid
-flowchart LR
-    In[Request] --> Search[Web Search]
-    Search --> Analyzer[Options Analyzer]
-    Analyzer --> Generator[Plan Generator]
-    Generator --> Out[Itinerary]
-    
-    style In fill:#8B0000,color:#fff
-    style Search fill:#2E8B57,color:#fff
-    style Analyzer fill:#2E8B57,color:#fff
-    style Generator fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Planning Agent can search for travel options and create detailed itineraries.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `travel_planner.py`:
-        ```python
-        from praisonaiagents import Agent, Tools
-        from praisonaiagents.tools import duckduckgo
-
-        # Create Planning Agent
-        planning_agent = Agent(
-            name="TravelPlanner",
-            role="Travel Planning Specialist",
-            goal="Create comprehensive travel plans and itineraries",
-            instructions="You are a Planning Agent",
-            tools=[duckduckgo]
-        )
-
-        # Generate travel plan
-        response = planning_agent.start(
-            "I want to go to London next week, find me a good hotel and flight"
-        )
-        
-        # Save plan
-        with open('travel_plan.txt', 'w') as f:
-            f.write(response)
-        ```
-    </Step>
-</Steps>
-
-## Understanding Travel Planning
-
-The Planning Agent combines multiple capabilities to create comprehensive travel plans:
-
-1. **Web Search**: Uses DuckDuckGo to find current travel options
-2. **Options Analysis**: Evaluates and compares different choices
-3. **Plan Generation**: Creates detailed itineraries
-4. **Recommendations**: Provides personalized suggestions
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Flight Search" icon="plane">
-    Finds and compares flight options.
-  </Card>
-  <Card title="Hotel Booking" icon="hotel">
-    Recommends suitable accommodations.
-  </Card>
-  <Card title="Itinerary Creation" icon="calendar-days">
-    Generates detailed travel schedules.
-  </Card>
-  <Card title="Real-time Data" icon="clock">
-    Uses current pricing and availability.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Create a weekend trip plan
-from praisonaiagents import Agent, Tools
-from praisonaiagents.tools import duckduckgo
-
-agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
-agent.start("I want to go London next week, find me a good hotel and flight")
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex travel planning
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendations
-- Check out the [Research Agent](/agents/research) for detailed destination research

docs/agents/programming.mdx

@@ -1,125 +0,0 @@
----
-title: "Programming Agent"
-sidebarTitle: "Programming"
-description: "Learn how to create AI agents for code development, analysis, and execution."
-icon: "code"
----
-
-```mermaid
-flowchart LR
-    In[Code Request] --> Analyzer[Code Analyzer]
-    Analyzer --> Generator[Code Generator]
-    Generator --> Executor[Code Executor]
-    Executor --> Debugger[Code Debugger]
-    Debugger --> Out[Output]
-    
-    style In fill:#8B0000,color:#fff
-    style Analyzer fill:#2E8B57,color:#fff
-    style Generator fill:#2E8B57,color:#fff
-    style Executor fill:#2E8B57,color:#fff
-    style Debugger fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Programming Agent can analyze, generate, execute, and debug code.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `code_assistant.py`:
-        ```python
-        from praisonaiagents import Agent, Tools
-        from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
-        from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
-        from praisonaiagents.tools import duckduckgo # Web Search Tool
-
-        agent = Agent(
-            instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10, 
-            tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
-        )
-        agent.start(
-            "Write a python script using yfinance to find the stock price of Tesla"
-            "First check if required packages are installed"
-            "Run it using execute_code"
-            "execute_command if you want to run any terminal command"
-            "search internet using duckduckgo if you want to know update python package information"
-            "Analyse the output using analyze_code and fix error if required"
-            "if no package is installed, install it"
-            "then run the code"
-        )
-        ```
-    </Step>
-</Steps>
-
-## Understanding Code Development
-
-The Programming Agent combines multiple tools for comprehensive code development:
-
-1. **Code Tools**:
-   - `execute_code`: Run Python code
-   - `analyze_code`: Analyze code structure
-   - `format_code`: Format code to standards
-   - `lint_code`: Check code quality
-   - `disassemble_code`: View bytecode
-
-2. **Shell Tools**:
-   - `execute_command`: Run terminal commands
-   - `list_processes`: View running processes
-   - `kill_process`: Terminate processes
-   - `get_system_info`: System information
-
-3. **Web Tools**:
-   - `duckduckgo`: Search for programming resources
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Code Execution" icon="play">
-    Run and test code directly.
-  </Card>
-  <Card title="Code Analysis" icon="magnifying-glass-code">
-    Analyze and improve code quality.
-  </Card>
-  <Card title="System Integration" icon="terminal">
-    Execute system commands and manage processes.
-  </Card>
-  <Card title="Package Management" icon="box">
-    Handle dependencies and installations.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Create and run a data analysis script
-response = agent.start("""
-    1. Create a script that:
-       - Uses pandas for data analysis
-       - Reads a CSV file
-       - Performs basic statistics
-       - Creates a visualization
-    2. Check and install required packages
-    3. Execute and analyze the results
-""")
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex development workflows
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving code quality
-- Check out the [Data Analyst Agent](/agents/data-analyst) for specialized data analysis

docs/agents/recommendation.mdx

@@ -1,109 +0,0 @@
----
-title: "Recommendation Agent"
-sidebarTitle: "Recommendation"
-description: "Learn how to create AI agents for personalized recommendations across various domains."
-icon: "thumbs-up"
----
-
-```mermaid
-flowchart LR
-    In[User Preferences] --> Search[Content Search]
-    Search --> Analyzer[Preference Analyzer]
-    Analyzer --> Generator[Recommendation Generator]
-    Generator --> Out[Recommendations]
-    
-    style In fill:#8B0000,color:#fff
-    style Search fill:#2E8B57,color:#fff
-    style Analyzer fill:#2E8B57,color:#fff
-    style Generator fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Recommendation Agent can analyze preferences and generate personalized recommendations.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `recommendation_system.py`:
-        ```python
-        from praisonaiagents import Agent, Tools
-        from praisonaiagents.tools import duckduckgo
-
-        # Create Recommendation Agent
-        recommendation_agent = Agent(
-            name="RecommendationEngine",
-            role="Recommendation Specialist",
-            goal="Generate personalized recommendations based on preferences",
-            instructions="You are a Recommendation Agent",
-            tools=[duckduckgo]
-        )
-
-        # Get recommendations
-        response = recommendation_agent.start(
-            "Recommend me a good movie to watch in 2025"
-        )
-        
-        # Save recommendations
-        with open('recommendations.txt', 'w') as f:
-            f.write(response)
-        ```
-    </Step>
-</Steps>
-
-## Understanding Recommendation System
-
-The Recommendation Agent uses multiple approaches to generate personalized suggestions:
-
-1. **Content Search**: Uses DuckDuckGo to find current options
-2. **Preference Analysis**: Understands user preferences
-3. **Recommendation Generation**: Creates personalized suggestions
-4. **Content Filtering**: Filters based on relevance and quality
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Personalization" icon="user">
-    Tailored recommendations based on preferences.
-  </Card>
-  <Card title="Real-time Data" icon="clock">
-    Up-to-date content and information.
-  </Card>
-  <Card title="Multi-domain" icon="layer-group">
-    Recommendations across various categories.
-  </Card>
-  <Card title="Content Filtering" icon="filter">
-    Quality-based content selection.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Get personalized recommendations
-from praisonaiagents import Agent, Tools
-from praisonaiagents.tools import duckduckgo
-
-agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
-agent.start("Recommend me a good movie to watch in 2025")
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex recommendation systems
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
-- Check out the [Research Agent](/agents/research) for detailed content research

docs/agents/research.mdx

@@ -1,108 +0,0 @@
----
-title: "Research Agent"
-sidebarTitle: "Research"
-description: "Learn how to create AI agents for conducting comprehensive research and analysis."
-icon: "magnifying-glass-chart"
----
-
-```mermaid
-flowchart LR
-    In[Research Query] --> Search[Web Search]
-    Search --> Analyzer[Content Analyzer]
-    Analyzer --> Synthesizer[Information Synthesizer]
-    Synthesizer --> Out[Research Report]
-    
-    style In fill:#8B0000,color:#fff
-    style Search fill:#2E8B57,color:#fff
-    style Analyzer fill:#2E8B57,color:#fff
-    style Synthesizer fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Research Agent can gather, analyze, and synthesize information from various sources.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `research_assistant.py`:
-        ```python
-        from praisonaiagents import Agent, Tools
-        from praisonaiagents.tools import duckduckgo
-
-        # Create Research Agent
-        research_agent = Agent(
-            name="ResearchAssistant",
-            role="Research Specialist",
-            goal="Conduct comprehensive research and analysis",
-            instructions="You are a Research Agent",
-            tools=[duckduckgo]
-        )
-
-        # Conduct research
-        response = research_agent.start(
-            "Research about AI developments in 2024"
-        )
-        
-        # Save research findings
-        with open('research_report.md', 'w') as f:
-            f.write(response)
-        ```
-    </Step>
-</Steps>
-
-## Understanding Research Process
-
-The Research Agent employs a systematic approach to information gathering and analysis:
-
-1. **Web Search**: Uses DuckDuckGo to find relevant sources
-2. **Content Analysis**: Evaluates source credibility and relevance
-3. **Information Synthesis**: Combines findings into coherent insights
-4. **Report Generation**: Creates structured research reports
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Web Research" icon="globe">
-    Comprehensive online information gathering.
-  </Card>
-  <Card title="Content Analysis" icon="microscope">
-    In-depth evaluation of sources.
-  </Card>
-  <Card title="Information Synthesis" icon="layer-group">
-    Integration of multiple sources.
-  </Card>
-  <Card title="Report Generation" icon="file-lines">
-    Structured research documentation.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-from praisonaiagents import Agent, Tools
-from praisonaiagents.tools import duckduckgo
-
-agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
-agent.start("Research about AI 2024")
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex research projects
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
-- Check out the [Data Analyst Agent](/agents/data-analyst) for data-driven research

docs/agents/shopping.mdx

@@ -1,109 +0,0 @@
----
-title: "Shopping Agent"
-sidebarTitle: "Shopping"
-description: "Learn how to create AI agents for price comparison and shopping assistance."
-icon: "shop"
----
-
-```mermaid
-flowchart LR
-    In[Product Query] --> Search[Store Search]
-    Search --> Analyzer[Price Analyzer]
-    Analyzer --> Comparator[Price Comparator]
-    Comparator --> Out[Price Report]
-    
-    style In fill:#8B0000,color:#fff
-    style Search fill:#2E8B57,color:#fff
-    style Analyzer fill:#2E8B57,color:#fff
-    style Comparator fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Shopping Agent can search for products, compare prices, and provide shopping recommendations.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `shopping_assistant.py`:
-        ```python
-        from praisonaiagents import Agent, Tools
-        from praisonaiagents.tools import duckduckgo
-
-        # Create Shopping Agent
-        shopping_agent = Agent(
-            name="ShoppingAssistant",
-            role="Shopping Specialist",
-            goal="Find the best prices and deals across stores",
-            instructions="You are a Shopping Agent",
-            tools=[duckduckgo]
-        )
-
-        # Search for product prices
-        response = shopping_agent.start(
-            "I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table"
-        )
-        
-        # Save price comparison
-        with open('price_comparison.md', 'w') as f:
-            f.write(response)
-        ```
-    </Step>
-</Steps>
-
-## Understanding Shopping Assistant
-
-The Shopping Agent combines multiple capabilities for effective price comparison:
-
-1. **Store Search**: Uses DuckDuckGo to find retailers
-2. **Price Analysis**: Gathers pricing information
-3. **Price Comparison**: Compares prices across stores
-4. **Deal Finding**: Identifies special offers and discounts
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Price Comparison" icon="scale-balanced">
-    Compare prices across multiple stores.
-  </Card>
-  <Card title="Deal Detection" icon="tag">
-    Find special offers and discounts.
-  </Card>
-  <Card title="Store Analysis" icon="store">
-    Evaluate retailer reliability.
-  </Card>
-  <Card title="Price Tracking" icon="chart-line">
-    Monitor price changes over time.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Compare product prices
-from praisonaiagents import Agent, Tools
-from praisonaiagents.tools import duckduckgo
-
-agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
-agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex shopping research
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving price accuracy
-- Check out the [Research Agent](/agents/research) for detailed product research

docs/agents/single.mdx

@@ -1,102 +0,0 @@
----
-title: "Single Agent"
-sidebarTitle: "Single"
-description: "Learn how to create a basic single-purpose AI agent for simple tasks."
-icon: "circle-1"
----
-
-```mermaid
-flowchart LR
-    In[Input] --> Agent[Single Agent]
-    Agent --> Out[Output]
-    
-    style In fill:#8B0000,color:#fff
-    style Agent fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how to create and use a simple single-purpose agent.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `simple_agent.py`:
-        ```python
-        from praisonaiagents import Agent
-
-        agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
-        agent.start("Write a blog post about AI")
-        ```
-    </Step>
-</Steps>
-
-## Understanding Single Agent
-
-The Single Agent is the simplest form of PraisonAI agent:
-
-1. **Simple Setup**: Minimal configuration required
-2. **Single Purpose**: Focused on one specific task
-3. **Direct Execution**: Straightforward input-output flow
-4. **No Dependencies**: No external tools required
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Simple Setup" icon="square-1">
-    Minimal configuration needed.
-  </Card>
-  <Card title="Focused Task" icon="bullseye">
-    Single-purpose execution.
-  </Card>
-  <Card title="Quick Start" icon="bolt">
-    Rapid implementation.
-  </Card>
-  <Card title="No Tools" icon="toolbox">
-    No external dependencies.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Create a content generation agent
-from praisonaiagents import Agent
-
-# Create content agent
-agent = Agent(
-    instructions="You are a Content Generation Agent, create engaging content"
-)
-
-# Generate content
-response = agent.start("""
-    Write a short story about:
-    - A future world
-    - With advanced AI
-    - And human collaboration
-""")
-
-# Save the story
-with open('ai_story.txt', 'w') as f:
-    f.write(response)
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for more complex workflows
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving output quality
-- Check out other specialized agents like the [Markdown Agent](/agents/markdown) for specific use cases

docs/agents/video.mdx

@@ -1,149 +0,0 @@
----
-title: "Video Agent"
-sidebarTitle: "Video"
-description: "Learn how to create AI agents for video analysis and content understanding."
-icon: "video"
----
-
-```mermaid
-flowchart LR
-    In[Video Input] --> Analyzer[Content Analyzer]
-    Analyzer --> Detector[Object Detector]
-    Detector --> Transcriber[Text Transcriber]
-    Transcriber --> Out[Analysis Report]
-    
-    style In fill:#8B0000,color:#fff
-    style Analyzer fill:#2E8B57,color:#fff
-    style Detector fill:#2E8B57,color:#fff
-    style Transcriber fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Video Agent can analyze video content, detect objects, and extract meaningful information.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `video_analyzer.py`:
-        ```python
-        from praisonaiagents import Agent, Task, PraisonAIAgents
-
-        # Create Video Analysis Agent
-        video_agent = Agent(
-            name="VideoAnalyst",
-            role="Video Analysis Specialist",
-            goal="Analyze videos to extract meaningful information",
-            backstory="""You are an expert in computer vision and video analysis.
-            You excel at describing content, detecting objects, and understanding context.""",
-            llm="gpt-4o-mini",
-            self_reflect=False
-        )
-
-        # Create video analysis task
-        analysis_task = Task(
-            name="analyze_video",
-            description="""Analyze this video and provide:
-                1. Summary of main events
-                2. Key objects and people
-                3. Text and important information
-                4. Context and setting""",
-            expected_output="Comprehensive video analysis",
-            agent=video_agent,
-            images=["video.mp4"]
-        )
-
-        # Create PraisonAIAgents instance
-        agents = PraisonAIAgents(
-            agents=[video_agent],
-            tasks=[analysis_task],
-            process="sequential",
-            verbose=1
-        )
-
-        # Run analysis
-        agents.start()
-        ```
-    </Step>
-</Steps>
-
-## Understanding Video Analysis
-
-The Video Agent combines multiple capabilities for comprehensive video understanding:
-
-1. **Content Analysis**: Analyzes video scenes and events
-2. **Object Detection**: Identifies objects and people
-3. **Text Extraction**: Captures text shown in videos
-4. **Context Understanding**: Interprets settings and situations
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Scene Analysis" icon="film">
-    Detailed analysis of video scenes.
-  </Card>
-  <Card title="Object Detection" icon="cube">
-    Identification of objects and people.
-  </Card>
-  <Card title="Text Extraction" icon="closed-captioning">
-    Capture of text and captions.
-  </Card>
-  <Card title="Context Analysis" icon="sitemap">
-    Understanding of video context.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Analyze a presentation video
-from praisonaiagents import Agent, Task, PraisonAIAgents
-
-video_agent = Agent(
-    name="VideoAnalyst",
-    role="Video Analysis Specialist",
-    goal="Extract information from presentation videos",
-    llm="gpt-4o-mini"
-)
-
-# Create presentation analysis task
-presentation_task = Task(
-    name="analyze_presentation",
-    description="""Analyze this presentation video:
-        1. Extract key points
-        2. Capture slide content
-        3. Note speaker's main arguments
-        4. Summarize Q&A session""",
-    expected_output="Detailed presentation summary",
-    agent=video_agent,
-    images=["presentation.mp4"]
-)
-
-# Run analysis
-agents = PraisonAIAgents(
-    agents=[video_agent],
-    tasks=[presentation_task],
-    process="sequential"
-)
-agents.start()
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex video analysis
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
-- Check out the [Image Agent](/agents/image) for still image analysis

docs/agents/websearch.mdx

@@ -1,124 +0,0 @@
----
-title: "Web Search Agent"
-sidebarTitle: "Web Search"
-description: "Learn how to create AI agents for intelligent web searching and information gathering."
-icon: "globe"
----
-
-```mermaid
-flowchart LR
-    In[Search Query] --> Search[Web Search]
-    Search --> Filter[Content Filter]
-    Filter --> Summarizer[Content Summarizer]
-    Summarizer --> Out[Search Results]
-    
-    style In fill:#8B0000,color:#fff
-    style Search fill:#2E8B57,color:#fff
-    style Filter fill:#2E8B57,color:#fff
-    style Summarizer fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Web Search Agent can perform intelligent searches and process web content.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `web_search.py`:
-        ```python
-        from praisonaiagents import Agent, Tools
-        from praisonaiagents.tools import duckduckgo
-
-        # Create Web Search Agent
-        search_agent = Agent(
-            name="WebSearcher",
-            role="Web Search Specialist",
-            goal="Perform intelligent web searches and gather information",
-            instructions="You are a Web Search Agent",
-            tools=[duckduckgo]
-        )
-
-        # Perform search
-        response = search_agent.start(
-            "Search about AI developments in 2024"
-        )
-        
-        # Save search results
-        with open('search_results.md', 'w') as f:
-            f.write(response)
-        ```
-    </Step>
-</Steps>
-
-## Understanding Web Search
-
-The Web Search Agent employs sophisticated search strategies:
-
-1. **Query Processing**: Optimizes search queries
-2. **Content Filtering**: Filters relevant results
-3. **Information Extraction**: Extracts key information
-4. **Result Summarization**: Summarizes findings
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Smart Search" icon="magnifying-glass">
-    Intelligent query processing.
-  </Card>
-  <Card title="Content Filtering" icon="filter">
-    Relevance-based filtering.
-  </Card>
-  <Card title="Information Extraction" icon="scissors">
-    Key information extraction.
-  </Card>
-  <Card title="Result Summary" icon="list">
-    Concise result summaries.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Perform topic research
-from praisonaiagents import Agent, Tools
-from praisonaiagents.tools import duckduckgo
-
-agent = Agent(
-    instructions="You are a Web Search Agent",
-    tools=[duckduckgo]
-)
-
-# Research specific topic
-response = agent.start("""
-    Search for information about quantum computing:
-    - Recent breakthroughs
-    - Leading companies
-    - Current applications
-    - Future predictions
-""")
-
-# Save research results
-with open('quantum_computing_research.md', 'w') as f:
-    f.write(response)
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex search workflows
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving search relevance
-- Check out the [Research Agent](/agents/research) for comprehensive research capabilities

docs/agents/wikipedia.mdx

@@ -1,139 +0,0 @@
----
-title: "Wikipedia Agent"
-sidebarTitle: "Wikipedia"
-description: "Learn how to create AI agents for searching and extracting information from Wikipedia."
-icon: "book"
----
-
-```mermaid
-flowchart LR
-    In[Query] --> Search[Wiki Search]
-    Search --> Page[Page Retrieval]
-    Page --> Summary[Content Summary]
-    Summary --> Out[Knowledge Output]
-    
-    style In fill:#8B0000,color:#fff
-    style Search fill:#2E8B57,color:#fff
-    style Page fill:#2E8B57,color:#fff
-    style Summary fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-A workflow demonstrating how the Wikipedia Agent can search, retrieve, and summarize Wikipedia content.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Package">
-        First, install the PraisonAI Agents package:
-        ```bash
-        pip install praisonaiagents
-        ```
-    </Step>
-
-    <Step title="Set API Key">
-        Set your OpenAI API key as an environment variable:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-    </Step>
-
-    <Step title="Create Script">
-        Create a new file `wikipedia_assistant.py`:
-        ```python
-        from praisonaiagents import Agent, Task, PraisonAIAgents
-        from praisonaiagents.tools import (
-            wiki_search,
-            wiki_summary,
-            wiki_page,
-            wiki_random,
-            wiki_language
-        )
-
-        # Create Wikipedia Agent
-        wiki_agent = Agent(
-            name="WikipediaAssistant",
-            role="Wikipedia Research Specialist",
-            goal="Extract and summarize Wikipedia content",
-            instructions="You are a Wikipedia Agent",
-            tools=[
-                wiki_search,
-                wiki_summary,
-                wiki_page,
-                wiki_random,
-                wiki_language
-            ],
-            self_reflect=True,
-            min_reflect=3,
-            max_reflect=5
-        )
-
-        # Research a topic
-        response = wiki_agent.start("""
-            What is the history of AI?
-            First search the history of AI
-            Read the page of the history of AI
-            Get the summary of the page
-        """)
-        
-        # Save research results
-        with open('ai_history.md', 'w') as f:
-            f.write(response)
-        ```
-    </Step>
-</Steps>
-
-## Understanding Wikipedia Research
-
-The Wikipedia Agent provides comprehensive Wikipedia access through multiple tools:
-
-1. **Wiki Search**: `wiki_search` for finding relevant articles
-2. **Page Access**: `wiki_page` for retrieving full articles
-3. **Summarization**: `wiki_summary` for concise overviews
-4. **Random Articles**: `wiki_random` for discovering content
-5. **Language Support**: `wiki_language` for multilingual access
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Article Search" icon="magnifying-glass">
-    Find relevant Wikipedia articles.
-  </Card>
-  <Card title="Content Retrieval" icon="book-open">
-    Access complete article content.
-  </Card>
-  <Card title="Summarization" icon="compress">
-    Generate concise article summaries.
-  </Card>
-  <Card title="Multilingual" icon="language">
-    Access content in multiple languages.
-  </Card>
-</CardGroup>
-
-## Example Usage
-
-```python
-# Example: Research a scientific topic
-from praisonaiagents import Agent
-from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
-
-agent = Agent(
-    instructions="You are a Wikipedia Agent", 
-    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
-    self_reflect=True,
-    min_reflect=3,
-    max_reflect=5,
-)
-agent.start(
-    "What is the history of AI?"
-    "First search the history of AI"
-    "Read the page of the history of AI"
-    "Get the summary of the page"
-)
-```
-
-## Next Steps
-
-- Learn about [Prompt Chaining](/features/promptchaining) for complex Wikipedia research
-- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
-- Check out the [Research Agent](/agents/research) for broader research capabilities

docs/api-reference/index.mdx

@@ -1,45 +0,0 @@
----
-title: 'API Reference'
-description: 'PraisonAI API documentation with framework-specific details'
-icon: "book-open"
----
-
-# API Reference
-
-This section provides detailed information about the PraisonAI API and its framework-specific implementations.
-
-## Core Modules
-
-- [praisonai](../api/praisonai/index) - Core package functionality
-- [praisonai.auto](../api/praisonai/auto) - Automated agent generation
-- [praisonai.agents_generator](../api/praisonai/agents_generator) - Framework-specific agent generation
-- [praisonai.cli](../api/praisonai/cli) - Command-line interface
-- [praisonai.deploy](../api/praisonai/deploy) - Deployment utilities
-
-## Framework Support
-
-### CrewAI Integration
-Requires installation with:
-```bash
-pip install "praisonai[crewai]"
-```
-
-Features:
-- Task delegation
-- Sequential/parallel execution
-- Built-in tools
-- Structured workflows
-
-### AG2 (Formerly AutoGen) Integration
-Requires installation with:
-```bash
-pip install "praisonai[autogen]"
-```
-
-Features:
-- Multi-agent conversations
-- Code execution
-- Built-in tools
-- Flexible interactions
-
-For detailed API documentation of each module, please refer to the generated files in the `api` folder.

docs/api.mdx

@@ -1,79 +0,0 @@
----
-title: "API Reference"
-description: "Complete API reference for PraisonAI, including core modules, installation options, and framework-specific features"
-icon: "code"
----
-
-## Core Modules
-
-### praisonai
-The main package containing core functionality.
-
-```python
-from praisonai import PraisonAI
-```
-
-### praisonai.auto
-Automated agent generation functionality.
-
-```python
-from praisonai.auto import AutoGenerator
-```
-
-### praisonai.agents_generator
-Framework-specific agent generation and orchestration:
-- CrewAI support (requires `praisonai[crewai]`)
-- AG2 (Formerly AutoGen) support (requires `praisonai[autogen]`)
-
-```python
-from praisonai.agents_generator import AgentsGenerator
-```
-
-### praisonai.cli
-Command-line interface with framework-specific handling.
-
-```python
-from praisonai.cli import PraisonAI
-```
-
-### praisonai.deploy
-Deployment utilities.
-
-```python
-from praisonai.deploy import CloudDeployer
-```
-
-## Installation Options
-
-```bash
-# Basic installation
-pip install praisonai
-
-# Framework-specific installations
-pip install "praisonai[crewai]"    # Install with CrewAI support
-pip install "praisonai[autogen]"   # Install with AG2 support
-pip install "praisonai[crewai,autogen]"  # Install both frameworks
-
-# Additional features
-pip install "praisonai[ui]"        # Install UI support
-pip install "praisonai[chat]"      # Install Chat interface
-pip install "praisonai[code]"      # Install Code interface
-pip install "praisonai[realtime]"  # Install Realtime voice interaction
-pip install "praisonai[call]"      # Install Call feature
-```
-
-## Framework-specific Features
-
-### CrewAI
-When installing with `pip install "praisonai[crewai]"`, you get:
-- CrewAI framework support
-- PraisonAI tools integration
-- Task delegation capabilities
-- Sequential and parallel task execution
-
-### AG2 (Formerly AutoGen)
-When installing with `pip install "praisonai[autogen]"`, you get:
-- AG2 framework support
-- PraisonAI tools integration
-- Multi-agent conversation capabilities
-- Code execution environment

docs/api/praisonai/agents_generator.html

@@ -1,566 +0,0 @@
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-</head>
-<body>
-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.agents_generator</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-functions">Functions</h2>
-<dl>
-<dt id="praisonai.agents_generator.disable_crewai_telemetry"><code class="name flex">
-<span>def <span class="ident">disable_crewai_telemetry</span></span>(<span>)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.agents_generator.noop"><code class="name flex">
-<span>def <span class="ident">noop</span></span>(<span>*args, **kwargs)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.agents_generator.AgentsGenerator"><code class="flex name class">
-<span>class <span class="ident">AgentsGenerator</span></span>
-<span>(</span><span>agent_file, framework, config_list, log_level=None, agent_callback=None, task_callback=None, agent_yaml=None, tools=None)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Initialize the AgentsGenerator object.</p>
-<h2 id="parameters">Parameters</h2>
-<p>agent_file (str): The path to the agent file.
-framework (str): The framework to be used for the agents.
-config_list (list): A list of configurations for the agents.
-log_level (int, optional): The logging level to use. Defaults to logging.INFO.
-agent_callback (callable, optional): A callback function to be executed after each agent step.
-task_callback (callable, optional): A callback function to be executed after each tool run.
-agent_yaml (str, optional): The content of the YAML file. Defaults to None.
-tools (dict, optional): A dictionary containing the tools to be used for the agents. Defaults to None.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code></dt>
-<dd>The path to the agent file.</dd>
-<dt><strong><code>framework</code></strong> :&ensp;<code>str</code></dt>
-<dd>The framework to be used for the agents.</dd>
-<dt><strong><code>config_list</code></strong> :&ensp;<code>list</code></dt>
-<dd>A list of configurations for the agents.</dd>
-<dt><strong><code>log_level</code></strong> :&ensp;<code>int</code></dt>
-<dd>The logging level to use.</dd>
-<dt><strong><code>agent_callback</code></strong> :&ensp;<code>callable</code>, optional</dt>
-<dd>A callback function to be executed after each agent step.</dd>
-<dt><strong><code>task_callback</code></strong> :&ensp;<code>callable</code>, optional</dt>
-<dd>A callback function to be executed after each tool run.</dd>
-<dt><strong><code>tools</code></strong> :&ensp;<code>dict</code></dt>
-<dd>A dictionary containing the tools to be used for the agents.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class AgentsGenerator:
-    def __init__(self, agent_file, framework, config_list, log_level=None, agent_callback=None, task_callback=None, agent_yaml=None, tools=None):
-        &#34;&#34;&#34;
-        Initialize the AgentsGenerator object.
-
-        Parameters:
-            agent_file (str): The path to the agent file.
-            framework (str): The framework to be used for the agents.
-            config_list (list): A list of configurations for the agents.
-            log_level (int, optional): The logging level to use. Defaults to logging.INFO.
-            agent_callback (callable, optional): A callback function to be executed after each agent step.
-            task_callback (callable, optional): A callback function to be executed after each tool run.
-            agent_yaml (str, optional): The content of the YAML file. Defaults to None.
-            tools (dict, optional): A dictionary containing the tools to be used for the agents. Defaults to None.
-
-        Attributes:
-            agent_file (str): The path to the agent file.
-            framework (str): The framework to be used for the agents.
-            config_list (list): A list of configurations for the agents.
-            log_level (int): The logging level to use.
-            agent_callback (callable, optional): A callback function to be executed after each agent step.
-            task_callback (callable, optional): A callback function to be executed after each tool run.
-            tools (dict): A dictionary containing the tools to be used for the agents.
-        &#34;&#34;&#34;
-        self.agent_file = agent_file
-        self.framework = framework
-        self.config_list = config_list
-        self.log_level = log_level
-        self.agent_callback = agent_callback
-        self.task_callback = task_callback
-        self.agent_yaml = agent_yaml
-        self.tools = tools or []  # Store tool class names as a list
-        self.log_level = log_level or logging.getLogger().getEffectiveLevel()
-        if self.log_level == logging.NOTSET:
-            self.log_level = os.environ.get(&#39;LOGLEVEL&#39;, &#39;INFO&#39;).upper()
-        
-        logging.basicConfig(level=self.log_level, format=&#39;%(asctime)s - %(levelname)s - %(message)s&#39;)
-        self.logger = logging.getLogger(__name__)
-        self.logger.setLevel(self.log_level)
-        
-    def is_function_or_decorated(self, obj):
-        &#34;&#34;&#34;
-        Checks if the given object is a function or has a __call__ method.
-
-        Parameters:
-            obj (object): The object to be checked.
-
-        Returns:
-            bool: True if the object is a function or has a __call__ method, False otherwise.
-        &#34;&#34;&#34;
-        return inspect.isfunction(obj) or hasattr(obj, &#39;__call__&#39;)
-
-    def load_tools_from_module(self, module_path):
-        &#34;&#34;&#34;
-        Loads tools from a specified module path.
-
-        Parameters:
-            module_path (str): The path to the module containing the tools.
-
-        Returns:
-            dict: A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.
-
-        Raises:
-            FileNotFoundError: If the specified module path does not exist.
-        &#34;&#34;&#34;
-        spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
-        module = importlib.util.module_from_spec(spec)
-        spec.loader.exec_module(module)
-        return {name: obj for name, obj in inspect.getmembers(module, self.is_function_or_decorated)}
-    
-    def load_tools_from_module_class(self, module_path):
-        &#34;&#34;&#34;
-        Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.
-
-        Parameters:
-            module_path (str): The path to the module containing the tools.
-
-        Returns:
-            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
-
-        Raises:
-            FileNotFoundError: If the specified module path does not exist.
-        &#34;&#34;&#34;
-        spec = importlib.util.spec_from_file_location(&#34;tools_module&#34;, module_path)
-        module = importlib.util.module_from_spec(spec)
-        spec.loader.exec_module(module)
-        return {name: obj() for name, obj in inspect.getmembers(module, lambda x: inspect.isclass(x) and (x.__module__.startswith(&#39;langchain_community.tools&#39;) or issubclass(x, BaseTool)) and x is not BaseTool)}
-
-    def load_tools_from_package(self, package_path):
-        &#34;&#34;&#34;
-        Loads tools from a specified package path containing modules with functions or classes.
-
-        Parameters:
-            package_path (str): The path to the package containing the tools.
-
-        Returns:
-            dict: A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
-
-        Raises:
-            FileNotFoundError: If the specified package path does not exist.
-
-        This function iterates through all the .py files in the specified package path, excluding those that start with &#34;__&#34;. For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.
-        &#34;&#34;&#34;
-        tools_dict = {}
-        for module_file in os.listdir(package_path):
-            if module_file.endswith(&#39;.py&#39;) and not module_file.startswith(&#39;__&#39;):
-                module_name = f&#34;{package_path.name}.{module_file[:-3]}&#34;  # Remove .py for import
-                module = importlib.import_module(module_name)
-                for name, obj in inspect.getmembers(module, self.is_function_or_decorated):
-                    tools_dict[name] = obj
-        return tools_dict
-
-    def generate_crew_and_kickoff(self):
-        &#34;&#34;&#34;
-        Generates a crew of agents and initiates tasks based on the provided configuration.
-
-        Parameters:
-            agent_file (str): The path to the agent file.
-            framework (str): The framework to be used for the agents.
-            config_list (list): A list of configurations for the agents.
-
-        Returns:
-            str: The output of the tasks performed by the crew of agents.
-
-        Raises:
-            FileNotFoundError: If the specified agent file does not exist.
-
-        This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is &#34;autogen&#34;, it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not &#34;autogen&#34;, it creates a crew of agents and initiates tasks based on the configuration.
-        &#34;&#34;&#34;
-        if self.agent_yaml:
-            config = yaml.safe_load(self.agent_yaml)
-        else:
-            if self.agent_file == &#39;/app/api:app&#39; or self.agent_file == &#39;api:app&#39;:
-                self.agent_file = &#39;agents.yaml&#39;
-            try:
-                with open(self.agent_file, &#39;r&#39;) as f:
-                    config = yaml.safe_load(f)
-            except FileNotFoundError:
-                print(f&#34;File not found: {self.agent_file}&#34;)
-                return
-
-        topic = config[&#39;topic&#39;]
-        tools_dict = {
-            &#39;CodeDocsSearchTool&#39;: CodeDocsSearchTool(),
-            &#39;CSVSearchTool&#39;: CSVSearchTool(),
-            &#39;DirectorySearchTool&#39;: DirectorySearchTool(),
-            &#39;DOCXSearchTool&#39;: DOCXSearchTool(),
-            &#39;DirectoryReadTool&#39;: DirectoryReadTool(),
-            &#39;FileReadTool&#39;: FileReadTool(),
-            # &#39;GithubSearchTool&#39;: GithubSearchTool(),
-            # &#39;SeperDevTool&#39;: SeperDevTool(),
-            &#39;TXTSearchTool&#39;: TXTSearchTool(),
-            &#39;JSONSearchTool&#39;: JSONSearchTool(),
-            &#39;MDXSearchTool&#39;: MDXSearchTool(),
-            &#39;PDFSearchTool&#39;: PDFSearchTool(),
-            # &#39;PGSearchTool&#39;: PGSearchTool(),
-            &#39;RagTool&#39;: RagTool(),
-            &#39;ScrapeElementFromWebsiteTool&#39;: ScrapeElementFromWebsiteTool(),
-            &#39;ScrapeWebsiteTool&#39;: ScrapeWebsiteTool(),
-            &#39;WebsiteSearchTool&#39;: WebsiteSearchTool(),
-            &#39;XMLSearchTool&#39;: XMLSearchTool(),
-            &#39;YoutubeChannelSearchTool&#39;: YoutubeChannelSearchTool(),
-            &#39;YoutubeVideoSearchTool&#39;: YoutubeVideoSearchTool(),
-        }
-        
-        # Add tools from class names
-        for tool_class in self.tools:
-            if isinstance(tool_class, type) and issubclass(tool_class, BaseTool):
-                tool_name = tool_class.__name__
-                tools_dict[tool_name] = tool_class()
-                self.logger.debug(f&#34;Added tool: {tool_name}&#34;)
-        
-        root_directory = os.getcwd()
-        tools_py_path = os.path.join(root_directory, &#39;tools.py&#39;)
-        tools_dir_path = Path(root_directory) / &#39;tools&#39;
-        
-        if os.path.isfile(tools_py_path):
-            tools_dict.update(self.load_tools_from_module_class(tools_py_path))
-            self.logger.debug(&#34;tools.py exists in the root directory. Loading tools.py and skipping tools folder.&#34;)
-        elif tools_dir_path.is_dir():
-            tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
-            self.logger.debug(&#34;tools folder exists in the root directory&#34;)
-        
-        framework = self.framework or config.get(&#39;framework&#39;)
-
-        agents = {}
-        tasks = []
-        if framework == &#34;autogen&#34;:
-            # Load the LLM configuration dynamically
-            # print(self.config_list)
-            llm_config = {&#34;config_list&#34;: self.config_list}
-            
-            if agentops_exists:
-                agentops.init(os.environ.get(&#34;AGENTOPS_API_KEY&#34;), tags=[&#34;autogen&#34;])
-            # Assuming the user proxy agent is set up as per your requirements
-            user_proxy = autogen.UserProxyAgent(
-                name=&#34;User&#34;,
-                human_input_mode=&#34;NEVER&#34;,
-                is_termination_msg=lambda x: (x.get(&#34;content&#34;) or &#34;&#34;).rstrip().rstrip(&#34;.&#34;).lower().endswith(&#34;terminate&#34;) or &#34;TERMINATE&#34; in (x.get(&#34;content&#34;) or &#34;&#34;),
-                code_execution_config={
-                    &#34;work_dir&#34;: &#34;coding&#34;,
-                    &#34;use_docker&#34;: False,
-                },
-                # additional setup for the user proxy agent
-            )
-            
-            for role, details in config[&#39;roles&#39;].items():
-                agent_name = details[&#39;role&#39;].format(topic=topic).replace(&#34;{topic}&#34;, topic)
-                agent_goal = details[&#39;goal&#39;].format(topic=topic)
-                # Creating an AssistantAgent for each role dynamically
-                agents[role] = autogen.AssistantAgent(
-                    name=agent_name,
-                    llm_config=llm_config,
-                    system_message=details[&#39;backstory&#39;].format(topic=topic)+&#34;. Must Reply \&#34;TERMINATE\&#34; in the end when everything is done.&#34;,
-                )
-                for tool in details.get(&#39;tools&#39;, []):
-                    if tool in tools_dict:
-                        try:
-                            tool_class = globals()[f&#39;autogen_{type(tools_dict[tool]).__name__}&#39;]
-                            print(f&#34;Found {tool_class.__name__} for {tool}&#34;)
-                        except KeyError:
-                            print(f&#34;Warning: autogen_{type(tools_dict[tool]).__name__} function not found. Skipping this tool.&#34;)
-                            continue
-                        tool_class(agents[role], user_proxy)
-
-                # Preparing tasks for initiate_chats
-                for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
-                    description_filled = task_details[&#39;description&#39;].format(topic=topic)
-                    expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
-                    
-                    chat_task = {
-                        &#34;recipient&#34;: agents[role],
-                        &#34;message&#34;: description_filled,
-                        &#34;summary_method&#34;: &#34;last_msg&#34;, 
-                        # Additional fields like carryover can be added based on dependencies
-                    }
-                    tasks.append(chat_task)
-            response = user_proxy.initiate_chats(tasks)
-            result = &#34;### Output ###\n&#34;+response[-1].summary if hasattr(response[-1], &#39;summary&#39;) else &#34;&#34;
-            if agentops_exists:
-                agentops.end_session(&#34;Success&#34;)
-        else: # framework=crewai
-            if agentops_exists:
-                agentops.init(os.environ.get(&#34;AGENTOPS_API_KEY&#34;), tags=[&#34;crewai&#34;])
-            
-            tasks_dict = {}
-            
-            for role, details in config[&#39;roles&#39;].items():
-                role_filled = details[&#39;role&#39;].format(topic=topic)
-                goal_filled = details[&#39;goal&#39;].format(topic=topic)
-                backstory_filled = details[&#39;backstory&#39;].format(topic=topic)
-                
-                # Adding tools to the agent if exists
-                agent_tools = [tools_dict[tool] for tool in details.get(&#39;tools&#39;, []) if tool in tools_dict]
-                
-                llm_model = details.get(&#39;llm&#39;)  # Get the llm configuration
-                if llm_model:
-                    llm = PraisonAIModel(
-                        model=llm_model.get(&#34;model&#34;, os.environ.get(&#34;MODEL_NAME&#34;, &#34;openai/gpt-4o&#34;)),
-                    ).get_model()
-                else:
-                    llm = PraisonAIModel().get_model()
-
-                function_calling_llm_model = details.get(&#39;function_calling_llm&#39;)
-                if function_calling_llm_model:
-                    function_calling_llm = PraisonAIModel(
-                        model=function_calling_llm_model.get(&#34;model&#34;, os.environ.get(&#34;MODEL_NAME&#34;, &#34;openai/gpt-4o&#34;)),
-                    ).get_model()
-                else:
-                    function_calling_llm = PraisonAIModel().get_model()
-                
-                agent = Agent(
-                    role=role_filled, 
-                    goal=goal_filled, 
-                    backstory=backstory_filled, 
-                    tools=agent_tools, 
-                    allow_delegation=details.get(&#39;allow_delegation&#39;, False),
-                    llm=llm,
-                    function_calling_llm=function_calling_llm,
-                    max_iter=details.get(&#39;max_iter&#39;, 15),
-                    max_rpm=details.get(&#39;max_rpm&#39;),
-                    max_execution_time=details.get(&#39;max_execution_time&#39;),
-                    verbose=details.get(&#39;verbose&#39;, True),
-                    cache=details.get(&#39;cache&#39;, True),
-                    system_template=details.get(&#39;system_template&#39;),
-                    prompt_template=details.get(&#39;prompt_template&#39;),
-                    response_template=details.get(&#39;response_template&#39;),
-                )
-                
-                # Set agent callback if provided
-                if self.agent_callback:
-                    agent.step_callback = self.agent_callback
-
-                agents[role] = agent
-
-                for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
-                    description_filled = task_details[&#39;description&#39;].format(topic=topic)
-                    expected_output_filled = task_details[&#39;expected_output&#39;].format(topic=topic)
-
-                    task = Task(
-                        description=description_filled,  # Clear, concise statement of what the task entails
-                        expected_output=expected_output_filled,  # Detailed description of what task&#39;s completion looks like
-                        agent=agent,  # The agent responsible for the task
-                        tools=task_details.get(&#39;tools&#39;, []),  # Functions or capabilities the agent can utilize
-                        async_execution=task_details.get(&#39;async_execution&#39;) if task_details.get(&#39;async_execution&#39;) is not None else False,  # Execute asynchronously if set
-                        context=[], ## TODO: 
-                        config=task_details.get(&#39;config&#39;) if task_details.get(&#39;config&#39;) is not None else {},  # Additional configuration details
-                        output_json=task_details.get(&#39;output_json&#39;) if task_details.get(&#39;output_json&#39;) is not None else None,  # Outputs a JSON object
-                        output_pydantic=task_details.get(&#39;output_pydantic&#39;) if task_details.get(&#39;output_pydantic&#39;) is not None else None,  # Outputs a Pydantic model object
-                        output_file=task_details.get(&#39;output_file&#39;) if task_details.get(&#39;output_file&#39;) is not None else &#34;&#34;,  # Saves the task output to a file
-                        callback=task_details.get(&#39;callback&#39;) if task_details.get(&#39;callback&#39;) is not None else None,  # Python callable executed with the task&#39;s output
-                        human_input=task_details.get(&#39;human_input&#39;) if task_details.get(&#39;human_input&#39;) is not None else False,  # Indicates if the task requires human feedback
-                        create_directory=task_details.get(&#39;create_directory&#39;) if task_details.get(&#39;create_directory&#39;) is not None else False  # Indicates if a directory needs to be created
-                    )
-                    
-                    # Set tool callback if provided
-                    if self.task_callback:
-                        task.callback = self.task_callback
-
-                    tasks.append(task)
-                    tasks_dict[task_name] = task
-            
-            for role, details in config[&#39;roles&#39;].items():
-                for task_name, task_details in details.get(&#39;tasks&#39;, {}).items():
-                    task = tasks_dict[task_name]
-                    context_tasks = [tasks_dict[ctx] for ctx in task_details.get(&#39;context&#39;, []) if ctx in tasks_dict]
-                    task.context = context_tasks
-
-            crew = Crew(
-                agents=list(agents.values()),
-                tasks=tasks,
-                verbose=2
-            )
-            
-            self.logger.debug(&#34;Final Crew Configuration:&#34;)
-            self.logger.debug(f&#34;Agents: {crew.agents}&#34;)
-            self.logger.debug(f&#34;Tasks: {crew.tasks}&#34;)
-
-            response = crew.kickoff()
-            result = f&#34;### Task Output ###\n{response}&#34;
-            if agentops_exists:
-                agentops.end_session(&#34;Success&#34;)
-        return result</code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff"><code class="name flex">
-<span>def <span class="ident">generate_crew_and_kickoff</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Generates a crew of agents and initiates tasks based on the provided configuration.</p>
-<h2 id="parameters">Parameters</h2>
-<p>agent_file (str): The path to the agent file.
-framework (str): The framework to be used for the agents.
-config_list (list): A list of configurations for the agents.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>str</code></dt>
-<dd>The output of the tasks performed by the crew of agents.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>FileNotFoundError</code></dt>
-<dd>If the specified agent file does not exist.</dd>
-</dl>
-<p>This function first loads the agent configuration from the specified file. It then initializes the tools required for the agents based on the specified framework. If the specified framework is "autogen", it loads the LLM configuration dynamically and creates an AssistantAgent for each role in the configuration. It then adds tools to the agents if specified in the configuration. Finally, it prepares tasks for the agents based on the configuration and initiates the tasks using the crew of agents. If the specified framework is not "autogen", it creates a crew of agents and initiates tasks based on the configuration.</p></div>
-</dd>
-<dt id="praisonai.agents_generator.AgentsGenerator.is_function_or_decorated"><code class="name flex">
-<span>def <span class="ident">is_function_or_decorated</span></span>(<span>self, obj)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Checks if the given object is a function or has a <strong>call</strong> method.</p>
-<h2 id="parameters">Parameters</h2>
-<p>obj (object): The object to be checked.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>bool</code></dt>
-<dd>True if the object is a function or has a <strong>call</strong> method, False otherwise.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_module"><code class="name flex">
-<span>def <span class="ident">load_tools_from_module</span></span>(<span>self, module_path)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Loads tools from a specified module path.</p>
-<h2 id="parameters">Parameters</h2>
-<p>module_path (str): The path to the module containing the tools.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>dict</code></dt>
-<dd>A dictionary containing the names of the tools as keys and the corresponding functions or objects as values.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>FileNotFoundError</code></dt>
-<dd>If the specified module path does not exist.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class"><code class="name flex">
-<span>def <span class="ident">load_tools_from_module_class</span></span>(<span>self, module_path)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Loads tools from a specified module path containing classes that inherit from BaseTool or are part of langchain_community.tools package.</p>
-<h2 id="parameters">Parameters</h2>
-<p>module_path (str): The path to the module containing the tools.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>dict</code></dt>
-<dd>A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>FileNotFoundError</code></dt>
-<dd>If the specified module path does not exist.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.agents_generator.AgentsGenerator.load_tools_from_package"><code class="name flex">
-<span>def <span class="ident">load_tools_from_package</span></span>(<span>self, package_path)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Loads tools from a specified package path containing modules with functions or classes.</p>
-<h2 id="parameters">Parameters</h2>
-<p>package_path (str): The path to the package containing the tools.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>dict</code></dt>
-<dd>A dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>FileNotFoundError</code></dt>
-<dd>If the specified package path does not exist.</dd>
-</dl>
-<p>This function iterates through all the .py files in the specified package path, excluding those that start with "__". For each file, it imports the corresponding module and checks if it contains any functions or classes that can be loaded as tools. The function then returns a dictionary containing the names of the tools as keys and the corresponding initialized instances of the classes as values.</p></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-functions">Functions</a></h3>
-<ul class="">
-<li><code><a title="praisonai.agents_generator.disable_crewai_telemetry" href="#praisonai.agents_generator.disable_crewai_telemetry">disable_crewai_telemetry</a></code></li>
-<li><code><a title="praisonai.agents_generator.noop" href="#praisonai.agents_generator.noop">noop</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="praisonai.agents_generator.AgentsGenerator" href="#praisonai.agents_generator.AgentsGenerator">AgentsGenerator</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff" href="#praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff">generate_crew_and_kickoff</a></code></li>
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.is_function_or_decorated" href="#praisonai.agents_generator.AgentsGenerator.is_function_or_decorated">is_function_or_decorated</a></code></li>
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_module" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_module">load_tools_from_module</a></code></li>
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_module_class">load_tools_from_module_class</a></code></li>
-<li><code><a title="praisonai.agents_generator.AgentsGenerator.load_tools_from_package" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_package">load_tools_from_package</a></code></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</nav>
-</main>
-<footer id="footer">
-<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.1</a>.</p>
-</footer>
-</body>
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-</head>
-<body>
-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.auto</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.auto.AutoGenerator"><code class="flex name class">
-<span>class <span class="ident">AutoGenerator</span></span>
-<span>(</span><span>topic='Movie Story writing about AI', agent_file='test.yaml', framework='crewai', config_list: Optional[List[Dict]] = None)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Initialize the AutoGenerator class with the specified topic, agent file, and framework.
-Note: autogen framework is different from this AutoGenerator class.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>topic</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The topic for the generated team structure. Defaults to "Movie Story writing about AI".</dd>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The name of the YAML file to save the generated team structure. Defaults to "test.yaml".</dd>
-<dt><strong><code>framework</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The framework for the generated team structure. Defaults to "crewai".</dd>
-<dt><strong><code>config_list</code></strong> :&ensp;<code>Optional[List[Dict]]</code>, optional</dt>
-<dd>A list containing the configuration details for the OpenAI API.
-If None, it defaults to using environment variables or hardcoded values.</dd>
-</dl>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>config_list</code></strong> :&ensp;<code>list</code></dt>
-<dd>A list containing the configuration details for the OpenAI API.</dd>
-<dt><strong><code>topic</code></strong> :&ensp;<code>str</code></dt>
-<dd>The specified topic for the generated team structure.</dd>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code></dt>
-<dd>The specified name of the YAML file to save the generated team structure.</dd>
-<dt><strong><code>framework</code></strong> :&ensp;<code>str</code></dt>
-<dd>The specified framework for the generated team structure.</dd>
-<dt><strong><code>client</code></strong> :&ensp;<code>instructor.Client</code></dt>
-<dd>An instance of the instructor.Client class initialized with the specified OpenAI API configuration.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class AutoGenerator:
-    def __init__(self, topic=&#34;Movie Story writing about AI&#34;, agent_file=&#34;test.yaml&#34;, framework=&#34;crewai&#34;, config_list: Optional[List[Dict]] = None):
-        &#34;&#34;&#34;
-        Initialize the AutoGenerator class with the specified topic, agent file, and framework.
-        Note: autogen framework is different from this AutoGenerator class.
-
-        Args:
-            topic (str, optional): The topic for the generated team structure. Defaults to &#34;Movie Story writing about AI&#34;.
-            agent_file (str, optional): The name of the YAML file to save the generated team structure. Defaults to &#34;test.yaml&#34;.
-            framework (str, optional): The framework for the generated team structure. Defaults to &#34;crewai&#34;.
-            config_list (Optional[List[Dict]], optional): A list containing the configuration details for the OpenAI API. 
-                                                          If None, it defaults to using environment variables or hardcoded values.
-        Attributes:
-            config_list (list): A list containing the configuration details for the OpenAI API.
-            topic (str): The specified topic for the generated team structure.
-            agent_file (str): The specified name of the YAML file to save the generated team structure.
-            framework (str): The specified framework for the generated team structure.
-            client (instructor.Client): An instance of the instructor.Client class initialized with the specified OpenAI API configuration.
-        &#34;&#34;&#34;
-        self.config_list = config_list or [
-            {
-                &#39;model&#39;: os.environ.get(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;),
-                &#39;base_url&#39;: os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;),
-                &#39;api_key&#39;: os.environ.get(&#34;OPENAI_API_KEY&#34;)
-            }
-        ]
-        self.topic = topic
-        self.agent_file = agent_file
-        self.framework = framework or &#34;crewai&#34;
-        self.client = instructor.patch(
-            OpenAI(
-                base_url=self.config_list[0][&#39;base_url&#39;],
-                api_key=os.getenv(&#34;OPENAI_API_KEY&#34;),
-            ),
-            mode=instructor.Mode.JSON,
-        )
-
-    def generate(self):
-        &#34;&#34;&#34;
-        Generates a team structure for the specified topic.
-
-        Args:
-            None
-
-        Returns:
-            str: The full path of the YAML file containing the generated team structure.
-
-        Raises:
-            Exception: If the generation process fails.
-
-        Usage:
-            generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
-            path = generator.generate()
-            print(path)
-        &#34;&#34;&#34;
-        response = self.client.chat.completions.create(
-            model=self.config_list[0][&#39;model&#39;],
-            response_model=TeamStructure,
-            max_retries=10,
-            messages=[
-                {&#34;role&#34;: &#34;system&#34;, &#34;content&#34;: &#34;You are a helpful assistant designed to output complex team structures.&#34;},
-                {&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: self.get_user_content()}
-            ]
-        )
-        json_data = json.loads(response.model_dump_json())
-        self.convert_and_save(json_data)
-        full_path = os.path.abspath(self.agent_file)
-        return full_path
-
-    def convert_and_save(self, json_data):
-        &#34;&#34;&#34;Converts the provided JSON data into the desired YAML format and saves it to a file.
-
-        Args:
-            json_data (dict): The JSON data representing the team structure.
-            topic (str, optional): The topic to be inserted into the YAML. Defaults to &#34;Artificial Intelligence&#34;.
-            agent_file (str, optional): The name of the YAML file to save. Defaults to &#34;test.yaml&#34;.
-        &#34;&#34;&#34;
-
-        yaml_data = {
-            &#34;framework&#34;: self.framework,
-            &#34;topic&#34;: self.topic,
-            &#34;roles&#34;: {},
-            &#34;dependencies&#34;: []
-        }
-
-        for role_id, role_details in json_data[&#39;roles&#39;].items():
-            yaml_data[&#39;roles&#39;][role_id] = {
-                &#34;backstory&#34;: &#34;&#34; + role_details[&#39;backstory&#39;],
-                &#34;goal&#34;: role_details[&#39;goal&#39;],
-                &#34;role&#34;: role_details[&#39;role&#39;],
-                &#34;tasks&#34;: {},
-                # &#34;tools&#34;: role_details.get(&#39;tools&#39;, []),
-                &#34;tools&#34;: [&#39;&#39;]
-            }
-
-            for task_id, task_details in role_details[&#39;tasks&#39;].items():
-                yaml_data[&#39;roles&#39;][role_id][&#39;tasks&#39;][task_id] = {
-                    &#34;description&#34;: &#34;&#34; + task_details[&#39;description&#39;],
-                    &#34;expected_output&#34;: &#34;&#34; + task_details[&#39;expected_output&#39;]
-                }
-
-        # Save to YAML file, maintaining the order
-        with open(self.agent_file, &#39;w&#39;) as f:
-            yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
-
-    def get_user_content(self):
-        &#34;&#34;&#34;
-        Generates a prompt for the OpenAI API to generate a team structure.
-
-        Args:
-            None
-
-        Returns:
-            str: The prompt for the OpenAI API.
-
-        Usage:
-            generator = AutoGenerator(framework=&#34;crewai&#34;, topic=&#34;Create a movie script about Cat in Mars&#34;)
-            prompt = generator.get_user_content()
-            print(prompt)
-        &#34;&#34;&#34;
-        user_content = &#34;&#34;&#34;Generate a team structure for  \&#34;&#34;&#34;&#34; + self.topic + &#34;&#34;&#34;\&#34; task. 
-No Input data will be provided to the team.
-The team will work in sequence. First role will pass the output to the next role, and so on.
-The last role will generate the final output.
-Think step by step.
-With maximum 3 roles, each with 1 task. Include role goals, backstories, task descriptions, and expected outputs.
-List of Available Tools: CodeDocsSearchTool, CSVSearchTool, DirectorySearchTool, DOCXSearchTool, DirectoryReadTool, FileReadTool, TXTSearchTool, JSONSearchTool, MDXSearchTool, PDFSearchTool, RagTool, ScrapeElementFromWebsiteTool, ScrapeWebsiteTool, WebsiteSearchTool, XMLSearchTool, YoutubeChannelSearchTool, YoutubeVideoSearchTool.
-Only use Available Tools. Do Not use any other tools. 
-Example Below: 
-Use below example to understand the structure of the output. 
-The final role you create should satisfy the provided task: &#34;&#34;&#34; + self.topic + &#34;&#34;&#34;.
-{
-&#34;roles&#34;: {
-&#34;narrative_designer&#34;: {
-&#34;role&#34;: &#34;Narrative Designer&#34;,
-&#34;goal&#34;: &#34;Create AI storylines&#34;,
-&#34;backstory&#34;: &#34;Skilled in narrative development for AI, with a focus on story resonance.&#34;,
-&#34;tools&#34;: [&#34;ScrapeWebsiteTool&#34;],
-&#34;tasks&#34;: {
-&#34;story_concept_development&#34;: {
-&#34;description&#34;: &#34;Craft a unique AI story concept with depth and engagement using concept from this page the content https://www.asthebirdfliesblog.com/posts/how-to-write-book-story-development .&#34;,
-&#34;expected_output&#34;: &#34;Document with narrative arcs, character bios, and settings.&#34;
-}
-}
-},
-&#34;scriptwriter&#34;: {
-&#34;role&#34;: &#34;Scriptwriter&#34;,
-&#34;goal&#34;: &#34;Write scripts from AI concepts&#34;,
-&#34;backstory&#34;: &#34;Expert in dialogue and script structure, translating concepts into scripts.&#34;,
-&#34;tasks&#34;: {
-&#34;scriptwriting_task&#34;: {
-&#34;description&#34;: &#34;Turn narrative concepts into scripts, including dialogue and scenes.&#34;,
-&#34;expected_output&#34;: &#34;Production-ready script with dialogue and scene details.&#34;
-}
-}
-}
-}
-}
-        &#34;&#34;&#34;
-        return user_content</code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.auto.AutoGenerator.convert_and_save"><code class="name flex">
-<span>def <span class="ident">convert_and_save</span></span>(<span>self, json_data)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Converts the provided JSON data into the desired YAML format and saves it to a file.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>json_data</code></strong> :&ensp;<code>dict</code></dt>
-<dd>The JSON data representing the team structure.</dd>
-<dt><strong><code>topic</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".</dd>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The name of the YAML file to save. Defaults to "test.yaml".</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.auto.AutoGenerator.generate"><code class="name flex">
-<span>def <span class="ident">generate</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Generates a team structure for the specified topic.</p>
-<h2 id="args">Args</h2>
-<p>None</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>str</code></dt>
-<dd>The full path of the YAML file containing the generated team structure.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>Exception</code></dt>
-<dd>If the generation process fails.</dd>
-</dl>
-<h2 id="usage">Usage</h2>
-<p>generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
-path = generator.generate()
-print(path)</p></div>
-</dd>
-<dt id="praisonai.auto.AutoGenerator.get_user_content"><code class="name flex">
-<span>def <span class="ident">get_user_content</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Generates a prompt for the OpenAI API to generate a team structure.</p>
-<h2 id="args">Args</h2>
-<p>None</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>str</code></dt>
-<dd>The prompt for the OpenAI API.</dd>
-</dl>
-<h2 id="usage">Usage</h2>
-<p>generator = AutoGenerator(framework="crewai", topic="Create a movie script about Cat in Mars")
-prompt = generator.get_user_content()
-print(prompt)</p></div>
-</dd>
-</dl>
-</dd>
-<dt id="praisonai.auto.RoleDetails"><code class="flex name class">
-<span>class <span class="ident">RoleDetails</span></span>
-<span>(</span><span>**data: Any)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.9/concepts/models/">https://docs.pydantic.dev/2.9/concepts/models/</a></p>
-<p>A base class for creating Pydantic models.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>__class_vars__</code></strong></dt>
-<dd>The names of the class variables defined on the model.</dd>
-<dt><strong><code>__private_attributes__</code></strong></dt>
-<dd>Metadata about the private attributes of the model.</dd>
-<dt><strong><code>__signature__</code></strong></dt>
-<dd>The synthesized <code>__init__</code> [<code>Signature</code>][inspect.Signature] of the model.</dd>
-<dt><strong><code>__pydantic_complete__</code></strong></dt>
-<dd>Whether model building is completed, or if there are still undefined fields.</dd>
-<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
-<dd>The core schema of the model.</dd>
-<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
-<dd>Whether the model has a custom <code>__init__</code> function.</dd>
-<dt><strong><code>__pydantic_decorators__</code></strong></dt>
-<dd>Metadata containing the decorators defined on the model.
-This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
-<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
-<dd>Metadata for generic models; contains data used for a similar purpose to
-<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
-<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
-<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
-<dt><strong><code>__pydantic_post_init__</code></strong></dt>
-<dd>The name of the post-init method for the model, if defined.</dd>
-<dt><strong><code>__pydantic_root_model__</code></strong></dt>
-<dd>Whether the model is a [<code>RootModel</code>][pydantic.root_model.RootModel].</dd>
-<dt><strong><code>__pydantic_serializer__</code></strong></dt>
-<dd>The <code>pydantic-core</code> <code>SchemaSerializer</code> used to dump instances of the model.</dd>
-<dt><strong><code>__pydantic_validator__</code></strong></dt>
-<dd>The <code>pydantic-core</code> <code>SchemaValidator</code> used to validate instances of the model.</dd>
-<dt><strong><code>__pydantic_extra__</code></strong></dt>
-<dd>A dictionary containing extra values, if [<code>extra</code>][pydantic.config.ConfigDict.extra]
-is set to <code>'allow'</code>.</dd>
-<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
-<dd>The names of fields explicitly set during instantiation.</dd>
-<dt><strong><code>__pydantic_private__</code></strong></dt>
-<dd>Values of private attributes set on the model instance.</dd>
-</dl>
-<p>Create a new model by parsing and validating input data from keyword arguments.</p>
-<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
-validated to form a valid model.</p>
-<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class RoleDetails(BaseModel):
-    role: str
-    goal: str
-    backstory: str
-    tasks: Dict[str, TaskDetails]
-    tools: List[str]</code></pre>
-</details>
-<h3>Ancestors</h3>
-<ul class="hlist">
-<li>pydantic.main.BaseModel</li>
-</ul>
-<h3>Class variables</h3>
-<dl>
-<dt id="praisonai.auto.RoleDetails.backstory"><code class="name">var <span class="ident">backstory</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.goal"><code class="name">var <span class="ident">goal</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.role"><code class="name">var <span class="ident">role</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.tasks"><code class="name">var <span class="ident">tasks</span> : Dict[str, <a title="praisonai.auto.TaskDetails" href="#praisonai.auto.TaskDetails">TaskDetails</a>]</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.RoleDetails.tools"><code class="name">var <span class="ident">tools</span> : List[str]</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</dd>
-<dt id="praisonai.auto.TaskDetails"><code class="flex name class">
-<span>class <span class="ident">TaskDetails</span></span>
-<span>(</span><span>**data: Any)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.9/concepts/models/">https://docs.pydantic.dev/2.9/concepts/models/</a></p>
-<p>A base class for creating Pydantic models.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>__class_vars__</code></strong></dt>
-<dd>The names of the class variables defined on the model.</dd>
-<dt><strong><code>__private_attributes__</code></strong></dt>
-<dd>Metadata about the private attributes of the model.</dd>
-<dt><strong><code>__signature__</code></strong></dt>
-<dd>The synthesized <code>__init__</code> [<code>Signature</code>][inspect.Signature] of the model.</dd>
-<dt><strong><code>__pydantic_complete__</code></strong></dt>
-<dd>Whether model building is completed, or if there are still undefined fields.</dd>
-<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
-<dd>The core schema of the model.</dd>
-<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
-<dd>Whether the model has a custom <code>__init__</code> function.</dd>
-<dt><strong><code>__pydantic_decorators__</code></strong></dt>
-<dd>Metadata containing the decorators defined on the model.
-This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
-<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
-<dd>Metadata for generic models; contains data used for a similar purpose to
-<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
-<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
-<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
-<dt><strong><code>__pydantic_post_init__</code></strong></dt>
-<dd>The name of the post-init method for the model, if defined.</dd>
-<dt><strong><code>__pydantic_root_model__</code></strong></dt>
-<dd>Whether the model is a [<code>RootModel</code>][pydantic.root_model.RootModel].</dd>
-<dt><strong><code>__pydantic_serializer__</code></strong></dt>
-<dd>The <code>pydantic-core</code> <code>SchemaSerializer</code> used to dump instances of the model.</dd>
-<dt><strong><code>__pydantic_validator__</code></strong></dt>
-<dd>The <code>pydantic-core</code> <code>SchemaValidator</code> used to validate instances of the model.</dd>
-<dt><strong><code>__pydantic_extra__</code></strong></dt>
-<dd>A dictionary containing extra values, if [<code>extra</code>][pydantic.config.ConfigDict.extra]
-is set to <code>'allow'</code>.</dd>
-<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
-<dd>The names of fields explicitly set during instantiation.</dd>
-<dt><strong><code>__pydantic_private__</code></strong></dt>
-<dd>Values of private attributes set on the model instance.</dd>
-</dl>
-<p>Create a new model by parsing and validating input data from keyword arguments.</p>
-<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
-validated to form a valid model.</p>
-<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class TaskDetails(BaseModel):
-    description: str
-    expected_output: str</code></pre>
-</details>
-<h3>Ancestors</h3>
-<ul class="hlist">
-<li>pydantic.main.BaseModel</li>
-</ul>
-<h3>Class variables</h3>
-<dl>
-<dt id="praisonai.auto.TaskDetails.description"><code class="name">var <span class="ident">description</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TaskDetails.expected_output"><code class="name">var <span class="ident">expected_output</span> : str</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TaskDetails.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TaskDetails.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TaskDetails.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</dd>
-<dt id="praisonai.auto.TeamStructure"><code class="flex name class">
-<span>class <span class="ident">TeamStructure</span></span>
-<span>(</span><span>**data: Any)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Usage docs: <a href="https://docs.pydantic.dev/2.9/concepts/models/">https://docs.pydantic.dev/2.9/concepts/models/</a></p>
-<p>A base class for creating Pydantic models.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>__class_vars__</code></strong></dt>
-<dd>The names of the class variables defined on the model.</dd>
-<dt><strong><code>__private_attributes__</code></strong></dt>
-<dd>Metadata about the private attributes of the model.</dd>
-<dt><strong><code>__signature__</code></strong></dt>
-<dd>The synthesized <code>__init__</code> [<code>Signature</code>][inspect.Signature] of the model.</dd>
-<dt><strong><code>__pydantic_complete__</code></strong></dt>
-<dd>Whether model building is completed, or if there are still undefined fields.</dd>
-<dt><strong><code>__pydantic_core_schema__</code></strong></dt>
-<dd>The core schema of the model.</dd>
-<dt><strong><code>__pydantic_custom_init__</code></strong></dt>
-<dd>Whether the model has a custom <code>__init__</code> function.</dd>
-<dt><strong><code>__pydantic_decorators__</code></strong></dt>
-<dd>Metadata containing the decorators defined on the model.
-This replaces <code>Model.__validators__</code> and <code>Model.__root_validators__</code> from Pydantic V1.</dd>
-<dt><strong><code>__pydantic_generic_metadata__</code></strong></dt>
-<dd>Metadata for generic models; contains data used for a similar purpose to
-<strong>args</strong>, <strong>origin</strong>, <strong>parameters</strong> in typing-module generics. May eventually be replaced by these.</dd>
-<dt><strong><code>__pydantic_parent_namespace__</code></strong></dt>
-<dd>Parent namespace of the model, used for automatic rebuilding of models.</dd>
-<dt><strong><code>__pydantic_post_init__</code></strong></dt>
-<dd>The name of the post-init method for the model, if defined.</dd>
-<dt><strong><code>__pydantic_root_model__</code></strong></dt>
-<dd>Whether the model is a [<code>RootModel</code>][pydantic.root_model.RootModel].</dd>
-<dt><strong><code>__pydantic_serializer__</code></strong></dt>
-<dd>The <code>pydantic-core</code> <code>SchemaSerializer</code> used to dump instances of the model.</dd>
-<dt><strong><code>__pydantic_validator__</code></strong></dt>
-<dd>The <code>pydantic-core</code> <code>SchemaValidator</code> used to validate instances of the model.</dd>
-<dt><strong><code>__pydantic_extra__</code></strong></dt>
-<dd>A dictionary containing extra values, if [<code>extra</code>][pydantic.config.ConfigDict.extra]
-is set to <code>'allow'</code>.</dd>
-<dt><strong><code>__pydantic_fields_set__</code></strong></dt>
-<dd>The names of fields explicitly set during instantiation.</dd>
-<dt><strong><code>__pydantic_private__</code></strong></dt>
-<dd>Values of private attributes set on the model instance.</dd>
-</dl>
-<p>Create a new model by parsing and validating input data from keyword arguments.</p>
-<p>Raises [<code>ValidationError</code>][pydantic_core.ValidationError] if the input data cannot be
-validated to form a valid model.</p>
-<p><code>self</code> is explicitly positional-only to allow <code>self</code> as a field name.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class TeamStructure(BaseModel):
-    roles: Dict[str, RoleDetails]</code></pre>
-</details>
-<h3>Ancestors</h3>
-<ul class="hlist">
-<li>pydantic.main.BaseModel</li>
-</ul>
-<h3>Class variables</h3>
-<dl>
-<dt id="praisonai.auto.TeamStructure.model_computed_fields"><code class="name">var <span class="ident">model_computed_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TeamStructure.model_config"><code class="name">var <span class="ident">model_config</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TeamStructure.model_fields"><code class="name">var <span class="ident">model_fields</span></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.auto.TeamStructure.roles"><code class="name">var <span class="ident">roles</span> : Dict[str, <a title="praisonai.auto.RoleDetails" href="#praisonai.auto.RoleDetails">RoleDetails</a>]</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="praisonai.auto.AutoGenerator" href="#praisonai.auto.AutoGenerator">AutoGenerator</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.auto.AutoGenerator.convert_and_save" href="#praisonai.auto.AutoGenerator.convert_and_save">convert_and_save</a></code></li>
-<li><code><a title="praisonai.auto.AutoGenerator.generate" href="#praisonai.auto.AutoGenerator.generate">generate</a></code></li>
-<li><code><a title="praisonai.auto.AutoGenerator.get_user_content" href="#praisonai.auto.AutoGenerator.get_user_content">get_user_content</a></code></li>
-</ul>
-</li>
-<li>
-<h4><code><a title="praisonai.auto.RoleDetails" href="#praisonai.auto.RoleDetails">RoleDetails</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.auto.RoleDetails.backstory" href="#praisonai.auto.RoleDetails.backstory">backstory</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.goal" href="#praisonai.auto.RoleDetails.goal">goal</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.model_computed_fields" href="#praisonai.auto.RoleDetails.model_computed_fields">model_computed_fields</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.model_config" href="#praisonai.auto.RoleDetails.model_config">model_config</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.model_fields" href="#praisonai.auto.RoleDetails.model_fields">model_fields</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.role" href="#praisonai.auto.RoleDetails.role">role</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.tasks" href="#praisonai.auto.RoleDetails.tasks">tasks</a></code></li>
-<li><code><a title="praisonai.auto.RoleDetails.tools" href="#praisonai.auto.RoleDetails.tools">tools</a></code></li>
-</ul>
-</li>
-<li>
-<h4><code><a title="praisonai.auto.TaskDetails" href="#praisonai.auto.TaskDetails">TaskDetails</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.auto.TaskDetails.description" href="#praisonai.auto.TaskDetails.description">description</a></code></li>
-<li><code><a title="praisonai.auto.TaskDetails.expected_output" href="#praisonai.auto.TaskDetails.expected_output">expected_output</a></code></li>
-<li><code><a title="praisonai.auto.TaskDetails.model_computed_fields" href="#praisonai.auto.TaskDetails.model_computed_fields">model_computed_fields</a></code></li>
-<li><code><a title="praisonai.auto.TaskDetails.model_config" href="#praisonai.auto.TaskDetails.model_config">model_config</a></code></li>
-<li><code><a title="praisonai.auto.TaskDetails.model_fields" href="#praisonai.auto.TaskDetails.model_fields">model_fields</a></code></li>
-</ul>
-</li>
-<li>
-<h4><code><a title="praisonai.auto.TeamStructure" href="#praisonai.auto.TeamStructure">TeamStructure</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.auto.TeamStructure.model_computed_fields" href="#praisonai.auto.TeamStructure.model_computed_fields">model_computed_fields</a></code></li>
-<li><code><a title="praisonai.auto.TeamStructure.model_config" href="#praisonai.auto.TeamStructure.model_config">model_config</a></code></li>
-<li><code><a title="praisonai.auto.TeamStructure.model_fields" href="#praisonai.auto.TeamStructure.model_fields">model_fields</a></code></li>
-<li><code><a title="praisonai.auto.TeamStructure.roles" href="#praisonai.auto.TeamStructure.roles">roles</a></code></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
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-<footer id="footer">
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-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.cli</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-functions">Functions</h2>
-<dl>
-<dt id="praisonai.cli.stream_subprocess"><code class="name flex">
-<span>def <span class="ident">stream_subprocess</span></span>(<span>command, env=None)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Execute a subprocess command and stream the output to the terminal in real-time.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>command</code></strong> :&ensp;<code>list</code></dt>
-<dd>A list containing the command and its arguments.</dd>
-<dt><strong><code>env</code></strong> :&ensp;<code>dict</code>, optional</dt>
-<dd>Environment variables for the subprocess.</dd>
-</dl></div>
-</dd>
-</dl>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.cli.PraisonAI"><code class="flex name class">
-<span>class <span class="ident">PraisonAI</span></span>
-<span>(</span><span>agent_file='agents.yaml', framework='', auto=False, init=False, agent_yaml=None, tools=None)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Initialize the PraisonAI object with default parameters.</p>
-<h2 id="parameters">Parameters</h2>
-<p>agent_file (str): The default agent file to use. Defaults to "agents.yaml".
-framework (str): The default framework to use. Defaults to "crewai".
-auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
-init (bool): A flag indicating whether to enable initialization mode. Defaults to False.</p>
-<h2 id="attributes">Attributes</h2>
-<dl>
-<dt><strong><code>config_list</code></strong> :&ensp;<code>list</code></dt>
-<dd>A list of configuration dictionaries for the OpenAI API.</dd>
-<dt><strong><code>agent_file</code></strong> :&ensp;<code>str</code></dt>
-<dd>The agent file to use.</dd>
-<dt><strong><code>framework</code></strong> :&ensp;<code>str</code></dt>
-<dd>The framework to use.</dd>
-<dt><strong><code>auto</code></strong> :&ensp;<code>bool</code></dt>
-<dd>A flag indicating whether to enable auto mode.</dd>
-<dt><strong><code>init</code></strong> :&ensp;<code>bool</code></dt>
-<dd>A flag indicating whether to enable initialization mode.</dd>
-<dt><strong><code>agent_yaml</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The content of the YAML file. Defaults to None.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class PraisonAI:
-    def __init__(self, agent_file=&#34;agents.yaml&#34;, framework=&#34;&#34;, auto=False, init=False, agent_yaml=None, tools=None):
-        &#34;&#34;&#34;
-        Initialize the PraisonAI object with default parameters.
-
-        Parameters:
-            agent_file (str): The default agent file to use. Defaults to &#34;agents.yaml&#34;.
-            framework (str): The default framework to use. Defaults to &#34;crewai&#34;.
-            auto (bool): A flag indicating whether to enable auto mode. Defaults to False.
-            init (bool): A flag indicating whether to enable initialization mode. Defaults to False.
-
-        Attributes:
-            config_list (list): A list of configuration dictionaries for the OpenAI API.
-            agent_file (str): The agent file to use.
-            framework (str): The framework to use.
-            auto (bool): A flag indicating whether to enable auto mode.
-            init (bool): A flag indicating whether to enable initialization mode.
-            agent_yaml (str, optional): The content of the YAML file. Defaults to None.
-        &#34;&#34;&#34;
-        self.agent_yaml = agent_yaml
-        self.config_list = [
-            {
-                &#39;model&#39;: os.environ.get(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;),
-                &#39;base_url&#39;: os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;),
-                &#39;api_key&#39;: os.environ.get(&#34;OPENAI_API_KEY&#34;)
-            }
-        ]
-        self.agent_file = agent_file
-        self.framework = framework
-        self.auto = auto
-        self.init = init
-        self.tools = tools or []  # Store tool class names as a list
-
-    def run(self):
-        &#34;&#34;&#34;
-        Run the PraisonAI application.
-        &#34;&#34;&#34;
-        self.main()
-
-    def main(self):
-        &#34;&#34;&#34;
-        The main function of the PraisonAI object. It parses the command-line arguments,
-        initializes the necessary attributes, and then calls the appropriate methods based on the
-        provided arguments.
-
-        Args:
-            self (PraisonAI): An instance of the PraisonAI class.
-    
-        Returns:
-            Any: Depending on the arguments provided, the function may return a result from the
-            AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
-            the successful creation of a file.
-        &#34;&#34;&#34;
-        args = self.parse_args()
-        if args is None:
-            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
-        if args.deploy:
-            from .deploy import CloudDeployer
-            deployer = CloudDeployer()
-            deployer.run_commands()
-            return
-        
-        if getattr(args, &#39;chat&#39;, False):
-            self.create_chainlit_chat_interface()
-            return
-        
-        if getattr(args, &#39;code&#39;, False):
-            self.create_code_interface()
-            return
-        
-        if getattr(args, &#39;realtime&#39;, False):
-            self.create_realtime_interface()
-            return
-        
-        if getattr(args, &#39;call&#39;, False):
-            call_args = []
-            if args.public:
-                call_args.append(&#39;--public&#39;)
-            call_module.main(call_args)
-            return
-        
-        if args.agent_file == &#39;train&#39;:
-            package_root = os.path.dirname(os.path.abspath(__file__))
-            config_yaml_destination = os.path.join(os.getcwd(), &#39;config.yaml&#39;)
-
-            # Create config.yaml only if it doesn&#39;t exist or --model or --dataset is provided
-            if not os.path.exists(config_yaml_destination) or args.model or args.dataset:
-                config = generate_config(
-                    model_name=args.model,
-                    hf_model_name=args.hf,
-                    ollama_model_name=args.ollama,
-                    dataset=[{
-                        &#34;name&#34;: args.dataset
-                    }]
-                )
-                with open(&#39;config.yaml&#39;, &#39;w&#39;) as f:
-                    yaml.dump(config, f, default_flow_style=False, indent=2) 
-
-            # Overwrite huggingface_save and ollama_save if --hf or --ollama are provided 
-            if args.hf:
-                config[&#34;huggingface_save&#34;] = &#34;true&#34;
-            if args.ollama:
-                config[&#34;ollama_save&#34;] = &#34;true&#34;
-
-            if &#39;init&#39; in sys.argv:
-                from praisonai.setup.setup_conda_env import main as setup_conda_main
-                setup_conda_main()
-                print(&#34;All packages installed&#34;)
-                return
-
-            try:
-                result = subprocess.check_output([&#39;conda&#39;, &#39;env&#39;, &#39;list&#39;])
-                if &#39;praison_env&#39; in result.decode(&#39;utf-8&#39;):
-                    print(&#34;Conda environment &#39;praison_env&#39; found.&#34;)
-                else:
-                    raise subprocess.CalledProcessError(1, &#39;grep&#39;)
-            except subprocess.CalledProcessError:
-                print(&#34;Conda environment &#39;praison_env&#39; not found. Setting it up...&#34;)
-                from praisonai.setup.setup_conda_env import main as setup_conda_main
-                setup_conda_main()
-                print(&#34;All packages installed.&#34;)
-
-            train_args = sys.argv[2:]  # Get all arguments after &#39;train&#39;
-            train_script_path = os.path.join(package_root, &#39;train.py&#39;)
-            
-            # Set environment variables
-            env = os.environ.copy()
-            env[&#39;PYTHONUNBUFFERED&#39;] = &#39;1&#39;
-            
-            stream_subprocess([&#39;conda&#39;, &#39;run&#39;, &#39;--no-capture-output&#39;, &#39;--name&#39;, &#39;praison_env&#39;, &#39;python&#39;, &#39;-u&#39;, train_script_path, &#39;train&#39;], env=env)
-            return
-        
-        invocation_cmd = &#34;praisonai&#34;
-        version_string = f&#34;PraisonAI version {__version__}&#34;
-        
-        self.framework = args.framework or self.framework 
-        
-        if args.agent_file:
-            if args.agent_file.startswith(&#34;tests.test&#34;): # Argument used for testing purposes. eg: python -m unittest tests.test 
-                print(&#34;test&#34;)
-            else:
-                self.agent_file = args.agent_file
-        
-        
-        if args.auto or args.init:
-            temp_topic = &#39; &#39;.join(args.auto) if args.auto else &#39; &#39;.join(args.init)
-            self.topic = temp_topic
-        elif self.auto or self.init:  # Use the auto attribute if args.auto is not provided
-            self.topic = self.auto
-            
-        if args.auto or self.auto:
-            self.agent_file = &#34;test.yaml&#34;
-            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
-            self.agent_file = generator.generate()
-            agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
-        elif args.init or self.init:
-            self.agent_file = &#34;agents.yaml&#34;
-            generator = AutoGenerator(topic=self.topic , framework=self.framework, agent_file=self.agent_file)
-            self.agent_file = generator.generate()
-            print(&#34;File {} created successfully&#34;.format(self.agent_file))
-            return &#34;File {} created successfully&#34;.format(self.agent_file)
-        
-        if args.ui:
-            if args.ui == &#34;gradio&#34;:
-                self.create_gradio_interface()
-            elif args.ui == &#34;chainlit&#34;:
-                self.create_chainlit_interface()
-            else:
-                # Modify below code to allow default ui
-                agents_generator = AgentsGenerator(
-                    self.agent_file, 
-                    self.framework, 
-                    self.config_list, 
-                    agent_yaml=self.agent_yaml,
-                    tools=self.tools  # Pass tools to AgentsGenerator
-                )
-                result = agents_generator.generate_crew_and_kickoff()
-                return result
-        else:
-            agents_generator = AgentsGenerator(
-                self.agent_file, 
-                self.framework, 
-                self.config_list, 
-                agent_yaml=self.agent_yaml,
-                tools=self.tools  # Pass tools to AgentsGenerator
-            )
-            result = agents_generator.generate_crew_and_kickoff()
-            return result
-            
-    def parse_args(self):
-        &#34;&#34;&#34;
-        Parse the command-line arguments for the PraisonAI CLI.
-
-        Args:
-            self (PraisonAI): An instance of the PraisonAI class.
-
-        Returns:
-            argparse.Namespace: An object containing the parsed command-line arguments.
-
-        Raises:
-            argparse.ArgumentError: If the arguments provided are invalid.
-
-        Example:
-            &gt;&gt;&gt; args = praison_ai.parse_args()
-            &gt;&gt;&gt; print(args.agent_file)  # Output: &#39;agents.yaml&#39;
-        &#34;&#34;&#34;
-        parser = argparse.ArgumentParser(prog=&#34;praisonai&#34;, description=&#34;praisonAI command-line interface&#34;)
-        parser.add_argument(&#34;--framework&#34;, choices=[&#34;crewai&#34;, &#34;autogen&#34;], help=&#34;Specify the framework&#34;)
-        parser.add_argument(&#34;--ui&#34;, choices=[&#34;chainlit&#34;, &#34;gradio&#34;], help=&#34;Specify the UI framework (gradio or chainlit).&#34;)
-        parser.add_argument(&#34;--auto&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
-        parser.add_argument(&#34;--init&#34;, nargs=argparse.REMAINDER, help=&#34;Enable auto mode and pass arguments for it&#34;)
-        parser.add_argument(&#34;agent_file&#34;, nargs=&#34;?&#34;, help=&#34;Specify the agent file&#34;)
-        parser.add_argument(&#34;--deploy&#34;, action=&#34;store_true&#34;, help=&#34;Deploy the application&#34;) 
-        parser.add_argument(&#34;--model&#34;, type=str, help=&#34;Model name&#34;)
-        parser.add_argument(&#34;--hf&#34;, type=str, help=&#34;Hugging Face model name&#34;)
-        parser.add_argument(&#34;--ollama&#34;, type=str, help=&#34;Ollama model name&#34;)
-        parser.add_argument(&#34;--dataset&#34;, type=str, help=&#34;Dataset name for training&#34;, default=&#34;yahma/alpaca-cleaned&#34;)
-        parser.add_argument(&#34;--realtime&#34;, action=&#34;store_true&#34;, help=&#34;Start the realtime voice interaction interface&#34;)
-        parser.add_argument(&#34;--call&#34;, action=&#34;store_true&#34;, help=&#34;Start the PraisonAI Call server&#34;)
-        parser.add_argument(&#34;--public&#34;, action=&#34;store_true&#34;, help=&#34;Use ngrok to expose the server publicly (only with --call)&#34;)
-        args, unknown_args = parser.parse_known_args()
-
-        if unknown_args and unknown_args[0] == &#39;-b&#39; and unknown_args[1] == &#39;api:app&#39;:
-            args.agent_file = &#39;agents.yaml&#39;
-        if args.agent_file == &#39;api:app&#39; or args.agent_file == &#39;/app/api:app&#39;:
-            args.agent_file = &#39;agents.yaml&#39;
-        if args.agent_file == &#39;ui&#39;:
-            args.ui = &#39;chainlit&#39;
-        if args.agent_file == &#39;chat&#39;:
-            args.ui = &#39;chainlit&#39;
-            args.chat = True
-        if args.agent_file == &#39;code&#39;:
-            args.ui = &#39;chainlit&#39;
-            args.code = True
-        if args.agent_file == &#39;realtime&#39;:
-            args.realtime = True
-        if args.agent_file == &#39;call&#39;:
-            args.call = True
-
-        return args
-    
-    def create_chainlit_chat_interface(self):
-        &#34;&#34;&#34;
-        Create a Chainlit interface for the chat application.
-
-        This function sets up a Chainlit application that listens for messages.
-        When a message is received, it runs PraisonAI with the provided message as the topic.
-        The generated agents are then used to perform tasks.
-
-        Returns:
-            None: This function does not return any value. It starts the Chainlit application.
-        &#34;&#34;&#34;
-        if CHAINLIT_AVAILABLE:
-            import praisonai
-            os.environ[&#34;CHAINLIT_PORT&#34;] = &#34;8084&#34;
-            root_path = os.path.join(os.path.expanduser(&#34;~&#34;), &#34;.praison&#34;)
-            os.environ[&#34;CHAINLIT_APP_ROOT&#34;] = root_path
-            public_folder = os.path.join(os.path.dirname(praisonai.__file__), &#39;public&#39;)
-            if not os.path.exists(os.path.join(root_path, &#34;public&#34;)):  # Check if the folder exists in the current directory
-                if os.path.exists(public_folder):
-                    shutil.copytree(public_folder, os.path.join(root_path, &#34;public&#34;), dirs_exist_ok=True)
-                    logging.info(&#34;Public folder copied successfully!&#34;)
-                else:
-                    logging.info(&#34;Public folder not found in the package.&#34;)
-            else:
-                logging.info(&#34;Public folder already exists.&#34;)
-            chat_ui_path = os.path.join(os.path.dirname(praisonai.__file__), &#39;ui&#39;, &#39;chat.py&#39;)
-            chainlit_run([chat_ui_path])
-        else:
-            print(&#34;ERROR: Chat UI is not installed. Please install it with &#39;pip install \&#34;praisonai\[chat]\&#34;&#39; to use the chat UI.&#34;)
-            
-    def create_code_interface(self):
-        &#34;&#34;&#34;
-        Create a Chainlit interface for the code application.
-
-        This function sets up a Chainlit application that listens for messages.
-        When a message is received, it runs PraisonAI with the provided message as the topic.
-        The generated agents are then used to perform tasks.
-
-        Returns:
-            None: This function does not return any value. It starts the Chainlit application.
-        &#34;&#34;&#34;
-        if CHAINLIT_AVAILABLE:
-            import praisonai
-            os.environ[&#34;CHAINLIT_PORT&#34;] = &#34;8086&#34;
-            root_path = os.path.join(os.path.expanduser(&#34;~&#34;), &#34;.praison&#34;)
-            os.environ[&#34;CHAINLIT_APP_ROOT&#34;] = root_path
-            public_folder = os.path.join(os.path.dirname(__file__), &#39;public&#39;)
-            if not os.path.exists(os.path.join(root_path, &#34;public&#34;)):  # Check if the folder exists in the current directory
-                if os.path.exists(public_folder):
-                    shutil.copytree(public_folder, os.path.join(root_path, &#34;public&#34;), dirs_exist_ok=True)
-                    logging.info(&#34;Public folder copied successfully!&#34;)
-                else:
-                    logging.info(&#34;Public folder not found in the package.&#34;)
-            else:
-                logging.info(&#34;Public folder already exists.&#34;)
-            code_ui_path = os.path.join(os.path.dirname(praisonai.__file__), &#39;ui&#39;, &#39;code.py&#39;)
-            chainlit_run([code_ui_path])
-        else:
-            print(&#34;ERROR: Code UI is not installed. Please install it with &#39;pip install \&#34;praisonai\[code]\&#34;&#39; to use the code UI.&#34;)
-
-    def create_gradio_interface(self):
-        &#34;&#34;&#34;
-        Create a Gradio interface for generating agents and performing tasks.
-
-        Args:
-            self (PraisonAI): An instance of the PraisonAI class.
-
-        Returns:
-            None: This method does not return any value. It launches the Gradio interface.
-
-        Raises:
-            None: This method does not raise any exceptions.
-
-        Example:
-            &gt;&gt;&gt; praison_ai.create_gradio_interface()
-        &#34;&#34;&#34;
-        if GRADIO_AVAILABLE:
-            def generate_crew_and_kickoff_interface(auto_args, framework):
-                &#34;&#34;&#34;
-                Generate a crew and kick off tasks based on the provided auto arguments and framework.
-
-                Args:
-                    auto_args (list): Topic.
-                    framework (str): The framework to use for generating agents.
-
-                Returns:
-                    str: A string representing the result of generating the crew and kicking off tasks.
-
-                Raises:
-                    None: This method does not raise any exceptions.
-
-                Example:
-                    &gt;&gt;&gt; result = generate_crew_and_kickoff_interface(&#34;Create a movie about Cat in Mars&#34;, &#34;crewai&#34;)
-                    &gt;&gt;&gt; print(result)
-                &#34;&#34;&#34;
-                self.framework = framework
-                self.agent_file = &#34;test.yaml&#34;
-                generator = AutoGenerator(topic=auto_args , framework=self.framework)
-                self.agent_file = generator.generate()
-                agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
-                result = agents_generator.generate_crew_and_kickoff()
-                return result
-
-            gr.Interface(
-                fn=generate_crew_and_kickoff_interface,
-                inputs=[gr.Textbox(lines=2, label=&#34;Auto Args&#34;), gr.Dropdown(choices=[&#34;crewai&#34;, &#34;autogen&#34;], label=&#34;Framework&#34;)],
-                outputs=&#34;textbox&#34;,
-                title=&#34;Praison AI Studio&#34;,
-                description=&#34;Create Agents and perform tasks&#34;,
-                theme=&#34;default&#34;
-            ).launch()
-        else:
-            print(&#34;ERROR: Gradio is not installed. Please install it with &#39;pip install gradio&#39; to use this feature.&#34;) 
-        
-    def create_chainlit_interface(self):
-        &#34;&#34;&#34;
-        Create a Chainlit interface for generating agents and performing tasks.
-
-        This function sets up a Chainlit application that listens for messages.
-        When a message is received, it runs PraisonAI with the provided message as the topic.
-        The generated agents are then used to perform tasks.
-
-        Returns:
-            None: This function does not return any value. It starts the Chainlit application.
-        &#34;&#34;&#34;
-        if CHAINLIT_AVAILABLE:
-            import praisonai
-            os.environ[&#34;CHAINLIT_PORT&#34;] = &#34;8082&#34;
-            # Get the path to the &#39;public&#39; folder within the package
-            public_folder = os.path.join(os.path.dirname(praisonai.__file__), &#39;public&#39;)
-            if not os.path.exists(&#34;public&#34;):  # Check if the folder exists in the current directory
-                if os.path.exists(public_folder):
-                    shutil.copytree(public_folder, &#39;public&#39;, dirs_exist_ok=True)
-                    logging.info(&#34;Public folder copied successfully!&#34;)
-                else:
-                    logging.info(&#34;Public folder not found in the package.&#34;)
-            else:
-                logging.info(&#34;Public folder already exists.&#34;)
-            chainlit_ui_path = os.path.join(os.path.dirname(praisonai.__file__), &#39;chainlit_ui.py&#39;)
-            chainlit_run([chainlit_ui_path])
-        else:
-            print(&#34;ERROR: Chainlit is not installed. Please install it with &#39;pip install \&#34;praisonai\[ui]\&#34;&#39; to use the UI.&#34;)        
-
-    def create_realtime_interface(self):
-        &#34;&#34;&#34;
-        Create a Chainlit interface for the realtime voice interaction application.
-        &#34;&#34;&#34;
-        if CHAINLIT_AVAILABLE:
-            import praisonai
-            os.environ[&#34;CHAINLIT_PORT&#34;] = &#34;8088&#34;  # Ensure this port is not in use by another service
-            root_path = os.path.join(os.path.expanduser(&#34;~&#34;), &#34;.praison&#34;)
-            os.environ[&#34;CHAINLIT_APP_ROOT&#34;] = root_path
-            public_folder = os.path.join(os.path.dirname(praisonai.__file__), &#39;public&#39;)
-            if not os.path.exists(os.path.join(root_path, &#34;public&#34;)):
-                if os.path.exists(public_folder):
-                    shutil.copytree(public_folder, os.path.join(root_path, &#34;public&#34;), dirs_exist_ok=True)
-                    logging.info(&#34;Public folder copied successfully!&#34;)
-                else:
-                    logging.info(&#34;Public folder not found in the package.&#34;)
-            else:
-                logging.info(&#34;Public folder already exists.&#34;)
-            realtime_ui_path = os.path.join(os.path.dirname(praisonai.__file__), &#39;ui&#39;, &#39;realtime.py&#39;)
-            chainlit_run([realtime_ui_path])
-        else:
-            print(&#34;ERROR: Realtime UI is not installed. Please install it with &#39;pip install \&#34;praisonai[realtime]\&#34;&#39; to use the realtime UI.&#34;)</code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.cli.PraisonAI.create_chainlit_chat_interface"><code class="name flex">
-<span>def <span class="ident">create_chainlit_chat_interface</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Create a Chainlit interface for the chat application.</p>
-<p>This function sets up a Chainlit application that listens for messages.
-When a message is received, it runs PraisonAI with the provided message as the topic.
-The generated agents are then used to perform tasks.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd>This function does not return any value. It starts the Chainlit application.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.create_chainlit_interface"><code class="name flex">
-<span>def <span class="ident">create_chainlit_interface</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Create a Chainlit interface for generating agents and performing tasks.</p>
-<p>This function sets up a Chainlit application that listens for messages.
-When a message is received, it runs PraisonAI with the provided message as the topic.
-The generated agents are then used to perform tasks.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd>This function does not return any value. It starts the Chainlit application.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.create_code_interface"><code class="name flex">
-<span>def <span class="ident">create_code_interface</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Create a Chainlit interface for the code application.</p>
-<p>This function sets up a Chainlit application that listens for messages.
-When a message is received, it runs PraisonAI with the provided message as the topic.
-The generated agents are then used to perform tasks.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd>This function does not return any value. It starts the Chainlit application.</dd>
-</dl></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.create_gradio_interface"><code class="name flex">
-<span>def <span class="ident">create_gradio_interface</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Create a Gradio interface for generating agents and performing tasks.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>self</code></strong> :&ensp;<code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
-<dd>An instance of the PraisonAI class.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd>This method does not return any value. It launches the Gradio interface.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>None</code></dt>
-<dd>This method does not raise any exceptions.</dd>
-</dl>
-<h2 id="example">Example</h2>
-<pre><code class="language-python-repl">&gt;&gt;&gt; praison_ai.create_gradio_interface()
-</code></pre></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.create_realtime_interface"><code class="name flex">
-<span>def <span class="ident">create_realtime_interface</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Create a Chainlit interface for the realtime voice interaction application.</p></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.main"><code class="name flex">
-<span>def <span class="ident">main</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>The main function of the PraisonAI object. It parses the command-line arguments,
-initializes the necessary attributes, and then calls the appropriate methods based on the
-provided arguments.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>self</code></strong> :&ensp;<code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
-<dd>An instance of the PraisonAI class.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>Any</code></dt>
-<dd>Depending on the arguments provided, the function may return a result from the</dd>
-</dl>
-<p>AgentsGenerator, a deployment result from the CloudDeployer, or a message indicating
-the successful creation of a file.</p></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.parse_args"><code class="name flex">
-<span>def <span class="ident">parse_args</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Parse the command-line arguments for the PraisonAI CLI.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>self</code></strong> :&ensp;<code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></dt>
-<dd>An instance of the PraisonAI class.</dd>
-</dl>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>argparse.Namespace</code></dt>
-<dd>An object containing the parsed command-line arguments.</dd>
-</dl>
-<h2 id="raises">Raises</h2>
-<dl>
-<dt><code>argparse.ArgumentError</code></dt>
-<dd>If the arguments provided are invalid.</dd>
-</dl>
-<h2 id="example">Example</h2>
-<pre><code class="language-python-repl">&gt;&gt;&gt; args = praison_ai.parse_args()
-&gt;&gt;&gt; print(args.agent_file)  # Output: 'agents.yaml'
-</code></pre></div>
-</dd>
-<dt id="praisonai.cli.PraisonAI.run"><code class="name flex">
-<span>def <span class="ident">run</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Run the PraisonAI application.</p></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai" href="index.html">praisonai</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-functions">Functions</a></h3>
-<ul class="">
-<li><code><a title="praisonai.cli.stream_subprocess" href="#praisonai.cli.stream_subprocess">stream_subprocess</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="praisonai.cli.PraisonAI" href="#praisonai.cli.PraisonAI">PraisonAI</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.cli.PraisonAI.create_chainlit_chat_interface" href="#praisonai.cli.PraisonAI.create_chainlit_chat_interface">create_chainlit_chat_interface</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.create_chainlit_interface" href="#praisonai.cli.PraisonAI.create_chainlit_interface">create_chainlit_interface</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.create_code_interface" href="#praisonai.cli.PraisonAI.create_code_interface">create_code_interface</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.create_gradio_interface" href="#praisonai.cli.PraisonAI.create_gradio_interface">create_gradio_interface</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.create_realtime_interface" href="#praisonai.cli.PraisonAI.create_realtime_interface">create_realtime_interface</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.main" href="#praisonai.cli.PraisonAI.main">main</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.parse_args" href="#praisonai.cli.PraisonAI.parse_args">parse_args</a></code></li>
-<li><code><a title="praisonai.cli.PraisonAI.run" href="#praisonai.cli.PraisonAI.run">run</a></code></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-</nav>
-</main>
-<footer id="footer">
-<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.1</a>.</p>
-</footer>
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docs/api/praisonai/deploy.html

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-</head>
-<body>
-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.deploy</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.deploy.CloudDeployer"><code class="flex name class">
-<span>class <span class="ident">CloudDeployer</span></span>
-</code></dt>
-<dd>
-<div class="desc"><p>A class for deploying a cloud-based application.</p>
-<h2 id="attributes">Attributes</h2>
-<p>None</p>
-<h2 id="methods">Methods</h2>
-<p><strong>init</strong>(self):
-Loads environment variables from .env file or system and sets them.</p>
-<p>Loads environment variables from .env file or system and sets them.</p>
-<h2 id="parameters">Parameters</h2>
-<p>self: An instance of the CloudDeployer class.</p>
-<h2 id="returns">Returns</h2>
-<p>None</p>
-<h2 id="raises">Raises</h2>
-<p>None</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class CloudDeployer:
-    &#34;&#34;&#34;
-    A class for deploying a cloud-based application.
-
-    Attributes:
-        None
-
-    Methods:
-        __init__(self):
-            Loads environment variables from .env file or system and sets them.
-
-    &#34;&#34;&#34;
-    def __init__(self):
-        &#34;&#34;&#34;
-        Loads environment variables from .env file or system and sets them.
-
-        Parameters:
-            self: An instance of the CloudDeployer class.
-
-        Returns:
-            None
-
-        Raises:
-            None
-
-        &#34;&#34;&#34;
-        # Load environment variables from .env file or system
-        load_dotenv()
-        self.set_environment_variables()
-
-    def create_dockerfile(self):
-        &#34;&#34;&#34;
-        Creates a Dockerfile for the application.
-
-        Parameters:
-            self: An instance of the CloudDeployer class.
-
-        Returns:
-            None
-
-        Raises:
-            None
-
-        This method creates a Dockerfile in the current directory with the specified content.
-        The Dockerfile is used to build a Docker image for the application.
-        The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
-        set the working directory to /app, copy the current directory contents into the container,
-        install the required Python packages (flask, praisonai, gunicorn, and markdown),
-        expose port 8080, and run the application using Gunicorn.
-        &#34;&#34;&#34;
-        with open(&#34;Dockerfile&#34;, &#34;w&#34;) as file:
-            file.write(&#34;FROM python:3.11-slim\n&#34;)
-            file.write(&#34;WORKDIR /app\n&#34;)
-            file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.2.22 gunicorn markdown\n&#34;)
-            file.write(&#34;EXPOSE 8080\n&#34;)
-            file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
-            
-    def create_api_file(self):
-        &#34;&#34;&#34;
-        Creates an API file for the application.
-
-        Parameters:
-            self (CloudDeployer): An instance of the CloudDeployer class.
-
-        Returns:
-            None
-
-        This method creates an API file named &#34;api.py&#34; in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path (&#34;/&#34;) and uses the Markdown library to format the output.
-        &#34;&#34;&#34;
-        with open(&#34;api.py&#34;, &#34;w&#34;) as file:
-            file.write(&#34;from flask import Flask\n&#34;)
-            file.write(&#34;from praisonai import PraisonAI\n&#34;)
-            file.write(&#34;import markdown\n\n&#34;)
-            file.write(&#34;app = Flask(__name__)\n\n&#34;)
-            file.write(&#34;def basic():\n&#34;)
-            file.write(&#34;    praisonai = PraisonAI(agent_file=\&#34;agents.yaml\&#34;)\n&#34;)
-            file.write(&#34;    return praisonai.run()\n\n&#34;)
-            file.write(&#34;@app.route(&#39;/&#39;)\n&#34;)
-            file.write(&#34;def home():\n&#34;)
-            file.write(&#34;    output = basic()\n&#34;)
-            file.write(&#34;    html_output = markdown.markdown(output)\n&#34;)
-            file.write(&#34;    return f&#39;&lt;html&gt;&lt;body&gt;{html_output}&lt;/body&gt;&lt;/html&gt;&#39;\n\n&#34;)
-            file.write(&#34;if __name__ == \&#34;__main__\&#34;:\n&#34;)
-            file.write(&#34;    app.run(debug=True)\n&#34;)
-    
-    def set_environment_variables(self):
-        &#34;&#34;&#34;Sets environment variables with fallback to .env values or defaults.&#34;&#34;&#34;
-        os.environ[&#34;OPENAI_MODEL_NAME&#34;] = os.getenv(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;)
-        os.environ[&#34;OPENAI_API_KEY&#34;] = os.getenv(&#34;OPENAI_API_KEY&#34;, &#34;Enter your API key&#34;)
-        os.environ[&#34;OPENAI_API_BASE&#34;] = os.getenv(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;)
-
-    def run_commands(self):
-        &#34;&#34;&#34;
-        Sets environment variables with fallback to .env values or defaults.
-
-        Parameters:
-            None
-
-        Returns:
-            None
-
-        Raises:
-            None
-
-        This method sets environment variables for the application. It uses the `os.environ` dictionary to set the following environment variables:
-
-        - `OPENAI_MODEL_NAME`: The name of the OpenAI model to use. If not specified in the .env file, it defaults to &#34;gpt-4o&#34;.
-        - `OPENAI_API_KEY`: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to &#34;Enter your API key&#34;.
-        - `OPENAI_API_BASE`: The base URL for the OpenAI API. If not specified in the .env file, it defaults to &#34;https://api.openai.com/v1&#34;.
-        &#34;&#34;&#34;
-        self.create_api_file()
-        self.create_dockerfile()
-        &#34;&#34;&#34;Runs a sequence of shell commands for deployment, continues on error.&#34;&#34;&#34;
-        commands = [
-            &#34;yes | gcloud auth configure-docker us-central1-docker.pkg.dev&#34;,
-            &#34;gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1&#34;,
-            &#34;docker build --platform linux/amd64 -t gcr.io/$(gcloud config get-value project)/praisonai-app:latest .&#34;,
-            &#34;docker tag gcr.io/$(gcloud config get-value project)/praisonai-app:latest us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
-            &#34;docker push us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest&#34;,
-            &#34;gcloud run deploy praisonai-service --image us-central1-docker.pkg.dev/$(gcloud config get-value project)/praisonai-repository/praisonai-app:latest --platform managed --region us-central1 --allow-unauthenticated --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}&#34;
-        ]
-
-        for cmd in commands:
-            try:
-                subprocess.run(cmd, shell=True, check=True)
-            except subprocess.CalledProcessError as e:
-                print(f&#34;ERROR: Command &#39;{e.cmd}&#39; failed with exit status {e.returncode}&#34;)
-                print(f&#34;Continuing with the next command...&#34;)</code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.deploy.CloudDeployer.create_api_file"><code class="name flex">
-<span>def <span class="ident">create_api_file</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Creates an API file for the application.</p>
-<h2 id="parameters">Parameters</h2>
-<p>self (CloudDeployer): An instance of the CloudDeployer class.</p>
-<h2 id="returns">Returns</h2>
-<p>None
-This method creates an API file named "api.py" in the current directory. The file contains a basic Flask application that uses the PraisonAI library to run a simple agent and returns the output as an HTML page. The application listens on the root path ("/") and uses the Markdown library to format the output.</p></div>
-</dd>
-<dt id="praisonai.deploy.CloudDeployer.create_dockerfile"><code class="name flex">
-<span>def <span class="ident">create_dockerfile</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Creates a Dockerfile for the application.</p>
-<h2 id="parameters">Parameters</h2>
-<p>self: An instance of the CloudDeployer class.</p>
-<h2 id="returns">Returns</h2>
-<p>None</p>
-<h2 id="raises">Raises</h2>
-<p>None
-This method creates a Dockerfile in the current directory with the specified content.
-The Dockerfile is used to build a Docker image for the application.
-The content of the Dockerfile includes instructions to use the Python 3.11-slim base image,
-set the working directory to /app, copy the current directory contents into the container,
-install the required Python packages (flask, praisonai, gunicorn, and markdown),
-expose port 8080, and run the application using Gunicorn.</p></div>
-</dd>
-<dt id="praisonai.deploy.CloudDeployer.run_commands"><code class="name flex">
-<span>def <span class="ident">run_commands</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p>
-<h2 id="parameters">Parameters</h2>
-<p>None</p>
-<h2 id="returns">Returns</h2>
-<p>None</p>
-<h2 id="raises">Raises</h2>
-<p>None
-This method sets environment variables for the application. It uses the <code>os.environ</code> dictionary to set the following environment variables:</p>
-<ul>
-<li><code>OPENAI_MODEL_NAME</code>: The name of the OpenAI model to use. If not specified in the .env file, it defaults to "gpt-4o".</li>
-<li><code>OPENAI_API_KEY</code>: The API key for accessing the OpenAI API. If not specified in the .env file, it defaults to "Enter your API key".</li>
-<li><code>OPENAI_API_BASE</code>: The base URL for the OpenAI API. If not specified in the .env file, it defaults to "https://api.openai.com/v1".</li>
-</ul></div>
-</dd>
-<dt id="praisonai.deploy.CloudDeployer.set_environment_variables"><code class="name flex">
-<span>def <span class="ident">set_environment_variables</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Sets environment variables with fallback to .env values or defaults.</p></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
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docs/api/praisonai/inbuilt_tools/autogen_tools.html

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-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.inbuilt_tools.autogen_tools</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-functions">Functions</h2>
-<dl>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_CSVSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_CodeDocsSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_DOCXSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool"><code class="name flex">
-<span>def <span class="ident">autogen_DirectoryReadTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_DirectorySearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool"><code class="name flex">
-<span>def <span class="ident">autogen_FileReadTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_JSONSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_MDXSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_PDFSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_RagTool"><code class="name flex">
-<span>def <span class="ident">autogen_RagTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool"><code class="name flex">
-<span>def <span class="ident">autogen_ScrapeElementFromWebsiteTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool"><code class="name flex">
-<span>def <span class="ident">autogen_ScrapeWebsiteTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_TXTSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_WebsiteSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_XMLSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_YoutubeChannelSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool"><code class="name flex">
-<span>def <span class="ident">autogen_YoutubeVideoSearchTool</span></span>(<span>assistant, user_proxy)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function"><code class="name flex">
-<span>def <span class="ident">create_autogen_tool_function</span></span>(<span>tool_name)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</section>
-<section>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai.inbuilt_tools" href="index.html">praisonai.inbuilt_tools</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-functions">Functions</a></h3>
-<ul class="">
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool">autogen_CSVSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool">autogen_CodeDocsSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool">autogen_DOCXSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool">autogen_DirectoryReadTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool">autogen_DirectorySearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool">autogen_FileReadTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool">autogen_JSONSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool">autogen_MDXSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool">autogen_PDFSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_RagTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_RagTool">autogen_RagTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool">autogen_ScrapeElementFromWebsiteTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool">autogen_ScrapeWebsiteTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool">autogen_TXTSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool">autogen_WebsiteSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool">autogen_XMLSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool">autogen_YoutubeChannelSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool">autogen_YoutubeVideoSearchTool</a></code></li>
-<li><code><a title="praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function" href="#praisonai.inbuilt_tools.autogen_tools.create_autogen_tool_function">create_autogen_tool_function</a></code></li>
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docs/api/praisonai/inbuilt_tools/index.html

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docs/api/praisonai/inc/config.html

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docs/api/praisonai/inc/models.html

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-</head>
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-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.inc.models</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.inc.models.PraisonAIModel"><code class="flex name class">
-<span>class <span class="ident">PraisonAIModel</span></span>
-<span>(</span><span>model=None, api_key_var=None, base_url=None)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Initializes the PraisonAIModel with the provided parameters or environment variables.</p>
-<h2 id="args">Args</h2>
-<dl>
-<dt><strong><code>model</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The name of the OpenAI model. Defaults to None.</dd>
-<dt><strong><code>api_key_var</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The OpenAI API key. Defaults to None.</dd>
-<dt><strong><code>base_url</code></strong> :&ensp;<code>str</code>, optional</dt>
-<dd>The base URL for the OpenAI API. Defaults to None.</dd>
-</dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class PraisonAIModel:
-    def __init__(self, model=None, api_key_var=None, base_url=None):
-        &#34;&#34;&#34;
-        Initializes the PraisonAIModel with the provided parameters or environment variables.
-
-        Args:
-            model (str, optional): The name of the OpenAI model. Defaults to None.
-            api_key_var (str, optional): The OpenAI API key. Defaults to None.
-            base_url (str, optional): The base URL for the OpenAI API. Defaults to None.
-        &#34;&#34;&#34;
-        self.model =  model or os.getenv(&#34;OPENAI_MODEL_NAME&#34;, &#34;gpt-4o&#34;)
-        if self.model.startswith(&#34;openai/&#34;):
-            self.api_key_var = &#34;OPENAI_API_KEY&#34;
-            self.base_url = base_url or &#34;https://api.openai.com/v1&#34;
-            self.model_name = self.model.replace(&#34;openai/&#34;, &#34;&#34;)
-        elif self.model.startswith(&#34;groq/&#34;):
-            self.api_key_var = &#34;GROQ_API_KEY&#34;
-            self.base_url = base_url or &#34;https://api.groq.com/openai/v1&#34;
-            self.model_name = self.model.replace(&#34;groq/&#34;, &#34;&#34;)
-        elif self.model.startswith(&#34;cohere/&#34;):
-            self.api_key_var = &#34;COHERE_API_KEY&#34;
-            self.base_url = &#34;&#34;
-            self.model_name = self.model.replace(&#34;cohere/&#34;, &#34;&#34;)
-        elif self.model.startswith(&#34;ollama/&#34;):
-            self.api_key_var = &#34;OLLAMA_API_KEY&#34;
-            self.base_url = base_url or &#34;http://localhost:11434/v1&#34;
-            self.model_name = self.model.replace(&#34;ollama/&#34;, &#34;&#34;)
-        elif self.model.startswith(&#34;anthropic/&#34;):
-            self.api_key_var = &#34;ANTHROPIC_API_KEY&#34;
-            self.base_url = &#34;&#34;
-            self.model_name = self.model.replace(&#34;anthropic/&#34;, &#34;&#34;)
-        elif self.model.startswith(&#34;google/&#34;):
-            self.api_key_var = &#34;GOOGLE_API_KEY&#34;
-            self.base_url = &#34;&#34;
-            self.model_name = self.model.replace(&#34;google/&#34;, &#34;&#34;)
-        elif self.model.startswith(&#34;openrouter/&#34;):
-            self.api_key_var = &#34;OPENROUTER_API_KEY&#34;
-            self.base_url = base_url or &#34;https://openrouter.ai/api/v1&#34;
-            self.model_name = self.model.replace(&#34;openrouter/&#34;, &#34;&#34;)
-        else: 
-            self.api_key_var = api_key_var or &#34;OPENAI_API_KEY&#34; 
-            self.base_url = base_url or os.environ.get(&#34;OPENAI_API_BASE&#34;, &#34;https://api.openai.com/v1&#34;)
-            self.model_name = self.model
-        logger.debug(f&#34;Initialized PraisonAIModel with model {self.model_name}, api_key_var {self.api_key_var}, and base_url {self.base_url}&#34;)
-        self.api_key = os.environ.get(self.api_key_var, &#34;nokey&#34;)
-
-    def get_model(self):
-        &#34;&#34;&#34;
-        Returns an instance of the langchain Chat client with the configured parameters.
-
-        Returns:
-            Chat: An instance of the langchain Chat client.
-        &#34;&#34;&#34;
-        if self.model.startswith(&#34;google/&#34;):
-            if GOOGLE_GENAI_AVAILABLE:
-                return ChatGoogleGenerativeAI(
-                    model=self.model_name,
-                    google_api_key=self.api_key
-                )
-            else:
-                raise ImportError(
-                    &#34;Required Langchain Integration &#39;langchain-google-genai&#39; not found. &#34;
-                    &#34;Please install with &#39;pip install langchain-google-genai&#39;&#34;
-                )
-        elif self.model.startswith(&#34;cohere/&#34;):
-            if COHERE_AVAILABLE:
-                return ChatCohere(
-                    model=self.model_name,
-                    cohere_api_key=self.api_key,
-                )
-            else:
-                raise ImportError(
-                    &#34;Required Langchain Integration &#39;langchain-cohere&#39; not found. &#34;
-                    &#34;Please install with &#39;pip install langchain-cohere&#39;&#34;
-                )
-        elif self.model.startswith(&#34;anthropic/&#34;):
-            if ANTHROPIC_AVAILABLE:
-                return ChatAnthropic(
-                    model=self.model_name,
-                    anthropic_api_key=self.api_key,
-                )
-            else:
-                raise ImportError(
-                    &#34;Required Langchain Integration &#39;langchain-anthropic&#39; not found. &#34;
-                    &#34;Please install with &#39;pip install langchain-anthropic&#39;&#34;
-                )
-        elif OPENAI_AVAILABLE:
-            return ChatOpenAI(
-                model=self.model_name,
-                api_key=self.api_key,
-                base_url=self.base_url,
-            )
-        else:
-            raise ImportError(
-                &#34;Required Langchain Integration &#39;langchain-openai&#39; not found. &#34;
-                &#34;Please install with &#39;pip install langchain-openai&#39;&#34;
-            )</code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.inc.models.PraisonAIModel.get_model"><code class="name flex">
-<span>def <span class="ident">get_model</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Returns an instance of the langchain Chat client with the configured parameters.</p>
-<h2 id="returns">Returns</h2>
-<dl>
-<dt><code>Chat</code></dt>
-<dd>An instance of the langchain Chat client.</dd>
-</dl></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
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-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai.inc" href="index.html">praisonai.inc</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="praisonai.inc.models.PraisonAIModel" href="#praisonai.inc.models.PraisonAIModel">PraisonAIModel</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.inc.models.PraisonAIModel.get_model" href="#praisonai.inc.models.PraisonAIModel.get_model">get_model</a></code></li>
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docs/api/praisonai/index.html

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-<header>
-<h1 class="title">Package <code>praisonai</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-<h2 class="section-title" id="header-submodules">Sub-modules</h2>
-<dl>
-<dt><code class="name"><a title="praisonai.agents_generator" href="agents_generator.html">praisonai.agents_generator</a></code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt><code class="name"><a title="praisonai.auto" href="auto.html">praisonai.auto</a></code></dt>
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-<div class="desc"></div>
-</dd>
-<dt><code class="name"><a title="praisonai.cli" href="cli.html">praisonai.cli</a></code></dt>
-<dd>
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docs/api/praisonai/test.html

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-</head>
-<body>
-<main>
-<article id="content">
-<header>
-<h1 class="title">Module <code>praisonai.ui.context</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-functions">Functions</h2>
-<dl>
-<dt id="praisonai.ui.context.main"><code class="name flex">
-<span>def <span class="ident">main</span></span>(<span>)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.ui.context.ContextGatherer"><code class="flex name class">
-<span>class <span class="ident">ContextGatherer</span></span>
-<span>(</span><span>directory='.', output_file='context.txt', relevant_extensions=None, max_file_size=1000000, max_tokens=900000)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class ContextGatherer:
-    def __init__(self, directory=&#39;.&#39;, output_file=&#39;context.txt&#39;,
-                 relevant_extensions=None, max_file_size=1_000_000, max_tokens=900000):
-        self.directory = directory
-        self.output_file = output_file
-        self.relevant_extensions = relevant_extensions or [
-            &#39;.py&#39;, &#39;.js&#39;, &#39;.ts&#39;, &#39;.java&#39;, &#39;.rb&#39;, &#39;.php&#39;, &#39;.pl&#39;, &#39;.pm&#39;, &#39;.c&#39;, &#39;.h&#39;,
-            &#39;.cpp&#39;, &#39;.hpp&#39;, &#39;.cs&#39;, &#39;.vb&#39;, &#39;.swift&#39;, &#39;.kt&#39;, &#39;.m&#39;, &#39;.mm&#39;, &#39;.go&#39;, &#39;.rs&#39;,
-            &#39;.hs&#39;, &#39;.r&#39;, &#39;.lua&#39;, &#39;.sh&#39;, &#39;.bat&#39;, &#39;.clj&#39;, &#39;.scala&#39;, &#39;.erl&#39;, &#39;.ex&#39;,
-            &#39;.ml&#39;, &#39;.fs&#39;, &#39;.groovy&#39;, &#39;.jsm&#39;, &#39;.jsx&#39;, &#39;.tsx&#39;, &#39;.yaml&#39;
-        ]
-        self.max_file_size = max_file_size
-        self.max_tokens = int(os.getenv(&#34;PRAISONAI_MAX_TOKENS&#34;, max_tokens))
-        self.ignore_patterns = self.get_ignore_patterns()
-        self.include_paths = self.get_include_paths()
-        self.included_files = []
-
-    def get_ignore_patterns(self):
-        &#34;&#34;&#34;
-        Loads ignore patterns from various sources, prioritizing them in
-        the following order:
-            1. .praisonignore
-            2. settings.yaml (under code.ignore_files)
-            3. PRAISONAI_IGNORE_FILES environment variable
-            4. .gitignore
-            5. Default patterns
-        &#34;&#34;&#34;
-        ignore_patterns = []
-
-        def load_from_file(filepath):
-            if os.path.exists(filepath):
-                with open(filepath, &#39;r&#39;) as f:
-                    ignore_patterns.extend(
-                        line.strip() for line in f 
-                        if line.strip() and not line.startswith(&#39;#&#39;)
-                    )
-
-        # 1. Load from .praisonignore
-        load_from_file(os.path.join(self.directory, &#39;.praisonignore&#39;))
-
-        # 2. Load from settings.yaml
-        settings_path = os.path.join(self.directory, &#39;settings.yaml&#39;)
-        if os.path.exists(settings_path):
-            with open(settings_path, &#39;r&#39;) as f:
-                settings = yaml.safe_load(f)
-                if &#39;code&#39; in settings and &#39;ignore_files&#39; in settings[&#39;code&#39;]:
-                    ignore_patterns.extend(settings[&#39;code&#39;][&#39;ignore_files&#39;])
-
-        # 3. Load from environment variable
-        ignore_files_env = os.getenv(&#34;PRAISONAI_IGNORE_FILES&#34;)
-        if ignore_files_env:
-            ignore_patterns.extend(ignore_files_env.split(&#34;,&#34;))
-
-        # 4. Load from .gitignore
-        load_from_file(os.path.join(self.directory, &#39;.gitignore&#39;))
-
-        # 5. Default patterns (only if no patterns loaded from above sources)
-        if not ignore_patterns:
-            ignore_patterns = [
-                &#34;.*&#34;, &#34;*.pyc&#34;, &#34;__pycache__&#34;, &#34;.git&#34;, &#34;.gitignore&#34;, &#34;.vscode&#34;,
-                &#34;.idea&#34;, &#34;.DS_Store&#34;, &#34;*.lock&#34;, &#34;*.pyc&#34;, &#34;.env&#34;, &#34;docs&#34;, &#34;tests&#34;, 
-                &#34;test&#34;, &#34;tmp&#34;, &#34;temp&#34;, &#34;*.txt&#34;, &#34;*.md&#34;, &#34;*.json&#34;, &#34;*.csv&#34;, &#34;*.tsv&#34;,
-                &#34;public&#34;, &#34;*.sql&#34;, &#34;*.sqlite&#34;, &#34;*.db&#34;, &#34;*.db3&#34;, &#34;*.sqlite3&#34;, 
-                &#34;*.log&#34;, &#34;*.zip&#34;, &#34;*.gz&#34;, &#34;*.tar&#34;, &#34;*.rar&#34;, &#34;*.7z&#34;, &#34;*.pdf&#34;, 
-                &#34;*.jpg&#34;, &#34;*.jpeg&#34;, &#34;*.png&#34;, &#34;*.gif&#34;, &#34;*.svg&#34;, &#34;cookbooks&#34;, 
-                &#34;assets&#34;, &#34;__pycache__&#34;, &#34;dist&#34;, &#34;build&#34;, &#34;node_modules&#34;, &#34;venv&#34;
-            ]
-            logger.debug(f&#34;Using default ignore patterns: {ignore_patterns}&#34;)
-
-        # Modify patterns to match directories and add leading &#39;*&#39; if necessary
-        modified_ignore_patterns = [
-            &#39;*&#39; + pattern if not pattern.startswith(&#39;.&#39;) and not pattern.startswith(&#39;*&#39;) else pattern
-            for pattern in ignore_patterns
-        ]
-        logger.debug(f&#34;Final ignore patterns: {modified_ignore_patterns}&#34;)
-        return modified_ignore_patterns
-
-    def get_include_paths(self):
-        &#34;&#34;&#34;
-        Loads include paths from:
-            1. .praisoninclude (includes ONLY files/directories listed)
-            2. .praisoncontext (if .praisoninclude doesn&#39;t exist, this is used
-               to include all other relevant files, excluding ignore patterns)
-        &#34;&#34;&#34;
-        include_paths = []
-        include_all = False  # Flag to indicate if we need to include all files
- 
-        include_file = os.path.join(self.directory, &#39;.praisoncontext&#39;)
-        if os.path.exists(include_file):
-            with open(include_file, &#39;r&#39;) as f:
-                include_paths.extend(
-                    line.strip() for line in f
-                    if line.strip() and not line.startswith(&#39;#&#39;)
-                )
- 
-        # If .praisoncontext doesn&#39;t exist, fall back to .praisoninclude
-        # for including all relevant files
-        if not include_paths: 
-            include_file = os.path.join(self.directory, &#39;.praisoninclude&#39;)
-            if os.path.exists(include_file):
-                with open(include_file, &#39;r&#39;) as f:
-                    include_paths.extend(
-                        line.strip() for line in f
-                        if line.strip() and not line.startswith(&#39;#&#39;)
-                    )
-                include_all = True  # Include all files along with specified paths
- 
-        return include_paths, include_all
-
-    def should_ignore(self, file_path):
-        &#34;&#34;&#34;
-        Check if a file or directory should be ignored based on patterns.
-        Handles both file names and directory names for more comprehensive filtering.
-        &#34;&#34;&#34;
-        relative_path = os.path.relpath(file_path, self.directory)
-        if relative_path.startswith(&#39;.&#39;):
-            return True
-        for pattern in self.ignore_patterns:
-            if fnmatch.fnmatch(relative_path, pattern) or \
-               fnmatch.fnmatch(os.path.basename(file_path), pattern):
-                return True
-        return False
-
-    def is_relevant_file(self, file_path):
-        &#34;&#34;&#34;Determine if a file is relevant for the context.&#34;&#34;&#34;
-        return os.path.isfile(file_path) and \
-               os.path.getsize(file_path) &lt;= self.max_file_size and \
-               any(file_path.endswith(ext) for ext in self.relevant_extensions)
-
-    def gather_context(self):
-        &#34;&#34;&#34;
-        Gather context from relevant files, respecting ignore patterns
-        and include options from .praisoninclude and .praisoncontext.
-        &#34;&#34;&#34;
-        context = []
-        total_files = 0
-        processed_files = 0
-        self.include_paths, include_all = self.get_include_paths()
-
-        def add_file_content(file_path):
-            &#34;&#34;&#34;Helper function to add file content to context.&#34;&#34;&#34;
-            try:
-                with open(file_path, &#39;r&#39;, encoding=&#39;utf-8&#39;) as f:
-                    content = f.read()
-                    context.append(
-                        f&#34;File: {file_path}\n\n{content}\n\n{&#39;=&#39; * 50}\n&#34;
-                    )
-                    self.included_files.append(
-                        Path(file_path).relative_to(self.directory)
-                    )
-            except Exception as e:
-                logger.error(f&#34;Error reading {file_path}: {e}&#34;)
-
-        def process_path(path):
-            &#34;&#34;&#34;Helper function to process a single path (file or directory).&#34;&#34;&#34;
-            nonlocal total_files, processed_files
-            if os.path.isdir(path):
-                for root, dirs, files in os.walk(path):
-                    total_files += len(files)
-                    dirs[:] = [
-                        d
-                        for d in dirs
-                        if not self.should_ignore(os.path.join(root, d))
-                    ]
-                    for file in files:
-                        file_path = os.path.join(root, file)
-                        if not self.should_ignore(file_path) and self.is_relevant_file(file_path):
-                            add_file_content(file_path)
-                        processed_files += 1
-                        print(
-                            f&#34;\rProcessed {processed_files}/{total_files} files&#34;,
-                            end=&#34;&#34;,
-                            flush=True,
-                        )
-            elif os.path.isfile(path) and self.is_relevant_file(path):
-                add_file_content(path)
-                processed_files += 1
-                print(
-                    f&#34;\rProcessed {processed_files}/1 files&#34;,
-                    end=&#34;&#34;,
-                    flush=True,
-                )
-
-        if include_all:
-            # Include ALL relevant files from the entire directory
-            process_path(self.directory)
-            
-            # Include files from .praisoninclude specifically
-            for include_path in self.include_paths:
-                full_path = os.path.join(self.directory, include_path)
-                process_path(full_path)
-        elif self.include_paths:
-            # Include only files specified in .praisoncontext
-            for include_path in self.include_paths:
-                full_path = os.path.join(self.directory, include_path)
-                process_path(full_path)
-        else:
-            # No include options, process the entire directory
-            process_path(self.directory)
-
-        print()  # New line after progress indicator
-        return &#34;\n&#34;.join(context)
-
-    def count_tokens(self, text):
-        &#34;&#34;&#34;Count tokens using a simple whitespace-based tokenizer.&#34;&#34;&#34;
-        return len(text.split())
-
-    def truncate_context(self, context):
-        &#34;&#34;&#34;Truncate context to stay within the token limit.&#34;&#34;&#34;
-        tokens = context.split()
-        if len(tokens) &gt; self.max_tokens:
-            truncated_context = &#39; &#39;.join(tokens[:self.max_tokens])
-            logger.warning(&#34;Context truncated due to token limit.&#34;)
-            return truncated_context
-        return context
-
-    def save_context(self, context):
-        &#34;&#34;&#34;Save the gathered context to a file.&#34;&#34;&#34;
-        with open(self.output_file, &#39;w&#39;, encoding=&#39;utf-8&#39;) as f:
-            f.write(context)
-
-    def get_context_tree(self):
-        &#34;&#34;&#34;Generate a formatted tree structure of included files and folders.&#34;&#34;&#34;
-        tree = []
-        start_dir = Path(self.directory)
-
-        def add_to_tree(path, prefix=&#39;&#39;):
-            contents = sorted(path.iterdir())
-            pointers = [(&#39;└── &#39; if i == len(contents) - 1 else &#39;├── &#39;) for i in range(len(contents))]
-            for pointer, item in zip(pointers, contents):
-                rel_path = item.relative_to(start_dir)
-                if rel_path in self.included_files:
-                    tree.append(f&#34;{prefix}{pointer}{rel_path}&#34;)
-
-                if item.is_dir():
-                    add_to_tree(item, prefix + (&#39;    &#39; if pointer == &#39;└── &#39; else &#39;│   &#39;))
-
-        add_to_tree(start_dir)
-        return &#39;\n&#39;.join(tree)
-
-    def run(self):
-        &#34;&#34;&#34;Execute the context gathering, truncation, and reporting.&#34;&#34;&#34;
-        context = self.gather_context()
-        context = self.truncate_context(context)
-        token_count = self.count_tokens(context)
-        print(f&#34;Context gathered successfully.&#34;)
-        print(f&#34;Total number of tokens (estimated): {token_count}&#34;)
-        # self.save_context(context)
-        context_tree = self.get_context_tree()
-        logger.debug(f&#34;Context tree:\n{context_tree}&#34;)
-        return context, token_count, context_tree</code></pre>
-</details>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.ui.context.ContextGatherer.count_tokens"><code class="name flex">
-<span>def <span class="ident">count_tokens</span></span>(<span>self, text)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Count tokens using a simple whitespace-based tokenizer.</p></div>
-</dd>
-<dt id="praisonai.ui.context.ContextGatherer.gather_context"><code class="name flex">
-<span>def <span class="ident">gather_context</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Gather context from relevant files, respecting ignore patterns
-and include options from .praisoninclude and .praisoncontext.</p></div>
-</dd>
-<dt id="praisonai.ui.context.ContextGatherer.get_context_tree"><code class="name flex">
-<span>def <span class="ident">get_context_tree</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Generate a formatted tree structure of included files and folders.</p></div>
-</dd>
-<dt id="praisonai.ui.context.ContextGatherer.get_ignore_patterns"><code class="name flex">
-<span>def <span class="ident">get_ignore_patterns</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Loads ignore patterns from various sources, prioritizing them in
-the following order:
-1. .praisonignore
-2. settings.yaml (under code.ignore_files)
-3. PRAISONAI_IGNORE_FILES environment variable
-4. .gitignore
-5. Default patterns</p></div>
-</dd>
-<dt id="praisonai.ui.context.ContextGatherer.get_include_paths"><code class="name flex">
-<span>def <span class="ident">get_include_paths</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Loads include paths from:
-1. .praisoninclude (includes ONLY files/directories listed)
-2. .praisoncontext (if .praisoninclude doesn't exist, this is used
-to include all other relevant files, excluding ignore patterns)</p></div>
-</dd>
-<dt id="praisonai.ui.context.ContextGatherer.is_relevant_file"><code class="name flex">
-<span>def <span class="ident">is_relevant_file</span></span>(<span>self, file_path)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Determine if a file is relevant for the context.</p></div>
-</dd>
-<dt id="praisonai.ui.context.ContextGatherer.run"><code class="name flex">
-<span>def <span class="ident">run</span></span>(<span>self)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Execute the context gathering, truncation, and reporting.</p></div>
-</dd>
-<dt id="praisonai.ui.context.ContextGatherer.save_context"><code class="name flex">
-<span>def <span class="ident">save_context</span></span>(<span>self, context)</span>
-</code></dt>
-<dd>
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-<h1 class="title">Module <code>praisonai.ui.sql_alchemy</code></h1>
-</header>
-<section id="section-intro">
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-</section>
-<section>
-<h2 class="section-title" id="header-classes">Classes</h2>
-<dl>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer"><code class="flex name class">
-<span>class <span class="ident">SQLAlchemyDataLayer</span></span>
-<span>(</span><span>conninfo: str, ssl_require: bool = False, storage_provider: Optional[chainlit.data.base.BaseStorageClient] = None, user_thread_limit: Optional[int] = 1000, show_logger: Optional[bool] = False)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Base class for data persistence.</p></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">class SQLAlchemyDataLayer(BaseDataLayer):
-    def __init__(
-        self,
-        conninfo: str,
-        ssl_require: bool = False,
-        storage_provider: Optional[BaseStorageClient] = None,
-        user_thread_limit: Optional[int] = 1000,
-        show_logger: Optional[bool] = False,
-    ):
-        self._conninfo = conninfo
-        self.user_thread_limit = user_thread_limit
-        self.show_logger = show_logger
-        ssl_args = {}
-        if ssl_require:
-            # Create an SSL context to require an SSL connection
-            ssl_context = ssl.create_default_context()
-            ssl_context.check_hostname = False
-            ssl_context.verify_mode = ssl.CERT_NONE
-            ssl_args[&#34;ssl&#34;] = ssl_context
-        self.engine: AsyncEngine = create_async_engine(
-            self._conninfo, connect_args=ssl_args
-        )
-        self.async_session = sessionmaker(
-            bind=self.engine, expire_on_commit=False, class_=AsyncSession
-        )  # type: ignore
-        if storage_provider:
-            self.storage_provider: Optional[BaseStorageClient] = storage_provider
-            if self.show_logger:
-                logger.info(&#34;SQLAlchemyDataLayer storage client initialized&#34;)
-        else:
-            self.storage_provider = None
-            logger.warn(
-                &#34;SQLAlchemyDataLayer storage client is not initialized and elements will not be persisted!&#34;
-            )
-
-    async def build_debug_url(self) -&gt; str:
-        return &#34;&#34;
-
-    ###### SQL Helpers ######
-    async def execute_sql(
-        self, query: str, parameters: dict
-    ) -&gt; Union[List[Dict[str, Any]], int, None]:
-        parameterized_query = text(query)
-        async with self.async_session() as session:
-            try:
-                await session.begin()
-                result = await session.execute(parameterized_query, parameters)
-                await session.commit()
-                if result.returns_rows:
-                    json_result = [dict(row._mapping) for row in result.fetchall()]
-                    clean_json_result = self.clean_result(json_result)
-                    assert isinstance(clean_json_result, list) or isinstance(
-                        clean_json_result, int
-                    )
-                    return clean_json_result
-                else:
-                    return result.rowcount
-            except SQLAlchemyError as e:
-                await session.rollback()
-                logger.warn(f&#34;An error occurred: {e}&#34;)
-                return None
-            except Exception as e:
-                await session.rollback()
-                logger.warn(f&#34;An unexpected error occurred: {e}&#34;)
-                return None
-
-    async def get_current_timestamp(self) -&gt; str:
-        return datetime.now().isoformat() + &#34;Z&#34;
-
-    def clean_result(self, obj):
-        &#34;&#34;&#34;Recursively change UUID -&gt; str and serialize dictionaries&#34;&#34;&#34;
-        if isinstance(obj, dict):
-            return {k: self.clean_result(v) for k, v in obj.items()}
-        elif isinstance(obj, list):
-            return [self.clean_result(item) for item in obj]
-        elif isinstance(obj, uuid.UUID):
-            return str(obj)
-        return obj
-
-    ###### User ######
-    async def get_user(self, identifier: str) -&gt; Optional[PersistedUser]:
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: get_user, identifier={identifier}&#34;)
-        query = &#34;SELECT * FROM users WHERE identifier = :identifier&#34;
-        parameters = {&#34;identifier&#34;: identifier}
-        result = await self.execute_sql(query=query, parameters=parameters)
-        if result and isinstance(result, list):
-            user_data = result[0]
-
-            # SQLite returns JSON as string, we most convert it. (#1137)
-            metadata = user_data.get(&#34;metadata&#34;, {})
-            if isinstance(metadata, str):
-                metadata = json.loads(metadata)
-
-            assert isinstance(metadata, dict)
-            assert isinstance(user_data[&#34;id&#34;], str)
-            assert isinstance(user_data[&#34;identifier&#34;], str)
-            assert isinstance(user_data[&#34;createdAt&#34;], str)
-
-            return PersistedUser(
-                id=user_data[&#34;id&#34;],
-                identifier=user_data[&#34;identifier&#34;],
-                createdAt=user_data[&#34;createdAt&#34;],
-                metadata=metadata,
-            )
-        return None
-
-    async def _get_user_identifer_by_id(self, user_id: str) -&gt; str:
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: _get_user_identifer_by_id, user_id={user_id}&#34;)
-        query = &#34;SELECT identifier FROM users WHERE id = :user_id&#34;
-        parameters = {&#34;user_id&#34;: user_id}
-        result = await self.execute_sql(query=query, parameters=parameters)
-
-        assert result
-        assert isinstance(result, list)
-
-        return result[0][&#34;identifier&#34;]
-
-    async def _get_user_id_by_thread(self, thread_id: str) -&gt; Optional[str]:
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: _get_user_id_by_thread, thread_id={thread_id}&#34;)
-        query = &#34;SELECT userId FROM threads WHERE id = :thread_id&#34;
-        parameters = {&#34;thread_id&#34;: thread_id}
-        result = await self.execute_sql(query=query, parameters=parameters)
-        if result:
-            assert isinstance(result, list)
-            return result[0][&#34;userId&#34;]
-
-        return None
-
-    async def create_user(self, user: User) -&gt; Optional[PersistedUser]:
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: create_user, user_identifier={user.identifier}&#34;)
-        existing_user: Optional[&#34;PersistedUser&#34;] = await self.get_user(user.identifier)
-        user_dict: Dict[str, Any] = {
-            &#34;identifier&#34;: str(user.identifier),
-            &#34;metadata&#34;: json.dumps(user.metadata) or {},
-        }
-        if not existing_user:  # create the user
-            if self.show_logger:
-                logger.info(&#34;SQLAlchemy: create_user, creating the user&#34;)
-            user_dict[&#34;id&#34;] = str(uuid.uuid4())
-            user_dict[&#34;createdAt&#34;] = await self.get_current_timestamp()
-            query = &#34;&#34;&#34;INSERT INTO users (&#34;id&#34;, &#34;identifier&#34;, &#34;createdAt&#34;, &#34;metadata&#34;) VALUES (:id, :identifier, :createdAt, :metadata)&#34;&#34;&#34;
-            await self.execute_sql(query=query, parameters=user_dict)
-        else:  # update the user
-            if self.show_logger:
-                logger.info(&#34;SQLAlchemy: update user metadata&#34;)
-            query = &#34;&#34;&#34;UPDATE users SET &#34;metadata&#34; = :metadata WHERE &#34;identifier&#34; = :identifier&#34;&#34;&#34;
-            await self.execute_sql(
-                query=query, parameters=user_dict
-            )  # We want to update the metadata
-        return await self.get_user(user.identifier)
-
-    ###### Threads ######
-    async def get_thread_author(self, thread_id: str) -&gt; str:
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: get_thread_author, thread_id={thread_id}&#34;)
-        query = &#34;&#34;&#34;SELECT &#34;userIdentifier&#34; FROM threads WHERE &#34;id&#34; = :id&#34;&#34;&#34;
-        parameters = {&#34;id&#34;: thread_id}
-        result = await self.execute_sql(query=query, parameters=parameters)
-        if isinstance(result, list) and result:
-            author_identifier = result[0].get(&#34;userIdentifier&#34;)
-            if author_identifier is not None:
-                return author_identifier
-        raise ValueError(f&#34;Author not found for thread_id {thread_id}&#34;)
-
-    async def get_thread(self, thread_id: str) -&gt; Optional[ThreadDict]:
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: get_thread, thread_id={thread_id}&#34;)
-        user_threads: Optional[List[ThreadDict]] = await self.get_all_user_threads(
-            thread_id=thread_id
-        )
-        if user_threads:
-            return user_threads[0]
-        else:
-            return None
-
-    async def update_thread(
-        self,
-        thread_id: str,
-        name: Optional[str] = None,
-        user_id: Optional[str] = None,
-        metadata: Optional[Dict] = None,
-        tags: Optional[List[str]] = None,
-    ):
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: update_thread, thread_id={thread_id}&#34;)
-
-        user_identifier = None
-        if user_id:
-            user_identifier = await self._get_user_identifer_by_id(user_id)
-
-        data = {
-            &#34;id&#34;: thread_id,
-            &#34;createdAt&#34;: (
-                await self.get_current_timestamp() if metadata is None else None
-            ),
-            &#34;name&#34;: (
-                name
-                if name is not None
-                else (metadata.get(&#34;name&#34;) if metadata and &#34;name&#34; in metadata else None)
-            ),
-            &#34;userId&#34;: user_id,
-            &#34;userIdentifier&#34;: user_identifier,
-            &#34;tags&#34;: tags,
-            &#34;metadata&#34;: json.dumps(metadata) if metadata else None,
-        }
-        parameters = {
-            key: value for key, value in data.items() if value is not None
-        }  # Remove keys with None values
-        columns = &#34;, &#34;.join(f&#39;&#34;{key}&#34;&#39; for key in parameters.keys())
-        values = &#34;, &#34;.join(f&#34;:{key}&#34; for key in parameters.keys())
-        updates = &#34;, &#34;.join(
-            f&#39;&#34;{key}&#34; = EXCLUDED.&#34;{key}&#34;&#39; for key in parameters.keys() if key != &#34;id&#34;
-        )
-        query = f&#34;&#34;&#34;
-            INSERT INTO threads ({columns})
-            VALUES ({values})
-            ON CONFLICT (&#34;id&#34;) DO UPDATE
-            SET {updates};
-        &#34;&#34;&#34;
-        await self.execute_sql(query=query, parameters=parameters)
-
-    async def delete_thread(self, thread_id: str):
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: delete_thread, thread_id={thread_id}&#34;)
-        # Delete feedbacks/elements/steps/thread
-        feedbacks_query = &#34;&#34;&#34;DELETE FROM feedbacks WHERE &#34;forId&#34; IN (SELECT &#34;id&#34; FROM steps WHERE &#34;threadId&#34; = :id)&#34;&#34;&#34;
-        elements_query = &#34;&#34;&#34;DELETE FROM elements WHERE &#34;threadId&#34; = :id&#34;&#34;&#34;
-        steps_query = &#34;&#34;&#34;DELETE FROM steps WHERE &#34;threadId&#34; = :id&#34;&#34;&#34;
-        thread_query = &#34;&#34;&#34;DELETE FROM threads WHERE &#34;id&#34; = :id&#34;&#34;&#34;
-        parameters = {&#34;id&#34;: thread_id}
-        await self.execute_sql(query=feedbacks_query, parameters=parameters)
-        await self.execute_sql(query=elements_query, parameters=parameters)
-        await self.execute_sql(query=steps_query, parameters=parameters)
-        await self.execute_sql(query=thread_query, parameters=parameters)
-
-    async def list_threads(
-        self, pagination: Pagination, filters: ThreadFilter
-    ) -&gt; PaginatedResponse:
-        if self.show_logger:
-            logger.info(
-                f&#34;SQLAlchemy: list_threads, pagination={pagination}, filters={filters}&#34;
-            )
-        if not filters.userId:
-            raise ValueError(&#34;userId is required&#34;)
-        all_user_threads: List[ThreadDict] = (
-            await self.get_all_user_threads(user_id=filters.userId) or []
-        )
-
-        search_keyword = filters.search.lower() if filters.search else None
-        feedback_value = int(filters.feedback) if filters.feedback else None
-
-        filtered_threads = []
-        for thread in all_user_threads:
-            keyword_match = True
-            feedback_match = True
-            if search_keyword or feedback_value is not None:
-                if search_keyword:
-                    keyword_match = any(
-                        search_keyword in step[&#34;output&#34;].lower()
-                        for step in thread[&#34;steps&#34;]
-                        if &#34;output&#34; in step
-                    )
-                if feedback_value is not None:
-                    feedback_match = False  # Assume no match until found
-                    for step in thread[&#34;steps&#34;]:
-                        feedback = step.get(&#34;feedback&#34;)
-                        if feedback and feedback.get(&#34;value&#34;) == feedback_value:
-                            feedback_match = True
-                            break
-            if keyword_match and feedback_match:
-                filtered_threads.append(thread)
-
-        start = 0
-        if pagination.cursor:
-            for i, thread in enumerate(filtered_threads):
-                if (
-                    thread[&#34;id&#34;] == pagination.cursor
-                ):  # Find the start index using pagination.cursor
-                    start = i + 1
-                    break
-        end = start + pagination.first
-        paginated_threads = filtered_threads[start:end] or []
-
-        has_next_page = len(filtered_threads) &gt; end
-        start_cursor = paginated_threads[0][&#34;id&#34;] if paginated_threads else None
-        end_cursor = paginated_threads[-1][&#34;id&#34;] if paginated_threads else None
-
-        return PaginatedResponse(
-            pageInfo=PageInfo(
-                hasNextPage=has_next_page,
-                startCursor=start_cursor,
-                endCursor=end_cursor,
-            ),
-            data=paginated_threads,
-        )
-
-    ###### Steps ######
-    @queue_until_user_message()
-    async def create_step(self, step_dict: &#34;StepDict&#34;):
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: create_step, step_id={step_dict.get(&#39;id&#39;)}&#34;)
-
-        step_dict[&#34;showInput&#34;] = (
-            str(step_dict.get(&#34;showInput&#34;, &#34;&#34;)).lower()
-            if &#34;showInput&#34; in step_dict
-            else None
-        )
-        parameters = {
-            key: value
-            for key, value in step_dict.items()
-            if value is not None and not (isinstance(value, dict) and not value)
-        }
-        parameters[&#34;metadata&#34;] = json.dumps(step_dict.get(&#34;metadata&#34;, {}))
-        parameters[&#34;generation&#34;] = json.dumps(step_dict.get(&#34;generation&#34;, {}))
-        columns = &#34;, &#34;.join(f&#39;&#34;{key}&#34;&#39; for key in parameters.keys())
-        values = &#34;, &#34;.join(f&#34;:{key}&#34; for key in parameters.keys())
-        updates = &#34;, &#34;.join(
-            f&#39;&#34;{key}&#34; = :{key}&#39; for key in parameters.keys() if key != &#34;id&#34;
-        )
-        query = f&#34;&#34;&#34;
-            INSERT INTO steps ({columns})
-            VALUES ({values})
-            ON CONFLICT (id) DO UPDATE
-            SET {updates};
-        &#34;&#34;&#34;
-        await self.execute_sql(query=query, parameters=parameters)
-
-    @queue_until_user_message()
-    async def update_step(self, step_dict: &#34;StepDict&#34;):
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: update_step, step_id={step_dict.get(&#39;id&#39;)}&#34;)
-        await self.create_step(step_dict)
-
-    @queue_until_user_message()
-    async def delete_step(self, step_id: str):
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: delete_step, step_id={step_id}&#34;)
-        # Delete feedbacks/elements/steps
-        feedbacks_query = &#34;&#34;&#34;DELETE FROM feedbacks WHERE &#34;forId&#34; = :id&#34;&#34;&#34;
-        elements_query = &#34;&#34;&#34;DELETE FROM elements WHERE &#34;forId&#34; = :id&#34;&#34;&#34;
-        steps_query = &#34;&#34;&#34;DELETE FROM steps WHERE &#34;id&#34; = :id&#34;&#34;&#34;
-        parameters = {&#34;id&#34;: step_id}
-        await self.execute_sql(query=feedbacks_query, parameters=parameters)
-        await self.execute_sql(query=elements_query, parameters=parameters)
-        await self.execute_sql(query=steps_query, parameters=parameters)
-
-    ###### Feedback ######
-    async def upsert_feedback(self, feedback: Feedback) -&gt; str:
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: upsert_feedback, feedback_id={feedback.id}&#34;)
-        feedback.id = feedback.id or str(uuid.uuid4())
-        feedback_dict = asdict(feedback)
-        parameters = {
-            key: value for key, value in feedback_dict.items() if value is not None
-        }
-
-        columns = &#34;, &#34;.join(f&#39;&#34;{key}&#34;&#39; for key in parameters.keys())
-        values = &#34;, &#34;.join(f&#34;:{key}&#34; for key in parameters.keys())
-        updates = &#34;, &#34;.join(
-            f&#39;&#34;{key}&#34; = :{key}&#39; for key in parameters.keys() if key != &#34;id&#34;
-        )
-        query = f&#34;&#34;&#34;
-            INSERT INTO feedbacks ({columns})
-            VALUES ({values})
-            ON CONFLICT (id) DO UPDATE
-            SET {updates};
-        &#34;&#34;&#34;
-        await self.execute_sql(query=query, parameters=parameters)
-        return feedback.id
-
-    async def delete_feedback(self, feedback_id: str) -&gt; bool:
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: delete_feedback, feedback_id={feedback_id}&#34;)
-        query = &#34;&#34;&#34;DELETE FROM feedbacks WHERE &#34;id&#34; = :feedback_id&#34;&#34;&#34;
-        parameters = {&#34;feedback_id&#34;: feedback_id}
-        await self.execute_sql(query=query, parameters=parameters)
-        return True
-
-    ###### Elements ######
-    async def get_element(
-        self, thread_id: str, element_id: str
-    ) -&gt; Optional[&#34;ElementDict&#34;]:
-        if self.show_logger:
-            logger.info(
-                f&#34;SQLAlchemy: get_element, thread_id={thread_id}, element_id={element_id}&#34;
-            )
-        query = &#34;&#34;&#34;SELECT * FROM elements WHERE &#34;threadId&#34; = :thread_id AND &#34;id&#34; = :element_id&#34;&#34;&#34;
-        parameters = {&#34;thread_id&#34;: thread_id, &#34;element_id&#34;: element_id}
-        element: Union[List[Dict[str, Any]], int, None] = await self.execute_sql(
-            query=query, parameters=parameters
-        )
-        if isinstance(element, list) and element:
-            element_dict: Dict[str, Any] = element[0]
-            return ElementDict(
-                id=element_dict[&#34;id&#34;],
-                threadId=element_dict.get(&#34;threadId&#34;),
-                type=element_dict[&#34;type&#34;],
-                chainlitKey=element_dict.get(&#34;chainlitKey&#34;),
-                url=element_dict.get(&#34;url&#34;),
-                objectKey=element_dict.get(&#34;objectKey&#34;),
-                name=element_dict[&#34;name&#34;],
-                display=element_dict[&#34;display&#34;],
-                size=element_dict.get(&#34;size&#34;),
-                language=element_dict.get(&#34;language&#34;),
-                page=element_dict.get(&#34;page&#34;),
-                autoPlay=element_dict.get(&#34;autoPlay&#34;),
-                playerConfig=element_dict.get(&#34;playerConfig&#34;),
-                forId=element_dict.get(&#34;forId&#34;),
-                mime=element_dict.get(&#34;mime&#34;),
-            )
-        else:
-            return None
-
-    @queue_until_user_message()
-    async def create_element(self, element: &#34;Element&#34;):
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: create_element, element_id = {element.id}&#34;)
-
-        if not self.storage_provider:
-            logger.warn(
-                &#34;SQLAlchemy: create_element error. No blob_storage_client is configured!&#34;
-            )
-            return
-        if not element.for_id:
-            return
-
-        content: Optional[Union[bytes, str]] = None
-
-        if element.path:
-            async with aiofiles.open(element.path, &#34;rb&#34;) as f:
-                content = await f.read()
-        elif element.url:
-            async with aiohttp.ClientSession() as session:
-                async with session.get(element.url) as response:
-                    if response.status == 200:
-                        content = await response.read()
-                    else:
-                        content = None
-        elif element.content:
-            content = element.content
-        else:
-            raise ValueError(&#34;Element url, path or content must be provided&#34;)
-        if content is None:
-            raise ValueError(&#34;Content is None, cannot upload file&#34;)
-
-        user_id: str = await self._get_user_id_by_thread(element.thread_id) or &#34;unknown&#34;
-        file_object_key = f&#34;{user_id}/{element.id}&#34; + (
-            f&#34;/{element.name}&#34; if element.name else &#34;&#34;
-        )
-
-        if not element.mime:
-            element.mime = &#34;application/octet-stream&#34;
-
-        uploaded_file = await self.storage_provider.upload_file(
-            object_key=file_object_key, data=content, mime=element.mime, overwrite=True
-        )
-        if not uploaded_file:
-            raise ValueError(
-                &#34;SQLAlchemy Error: create_element, Failed to persist data in storage_provider&#34;
-            )
-
-        element_dict: ElementDict = element.to_dict()
-
-        element_dict[&#34;url&#34;] = uploaded_file.get(&#34;url&#34;)
-        element_dict[&#34;objectKey&#34;] = uploaded_file.get(&#34;object_key&#34;)
-        element_dict_cleaned = {k: v for k, v in element_dict.items() if v is not None}
-
-        columns = &#34;, &#34;.join(f&#39;&#34;{column}&#34;&#39; for column in element_dict_cleaned.keys())
-        placeholders = &#34;, &#34;.join(f&#34;:{column}&#34; for column in element_dict_cleaned.keys())
-        query = f&#34;INSERT INTO elements ({columns}) VALUES ({placeholders})&#34;
-        await self.execute_sql(query=query, parameters=element_dict_cleaned)
-
-    @queue_until_user_message()
-    async def delete_element(self, element_id: str, thread_id: Optional[str] = None):
-        if self.show_logger:
-            logger.info(f&#34;SQLAlchemy: delete_element, element_id={element_id}&#34;)
-        query = &#34;&#34;&#34;DELETE FROM elements WHERE &#34;id&#34; = :id&#34;&#34;&#34;
-        parameters = {&#34;id&#34;: element_id}
-        await self.execute_sql(query=query, parameters=parameters)
-
-    async def get_all_user_threads(
-        self, user_id: Optional[str] = None, thread_id: Optional[str] = None
-    ) -&gt; Optional[List[ThreadDict]]:
-        &#34;&#34;&#34;Fetch all user threads up to self.user_thread_limit, or one thread by id if thread_id is provided.&#34;&#34;&#34;
-        if self.show_logger:
-            logger.info(&#34;SQLAlchemy: get_all_user_threads&#34;)
-        user_threads_query = &#34;&#34;&#34;
-            SELECT
-                &#34;id&#34; AS thread_id,
-                &#34;createdAt&#34; AS thread_createdat,
-                &#34;name&#34; AS thread_name,
-                &#34;userId&#34; AS user_id,
-                &#34;userIdentifier&#34; AS user_identifier,
-                &#34;tags&#34; AS thread_tags,
-                &#34;metadata&#34; AS thread_metadata
-            FROM threads
-            WHERE &#34;userId&#34; = :user_id OR &#34;id&#34; = :thread_id
-            ORDER BY &#34;createdAt&#34; DESC
-            LIMIT :limit
-        &#34;&#34;&#34;
-        user_threads = await self.execute_sql(
-            query=user_threads_query,
-            parameters={
-                &#34;user_id&#34;: user_id,
-                &#34;limit&#34;: self.user_thread_limit,
-                &#34;thread_id&#34;: thread_id,
-            },
-        )
-        if not isinstance(user_threads, list):
-            return None
-        if not user_threads:
-            return []
-        else:
-            thread_ids = (
-                &#34;(&#39;&#34;
-                + &#34;&#39;,&#39;&#34;.join(map(str, [thread[&#34;thread_id&#34;] for thread in user_threads]))
-                + &#34;&#39;)&#34;
-            )
-
-        steps_feedbacks_query = f&#34;&#34;&#34;
-            SELECT
-                s.&#34;id&#34; AS step_id,
-                s.&#34;name&#34; AS step_name,
-                s.&#34;type&#34; AS step_type,
-                s.&#34;threadId&#34; AS step_threadid,
-                s.&#34;parentId&#34; AS step_parentid,
-                s.&#34;streaming&#34; AS step_streaming,
-                s.&#34;waitForAnswer&#34; AS step_waitforanswer,
-                s.&#34;isError&#34; AS step_iserror,
-                s.&#34;metadata&#34; AS step_metadata,
-                s.&#34;tags&#34; AS step_tags,
-                s.&#34;input&#34; AS step_input,
-                s.&#34;output&#34; AS step_output,
-                s.&#34;createdAt&#34; AS step_createdat,
-                s.&#34;start&#34; AS step_start,
-                s.&#34;end&#34; AS step_end,
-                s.&#34;generation&#34; AS step_generation,
-                s.&#34;showInput&#34; AS step_showinput,
-                s.&#34;language&#34; AS step_language,
-                s.&#34;indent&#34; AS step_indent,
-                f.&#34;value&#34; AS feedback_value,
-                f.&#34;comment&#34; AS feedback_comment,
-                f.&#34;id&#34; AS feedback_id
-            FROM steps s LEFT JOIN feedbacks f ON s.&#34;id&#34; = f.&#34;forId&#34;
-            WHERE s.&#34;threadId&#34; IN {thread_ids}
-            ORDER BY s.&#34;createdAt&#34; ASC
-        &#34;&#34;&#34;
-        steps_feedbacks = await self.execute_sql(
-            query=steps_feedbacks_query, parameters={}
-        )
-
-        elements_query = f&#34;&#34;&#34;
-            SELECT
-                e.&#34;id&#34; AS element_id,
-                e.&#34;threadId&#34; as element_threadid,
-                e.&#34;type&#34; AS element_type,
-                e.&#34;chainlitKey&#34; AS element_chainlitkey,
-                e.&#34;url&#34; AS element_url,
-                e.&#34;objectKey&#34; as element_objectkey,
-                e.&#34;name&#34; AS element_name,
-                e.&#34;display&#34; AS element_display,
-                e.&#34;size&#34; AS element_size,
-                e.&#34;language&#34; AS element_language,
-                e.&#34;page&#34; AS element_page,
-                e.&#34;forId&#34; AS element_forid,
-                e.&#34;mime&#34; AS element_mime
-            FROM elements e
-            WHERE e.&#34;threadId&#34; IN {thread_ids}
-        &#34;&#34;&#34;
-        elements = await self.execute_sql(query=elements_query, parameters={})
-
-        thread_dicts = {}
-        for thread in user_threads:
-            thread_id = thread[&#34;thread_id&#34;]
-            if thread_id is not None:
-                # Ensure metadata is a dictionary
-                metadata = thread[&#34;thread_metadata&#34;]
-                if isinstance(metadata, str):
-                    try:
-                        metadata = json.loads(metadata)
-                    except json.JSONDecodeError:
-                        metadata = {}
-                elif metadata is None:
-                    metadata = {}
-
-                thread_dicts[thread_id] = ThreadDict(
-                    id=thread_id,
-                    createdAt=thread[&#34;thread_createdat&#34;],
-                    name=thread[&#34;thread_name&#34;],
-                    userId=thread[&#34;user_id&#34;],
-                    userIdentifier=thread[&#34;user_identifier&#34;],
-                    tags=thread[&#34;thread_tags&#34;],
-                    metadata=metadata,
-                    steps=[],
-                    elements=[],
-                )
-        # Process steps_feedbacks to populate the steps in the corresponding ThreadDict
-        if isinstance(steps_feedbacks, list):
-            for step_feedback in steps_feedbacks:
-                thread_id = step_feedback[&#34;step_threadid&#34;]
-                if thread_id is not None:
-                    feedback = None
-                    if step_feedback[&#34;feedback_value&#34;] is not None:
-                        feedback = FeedbackDict(
-                            forId=step_feedback[&#34;step_id&#34;],
-                            id=step_feedback.get(&#34;feedback_id&#34;),
-                            value=step_feedback[&#34;feedback_value&#34;],
-                            comment=step_feedback.get(&#34;feedback_comment&#34;),
-                        )
-                    step_dict = StepDict(
-                        id=step_feedback[&#34;step_id&#34;],
-                        name=step_feedback[&#34;step_name&#34;],
-                        type=step_feedback[&#34;step_type&#34;],
-                        threadId=thread_id,
-                        parentId=step_feedback.get(&#34;step_parentid&#34;),
-                        streaming=step_feedback.get(&#34;step_streaming&#34;, False),
-                        waitForAnswer=step_feedback.get(&#34;step_waitforanswer&#34;),
-                        isError=step_feedback.get(&#34;step_iserror&#34;),
-                        metadata=(
-                            step_feedback[&#34;step_metadata&#34;]
-                            if step_feedback.get(&#34;step_metadata&#34;) is not None
-                            else {}
-                        ),
-                        tags=step_feedback.get(&#34;step_tags&#34;),
-                        input=(
-                            step_feedback.get(&#34;step_input&#34;, &#34;&#34;)
-                            if step_feedback.get(&#34;step_showinput&#34;)
-                            not in [None, &#34;false&#34;]
-                            else &#34;&#34;
-                        ),
-                        output=step_feedback.get(&#34;step_output&#34;, &#34;&#34;),
-                        createdAt=step_feedback.get(&#34;step_createdat&#34;),
-                        start=step_feedback.get(&#34;step_start&#34;),
-                        end=step_feedback.get(&#34;step_end&#34;),
-                        generation=step_feedback.get(&#34;step_generation&#34;),
-                        showInput=step_feedback.get(&#34;step_showinput&#34;),
-                        language=step_feedback.get(&#34;step_language&#34;),
-                        indent=step_feedback.get(&#34;step_indent&#34;),
-                        feedback=feedback,
-                    )
-                    # Append the step to the steps list of the corresponding ThreadDict
-                    thread_dicts[thread_id][&#34;steps&#34;].append(step_dict)
-
-        if isinstance(elements, list):
-            for element in elements:
-                thread_id = element[&#34;element_threadid&#34;]
-                if thread_id is not None:
-                    element_dict = ElementDict(
-                        id=element[&#34;element_id&#34;],
-                        threadId=thread_id,
-                        type=element[&#34;element_type&#34;],
-                        chainlitKey=element.get(&#34;element_chainlitkey&#34;),
-                        url=element.get(&#34;element_url&#34;),
-                        objectKey=element.get(&#34;element_objectkey&#34;),
-                        name=element[&#34;element_name&#34;],
-                        display=element[&#34;element_display&#34;],
-                        size=element.get(&#34;element_size&#34;),
-                        language=element.get(&#34;element_language&#34;),
-                        autoPlay=element.get(&#34;element_autoPlay&#34;),
-                        playerConfig=element.get(&#34;element_playerconfig&#34;),
-                        page=element.get(&#34;element_page&#34;),
-                        forId=element.get(&#34;element_forid&#34;),
-                        mime=element.get(&#34;element_mime&#34;),
-                    )
-                    thread_dicts[thread_id][&#34;elements&#34;].append(element_dict)  # type: ignore
-
-        return list(thread_dicts.values())</code></pre>
-</details>
-<h3>Ancestors</h3>
-<ul class="hlist">
-<li>chainlit.data.base.BaseDataLayer</li>
-<li>abc.ABC</li>
-</ul>
-<h3>Methods</h3>
-<dl>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.build_debug_url"><code class="name flex">
-<span>async def <span class="ident">build_debug_url</span></span>(<span>self) ‑> str</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.clean_result"><code class="name flex">
-<span>def <span class="ident">clean_result</span></span>(<span>self, obj)</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Recursively change UUID -&gt; str and serialize dictionaries</p></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.create_element"><code class="name flex">
-<span>async def <span class="ident">create_element</span></span>(<span>self, element: Element)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.create_step"><code class="name flex">
-<span>async def <span class="ident">create_step</span></span>(<span>self, step_dict: StepDict)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.create_user"><code class="name flex">
-<span>async def <span class="ident">create_user</span></span>(<span>self, user: chainlit.user.User) ‑> Optional[chainlit.user.PersistedUser]</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_element"><code class="name flex">
-<span>async def <span class="ident">delete_element</span></span>(<span>self, element_id: str, thread_id: Optional[str] = None)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_feedback"><code class="name flex">
-<span>async def <span class="ident">delete_feedback</span></span>(<span>self, feedback_id: str) ‑> bool</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_step"><code class="name flex">
-<span>async def <span class="ident">delete_step</span></span>(<span>self, step_id: str)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_thread"><code class="name flex">
-<span>async def <span class="ident">delete_thread</span></span>(<span>self, thread_id: str)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.execute_sql"><code class="name flex">
-<span>async def <span class="ident">execute_sql</span></span>(<span>self, query: str, parameters: dict) ‑> Union[List[Dict[str, Any]], int, ForwardRef(None)]</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_all_user_threads"><code class="name flex">
-<span>async def <span class="ident">get_all_user_threads</span></span>(<span>self, user_id: Optional[str] = None, thread_id: Optional[str] = None) ‑> Optional[List[chainlit.types.ThreadDict]]</span>
-</code></dt>
-<dd>
-<div class="desc"><p>Fetch all user threads up to self.user_thread_limit, or one thread by id if thread_id is provided.</p></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_current_timestamp"><code class="name flex">
-<span>async def <span class="ident">get_current_timestamp</span></span>(<span>self) ‑> str</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_element"><code class="name flex">
-<span>async def <span class="ident">get_element</span></span>(<span>self, thread_id: str, element_id: str) ‑> Optional[chainlit.element.ElementDict]</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_thread"><code class="name flex">
-<span>async def <span class="ident">get_thread</span></span>(<span>self, thread_id: str) ‑> Optional[chainlit.types.ThreadDict]</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_thread_author"><code class="name flex">
-<span>async def <span class="ident">get_thread_author</span></span>(<span>self, thread_id: str) ‑> str</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_user"><code class="name flex">
-<span>async def <span class="ident">get_user</span></span>(<span>self, identifier: str) ‑> Optional[chainlit.user.PersistedUser]</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.list_threads"><code class="name flex">
-<span>async def <span class="ident">list_threads</span></span>(<span>self, pagination: chainlit.types.Pagination, filters: chainlit.types.ThreadFilter) ‑> chainlit.types.PaginatedResponse</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.update_step"><code class="name flex">
-<span>async def <span class="ident">update_step</span></span>(<span>self, step_dict: StepDict)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.update_thread"><code class="name flex">
-<span>async def <span class="ident">update_thread</span></span>(<span>self, thread_id: str, name: Optional[str] = None, user_id: Optional[str] = None, metadata: Optional[Dict] = None, tags: Optional[List[str]] = None)</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-<dt id="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.upsert_feedback"><code class="name flex">
-<span>async def <span class="ident">upsert_feedback</span></span>(<span>self, feedback: chainlit.types.Feedback) ‑> str</span>
-</code></dt>
-<dd>
-<div class="desc"></div>
-</dd>
-</dl>
-</dd>
-</dl>
-</section>
-</article>
-<nav id="sidebar">
-<div class="toc">
-<ul></ul>
-</div>
-<ul id="index">
-<li><h3>Super-module</h3>
-<ul>
-<li><code><a title="praisonai.ui" href="index.html">praisonai.ui</a></code></li>
-</ul>
-</li>
-<li><h3><a href="#header-classes">Classes</a></h3>
-<ul>
-<li>
-<h4><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer">SQLAlchemyDataLayer</a></code></h4>
-<ul class="">
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.build_debug_url" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.build_debug_url">build_debug_url</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.clean_result" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.clean_result">clean_result</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.create_element" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.create_element">create_element</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.create_step" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.create_step">create_step</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.create_user" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.create_user">create_user</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_element" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_element">delete_element</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_feedback" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_feedback">delete_feedback</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_step" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_step">delete_step</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_thread" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.delete_thread">delete_thread</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.execute_sql" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.execute_sql">execute_sql</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_all_user_threads" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_all_user_threads">get_all_user_threads</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_current_timestamp" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_current_timestamp">get_current_timestamp</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_element" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_element">get_element</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_thread" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_thread">get_thread</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_thread_author" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_thread_author">get_thread_author</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_user" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.get_user">get_user</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.list_threads" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.list_threads">list_threads</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.update_step" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.update_step">update_step</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.update_thread" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.update_thread">update_thread</a></code></li>
-<li><code><a title="praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.upsert_feedback" href="#praisonai.ui.sql_alchemy.SQLAlchemyDataLayer.upsert_feedback">upsert_feedback</a></code></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
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-</body>
-</html>

docs/api/praisonaiagents/agent/agent.mdx

@@ -1,79 +0,0 @@
----
-title: "Agent Module"
-description: "Documentation for the praisonaiagents.agent.agent module"
-icon: "user"
----
-
-# Module praisonaiagents.agent.agent
-
-## Classes
-
-### Agent
-The main class representing an AI agent with specific role, goal, and capabilities.
-
-#### Parameters
-- `name: str` - Name of the agent
-- `role: str` - Role of the agent
-- `goal: str` - Goal the agent aims to achieve
-- `backstory: str` - Background story of the agent
-- `llm: str | Any | None = 'gpt-4o'` - Language model to use
-- `tools: List[Any] | None = None` - List of tools available to the agent
-- `function_calling_llm: Any | None = None` - LLM for function calling
-- `max_iter: int = 20` - Maximum iterations
-- `max_rpm: int | None = None` - Maximum requests per minute
-- `max_execution_time: int | None = None` - Maximum execution time
-- `memory: bool = True` - Enable memory
-- `verbose: bool = True` - Enable verbose output
-- `allow_delegation: bool = False` - Allow task delegation
-- `step_callback: Any | None = None` - Callback for each step
-- `cache: bool = True` - Enable caching
-- `system_template: str | None = None` - System prompt template
-- `prompt_template: str | None = None` - Prompt template
-- `response_template: str | None = None` - Response template
-- `allow_code_execution: bool | None = False` - Allow code execution
-- `max_retry_limit: int = 2` - Maximum retry attempts
-- `respect_context_window: bool = True` - Respect context window size
-- `code_execution_mode: Literal['safe', 'unsafe'] = 'safe'` - Code execution mode
-- `embedder_config: Dict[str, Any] | None = None` - Embedder configuration
-- `knowledge_sources: List[Any] | None = None` - Knowledge sources
-- `use_system_prompt: bool | None = True` - Use system prompt
-- `markdown: bool = True` - Enable markdown
-- `self_reflect: bool = True` - Enable self reflection
-- `max_reflect: int = 3` - Maximum reflections
-- `min_reflect: int = 1` - Minimum reflections
-- `reflect_llm: str | None = None` - LLM for reflection
-
-#### Methods
-- `chat(self, prompt, temperature=0.2, tools=None, output_json=None)` - Chat with the agent
-- `achat(self, prompt, temperature=0.2, tools=None, output_json=None)` - Async version of chat method
-- `clean_json_output(self, output: str) → str` - Clean and extract JSON from response text
-- `clear_history(self)` - Clear chat history
-- `execute_tool(self, function_name, arguments)` - Execute a tool dynamically based on the function name and arguments
-- `_achat_completion(self, response, tools)` - Async version of _chat_completion method
-
-#### Async Support
-The Agent class provides async support through the following methods:
-
-- `achat`: Async version of the chat method for non-blocking communication
-- `_achat_completion`: Internal async method for handling chat completions
-
-Example usage:
-```python
-async def main():
-    agent = Agent(
-        name="AsyncAgent",
-        role="Async Specialist",
-        goal="Handle async operations",
-        backstory="Expert in async processing"
-    )
-    
-    # Use async chat
-    result = await agent.achat(
-        prompt="Your prompt here",
-        tools=your_tools,
-        output_json=your_schema
-    )
-    print(result)
-
-asyncio.run(main())
-```

docs/api/praisonaiagents/agents/agents.mdx

@@ -1,113 +0,0 @@
----
-title: "Agents Module"
-description: "Documentation for the praisonaiagents.agents.agents module"
-icon: "users"
----
-
-# Module praisonaiagents.agents.agents
-
-## Functions
-
-### encode_file_to_base64(file_path: str) → str
-Base64-encode a file.
-
-### process_video(video_path: str, seconds_per_frame=2)
-Split video into frames (base64-encoded).
-
-## Classes
-
-### LoopItems
-A Pydantic model for handling loop items.
-
-#### Class Variables
-- `items: List[Any]` - List of items to loop through
-- `model_config` - Model configuration
-
-### PraisonAIAgents
-The main class for managing agents and tasks execution.
-
-#### Parameters
-- `agents: List[Agent]` - List of agents
-- `tasks: List[Task]` - List of tasks
-- `verbose: int = 0` - Verbosity level
-- `completion_checker: Optional[Callable] = None` - Custom completion checker
-- `max_retries: int = 5` - Maximum retry attempts
-- `process: str = "sequential"` - Process type (sequential, workflow, hierarchical)
-- `manager_llm: Optional[str] = None` - Language model for manager agent
-
-#### Methods
-- `add_task(self, task)` - Add a task to the crew
-- `clean_json_output(self, output: str) → str` - Clean JSON output
-- `clear_state(self) → None` - Clear all state values
-- `default_completion_checker(self, task, agent_output)` - Default completion checker
-- `execute_task(self, task_id)` - Execute a specific task
-- `get_agent_details(self, agent_name)` - Get agent details
-- `get_all_tasks_status(self)` - Get status of all tasks
-- `get_state(self, key: str, default: Any = None) → Any` - Get a state value
-- `get_task_details(self, task_id)` - Get task details
-- `get_task_result(self, task_id)` - Get task result
-- `get_task_status(self, task_id)` - Get task status
-- `run_all_tasks(self)` - Execute tasks based on execution mode
-- `run_task(self, task_id)` - Run a specific task
-- `save_output_to_file(self, task, task_output)` - Save task output to file
-- `set_state(self, key: str, value: Any) → None` - Set a state value
-- `start(self)` - Start the crew execution
-- `update_state(self, updates: Dict) → None` - Update multiple state values
-
-#### Async Methods
-- `astart(self)` - Async version of start method
-- `aexecute_task(self, task_id)` - Async version of execute_task
-- `arun_task(self, task_id)` - Async version of run_task
-- `arun_all_tasks(self)` - Async version of run_all_tasks
-
-#### Async Support
-The PraisonAIAgents class provides comprehensive async support through the following methods:
-
-```python
-class PraisonAIAgents:
-    async def astart(self):
-        """Async version of start method"""
-        await self.arun_all_tasks()
-        return {
-            "task_status": self.get_all_tasks_status(),
-            "task_results": self.get_task_results()
-        }
-        
-    async def aexecute_task(self, task_id):
-        """Async version of execute_task method"""
-        # Async task execution implementation
-        
-    async def arun_task(self, task_id):
-        """Async version of run_task method"""
-        # Async task running with retries
-        
-    async def arun_all_tasks(self):
-        """Async version of run_all_tasks method"""
-        # Async execution of all tasks
-```
-
-Example usage:
-```python
-async def main():
-    # Initialize agents and tasks
-    agents = PraisonAIAgents(
-        agents=[async_agent1, async_agent2],
-        tasks=[async_task1, async_task2],
-        process="workflow"
-    )
-    
-    # Start async execution
-    result = await agents.astart()
-    print(result)
-
-# Run the async workflow
-asyncio.run(main())
-```
-
-#### Async Features
-- Full async/await support
-- Parallel task execution
-- Non-blocking operations
-- Efficient resource utilization
-- Mixed sync/async task handling
-- Async process management

docs/api/praisonaiagents/agents/autoagents.mdx

@@ -1,187 +0,0 @@
----
-title: "AutoAgents Module"
-description: "Documentation for the praisonaiagents.agents.autoagents module"
-icon: "robot"
----
-
-# Module praisonaiagents.agents.autoagents
-
-The AutoAgents module provides automatic creation and management of AI agents and tasks based on high-level instructions.
-
-## Classes
-
-### AutoAgents
-The main class for automatically creating and managing AI agents and tasks.
-
-#### Parameters
-- `instructions: str` - High-level task description for the agents
-- `tools: Optional[List[Any]] = None` - List of tools available to the agents
-- `verbose: bool = False` - Enable detailed logging
-- `process: str = "sequential"` - Process type (sequential or hierarchical)
-- `manager_llm: Optional[str] = None` - Language model for manager agent
-- `max_retries: int = 5` - Maximum retry attempts
-- `completion_checker: Optional[Any] = None` - Custom completion checker
-- `allow_code_execution: bool = False` - Allow code execution
-- `memory: bool = True` - Enable agent memory
-- `markdown: bool = True` - Enable markdown formatting
-- `self_reflect: bool = False` - Enable agent self-reflection
-- `max_reflect: int = 3` - Maximum reflection iterations
-- `min_reflect: int = 1` - Minimum reflection iterations
-- `llm: Optional[str] = None` - Language model for agents
-- `function_calling_llm: Optional[str] = None` - Language model for tool calling
-- `respect_context_window: bool = True` - Respect model context window
-- `code_execution_mode: str = "safe"` - Code execution mode (safe/unsafe)
-- `embedder_config: Optional[Dict[str, Any]] = None` - Embedder configuration
-- `knowledge_sources: Optional[List[Any]] = None` - Knowledge sources
-- `use_system_prompt: bool = True` - Use system prompts
-- `cache: bool = True` - Enable caching
-- `allow_delegation: bool = False` - Allow task delegation
-- `step_callback: Optional[Any] = None` - Callback for each step
-- `system_template: Optional[str] = None` - Custom system template
-- `prompt_template: Optional[str] = None` - Custom prompt template
-- `response_template: Optional[str] = None` - Custom response template
-- `max_rpm: Optional[int] = None` - Maximum requests per minute
-- `max_execution_time: Optional[int] = None` - Maximum execution time
-- `max_iter: int = 20` - Maximum iterations
-- `reflect_llm: Optional[str] = None` - Language model for reflection
-- `max_agents: int = 3` - Maximum number of agents to create
-
-#### Methods
-
-##### start()
-Start the agents synchronously.
-
-```python
-def start(self):
-    """
-    Creates tasks based on the instructions, then starts execution.
-    Returns the task status and results dictionary.
-    """
-    return super().start()
-```
-
-##### astart()
-Start the agents asynchronously.
-
-```python
-async def astart(self):
-    """
-    Async version of start() method.
-    Creates tasks based on the instructions, then starts execution.
-    Returns the task status and results dictionary.
-    """
-    return await super().astart()
-```
-
-#### Internal Methods
-
-##### _generate_config()
-Generate the configuration for agents and tasks.
-
-```python
-def _generate_config(self) -> AutoAgentsConfig:
-    """
-    Generate the configuration for agents and tasks based on instructions.
-    Returns AutoAgentsConfig object containing agent and task configurations.
-    """
-```
-
-##### _create_agents_and_tasks()
-Create agents and tasks from configuration.
-
-```python
-def _create_agents_and_tasks(self, config: AutoAgentsConfig) -> tuple[List[Agent], List[Task]]:
-    """
-    Create agents and tasks based on the generated configuration.
-    Returns tuple of (agents, tasks).
-    """
-```
-
-##### _assign_tools_to_agent()
-Assign appropriate tools to an agent.
-
-```python
-def _assign_tools_to_agent(self, agent_config: AgentConfig) -> List[Any]:
-    """
-    Assign tools to an agent based on its role and tasks.
-    Returns list of assigned tools.
-    """
-```
-
-## Pydantic Models
-
-### TaskConfig
-Configuration for a task.
-
-#### Attributes
-- `name: str` - Task name
-- `description: str` - Task description
-- `expected_output: str` - Expected output description
-- `tools: List[str]` - Required tools for the task
-
-### AgentConfig
-Configuration for an agent.
-
-#### Attributes
-- `name: str` - Agent name
-- `role: str` - Agent role
-- `goal: str` - Agent goal
-- `backstory: str` - Agent backstory
-- `tools: List[str]` - Required tools
-- `tasks: List[TaskConfig]` - Tasks assigned to the agent
-
-### AutoAgentsConfig
-Overall configuration for AutoAgents.
-
-#### Attributes
-- `main_instruction: str` - Main instruction for the agents
-- `process_type: str` - Process type (sequential/hierarchical)
-- `agents: List[AgentConfig]` - List of agent configurations
-
-## Example Usage
-
-### Basic Usage
-```python
-from praisonaiagents import AutoAgents
-from praisonaiagents.tools import SerperDevTool
-
-agents = AutoAgents(
-    instructions="Research recent AI developments",
-    tools=[SerperDevTool()],
-    verbose=True
-)
-result = agents.start()
-```
-
-### Async Usage
-```python
-async def main():
-    agents = AutoAgents(
-        instructions="Research recent AI developments",
-        tools=[SerperDevTool()],
-        process="hierarchical"
-    )
-    result = await agents.astart()
-
-import asyncio
-asyncio.run(main())
-```
-
-### Advanced Configuration
-```python
-agents = AutoAgents(
-    instructions="Complex research task",
-    tools=[SerperDevTool()],
-    max_agents=5,
-    process="hierarchical",
-    manager_llm="gpt-4o",
-    memory=True,
-    allow_delegation=True,
-    max_execution_time=600,
-    self_reflect=True
-)
-```
-
-<Note>
-  For optimal results, provide clear instructions and appropriate tools for your use case.
-</Note>

docs/api/praisonaiagents/index.mdx

@@ -1,52 +0,0 @@
----
-title: "API Reference"
-description: "Praison AI Agents - SDK Documentation"
-icon: "code"
----
-
-<CardGroup cols={2}>
-  <Card title="Agent" icon="user" href="/api/praisonaiagents/agent/agent">
-    Documentation for individual AI agents and their capabilities
-  </Card>
-  <Card title="Agents" icon="users" href="/api/praisonaiagents/agents/agents">
-    Documentation for managing multiple agents and their interactions
-  </Card>
-  <Card title="Task" icon="list-check" href="/api/praisonaiagents/task/task">
-    Documentation for defining and managing tasks
-  </Card>
-  <Card title="Process" icon="diagram-project" href="/api/praisonaiagents/process/process">
-    Documentation for handling task execution flows
-  </Card>
-</CardGroup>
-
-# Module praisonaiagents
-
-## Sub-modules
-* praisonaiagents.agent - Agent module for defining individual AI agents
-* praisonaiagents.agents - Agents module for managing multiple agents
-* praisonaiagents.task - Task module for defining and managing tasks
-* praisonaiagents.process - Process module for handling task execution flows
-* praisonaiagents.main - Main module containing utility functions
-
-## Functions
-
-### clean_triple_backticks(text: str) → str
-Clean triple backticks from text output.
-
-### display_error(message: str, console=None)
-Display error messages in the console.
-
-### display_generating(content: str = '', start_time: float | None = None)
-Display generation status with optional timing information.
-
-### display_instruction(message: str, console=None)
-Display instruction messages in the console.
-
-### display_interaction(message, response, markdown=True, generation_time=None, console=None)
-Display interaction between user and agent.
-
-### display_self_reflection(message: str, console=None)
-Display agent self-reflection messages.
-
-### display_tool_call(message: str, console=None)
-Display tool call messages.

docs/api/praisonaiagents/process/process.mdx

@@ -1,98 +0,0 @@
----
-title: "Process Module"
-description: "Documentation for the praisonaiagents.process.process module"
-icon: "diagram-project"
----
-
-# Module praisonaiagents.process.process
-
-## Classes
-
-### Process
-The main class for handling task execution processes in different modes (sequential, hierarchical, workflow).
-
-#### Parameters
-- `tasks: Dict[str, Task]` - Dictionary of tasks to be executed
-- `agents: List[Agent]` - List of available agents
-- `manager_llm: Optional[str] = None` - Language model for manager agent in hierarchical process
-- `verbose: bool = False` - Enable verbose logging
-
-#### Methods
-- `workflow(self)` - Execute tasks in a workflow pattern with support for loops and conditions
-- `sequential(self)` - Execute tasks in sequential order
-- `hierarchical(self)` - Execute tasks in hierarchical order with a manager agent
-
-#### Async Methods
-- `aworkflow(self) -> AsyncGenerator[str, None]` - Async version of workflow process
-- `asequential(self) -> AsyncGenerator[str, None]` - Async version of sequential process
-- `ahierarchical(self) -> AsyncGenerator[str, None]` - Async version of hierarchical process
-
-#### Async Process Support
-Each process type has an async counterpart that supports non-blocking execution:
-
-```python
-class Process:
-    async def aworkflow(self):
-        """Async workflow process execution"""
-        # Async workflow implementation
-        
-    async def asequential(self):
-        """Async sequential process execution"""
-        # Async sequential implementation
-        
-    async def ahierarchical(self):
-        """Async hierarchical process execution"""
-        # Async hierarchical implementation
-```
-
-Example usage:
-```python
-# Create process instance
-process = Process(tasks=tasks, agents=agents)
-
-# Use async workflow
-async for task_id in process.aworkflow():
-    # Handle task execution
-    if tasks[task_id].async_execution:
-        await handle_async_task(task_id)
-    else:
-        handle_sync_task(task_id)
-```
-
-#### Async Process Features
-- Non-blocking task execution
-- Parallel task processing
-- Mixed sync/async task support
-- Efficient resource utilization
-- Async manager coordination (hierarchical)
-- Dynamic task scheduling (workflow)
-
-### LoopItems
-A Pydantic model for handling loop data in workflow process.
-
-#### Parameters
-- `items: List[Any]` - List of items to be processed in a loop
-
-## Process Types
-
-### Sequential Process
-- Tasks are executed one after another in a linear fashion
-- Each task is completed before moving to the next one
-- Simplest form of task execution
-
-### Hierarchical Process
-- Uses a manager agent to coordinate task execution
-- Manager decides task order and agent assignments
-- Provides more complex task orchestration
-- Requires manager_llm parameter
-- Supports dynamic agent assignment
-
-### Workflow Process
-- Supports complex task relationships
-- Features:
-  - Task dependencies
-  - Conditional execution
-  - Loops
-  - Context sharing between tasks
-- Maintains task state and relationships
-- Handles data flow between tasks

docs/api/praisonaiagents/task/task.mdx

@@ -1,78 +0,0 @@
----
-title: "Task Module"
-description: "Documentation for the praisonaiagents.task.task module"
-icon: "list-check"
----
-
-# Module praisonaiagents.task.task
-
-## Classes
-
-### Task
-Represents a task to be executed by an agent.
-
-#### Parameters
-- `description: str` - Task description
-- `expected_output: str` - Expected output description
-- `agent: Agent | None = None` - Agent assigned to the task
-- `name: str | None = None` - Task name
-- `tools: List[Any] | None = None` - Tools available for the task
-- `context: List[Task] | None = None` - Context from other tasks
-- `async_execution: bool | None = False` - Enable async execution
-- `config: Dict[str, Any] | None = None` - Task configuration
-- `output_file: str | None = None` - Output file path
-- `output_json: Type[BaseModel] | None = None` - JSON output schema
-- `output_pydantic: Type[BaseModel] | None = None` - Pydantic output schema
-- `callback: Any | None = None` - Task callback
-- `status: str = 'not started'` - Task status
-- `result: TaskOutput | None = None` - Task result
-- `create_directory: bool | None = False` - Create output directory
-- `id: int | None = None` - Task ID
-- `images: List[str] | None = None` - Task images
-- `next_tasks: List[str] | None = None` - Next tasks
-- `task_type: str = 'task'` - Task type
-- `condition: Dict[str, List[str]] | None = None` - Task conditions
-- `is_start: bool = False` - Is start task
-- `loop_state: Dict[str, str | int] | None = None` - Loop state
-
-#### Async Support
-The Task class supports asynchronous execution through the following features:
-
-- `async_execution`: Boolean flag to enable async execution
-- `callback`: Supports both sync and async callback functions
-- `execute_callback`: Internal async method for handling callbacks
-
-Example usage:
-```python
-from typing import Coroutine
-from praisonaiagents import Task, TaskOutput
-
-# Define an async callback
-async def async_callback(output: TaskOutput):
-    await some_async_operation()
-    print(f"Processed: {output.raw}")
-
-# Create task with async execution
-task = Task(
-    description="Async task example",
-    expected_output="Async result",
-    async_execution=True,  # Enable async execution
-    callback=async_callback  # Async callback
-)
-```
-
-#### Callback Types
-The Task class supports both synchronous and asynchronous callbacks:
-```python
-# Type definition
-callback: Optional[Union[
-    Callable[[TaskOutput], Any],  # Sync callback
-    Callable[[TaskOutput], Coroutine[Any, Any, Any]]  # Async callback
-]]
-```
-
-#### Async Task States
-- `not started`: Initial state
-- `in progress`: Task is being executed
-- `completed`: Task finished successfully
-- `failed`: Task execution failed

docs/call.mdx

@@ -1,126 +0,0 @@
----
-title: "PraisonAI Call"
-description: "Guide to PraisonAI's voice-based interaction feature enabling AI customer service through phone calls, including setup and tool integration"
-icon: "phone"
----
-
-<iframe width="560" height="315" src="https://www.youtube.com/embed/m1cwrUG2iAk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-
-## AI Customer Service
-
-PraisonAI Call is a feature that enables voice-based interaction with AI models through phone calls. This functionality allows users to have natural conversations with AI agents over traditional phone lines.
-
-## Installation
-
-### Step 1
-
-```bash
-pip install "praisonai[call]"
-export OPENAI_API_KEY="enter your openai api key here"
-export NGROK_AUTH_TOKEN="enter your ngrok auth token here"
-praisonai call --public
-```
-
-### Step 2
-
-Buy a number at [PraisonAI Dashboard](https://dashboard.praison.ai/)
-
-### Step 3
-
-Enter the Public URL in the PraisonAI Dashboard phone number field
-
-## Features
-
-- Make and receive phone calls with AI agents
-- Natural language processing for voice interactions
-- Support for multiple phone carriers and providers
-- Call recording and transcription capabilities
-- Integration with other PraisonAI features
-
-## Adding Tools
-
-1. Create a file called `tools.py`
-2. Add the following code:
-```python
-import yfinance as yf
-
-# Get Stock Price definition
-get_stock_price_def = {
-    "name": "get_stock_price",
-    "description": "Get the current stock price for a given ticker symbol",
-    "parameters": {
-        "type": "object", 
-        "properties": {
-            "ticker_symbol": {
-                "type": "string", 
-                "description": "The ticker symbol of the stock (e.g., AAPL, GOOGL)"
-            }
-        }, 
-        "required": ["ticker_symbol"]
-    }
-}
-
-# Get Stock Price function / Tool
-async def get_stock_price_handler(ticker_symbol):
-    try:
-        stock = yf.Ticker(ticker_symbol)
-        hist = stock.history(period="1d")
-        if hist.empty:
-            return {"error": f"No data found for ticker {ticker_symbol}"}
-        current_price = hist['Close'].iloc[-1]  # Using -1 is safer than 0
-        return {"price": str(current_price)}
-    except Exception as e:
-        return {"error": str(e)}
-
-
-
-get_stock_price = (get_stock_price_def, get_stock_price_handler)
-tools = [
-    get_stock_price
-]
-```
-
-3. ```bash
-pip install yfinance
-```
-
-4. ```bash
-export OPENAI_API_KEY="enter your openai api key here"
-export NGROK_AUTH_TOKEN="enter your ngrok auth token here"
-praisonai call --public
-```
-
-## Manage Google Calendar Events
-
-See [Google Calendar Tools](tools/googlecalendar.md)
-
-
-## Deploy
-
-### Docker Deployment
-
-```dockerfile
-# Use an official Python runtime as a parent image
-FROM python:3.11-slim
-
-# Set environment variables
-ENV PYTHONDONTWRITEBYTECODE 1
-ENV PYTHONUNBUFFERED 1
-
-# Set work directory
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/*
-
-# Install PraisonAI with the 'call' extra and ensure it's the latest version
-RUN pip install --no-cache-dir --upgrade "praisonai[call]"
-
-# Expose the port the app runs on
-EXPOSE 8090
-
-# Run the application
-CMD ["praisonai", "call"]
-```

docs/concepts/agents.mdx

@@ -1,368 +0,0 @@
----
-title: "Agents"
-description: "Understanding Agents in PraisonAI"
-icon: "user"
----
-
-# Understanding Agents
-Agents are the core building blocks of PraisonAI. Each agent is an autonomous AI entity with specific roles, goals, and capabilities.
-
-```mermaid
-graph LR
-    %% Define the main flow
-    Start([▶ Start]) --> Agent1
-    Agent1 --> Process[⚙ Process]
-    Process --> Agent2
-    Agent2 --> Output([✓ Output])
-    Process -.-> Agent1
-    
-    %% Define subgraphs for agents and their tasks
-    subgraph Agent1[ ]
-        Task1[📋 Task]
-        AgentIcon1[🤖 AI Agent]
-        Tools1[🔧 Tools]
-        
-        Task1 --- AgentIcon1
-        AgentIcon1 --- Tools1
-    end
-    
-    subgraph Agent2[ ]
-        Task2[📋 Task]
-        AgentIcon2[🤖 AI Agent]
-        Tools2[🔧 Tools]
-        
-        Task2 --- AgentIcon2
-        AgentIcon2 --- Tools2
-    end
-
-    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
-    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
-    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
-    classDef transparent fill:none,stroke:none
-
-    class Start,Output,Task1,Task2 input
-    class Process,AgentIcon1,AgentIcon2 process
-    class Tools1,Tools2 tools
-    class Agent1,Agent2 transparent
-```
-
-## Key Components
-
-<CardGroup cols={2}>
-  <Card title="Role & Goal" icon="target">
-    Defines the agent's purpose and objectives through role definition and specific goals
-  </Card>
-  <Card title="Capabilities" icon="toolbox">
-    Tools and functions available to the agent for task execution
-  </Card>
-  <Card title="Memory" icon="brain">
-    Context retention and learning capabilities across interactions
-  </Card>
-  <Card title="Language Model" icon="microchip">
-    The underlying AI model powering the agent's intelligence
-  </Card>
-</CardGroup>
-
-## Component Details
-
-### Role and Goal
-<Tip>
-  Clear role and goal definitions are crucial for optimal agent performance.
-</Tip>
-
-| Component | Description | Example |
-|:----------|:------------|:--------|
-| **Role** | Agent's function and expertise | Research Analyst, Code Developer |
-| **Goal** | Specific objectives to achieve | Analyze market trends, Generate reports |
-| **Backstory** | Contextual background | Expert with 10 years of experience |
-
-### Capabilities
-<CodeGroup>
-  ```python Basic
-  agent = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o",
-      markdown=True
-  )
-  ```
-</CodeGroup>
-
-
-<Steps>
-
-  <Step title="Install PraisonAI">
-    Install the core package:
-    ```bash Terminal
-    pip install praisonaiagents
-    ```
-  </Step>
-
-  <Step title="Configure Environment">
-    ```bash Terminal
-    export OPENAI_API_KEY=your_openai_key
-    ```
-    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
-
-  <Step title="Create Agent">
-    Create `app.py`:
-<CodeGroup>
-```python Single Agent
-from praisonaiagents import Agent
-
-agent = Agent(instructions="Your are a helpful AI assistant")
-agent.start("Write a movie script about a robot in Mars")
-```
-
-```python Multi Agents
-from praisonaiagents import Agent, PraisonAIAgents
-
-research_agent = Agent(instructions="Research about AI")
-summarise_agent = Agent(instructions="Summarise research agent's findings")
-
-agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
-agents.start()
-```
-</CodeGroup>
-  </Step>
-
-  <Step title="Start Agents">
-    Execute your script:
-    ```bash Terminal
-    python app.py
-    ```
-
-    You should see:
-    - Agent initialization
-    - Agents progress
-    - Final results
-    - Generated report
-  </Step>
-</Steps>
-
-## Agent Types
-
-<CardGroup cols={3}>
-  <Card title="Basic Agent" icon="user" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Overview">
-        Perfect for straightforward tasks and direct interactions
-      </Accordion>
-      <Accordion title="Features">
-        - Single-purpose focus
-        - Direct user interaction
-        - Limited tool set
-        - Ideal for simple tasks
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-
-  <Card title="Specialized Agent" icon="user-gear" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Overview">
-        Experts in specific domains with advanced capabilities
-      </Accordion>
-      <Accordion title="Features">
-        - Domain expertise
-        - Advanced capabilities
-        - Custom tools
-        - Deep knowledge base
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-
-  <Card title="Collaborative Agent" icon="users" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Overview">
-        Designed for team-based operations and complex workflows
-      </Accordion>
-      <Accordion title="Features">
-        - Team interaction
-        - Task delegation
-        - Shared context
-        - Coordinated actions
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-</CardGroup>
-
-## Best Practices
-
-<Note>
-Always implement proper error handling and resource management in your agent configurations.
-</Note>
-
-<CardGroup cols={2}>
-  <Card title="Agent Design" icon="compass-drafting" iconType="solid">
-    <Steps>
-      <Step title="Role Definition">
-        Define clear, specific roles for each agent
-      </Step>
-      <Step title="Goal Setting">
-        Set specific, measurable goals
-      </Step>
-      <Step title="Tool Selection">
-        Choose relevant tools for the task
-      </Step>
-      <Step title="Memory Setup">
-        Configure appropriate memory settings
-      </Step>
-    </Steps>
-  </Card>
-
-  <Card title="Agent Interaction" icon="network-wired" iconType="solid">
-    <Steps>
-      <Step title="Communication">
-        Establish clear communication protocols
-      </Step>
-      <Step title="Delegation">
-        Define explicit delegation rules
-      </Step>
-      <Step title="Error Handling">
-        Implement robust error handling
-      </Step>
-      <Step title="Resource Management">
-        Set up efficient resource allocation
-      </Step>
-    </Steps>
-  </Card>
-</CardGroup>
-
-## Async Capabilities
-
-<CardGroup cols={1}>
-  <Card title="Key Features" icon="bolt" iconType="solid">
-    <Tabs>
-      <Tab title="Async Support">
-        ```python
-        async def main():
-            agent = Agent(name="AsyncAgent")
-            result = await agent.aprocess_task()
-        ```
-        - Full async/await support
-        - Non-blocking operations
-        - Enhanced performance
-      </Tab>
-      
-      <Tab title="Parallel Execution">
-        ```python
-        async def parallel_tasks():
-            tasks = [agent1.task(), agent2.task()]
-            results = await asyncio.gather(*tasks)
-        ```
-        - Parallel task execution
-        - Efficient resource usage
-        - Improved throughput
-      </Tab>
-      
-      <Tab title="Integration">
-        ```python
-        @agent.async_tool
-        async def custom_tool():
-            async with aiohttp.ClientSession() as session:
-                # Async operations
-                pass
-        ```
-        - Async tool integration
-        - Callback support
-        - Mixed sync/async operations
-      </Tab>
-    </Tabs>
-  </Card>
-</CardGroup>
-
-## Advanced Features
-
-<CardGroup cols={2}>
-  <Card title="Memory Management" icon="brain">
-    - Short-term conversation memory
-    - Long-term knowledge retention
-    - Context preservation
-  </Card>
-  <Card title="Tool Integration" icon="plug">
-    - Custom tool development
-    - External API integration
-    - Resource access control
-  </Card>
-</CardGroup>
-
-## Async Support
-Agents now support asynchronous operations through the following methods:
-
-- `achat`: Async version of the chat method
-- `astart`: Async version of start method
-- `aexecute_task`: Async version of execute_task method
-- `arun_task`: Async version of run_task method
-- `arun_all_tasks`: Async version of run_all_tasks method
-
-### Example Usage:
-
-```python
-import asyncio
-from praisonaiagents import Agent, Task, PraisonAIAgents
-
-async def main():
-    # Create an async agent
-    async_agent = Agent(
-        name="AsyncAgent",
-        role="Async Task Specialist",
-        goal="Perform async operations",
-        backstory="Expert in async operations",
-        tools=[async_search_tool],  # Your async tool
-        verbose=True
-    )
-
-    # Create an async task
-    async_task = Task(
-        description="Perform async operation",
-        expected_output="Async result",
-        agent=async_agent,
-        async_execution=True  # Enable async execution
-    )
-
-    # Create and start agents with async support
-    agents = PraisonAIAgents(
-        agents=[async_agent],
-        tasks=[async_task],
-        verbose=True
-    )
-    
-    # Start async execution
-    result = await agents.astart()
-    print(result)
-
-# Run the async main function
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-### Key Features:
-- Full async/await support
-- Parallel task execution
-- Async tool integration
-- Async callback support
-- Mixed sync/async operations
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card
-    title="Create Your First Agent"
-    icon="code"
-    href="/code/quickstart"
-  >
-    Follow our quickstart guide to create your first agent
-  </Card>
-  <Card
-    title="API Reference"
-    icon="book"
-    href="/api/praisonaiagents/agent/agent"
-  >
-    Explore the complete Agent API documentation
-  </Card>
-</CardGroup> 

docs/concepts/knowledge.mdx

@@ -1,241 +0,0 @@
----
-title: "AI Agents with Knowledge"
-sidebarTitle: "Knowledge"
-description: "Learn how to create AI agents with custom knowledge bases"
-icon: "book"
----
-
-```mermaid
-flowchart LR
-    subgraph In[Input]
-        PDF[PDF]
-        TXT[TXT]
-        MD[MD]
-    end
-
-    subgraph Router[Vector Store]
-        DB[(Vector DB)]
-    end
-    
-    subgraph Out[Agents]
-        A1[Agent 1]
-        A2[Agent 2]
-        A3[Agent 3]
-    end
-
-    In --> Router
-    Router --> A1
-    Router --> A2
-    Router --> A3
-
-    style In fill:#8B0000,color:#fff
-    style Router fill:#2E8B57,color:#fff
-    style Out fill:#8B0000,color:#fff
-```
-
-| Feature | [Knowledge](/concepts/knowledge) | [Memory](/concepts/memory) |
-|---------|--------------------------------|---------------------------|
-| When Used | Pre-loaded before agent execution | Created and updated during runtime |
-| Purpose | Provide static reference information | Store dynamic context and interactions |
-| Storage | Read-only knowledge base | Read-write memory store |
-| Persistence | Permanent until explicitly changed | Can be temporary (STM) or persistent (LTM) |
-| Updates | Manual updates through knowledge files | Automatic updates during agent execution |
-
-## Quick Start
-
-<Steps>
-  <Step title="Install Package">
-    Install PraisonAI Agents with knowledge support:
-    ```bash
-    pip install "praisonaiagents[knowledge]"
-    ```
-  </Step>
-
-  <Step title="Set API Key">
-    Set your OpenAI API key:
-    ```bash
-    export OPENAI_API_KEY=xxxxx
-    ```
-  </Step>
-
-  <Step title="Create Script">
-    Create a new file `app.py`:
-    ```python
-    from praisonaiagents import Agent
-
-    agent = Agent(
-        name="Knowledge Agent",
-        instructions="You answer questions based on the provided knowledge.",
-        knowledge=["small.pdf"]
-    )
-
-    agent.start("What is KAG in one line?")
-    ```
-  </Step>
-</Steps>
-
-## Basic Usage
-
-The simplest way to create a knowledge-based agent is without any configuration:
-
-```python
-from praisonaiagents import Agent
-
-agent = Agent(
-    name="Knowledge Agent",
-    instructions="You answer questions based on the provided knowledge.",
-    knowledge=["small.pdf"]
-)
-
-agent.start("What is KAG in one line?")
-```
-
-## Advanced Configuration
-
-For more control over the knowledge base, you can specify a configuration:
-
-```python
-from praisonaiagents import Agent
-
-config = {
-    "vector_store": {
-        "provider": "chroma",
-        "config": {
-            "collection_name": "custom_knowledge",
-            "path": ".praison",
-        }
-    }
-}
-
-agent = Agent(
-    name="Knowledge Agent",
-    instructions="You answer questions based on the provided knowledge.",
-    knowledge=["small.pdf"],
-    knowledge_config=config
-)
-
-agent.start("What is KAG in one line?")
-```
-
-## Multi-Agent Knowledge System
-
-For more complex scenarios, you can create a knowledge-based system with multiple agents:
-
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-import logging
-import os
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-# Define the configuration for the Knowledge instance
-config = {
-    "vector_store": {
-        "provider": "chroma",
-        "config": {
-            "collection_name": "knowledge_test",
-            "path": ".praison",
-        }
-    }
-}
-
-# Create an agent with knowledge capabilities
-knowledge_agent = Agent(
-    name="KnowledgeAgent",
-    role="Information Specialist",
-    goal="Store and retrieve knowledge efficiently",
-    backstory="Expert in managing and utilizing stored knowledge",
-    knowledge=["sample.pdf"],
-    knowledge_config=config,
-    verbose=True
-)
-
-# Define a task for the agent
-knowledge_task = Task(
-    name="knowledge_task",
-    description="Who is Mervin Praison?",
-    expected_output="Answer to the question",
-    agent=knowledge_agent
-)
-
-# Create and start the agents
-agents = PraisonAIAgents(
-    agents=[knowledge_agent],
-    tasks=[knowledge_task],
-    process="sequential",
-    user_id="user1"
-)
-
-# Start execution
-result = agents.start()
-```
-
-## Understanding Knowledge Configuration
-
-<AccordionGroup>
-  <Accordion title="Vector Store Options">
-    - **Provider**: Choose between different vector store backends (e.g., "chroma")
-    - **Collection Name**: Name for your knowledge collection
-    - **Path**: Location to store the vector database
-  </Accordion>
-  
-  <Accordion title="Supported File Types">
-    - PDF documents (*.pdf)
-    - Text files (*.txt)
-    - Markdown files (*.md, *.mdx)
-    - And more...
-  </Accordion>
-
-  <Accordion title="Multi-Agent Setup">
-    - **Role**: Define specialized roles for knowledge agents
-    - **Goal**: Set specific knowledge management objectives
-    - **Process**: Choose between sequential or parallel execution
-  </Accordion>
-</AccordionGroup>
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Custom Knowledge" icon="book">
-    Import your own documents and data as knowledge sources
-  </Card>
-  <Card title="Vector Storage" icon="database">
-    Efficient storage and retrieval of knowledge embeddings
-  </Card>
-  <Card title="Multiple Sources" icon="layer-group">
-    Combine multiple documents and file types
-  </Card>
-  <Card title="Persistent Storage" icon="hard-drive">
-    Save and reuse knowledge bases across sessions
-  </Card>
-</CardGroup>
-
-## Best Practices
-
-1. **Document Preparation**
-   - Clean and well-formatted documents work best
-   - Break large documents into smaller chunks
-   - Use consistent formatting
-
-2. **Knowledge Organization**
-   - Group related documents together
-   - Use meaningful file names
-   - Keep knowledge bases focused and relevant
-
-3. **Performance Optimization**
-   - Monitor vector store size
-   - Clean up unused collections
-   - Use appropriate chunk sizes
-
-4. **Multi-Agent Coordination**
-   - Define clear roles and responsibilities
-   - Set appropriate logging levels for debugging
-   - Use unique collection names for different agent groups
-
-## Next Steps
-
-- Learn about [Memory Management](/concepts/memory) for long-term recall
-- Explore [Tool Integration](/concepts/tools) for enhanced capabilities
-- Check out [Examples](/examples) for implementation ideas

docs/concepts/memory.mdx

@@ -1,472 +0,0 @@
----
-title: "AI Agents with Memory"
-sidebarTitle: "Memory"
-description: "Learn how to create AI agents with memory capabilities for maintaining context and information across tasks."
-icon: "brain"
----
-
-```mermaid
-flowchart TB
-    subgraph Memory
-        direction TB
-        STM[Short Term]
-        LTM[Long Term]
-    end
-
-    subgraph Store
-        direction TB
-        DB[(Vector DB)]
-    end
-
-    Input[Input] ---> Agents
-    subgraph Agents
-        direction LR
-        A1[Agent 1]
-        A2[Agent 2]
-        A3[Agent 3]
-    end
-    Agents ---> Output[Output]
-
-    Memory <--> Store
-    Store <--> A1
-    Store <--> A2
-    Store <--> A3
-
-    style Memory fill:#189AB4,color:#fff
-    style Store fill:#2E8B57,color:#fff
-    style Agents fill:#8B0000,color:#fff
-    style Input fill:#8B0000,color:#fff
-    style Output fill:#8B0000,color:#fff
-```
-
-| Feature | [Knowledge](/concepts/knowledge) | [Memory](/concepts/memory) |
-|---------|--------------------------------|---------------------------|
-| When Used | Pre-loaded before agent execution | Created and updated during runtime |
-| Purpose | Provide static reference information | Store dynamic context and interactions |
-| Storage | Read-only knowledge base | Read-write memory store |
-| Persistence | Permanent until explicitly changed | Can be temporary (STM) or persistent (LTM) |
-| Updates | Manual updates through knowledge files | Automatic updates during agent execution |
-
-## Quick Start
-
-<Tabs>
-  <Tab title="Code">
-    <Steps>
-        <Step title="Install Package">
-            First, install the PraisonAI Agents package:
-            ```bash
-            pip install "praisonaiagents[memory]" duckduckgo_search 
-            ```
-            duckduckgo_search is a tool that allows agents to search the web.
-            It is required for the multiple agents example shown below.
-        </Step>
-
-        <Step title="Set API Key">
-            Set your OpenAI API key as an environment variable in your terminal:
-            ```bash
-            export OPENAI_API_KEY=your_api_key_here
-            ```
-        </Step>
-
-        <Step title="Create a file">
-            Create a new file `app.py` with the basic setup:
-<CodeGroup>
-  ```python Single Agent
-  from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
-
-  # Create blog writer agent
-  blog_agent = Agent(
-      role="Blog Writer",
-      goal="Write a blog post about AI",
-      backstory="Expert at writing blog posts",
-      llm="gpt-4o-mini"
-  )
-
-  # Create blog writing task
-  blog_task = Task(
-      description="Write a blog post about AI trends",
-      expected_output="Well-written blog post about AI trends",
-      agent=blog_agent
-  )
-
-  # Create and start the agents with memory enabled
-  agents = PraisonAIAgents(
-      agents=[blog_agent],
-      tasks=[blog_task],
-      memory=True
-  )   
-
-  # Start execution
-  result = agents.start()
-  print(result)
-  ```
-
-  ```python Multiple Agents
-  from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
-  from praisonaiagents.tools import duckduckgo
-
-  # Create research agent with memory
-  research_agent = Agent(
-      role="Research Analyst",
-      goal="Research and document key information about topics",
-      backstory="Expert at analyzing and storing information in memory",
-      llm="gpt-4o-mini",
-      tools=[duckduckgo]
-  )
-
-  # Create blog writer agent
-  blog_agent = Agent(
-      role="Blog Writer",
-      goal="Write a blog post about the research",
-      backstory="Expert at writing blog posts",
-      llm="gpt-4o-mini"
-  )
-
-  # Create research task
-  research_task = Task(
-      description="Research and document key information about AI trends",
-      expected_output="Detailed research findings about AI trends",
-      agent=research_agent
-  )
-
-  # Create blog writing task
-  blog_task = Task(
-      description="Write a blog post about the research findings",
-      expected_output="Well-written blog post based on research",
-      agent=blog_agent
-  )
-
-  # Create and start the agents with advanced memory configuration
-  agents = PraisonAIAgents(
-      agents=[research_agent, blog_agent],
-      tasks=[research_task, blog_task],
-      memory=True
-  )   
-
-  # Start execution
-  result = agents.start()
-  print(result)
-  ```
-</CodeGroup>
-        </Step>
-
-        <Step title="Start Agents">
-            Type this in your terminal to run your agents:
-            ```bash
-            python app.py
-            ```
-        </Step>
-    </Steps>
-  </Tab>
-  <Tab title="No Code">
-    <Steps>
-        <Step title="Install Package">
-            Install the PraisonAI package:
-            ```bash
-            pip install praisonai
-            ```
-        </Step>
-        <Step title="Set API Key">
-            Set your OpenAI API key as an environment variable in your terminal:
-            ```bash
-            export OPENAI_API_KEY=your_api_key_here
-            ```
-        </Step>
-        <Step title="Create a file">
-            Create a new file `agents.yaml` with the basic setup:
-```yaml
-framework: praisonai
-process: sequential
-memory: true
-roles:
-  researcher:
-    backstory: Expert at analyzing and storing information in memory.
-    goal: Research and document key information about topics
-    role: Research Analyst
-    llm: gpt-4o-mini
-    tools:
-      - duckduckgo
-    tasks:
-      research_task:
-        description: Research and document key information about topics.
-        expected_output: Detailed research findings.
-
-  writer:
-    backstory: Expert at writing blog posts.
-    goal: Write a blog post about the research
-    role: Blog Writer
-    llm: gpt-4o-mini
-    tasks:
-      blog_task:
-        description: Write a blog post about the research.
-        expected_output: Well-written blog post based on research.
-```
-        </Step>
-        <Step title="Start Agents">
-            Type this in your terminal to run your agents:
-```bash
-praisonai agents.yaml
-```
-        </Step>
-    </Steps>
-  </Tab>
-</Tabs>
-
-<Note>
-  **Requirements**
-  - Python 3.10 or higher
-  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
-  - Basic understanding of Python
-</Note>
-
-## Understanding Memory
-
-<Card title="What is Agent Memory?" icon="question">
-  Memory in AI agents enables them to:
-  - Maintain context across multiple tasks
-  - Remember previous interactions and findings
-  - Build upon past knowledge
-  - Share information between agents
-  - Create more coherent and contextual responses
-</Card>
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="Context Retention" icon="brain">
-    Maintain information across multiple interactions.
-  </Card>
-  <Card title="Information Sharing" icon="share-nodes">
-    Share knowledge between multiple agents.
-  </Card>
-  <Card title="Long-term Storage" icon="database">
-    Store and retrieve information over extended periods.
-  </Card>
-  <Card title="Memory Types" icon="layer-group">
-    Support for different memory types (short-term, long-term).
-  </Card>
-</CardGroup>
-
-## Multi-Agent Memory
-
-<Tabs>
-  <Tab title="Code">
-    ```python
-    from praisonaiagents import Agent, Task, PraisonAIAgents
-    from praisonaiagents.tools import duckduckgo
-
-    # Create first agent for research
-    researcher = Agent(
-        role="Research Analyst",
-        goal="Research and analyze market trends",
-        backstory="Expert in market research and data analysis",
-        tools=[duckduckgo],
-        verbose=True
-    )
-
-    # Create second agent for report writing
-    writer = Agent(
-        role="Report Writer",
-        goal="Create comprehensive market reports",
-        backstory="Expert in technical writing and report creation",
-        verbose=True
-    )
-
-    # Create research task
-    research_task = Task(
-        description="Research current market trends",
-        expected_output="Detailed market analysis",
-        agent=researcher
-    )
-
-    # Create writing task
-    report_task = Task(
-        description="Create a market report based on research",
-        expected_output="Comprehensive market report",
-        agent=writer
-    )
-
-    # Create and start the agents with memory
-    agents = PraisonAIAgents(
-        agents=[researcher, writer],
-        tasks=[research_task, report_task],
-        memory=True,
-        process="sequential"
-    )
-
-    # Start execution
-    result = agents.start()
-    ```
-  </Tab>
-  <Tab title="No Code">
-```yaml
-framework: praisonai
-process: sequential
-memory: true
-roles:
-  researcher:
-    backstory: Expert in market research and data analysis.
-    goal: Research and analyze market trends
-    role: Research Analyst
-    tools:
-      - duckduckgo
-    tasks:
-      research_task:
-        description: Research current market trends.
-        expected_output: Detailed market analysis.
-
-  writer:
-    backstory: Expert in technical writing and report creation.
-    goal: Create comprehensive market reports
-    role: Report Writer
-    tasks:
-      report_task:
-        description: Create a market report based on research.
-        expected_output: Comprehensive market report.
-```
-  </Tab>
-</Tabs>
-
-### Configuration Options
-
-```python
-# Create an agent with memory configuration
-agent = Agent(
-    role="Research Analyst",
-    goal="Research and retain information",
-    backstory="Expert in research and analysis",
-    tools=[duckduckgo],
-    verbose=True,  # Enable detailed logging
-    llm="gpt-4o"  # Language model to use
-)
-
-# Create agents with memory options
-agents = PraisonAIAgents(
-    agents=[agent],
-    tasks=[task],
-    memory=True,  # Enable memory
-    memory_config={
-        "provider": "rag",  # Use RAG for semantic search
-        "use_embedding": True,  # Enable embeddings for better search
-        "short_db": ".praison/short_term.db",  # Path for short-term memory
-        "long_db": ".praison/long_term.db",    # Path for long-term memory
-        "rag_db_path": ".praison/chroma_db"    # Path for vector database
-    }
-)
-```
-
-## Troubleshooting
-
-<CardGroup cols={2}>
-  <Card title="Memory Issues" icon="triangle-exclamation">
-    If memory isn't working as expected:
-    - Check memory configuration
-    - Enable verbose mode for debugging
-    - Verify memory provider settings
-  </Card>
-
-  <Card title="Context Flow" icon="code">
-    If context isn't being maintained:
-    - Review task dependencies
-    - Check memory configuration
-    - Verify agent communication
-  </Card>
-</CardGroup>
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card title="AutoAgents" icon="robot" href="../features/autoagents">
-    Learn about automatically created and managed AI agents
-  </Card>
-  <Card title="Mini Agents" icon="microchip" href="../features/mini">
-    Explore lightweight, focused AI agents
-  </Card>
-</CardGroup>
-
-<Note>
-  For optimal results, configure memory settings based on your specific use case requirements and expected interaction patterns.
-</Note>
-
-### Memory Configuration Options
-
-The memory system in PraisonAI supports various configuration options to customize how agents store and retrieve information:
-
-```python
-memory_config = {
-    # Memory Provider
-    "provider": "rag",        # Options: "rag", "mem0", "none"
-    "use_embedding": True,    # Enable semantic search with embeddings
-    
-    # Storage Paths
-    "short_db": ".praison/short_term.db",  # Short-term memory SQLite DB
-    "long_db": ".praison/long_term.db",    # Long-term memory SQLite DB
-    "rag_db_path": ".praison/chroma_db",   # Vector database path
-    
-    # Memory Settings
-    "ttl": 3600,             # Time to live for memory items (in seconds)
-    
-    # Optional Mem0 Config (if using mem0 provider)
-    "config": {
-        "api_key": "...",    # Mem0 API key
-        "org_id": "...",     # Organization ID
-        "project_id": "..."  # Project ID
-    }
-}
-
-# Create agents with memory configuration
-agents = PraisonAIAgents(
-    agents=[agent],
-    tasks=[task],
-    memory=True,
-    memory_config=memory_config
-)
-```
-
-### Memory Types
-
-PraisonAI's memory system includes several types of memory:
-
-<CardGroup cols={2}>
-  <Card title="Short-term Memory" icon="bolt">
-    - Temporary storage for current context
-    - Automatically cleared between sessions
-    - Fast access for immediate task context
-  </Card>
-  <Card title="Long-term Memory" icon="database">
-    - Persistent storage for important information
-    - Semantic search capabilities with RAG
-    - Quality-based storage decisions
-  </Card>
-</CardGroup>
-
-### Memory Quality Control
-
-PraisonAI includes built-in quality control for memory storage:
-
-```python
-# Example of storing with quality metrics
-agents.memory.store_long_term(
-    text="Important information to remember",
-    metadata={
-        "task_id": "task_123",
-        "agent": "research_agent"
-    },
-    completeness=0.9,    # How complete is the information
-    relevance=0.85,      # How relevant to the task
-    clarity=0.95,        # How clear and well-structured
-    accuracy=0.9,        # How accurate is the information
-    weights={            # Custom weights for quality score
-        "completeness": 0.3,
-        "relevance": 0.3,
-        "clarity": 0.2,
-        "accuracy": 0.2
-    }
-)
-
-# Search with quality filter
-results = agents.memory.search_long_term(
-    query="search query",
-    min_quality=0.8,     # Only return high-quality matches
-    limit=5              # Maximum number of results
-)

docs/concepts/process.mdx

@@ -1,453 +0,0 @@
----
-title: "Process"
-description: "Understanding Process Types in PraisonAI"
-icon: "diagram-project"
----
-
-# Understanding Process Types
-
-Process types in PraisonAI define how tasks are executed and how agents collaborate. Each process type offers different patterns for task execution and agent coordination.
-
-## Process Types Overview
-
-<CardGroup cols={3}>
-  <Card
-    title="Sequential Process"
-    icon="arrow-right"
-    href="/sequential-process"
-  >
-    Linear task execution in a predefined order
-  </Card>
-  <Card
-    title="Hierarchical Process"
-    icon="sitemap"
-    href="/hierarchical-process"
-  >
-    Manager-coordinated task execution with dynamic assignment
-  </Card>
-  <Card
-    title="Workflow Process"
-    icon="diagram-project"
-    href="/workflow-process"
-  >
-    Complex task flows with conditional execution and state management
-  </Card>
-</CardGroup>
-
-## Sequential Process
-
-```mermaid
-graph LR
-    Input[Input] --> A1
-    subgraph Agents
-        direction LR
-        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
-    end
-    A3 --> Output[Output]
-
-    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
-    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
-    classDef transparent fill:none,stroke:none
-
-    class Input,Output input
-    class A1,A2,A3 process
-    class Agents transparent
-```
-
-The simplest form of task execution where tasks are performed one after another.
-
-<Card>
-  ### Characteristics
-  - Linear execution flow
-  - Predictable order
-  - Simple dependency management
-  - Direct task progression
-
-  ### Usage
-  ```python
-  agents = PraisonAIAgents(
-      agents=[agent1, agent2],
-      tasks=[task1, task2, task3],
-      process="sequential"
-  )
-  ```
-</Card>
-
-## Hierarchical Process
-
-```mermaid
-graph TB
-    Input[Input] --> Manager
-    
-    subgraph Agents
-        Manager[Manager Agent]
-        
-        subgraph Workers
-            direction LR
-            W1[Worker 1]
-            W2[Worker 2]
-            W3[Worker 3]
-        end
-        
-        Manager --> W1
-        Manager --> W2
-        Manager --> W3
-    end
-    
-    W1 --> Manager
-    W2 --> Manager
-    W3 --> Manager
-    Manager --> Output[Output]
-
-    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
-    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
-    classDef transparent fill:none,stroke:none
-
-    class Input,Output input
-    class Manager,W1,W2,W3 process
-    class Agents,Workers transparent
-```
-
-Uses a manager agent to coordinate task execution and agent assignments.
-
-<Card>
-  ### Characteristics
-  - Manager-driven coordination
-  - Dynamic task assignment
-  - Flexible execution order
-  - Intelligent resource allocation
-
-  ### Configuration
-  ```python
-  agents = PraisonAIAgents(
-      agents=[manager_agent, worker_agent1, worker_agent2],
-      tasks=[task1, task2],
-      process="hierarchical",
-      manager_llm="gpt-4o"
-  )
-  ```
-</Card>
-
-## Workflow Process
-
-```mermaid
-graph LR
-    Input[Input] --> Start
-    
-    subgraph Workflow
-        direction LR
-        Start[Start] --> C1{Condition}
-        C1 --> |Yes| A1[Agent 1]
-        C1 --> |No| A2[Agent 2]
-        A1 --> Join
-        A2 --> Join
-        Join --> A3[Agent 3]
-    end
-    
-    A3 --> Output[Output]
-
-    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
-    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
-    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
-    classDef transparent fill:none,stroke:none
-
-    class Input,Output input
-    class Start,A1,A2,A3,Join process
-    class C1 decision
-    class Workflow transparent
-```
-
-Advanced process type supporting complex task relationships and conditional execution.
-
-<Card>
-  ### Features
-  - Task dependencies
-  - Conditional branching
-  - Loop handling
-  - Context sharing
-  - State management
-
-  ### Implementation
-  ```python
-  task_workflow = {
-      "start_task": {
-          "next": ["decision_task"],
-          "type": "task"
-      },
-      "decision_task": {
-          "type": "decision",
-          "conditions": {
-              "success": ["process_task"],
-              "failure": ["error_task"]
-          }
-      }
-  }
-  ```
-</Card>
-
-## Getting Started
-
-<Steps>
-  <Step title="Install PraisonAI">
-    Install the core package:
-    ```bash Terminal
-    pip install praisonaiagents
-    ```
-  </Step>
-
-  <Step title="Configure Environment">
-    ```bash Terminal
-    export OPENAI_API_KEY=your_openai_key
-    ```
-    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
-
-  <Step title="Create Agent">
-    Create `app.py`:
-<CodeGroup>
-  ```python Single Agent Sequential Process
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create an agent
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o"
-  )
-
-  # Define a task
-  task = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  # Run the agents
-  agents = PraisonAIAgents(
-      agents=[researcher],
-      tasks=[task],
-      verbose=False,
-      process="sequential"
-  )
-
-  result = agents.start()
-  ```
-
-  ```python Multiple Agents Hierarchical Process
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create multiple agents 
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  writer = Agent(
-      name="Writer",
-      role="Tech Content Strategist",
-      goal="Craft compelling content on tech advancements",
-      backstory="You are a content strategist",
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  # Define multiple tasks
-  task1 = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  task2 = Task(
-      name="writing_task",
-      description="Create a blog post about AI advancements",
-      expected_output="A blog post",
-      agent=writer
-  )
-
-  # Run with hierarchical process
-  agents = PraisonAIAgents(
-      agents=[researcher, writer],
-      tasks=[task1, task2],
-      verbose=False,
-      process="hierarchical",
-      manager_llm="gpt-4o"
-  )
-
-  result = agents.start()
-  ```
-</CodeGroup>
-  </Step>
-</Steps>
-<br /><br />
-<div className="relative w-full aspect-video">
-  <iframe
-    className="absolute top-0 left-0 w-full h-full"
-    src="https://www.youtube.com/embed/yWTH44QPl2A"
-    title="YouTube video player"
-    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-    allowFullScreen
-  ></iframe>
-</div>
-<br />
-
-## Advanced Features
-
-<CardGroup cols={2}>
-  <Card title="State Management" icon="database" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Task State Tracking">
-        Monitor and manage the progress of each task in real-time
-      </Accordion>
-      <Accordion title="Context Preservation">
-        Maintain important information across different stages of execution
-      </Accordion>
-      <Accordion title="Data Flow Control">
-        Manage how data moves between tasks and agents efficiently
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-
-  <Card title="Error Handling" icon="shield-check" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Graceful Recovery">
-        Automatically handle failures and continue execution
-      </Accordion>
-      <Accordion title="Alternative Paths">
-        Switch to backup plans when primary execution fails
-      </Accordion>
-      <Accordion title="Error Reporting">
-        Detailed error logs and diagnostic information
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-
-  <Card title="Monitoring" icon="chart-line" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Progress Tracking">
-        Real-time visibility into task completion status
-      </Accordion>
-      <Accordion title="Performance Metrics">
-        Measure execution time and resource efficiency
-      </Accordion>
-      <Accordion title="Resource Usage">
-        Monitor system resource utilization
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-
-  <Card title="Integration" icon="plug" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="External Systems">
-        Connect with other services and platforms
-      </Accordion>
-      <Accordion title="API Sync">
-        Maintain data consistency across systems
-      </Accordion>
-      <Accordion title="Event Handling">
-        React to system and external events
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-</CardGroup>
-
-## Async Processing
-
-<Note>
-All process types support asynchronous execution through async generators, enabling efficient parallel processing and non-blocking operations.
-</Note>
-
-### Core Async Methods
-
-<CardGroup cols={3}>
-  <Card title="asequential" icon="arrow-right">
-    Async version of sequential process for non-blocking linear execution
-  </Card>
-  <Card title="aworkflow" icon="diagram-project">
-    Async workflow process for complex parallel task execution
-  </Card>
-  <Card title="ahierarchical" icon="sitemap">
-    Async hierarchical process for distributed task management
-  </Card>
-</CardGroup>
-
-### Process-Specific Features
-
-<Tabs>
-  <Tab title="Sequential Process">
-    <Card>
-      - Tasks execute in order but don't block
-      - Maintains sequence while allowing async operations
-      - Perfect for I/O-heavy tasks
-      ```python
-      async def main():
-          agents = PraisonAIAgents(
-              process="sequential",
-              async_mode=True
-          )
-          await agents.astart()
-      ```
-    </Card>
-  </Tab>
-  
-  <Tab title="Workflow Process">
-    <Card>
-      - Parallel execution of independent tasks
-      - Async branching and merging
-      - Dynamic task scheduling based on conditions
-      ```python
-      async def main():
-          agents = PraisonAIAgents(
-              process="workflow",
-              async_mode=True,
-              parallel_tasks=True
-          )
-          await agents.astart()
-      ```
-    </Card>
-  </Tab>
-  
-  <Tab title="Hierarchical Process">
-    <Card>
-      - Async manager delegation
-      - Parallel subtask execution
-      - Dynamic worker assignment
-      ```python
-      async def main():
-          agents = PraisonAIAgents(
-              process="hierarchical",
-              async_mode=True,
-              dynamic_assignment=True
-          )
-          await agents.astart()
-      ```
-    </Card>
-  </Tab>
-</Tabs>
-
-### Key Benefits
-
-<CardGroup cols={2}>
-  <Card title="Performance" icon="bolt">
-    - Efficient resource utilization
-    - Reduced waiting time
-    - Better throughput
-  </Card>
-  
-  <Card title="Flexibility" icon="puzzle-piece">
-    - Mix sync and async tasks
-    - Adaptable execution patterns
-    - Easy scaling
-  </Card>
-</CardGroup>

docs/concepts/tasks.mdx

@@ -1,530 +0,0 @@
----
-title: "Tasks"
-description: "Understanding Tasks in PraisonAI"
-icon: "list-check"
----
-
-# Understanding Tasks
-Tasks are units of work that agents execute. Each task has a clear goal, input requirements, and expected outputs.
-
-```mermaid
-graph LR
-    Input[Input] ---> Task
-    
-    subgraph Task
-        direction LR
-        Role[Role]
-        Goal[Goal]
-        Story[Backstory]
-        Expected[Expected Output]
-        
-        Role --> Goal
-        Goal --> Story
-        Story --> Expected
-    end
-    
-    Task ---> Output[Output]
-
-    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
-    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
-    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
-    classDef transparent fill:none,stroke:none
-
-    class Input,Output input
-    class Role,Goal,Story,Expected process
-    class Task transparent
-```
-
-## Core Components
-
-<CardGroup cols={2}>
-  <Card title="Task Definition" icon="file-lines">
-    Clear description of work to be done and expected outputs
-  </Card>
-  <Card title="Agent Assignment" icon="user-check">
-    Matching tasks with capable agents
-  </Card>
-  <Card title="Tool Access" icon="toolbox">
-    Resources available for task completion
-  </Card>
-  <Card title="Output Handling" icon="file-export">
-    Managing and formatting task results
-  </Card>
-</CardGroup>
-
-## Task Configuration
-
-<CodeGroup>
-  ```python Basic
-  task = Task(
-      description="Research AI trends",
-      expected_output="Summary report",
-      agent=research_agent
-  )
-  ```
-  ```python Advanced
-  task = Task(
-      description="Analyze market data",
-      expected_output="Detailed analysis",
-      agent=analyst_agent,
-      tools=[AnalysisTool()],
-      output_file="analysis.md",
-      async_execution=True
-  )
-  ```
-</CodeGroup>
-
-# Understanding Tasks
-<br />
-```mermaid
-graph LR
-    %% Task Flow Steps
-    Start([▶ Start]) --> TaskDef
-    TaskDef --> AgentAssign
-    AgentAssign --> Exec
-    Exec --> Output([✓ Output])
-    
-    %% Define subgraphs for each step
-    subgraph TaskDef[" "]
-        TD_Icon[📋 Task Definition]
-        TD_Desc[Description & Requirements]
-        
-        TD_Icon --- TD_Desc
-        
-        style TD_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
-        style TD_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
-    end
-    
-    subgraph AgentAssign[" "]
-        AA_Icon[🤖 Agent Assignment]
-        AA_Desc[Matching & Delegation]
-        
-        AA_Icon --- AA_Desc
-        
-        style AA_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
-        style AA_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
-    end
-    
-    subgraph Exec[" "]
-        E_Icon[⚙️ Execution]
-        E_Desc[Processing & Tools]
-        
-        E_Icon --- E_Desc
-        
-        style E_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
-        style E_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
-    end
-    
-    %% Styling for main elements
-    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
-    
-    %% Style for containers
-    style TaskDef fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    style AgentAssign fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    style Exec fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    
-    %% Global styles
-    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
-    linkStyle default stroke:#7C90A0,stroke-width:2px
-```
-<br /><br />
-```mermaid
-graph LR
-    %% Task States
-    Pending([📥 Pending]) --> InProgress([⚡ In Progress])
-    InProgress --> Completed([✅ Completed])
-    InProgress --> Failed([❌ Failed])
-    
-    %% Styling
-    style Pending fill:#2D3047,stroke:#7C90A0,color:#fff
-    style InProgress fill:#5C80BC,stroke:#7C90A0,color:#fff
-    style Completed fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Failed fill:#2D3047,stroke:#7C90A0,color:#fff
-    
-    %% Global styles
-    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
-    linkStyle default stroke:#7C90A0,stroke-width:2px
-```
-
-## Task Types
-
-<Steps>
-  <Step title="Basic Task">
-    Simple, single-operation tasks with clear inputs and outputs
-  </Step>
-  <Step title="Decision Task">
-    Tasks involving choices and conditional paths
-    ```python
-    decision_task = Task(
-        type="decision",
-        conditions={
-            "success": ["next_task"],
-            "failure": ["error_task"]
-        }
-    )
-    ```
-  </Step>
-  <Step title="Loop Task">
-    Repetitive operations over collections
-    ```python
-    loop_task = Task(
-        type="loop",
-        items=data_list,
-        operation="process_item"
-    )
-    ```
-  </Step>
-</Steps>
-
-## Task Relationships
-
-<Warning>
-  Properly managing task dependencies is crucial for complex workflows. Always ensure proper context sharing and error handling.
-</Warning>
-
-### Context Sharing
-```python
-task_a = Task(name="research")
-task_b = Task(
-    name="analyze",
-    context=[task_a]  # Uses task_a's output
-)
-```
-
-### Task Dependencies
-
-| Relationship | Description | Example |
-|:-------------|:------------|:--------|
-| **Sequential** | Tasks run one after another | Research → Analysis → Report |
-| **Parallel** | Independent tasks run simultaneously | Data Collection + Processing |
-| **Conditional** | Tasks depend on previous results | Success → Next Task, Failure → Retry |
-
-## Advanced Features
-
-<CardGroup cols={2}>
-  <Card title="Output Handling" icon="file-code">
-    - Multiple output formats
-    - Structured data validation
-    - File system integration
-  </Card>
-  <Card title="Task Control" icon="sliders">
-    - Execution flow control
-    - Error recovery
-    - Progress tracking
-  </Card>
-</CardGroup>
-
-## Getting Started
-
-<Steps>
-
-  <Step title="Install PraisonAI">
-    Install the core package:
-    ```bash Terminal
-    pip install praisonaiagents
-    ```
-  </Step>
-
-  <Step title="Configure Environment">
-    ```bash Terminal
-    export OPENAI_API_KEY=your_openai_key
-    ```
-    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
-
-  <Step title="Create Agent">
-    Create `app.py`:
-<CodeGroup>
-  ```python Single Agent
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create an agent
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o"
-  )
-
-  # Define a task
-  task = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  # Run the agents
-  agents = PraisonAIAgents(
-      agents=[researcher],
-      tasks=[task],
-      verbose=False
-  )
-
-  result = agents.start()
-  ```
-
-  ```python Multiple Agents
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create multiple agents
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  writer = Agent(
-      name="Writer",
-      role="Tech Content Strategist",
-      goal="Craft compelling content on tech advancements",
-      backstory="You are a content strategist",
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  # Define multiple tasks
-  task1 = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  task2 = Task(
-      name="writing_task",
-      description="Create a blog post about AI advancements",
-      expected_output="A blog post",
-      agent=writer
-  )
-
-  # Run with hierarchical process
-  agents = PraisonAIAgents(
-      agents=[researcher, writer],
-      tasks=[task1, task2],
-      verbose=False,
-      process="hierarchical",
-      manager_llm="gpt-4o"
-  )
-
-  result = agents.start()
-  ```
-</CodeGroup>
-  </Step>
-</Steps>
-
-## Best Practices
-
-<Note>
-Design tasks to be atomic and focused. Each task should have a single, clear responsibility.
-</Note>
-
-<CardGroup cols={2}>
-  <Card title="Task Design Principles" icon="compass-drafting" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Clear Task Descriptions">
-        Write precise, unambiguous task descriptions that clearly state the task's purpose and requirements.
-        ```python
-        task = Task(
-            description=(
-                "Analyze Q4 2024 sales data "
-                "and generate growth metrics"
-            ),
-            expected_output="Sales growth report"
-        )
-        ```
-      </Accordion>
-
-      <Accordion title="Success Criteria">
-        Define measurable success criteria to ensure task completion can be properly evaluated.
-        ```python
-        task = Task(
-            success_criteria={
-                "format": "markdown",
-                "metrics": [
-                    "growth_rate",
-                    "revenue_change"
-                ],
-                "min_confidence": 0.95
-            }
-        )
-        ```
-      </Accordion>
-
-      <Accordion title="Task Dependencies">
-        Specify any dependencies or prerequisites for the task.
-        ```python
-        task = Task(
-            description="Generate final report",
-            dependencies=[
-                data_collection_task,
-                analysis_task
-            ],
-            wait_for_dependencies=True
-        )
-        ```
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-
-  <Card title="Resource Management" icon="server" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Tool Selection">
-        Choose appropriate tools that match your task's requirements.
-        ```python
-        task = Task(
-            tools=[
-                DataAnalyzer(),
-                ReportGenerator(),
-                MetricsCalculator()
-            ],
-            tool_config={
-                "timeout": 30,
-                "retry": True
-            }
-        )
-        ```
-      </Accordion>
-
-      <Accordion title="Error Handling">
-        Implement comprehensive error management strategies.
-        ```python
-        task = Task(
-            error_strategy="retry",
-            max_retries=3,
-            fallback_handler=handler,
-            error_callbacks=[
-                log_error,
-                notify_admin
-            ]
-        )
-        ```
-      </Accordion>
-
-      <Accordion title="Resource Limits">
-        Set appropriate resource constraints for optimal performance.
-        ```python
-        task = Task(
-            memory_limit="2GB",
-            timeout=300,
-            max_concurrent=5,
-            cpu_priority="high",
-            auto_scale=True
-        )
-        ```
-      </Accordion>
-
-      <Accordion title="Performance Monitoring">
-        Configure monitoring and logging for task execution.
-        ```python
-        task = Task(
-            monitoring={
-                "metrics": ["duration", "memory", "cpu"],
-                "log_level": "INFO",
-                "trace_enabled": True,
-                "alert_threshold": 0.85
-            }
-        )
-        ```
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-</CardGroup>
-
-## Async Task Execution
-
-<CardGroup cols={1}>
-  <Card title="Async Task Features" icon="bolt" iconType="solid">
-    <Tabs>
-      <Tab title="Basic Configuration">
-        ```python
-        async_task = Task(
-            description="Perform async operation",
-            expected_output="Async result",
-            agent=async_agent,
-            async_execution=True,  # Enable async execution
-            callback=async_callback  # Optional async callback
-        )
-        ```
-        - Enable async execution with `async_execution=True`
-        - Perfect for I/O-heavy operations
-        - Supports parallel task execution
-      </Tab>
-      
-      <Tab title="Async Callbacks">
-        ```python
-        async def async_callback(output: TaskOutput):
-            await asyncio.sleep(1)  # Simulate async operation
-            print(f"Processed output: {output.raw}")
-
-        task = Task(
-            description="Task with async callback",
-            expected_output="Result",
-            agent=agent,
-            async_execution=True,
-            callback=async_callback
-        )
-        ```
-        - Support for async callbacks
-        - Process results asynchronously
-        - Handle task completion events
-      </Tab>
-      
-      <Tab title="Mixed Execution">
-        ```python
-        # Create mixed task types
-        sync_task = Task(
-            description="Synchronous operation",
-            async_execution=False
-        )
-
-        async_task = Task(
-            description="Asynchronous operation",
-            async_execution=True
-        )
-
-        # Run with mixed execution
-        agents = PraisonAIAgents(
-            agents=[sync_agent, async_agent],
-            tasks=[sync_task, async_task]
-        )
-        result = await agents.astart()
-        ```
-        - Mix sync and async tasks
-        - Flexible execution patterns
-        - Optimal resource utilization
-      </Tab>
-    </Tabs>
-  </Card>
-</CardGroup>
-
-<Tip>
-**Pro Tip**: Use async execution for tasks that involve I/O operations (like API calls or file operations) to improve overall performance. Keep CPU-intensive tasks synchronous.
-</Tip>
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card
-    title="Create Tasks"
-    icon="plus"
-    href="/code/quickstart"
-  >
-    Learn how to create and configure tasks
-  </Card>
-  <Card
-    title="Task API"
-    icon="code"
-    href="/api/praisonaiagents/task/task"
-  >
-    Explore the complete Task API documentation
-  </Card>
-</CardGroup>

docs/concepts/tools.mdx

@@ -1,647 +0,0 @@
----
-title: "AI Agents with Tools"
-sidebarTitle: "Tools"
-description: "Learn how to create AI agents that can use tools to interact with external systems and perform actions."
-icon: "screwdriver-wrench"
----
-
-```mermaid
-flowchart TB
-    subgraph Tools
-        direction TB
-        T3[Internet Search]
-        T1[Code Execution]
-        T2[Formatting]
-    end
-
-    Input[Input] ---> Agents
-    subgraph Agents
-        direction LR
-        A1[Agent 1]
-        A2[Agent 2]
-        A3[Agent 3]
-    end
-    Agents ---> Output[Output]
-
-    T3 --> A1
-    T1 --> A2
-    T2 --> A3
-
-    style Tools fill:#189AB4,color:#fff
-    style Agents fill:#8B0000,color:#fff
-    style Input fill:#8B0000,color:#fff
-    style Output fill:#8B0000,color:#fff
-```
-
-| Feature | [Knowledge](/concepts/knowledge) | [Tools](/concepts/tools) |
-|---------|--------------------------------|---------------------------|
-| Purpose | Static reference information | Dynamic interaction capabilities |
-| Access | Read-only reference | Execute actions and commands |
-| Updates | Manual through files | Real-time through tool calls |
-| Storage | Knowledge base | Assigned to specific agents |
-| Persistence | Permanent until changed | Available during agent execution |
-
-## Quick Start
-
-Tools are functions that agents can use to interact with external systems and perform actions. They are essential for creating agents that can do more than just process text.
-
-## Creating Custom Tool
-<Steps>
-<Step>
-Create any function that you want to use as a tool, that performs a specific task.
-```python
-from duckduckgo_search import DDGS
-from typing import List, Dict
-
-# Tool Implementation
-def internet_search_tool(query: str) -> List[Dict]:
-    """
-    Perform Internet Search using DuckDuckGo
-    
-    Args:
-        query (str): The search query string
-        
-    Returns:
-        List[Dict]: List of search results containing title, URL, and snippet
-    """
-    results = []
-    ddgs = DDGS()
-    for result in ddgs.text(keywords=query, max_results=5):
-        results.append({
-            "title": result.get("title", ""),
-            "url": result.get("href", ""),
-            "snippet": result.get("body", "")
-        })
-    return results
-```
-</Step>
-<Step>
-Assign the tool to an agent
-```python
-    data_agent = Agent(
-        name="DataCollector",
-        role="Search Specialist",
-        goal="Perform internet searches to collect relevant information.",
-        backstory="Expert in finding and organising internet data.",
-        tools=[internet_search_tool], ## Add the tool to the agent i.e the function name
-    )
-```
-</Step>
-</Steps>
-
-<Card title="That's it!">
-<Check>You have created a custom tool and assigned it to an agent.</Check>
-</Card>
-
-## Implementing Tools Full Code Example
-<Tabs>
-
-<Tab title="Code">
-<Steps>
-
-  <Step title="Install PraisonAI">
-Install the core package:
-```bash Terminal
-pip install praisonaiagents duckduckgo-search
-```
-  </Step>
-
-  <Step title="Configure Environment">
-  ```bash Terminal
-  export OPENAI_API_KEY=your_openai_key
-  ```
-  Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-  Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
-
-  <Step title="Create Agent with Tool">
-    Create `app.py`
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-from duckduckgo_search import DDGS
-from typing import List, Dict
-
-# 1. Tool Implementation
-def internet_search_tool(query: str) -> List[Dict]:
-    """
-    Perform Internet Search using DuckDuckGo
-    
-    Args:
-        query (str): The search query string
-        
-    Returns:
-        List[Dict]: List of search results containing title, URL, and snippet
-    """
-    results = []
-    ddgs = DDGS()
-    for result in ddgs.text(keywords=query, max_results=5):
-        results.append({
-            "title": result.get("title", ""),
-            "url": result.get("href", ""),
-            "snippet": result.get("body", "")
-        })
-    return results
-
-# 2. Assign the tool to an agent
-data_agent = Agent(
-    name="DataCollector",
-    role="Search Specialist",
-    goal="Perform internet searches to collect relevant information.",
-    backstory="Expert in finding and organising internet data.",
-    tools=[internet_search_tool],
-    self_reflect=False
-)
-
-# 3. Task Definition
-collect_task = Task(
-    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
-    expected_output="List of search results with titles, URLs, and snippets.",
-    agent=data_agent,
-    name="collect_data",
-)
-
-# 4. Start Agents
-agents = PraisonAIAgents(
-    agents=[data_agent],
-    tasks=[collect_task],
-    process="sequential"
-)
-
-agents.start()
-```
-  </Step>
-
-  <Step title="Start Agents">
-    Execute your script:
-```bash Terminal
-python app.py
-```
-  </Step>
-</Steps>
-
-</Tab>
-<Tab title="No Code">
-<Steps>
-<Step title="Install PraisonAI">
-Install the core package and duckduckgo_search package:
-```bash Terminal
-pip install praisonai duckduckgo_search
-```
-</Step>
-<Step title="Create Custom Tool">
-<Info>
-To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM
-</Info>
-Create a new file `tools.py` with the following content:
-```python
-from duckduckgo_search import DDGS
-from typing import List, Dict
-
-# 1. Tool
-def internet_search_tool(query: str) -> List[Dict]:
-    """
-    Perform Internet Search
-    """
-    results = []
-    ddgs = DDGS()
-    for result in ddgs.text(keywords=query, max_results=5):
-        results.append({
-            "title": result.get("title", ""),
-            "url": result.get("href", ""),
-            "snippet": result.get("body", "")
-        })
-    return results  
-```
-</Step>
-<Step title="Create Agent">
-
-Create a new file `agents.yaml` with the following content:
-```yaml
-framework: praisonai
-topic: create movie script about cat in mars
-roles:
-  scriptwriter:
-    backstory: Expert in dialogue and script structure, translating concepts into
-      scripts.
-    goal: Write a movie script about a cat in Mars
-    role: Scriptwriter
-    tools:
-      - internet_search_tool # <-- Tool assigned to Agent here
-    tasks:
-      scriptwriting_task:
-        description: Turn the story concept into a production-ready movie script,
-          including dialogue and scene details.
-        expected_output: Final movie script with dialogue and scene details.
-```
-</Step>
-
-<Step title="Start Agents">
-
-Execute your script:
-```bash Terminal
-praisonai agents.yaml
-```
-</Step>
-</Steps>
-</Tab>
-</Tabs>
-
-## In-build Tools in PraisonAI
-
-<CardGroup cols={2}>
-  <Card title="Search Tools" icon="magnifying-glass" href="/tools/search">
-    Tools for searching and retrieving information from various sources
-  </Card>
-  <Card title="Python Tools" icon="python" href="/tools/python_tools">
-    Essential Python utilities for data manipulation and scripting
-  </Card>
-  <Card title="Spider Tools" icon="spider" href="/tools/spider_tools">
-    Web crawling and scraping capabilities for data extraction
-  </Card>
-  <Card title="Arxiv Tools" icon="book" href="/tools/arxiv_tools">
-    Access and search academic papers from arXiv repository
-  </Card>
-  <Card title="Newspaper Tools" icon="newspaper" href="/tools/newspaper_tools">
-    Extract and parse content from news articles and websites
-  </Card>
-  <Card title="DuckDB Tools" icon="database" href="/tools/duckdb_tools">
-    Fast analytical SQL database operations and queries
-  </Card>
-  <Card title="DuckDuckGo Tools" icon="duck" href="/tools/duckduckgo_tools">
-    Web search functionality using DuckDuckGo's API
-  </Card>
-  <Card title="Calculator Tools" icon="calculator" href="/tools/calculator_tools">
-    Perform mathematical calculations and conversions
-  </Card>
-  <Card title="YAML Tools" icon="file-code" href="/tools/yaml_tools">
-    Parse and manipulate YAML format data
-  </Card>
-  <Card title="JSON Tools" icon="brackets-curly" href="/tools/json_tools">
-    Handle JSON data structures and operations
-  </Card>
-  <Card title="Pandas Tools" icon="table" href="/tools/pandas_tools">
-    Data analysis and manipulation using Pandas
-  </Card>
-  <Card title="YFinance Tools" icon="chart-line" href="/tools/yfinance_tools">
-    Fetch financial market data from Yahoo Finance
-  </Card>
-  <Card title="Shell Tools" icon="terminal" href="/tools/shell_tools">
-    Execute shell commands and system operations
-  </Card>
-  <Card title="Wikipedia Tools" icon="book-open" href="/tools/wikipedia_tools">
-    Access and search Wikipedia articles and data
-  </Card>
-  <Card title="XML Tools" icon="code" href="/tools/xml_tools">
-    Process and manipulate XML format data
-  </Card>
-  <Card title="File Tools" icon="file" href="/tools/file_tools">
-    File system operations and management utilities
-  </Card>
-  <Card title="Excel Tools" icon="file-excel" href="/tools/excel_tools">
-    Work with Excel spreadsheets and workbooks
-  </Card>
-  <Card title="CSV Tools" icon="file-csv" href="/tools/csv_tools">
-    Handle CSV file operations and transformations
-  </Card>
-</CardGroup>
-
-## Tools Overview
-
-<CardGroup cols={3}>
-  <Card
-    title="Search Tools"
-    icon="magnifying-glass"
-    iconType="solid"
-  >
-    Tools for searching and retrieving information from various sources
-  </Card>
-
-  <Card
-    title="File Tools"
-    icon="file"
-    iconType="solid"
-  >
-    Tools for reading, writing, and manipulating files
-  </Card>
-
-  <Card
-    title="API Tools"
-    icon="code"
-    iconType="solid"
-  >
-    Tools for interacting with external APIs and services
-  </Card>
-</CardGroup>
-
-## Advanced Tool Features
-
-### Tool Configuration
-<Frame>
-```python
-def configured_tool(
-    query: str,
-    max_results: int = 5,
-    timeout: int = 10
-) -> List[Dict]:
-    """
-    Example of a configurable tool
-    
-    Args:
-        query (str): Search query
-        max_results (int): Maximum number of results
-        timeout (int): Request timeout in seconds
-        
-    Returns:
-        List[Dict]: Search results
-    """
-    # Tool implementation
-    pass
-```
-</Frame>
-
-### Tool Chaining
-<Frame>
-```python
-def chain_tools(input_data: str) -> Dict:
-    """
-    Example of chaining multiple tools
-    
-    Args:
-        input_data (str): Input data
-        
-    Returns:
-        Dict: Processed results
-    """
-    # 1. Search for data
-    search_results = internet_search_tool(input_data)
-    
-    # 2. Process results
-    processed_data = process_tool(search_results)
-    
-    # 3. Format output
-    return format_tool(processed_data)
-```
-</Frame>
-
-### Tool Categories
-
-<CardGroup cols={3}>
-  <Card title="Data Collection Tools">
-    - Web scraping
-    - API integration
-    - Database queries
-  </Card>
-  
-  <Card title="Processing Tools">
-    - Data transformation
-    - Text analysis
-    - Image processing
-  </Card>
-  
-  <Card title="Output Tools">
-    - File generation
-    - Report creation
-    - Data visualization
-  </Card>
-</CardGroup>
-
-## Tool Integration
-
-### Adding Tools to Agents
-<Frame>
-```python
-# Multiple tools
-agent = Agent(
-    name="MultiTool Agent",
-    tools=[
-        internet_search_tool,
-        file_processing_tool,
-        api_integration_tool
-    ]
-)
-```
-</Frame>
-
-### Tool Dependencies
-<Frame>
-```python
-# Tool with dependencies
-def advanced_tool(data: Dict) -> Dict:
-    """
-    Tool that depends on external libraries
-    
-    Args:
-        data (Dict): Input data
-        
-    Returns:
-        Dict: Processed data
-    """
-    try:
-        import required_library
-        # Tool implementation
-        return processed_result
-    except ImportError:
-        raise Exception("Required library not installed")
-```
-</Frame>
-
-## Tool Guidelines
-
-<CardGroup cols={2}>
-  <Card title="Best Practices">
-    1. **Type Hints**
-       - Use Python type hints
-       - Define clear input/output types
-       - Document complex types
-
-    2. **Documentation**
-       - Write clear docstrings
-       - Explain parameters
-       - Provide usage examples
-
-    3. **Error Handling**
-       - Handle exceptions gracefully
-       - Return meaningful errors
-       - Validate inputs
-  </Card>
-
-  <Card title="Tool Types">
-    1. **Search Tools**
-       - Web search
-       - Database queries
-       - Document search
-
-    2. **File Tools**
-       - Read/write operations
-       - File conversion
-       - Data extraction
-
-    3. **API Tools**
-       - REST API calls
-       - GraphQL queries
-       - Service integration
-  </Card>
-</CardGroup>
-
-## Best Practices Summary
-
-<Note>
-Following these best practices will help you create robust, efficient, and secure tools in PraisonAI.
-</Note>
-
-<CardGroup cols={1}>
-  <Card title="Design Principles" icon="compass-drafting" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Single Responsibility">
-        Each tool should have one clear purpose and do it well. Avoid creating tools that try to do too many things.
-        ```python
-        # Good Example
-        def process_image(image: np.array) -> np.array:
-            return processed_image
-
-        # Avoid
-        def process_and_save_and_upload(image):
-            # Too many responsibilities
-            pass
-        ```
-      </Accordion>
-      
-      <Accordion title="Clear Interfaces">
-        Define explicit input/output types and maintain consistent parameter naming.
-        ```python
-        def search_tool(
-            query: str,
-            max_results: int = 10
-        ) -> List[Dict[str, Any]]:
-            """
-            Search for information with clear parameters
-            """
-            pass
-        ```
-      </Accordion>
-      
-      <Accordion title="Documentation">
-        Always include detailed docstrings and type hints.
-        ```python
-        def analyze_text(
-            text: str,
-            language: str = "en"
-        ) -> Dict[str, float]:
-            """
-            Analyze text sentiment and emotions.
-            
-            Args:
-                text: Input text to analyze
-                language: ISO language code
-                
-            Returns:
-                Dict with sentiment scores
-            """
-            pass
-        ```
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-</CardGroup>
-
-<br />
-
-<CardGroup cols={1}>
-  <Card title="Performance Optimization" icon="bolt" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Efficient Processing">
-        Optimize resource usage and processing time.
-        ```python
-        # Use generators for large datasets
-        def process_large_data():
-            for chunk in data_generator():
-                yield process_chunk(chunk)
-        ```
-      </Accordion>
-      
-      <Accordion title="Resource Management">
-        Properly handle resource allocation and cleanup.
-        ```python
-        async with aiohttp.ClientSession() as session:
-            # Resource automatically managed
-            await process_data(session)
-        ```
-      </Accordion>
-      
-      <Accordion title="Caching">
-        Implement caching for frequently accessed data.
-        ```python
-        @cache.memoize(timeout=300)
-        def expensive_operation(data: str) -> Dict:
-            return process_expensive(data)
-        ```
-      </Accordion>
-      
-      <Accordion title="Async Operations">
-        Use async/await for I/O-bound operations.
-        ```python
-        async def fetch_data(urls: List[str]):
-            async with aiohttp.ClientSession() as session:
-                tasks = [fetch_url(session, url) for url in urls]
-                return await asyncio.gather(*tasks)
-        ```
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-</CardGroup>
-
-<br />
-
-<CardGroup cols={1}>
-  <Card title="Security Best Practices" icon="shield-check" iconType="solid">
-    <AccordionGroup>
-      <Accordion title="Input Validation">
-        Always validate and sanitize inputs to prevent security vulnerabilities.
-        ```python
-        def process_user_input(data: str) -> str:
-            if not isinstance(data, str):
-                raise ValueError("Input must be string")
-            return sanitize_input(data.strip())
-        ```
-      </Accordion>
-      
-      <Accordion title="Rate Limiting">
-        Implement rate limiting for API calls to prevent abuse.
-        ```python
-        @rate_limit(calls=100, period=60)
-        async def api_call():
-            return await make_request()
-        ```
-      </Accordion>
-      
-      <Accordion title="API Key Management">
-        Securely handle API keys and credentials using environment variables.
-        ```python
-        # Use environment variables
-        api_key = os.getenv('API_KEY')
-        if not api_key:
-            raise ConfigError("API key not found")
-        ```
-      </Accordion>
-      
-      <Accordion title="Error Masking">
-        Hide sensitive information in error messages to prevent information leakage.
-        ```python
-        try:
-            result = process_sensitive_data()
-        except Exception as e:
-            # Log detailed error internally
-            logger.error(f"Detailed error: {str(e)}")
-            # Return sanitized error to user
-            raise PublicError("Processing failed")
-        ```
-      </Accordion>
-    </AccordionGroup>
-  </Card>
-</CardGroup>
-
-<br />
-
-<Tip>
-**Pro Tip**: Start with these practices from the beginning of your project. It's easier to maintain good practices than to retrofit them later.
-</Tip>

docs/conf.py

@@ -1,28 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# For the full list of built-in configuration values, see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Project information -----------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
-
-project = 'PraisonAI'
-copyright = '2024, Mervin Praison'
-author = 'Mervin Praison'
-release = '7'
-
-# -- General configuration ---------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
-
-extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx_rtd_theme']
-
-templates_path = ['_templates']
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
-
-
-
-# -- Options for HTML output -------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
-
-html_theme = 'sphinx_rtd_theme'
-html_static_path = ['_static']

docs/contributing.mdx

@@ -1,14 +0,0 @@
----
-title: "Contributing"
-description: "Guide for contributing to PraisonAI through GitHub, including forking, cloning, and submitting pull requests"
-icon: "code-branch"
----
-
-- Fork on GitHub: Use the "Fork" button on the repository page.
-- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
-- Create a branch: `git checkout -b new-feature`
-- Make changes and commit: `git commit -am "Add some feature"`
-- Push to your fork: `git push origin new-feature`
-- Submit a pull request via GitHub's web interface.
-- Await feedback from project maintainers.
-

docs/course/agents/01-introduction.mdx

@@ -1,71 +0,0 @@
----
-title: "Introduction to AI Agents"
-description: "Welcome to the beginner's course on AI Agents"
-icon: "book-open"
----
-
-# Introduction to AI Agents
-
-Welcome to the beginners' course on AI Agents! This course is designed to help you understand and build your own AI agents, even if you have little to no programming experience.
-
-## What Are AI Agents?
-
-An AI agent is a software program that can:
-- Understand information from its environment
-- Make decisions based on that information
-- Take actions to achieve specific goals
-
-Think of an AI agent like a helpful assistant that can perform tasks for you automatically.
-
-```mermaid
-graph TD
-    A[Perceive Information] --> B[Make Decisions]
-    B --> C[Take Actions]
-    C --> A
-```
-
-## Examples in Daily Life
-
-You might already be using AI agents without realizing it:
-
-- **Virtual Assistants**: Siri, Alexa, and Google Assistant are AI agents that respond to your voice commands
-- **Recommendation Systems**: Netflix, YouTube, and Amazon use AI agents to suggest content you might like
-- **Smart Home Devices**: Devices that adjust temperature or lighting based on your preferences
-
-## What You'll Learn in This Course
-
-Throughout this course, you will:
-
-1. Understand the basic concepts of AI agents
-2. Learn about different types of AI agents
-3. Explore how AI agents make decisions
-4. Discover how to build simple AI agents without complex programming
-5. Create your own functional AI agents
-
-## Who This Course Is For
-
-This course is perfect for:
-- Complete beginners with no prior knowledge of AI
-- Non-technical professionals interested in how AI can solve problems
-- Students curious about artificial intelligence
-- Anyone who wants to understand and use AI agents in practical ways
-
-Let's begin our journey into the fascinating world of AI agents!
-<iframe 
-  id="codeExecutionFrame"
-  src="https://code-execution-server-praisonai.replit.app/?code=import%20openai%0A%0Aclient%20%3D%20openai.OpenAI()%0Aresult%20%3D%20client.chat.completions.create(%0A%20%20%20%20model%3D%22gpt-3.5-turbo%22%2C%0A%20%20%20%20messages%3D%5B%0A%20%20%20%20%20%20%20%20%7B%22role%22%3A%20%22user%22%2C%20%22content%22%3A%20%22Hello%20World%22%7D%0A%20%20%20%20%5D%0A)%0A%0Aprint(result.choices%5B0%5D.message.content)" 
-  width="100%" 
-  height="600px" 
-  frameborder="0"
-  allow="clipboard-read; clipboard-write"
-  scrolling="yes"
-  onload="resizeIframe(this)"
-></iframe>
-
-<IframeWithAutoHeight />
-
-<CardGroup cols={1}>
-  <Card title="Ready to Start?" icon="rocket">
-    Continue to the next lesson to learn about the different types of AI agents.
-  </Card>
-</CardGroup>

docs/course/agents/02-types-of-agents.mdx

@@ -1,113 +0,0 @@
----
-title: "Types of AI Agents"
-description: "Understanding the different categories of AI agents"
-icon: "sitemap"
----
-
-# Types of AI Agents
-
-AI agents come in different forms, each designed for specific purposes and with varying levels of complexity. Understanding these types will help you choose the right approach for your needs.
-
-## Simple Reflex Agents
-
-These are the most basic type of AI agents that operate using simple if-then rules.
-
-<CardGroup cols={1}>
-  <Card title="Key Characteristics" icon="lightbulb">
-    - React directly to current input only
-    - No memory of past events
-    - No prediction of future states
-    - Use predefined rules to determine actions
-  </Card>
-</CardGroup>
-
-**Real-world example**: A basic thermostat that turns heating on when temperature drops below a set point.
-
-```mermaid
-graph LR
-    A[Sensor Input] --> B{If-Then Rules}
-    B --> C[Action]
-```
-
-## Model-Based Agents
-
-These agents maintain an internal model of their environment to make better decisions.
-
-<CardGroup cols={1}>
-  <Card title="Key Characteristics" icon="brain">
-    - Build and maintain an internal representation of the world
-    - Can work with partial information
-    - Can consider "what if" scenarios
-    - Make decisions based on their understanding of how the world works
-  </Card>
-</CardGroup>
-
-**Real-world example**: A GPS navigation system that models roads and traffic conditions.
-
-## Goal-Based Agents
-
-These agents make decisions based on how their actions will help achieve specific goals.
-
-<CardGroup cols={1}>
-  <Card title="Key Characteristics" icon="bullseye">
-    - Consider future consequences of actions
-    - Evaluate which actions will lead toward goals
-    - Can plan sequences of actions
-    - Choose actions that maximize goal attainment
-  </Card>
-</CardGroup>
-
-**Real-world example**: A chess-playing AI that plans moves to achieve checkmate.
-
-## Utility-Based Agents
-
-These agents choose actions that maximize a specific utility (or happiness) function.
-
-<CardGroup cols={1}>
-  <Card title="Key Characteristics" icon="chart-line">
-    - Assign a utility value to different possible outcomes
-    - Consider probability and risk
-    - Choose actions that maximize expected utility
-    - Can balance competing objectives
-  </Card>
-</CardGroup>
-
-**Real-world example**: An investment robo-advisor that balances risk and return.
-
-## Learning Agents
-
-These agents improve their performance over time through experience.
-
-<CardGroup cols={1}>
-  <Card title="Key Characteristics" icon="graduation-cap">
-    - Learn from past experiences
-    - Adapt to changing environments
-    - Improve decision-making over time
-    - Can discover new strategies
-  </Card>
-</CardGroup>
-
-**Real-world example**: Recommendation systems that learn your preferences over time.
-
-## Understanding Agent Complexity
-
-<Tip>
-As you move from Simple Reflex Agents toward Learning Agents, both capability and complexity increase. For beginners, starting with simpler agent types is often best.
-</Tip>
-
-## Which Agent Type Should You Build?
-
-The best type of agent for your project depends on:
-
-1. **The problem complexity**: Simple problems may only need reflex agents
-2. **Available data**: Learning agents need training data
-3. **Required adaptability**: Will your agent need to adapt to new situations?
-4. **Available resources**: More complex agents require more computational power
-
-In this course, we'll focus primarily on goal-based and simple learning agents as they provide a good balance of capability and complexity for beginners.
-
-<CardGroup cols={1}>
-  <Card title="Next Steps" icon="arrow-right">
-    In the next lesson, we'll explore how agents process information and make decisions.
-  </Card>
-</CardGroup>

docs/course/agents/03-agent-architecture.mdx

@@ -1,157 +0,0 @@
----
-title: "Agent Architecture"
-description: "Understanding how AI agents are structured"
-icon: "layer-group"
----
-
-# Agent Architecture
-
-Understanding how AI agents are structured will help you build more effective agents. This lesson covers the fundamental components of an agent's architecture.
-
-## Basic Components of an AI Agent
-
-Every AI agent, regardless of complexity, has these basic components:
-
-```mermaid
-graph TD
-    A[Sensors/Input] --> B[Processing Unit]
-    B --> C[Decision-Making]
-    C --> D[Actions/Output]
-```
-
-### 1. Input (Sensors)
-
-This is how agents receive information from their environment.
-
-<CardGroup cols={1}>
-  <Card title="Examples of Input" icon="eye">
-    - Text input from users
-    - Data from databases
-    - Image or audio input
-    - API responses
-    - Sensor readings (in physical agents)
-  </Card>
-</CardGroup>
-
-### 2. Processing Unit
-
-This component processes information and converts it into a format the agent can understand.
-
-<CardGroup cols={1}>
-  <Card title="Processing Functions" icon="gears">
-    - Data cleaning and transformation
-    - Feature extraction
-    - Context building
-    - Information retrieval
-    - Pattern recognition
-  </Card>
-</CardGroup>
-
-### 3. Decision-Making Core
-
-The "brain" of the agent that determines what actions to take.
-
-<CardGroup cols={1}>
-  <Card title="Decision Components" icon="brain">
-    - Language models (like GPT-4)
-    - Rule systems
-    - Planning algorithms
-    - Knowledge base
-    - Memory systems
-  </Card>
-</CardGroup>
-
-### 4. Output (Actions)
-
-The actions the agent can perform to achieve its goals.
-
-<CardGroup cols={1}>
-  <Card title="Action Examples" icon="hand">
-    - Generating text responses
-    - Creating visual content
-    - Making API calls
-    - Controlling other systems
-    - Updating databases
-  </Card>
-</CardGroup>
-
-## The Agent Loop
-
-Agents operate in a continuous loop:
-
-```mermaid
-graph LR
-    A[Perceive] --> B[Process]
-    B --> C[Decide]
-    C --> D[Act]
-    D --> A
-```
-
-This cycle allows agents to continuously:
-1. Gather information
-2. Update their understanding
-3. Make new decisions
-4. Take appropriate actions
-
-## PraisonAI Agent Architecture
-
-In the PraisonAI framework, agents follow a specific architecture:
-
-<CardGroup cols={1}>
-  <Card title="PraisonAI Agent Components" icon="puzzle-piece">
-    - **Instructions**: Defines the agent's purpose and behavior
-    - **Language Model**: Powers the agent's intelligence (e.g., GPT-4)
-    - **Memory**: Stores context and previous interactions
-    - **Tools**: Specialized capabilities an agent can use
-  </Card>
-</CardGroup>
-
-### Simple Agent Structure
-
-```python
-from praisonaiagents import Agent
-
-# Create a simple agent
-research_agent = Agent(
-    instructions="Research the latest developments in renewable energy",
-    name="ResearchAgent"
-)
-
-# Start the agent
-research_agent.start()
-```
-
-## Understanding Agent Communication
-
-Multi-agent systems allow agents to communicate with each other:
-
-```mermaid
-graph LR
-    A[Agent 1] --> B[Agent 2]
-    B --> C[Agent 3]
-    C --> A
-```
-
-Each agent can:
-- Pass information to other agents
-- Request assistance from specialized agents
-- Collaborate on complex tasks
-
-## Key Takeaways
-
-<CardGroup cols={2}>
-  <Card title="Component Importance" icon="check">
-    Each component plays a vital role in the agent's functionality
-  </Card>
-  <Card title="Agent Customization" icon="gear">
-    You can customize each component based on your specific needs
-  </Card>
-  <Card title="Component Balance" icon="scale-balanced">
-    A well-designed agent balances all components effectively
-  </Card>
-  <Card title="Continuous Improvement" icon="arrow-trend-up">
-    Agents can be improved by enhancing individual components
-  </Card>
-</CardGroup>
-
-In the next lesson, we'll explore how to define effective instructions for your AI agents.

docs/course/agents/04-agent-instructions.mdx

@@ -1,187 +0,0 @@
----
-title: "Creating Effective Agent Instructions"
-description: "How to write clear instructions for your AI agents"
-icon: "file-lines"
----
-
-# Creating Effective Agent Instructions
-
-The instructions you give to your AI agent are crucial to its performance. Well-crafted instructions lead to agents that understand their purpose and execute tasks effectively.
-
-## Why Instructions Matter
-
-Instructions serve as the agent's "job description" and guide its behavior:
-
-<CardGroup cols={2}>
-  <Card title="Clarity" icon="glasses">
-    Clear instructions reduce confusion and misinterpretation
-  </Card>
-  <Card title="Focus" icon="bullseye">
-    Specific instructions keep the agent on task
-  </Card>
-  <Card title="Boundaries" icon="border-all">
-    Good instructions define what an agent should and shouldn't do
-  </Card>
-  <Card title="Personality" icon="face-smile">
-    Instructions can shape how an agent communicates
-  </Card>
-</CardGroup>
-
-## Elements of Effective Instructions
-
-### 1. Role Definition
-
-Tell the agent who it is and what its expertise should be.
-
-<CodeGroup>
-```text Good Example
-You are a financial analyst with expertise in market trends and investment strategies.
-```
-
-```text Weak Example
-You are a helper.
-```
-</CodeGroup>
-
-### 2. Purpose and Goals
-
-Clearly state what the agent should accomplish.
-
-<CodeGroup>
-```text Good Example
-Your goal is to analyze company quarterly reports and provide actionable investment insights.
-```
-
-```text Weak Example
-Help with finance stuff.
-```
-</CodeGroup>
-
-### 3. Process Guidelines
-
-Explain how the agent should approach tasks.
-
-<CodeGroup>
-```text Good Example
-First analyze the data trends, then identify key risk factors, and finally make recommendations based on the client's risk tolerance.
-```
-
-```text Weak Example
-Just do a good analysis.
-```
-</CodeGroup>
-
-### 4. Response Format
-
-Specify how you want information presented.
-
-<CodeGroup>
-```text Good Example
-Present your analysis in a structured format with: Summary, Key Findings, Risk Assessment, and Recommendations.
-```
-
-```text Weak Example
-Tell me what you find.
-```
-</CodeGroup>
-
-## Instructions Template
-
-Here's a template you can use to create effective agent instructions:
-
-```
-You are a [ROLE] specializing in [EXPERTISE AREAS].
-
-Your goal is to [MAIN OBJECTIVE].
-
-When approaching tasks:
-1. [FIRST STEP/APPROACH]
-2. [SECOND STEP/APPROACH]
-3. [THIRD STEP/APPROACH]
-
-Present your results in [FORMAT PREFERENCES].
-
-Additional guidelines:
-- [SPECIFIC BEHAVIOR 1]
-- [SPECIFIC BEHAVIOR 2]
-- [SPECIFIC BEHAVIOR 3]
-```
-
-## Examples in PraisonAI
-
-Let's look at how instructions are implemented in PraisonAI:
-
-<CodeGroup>
-```python Simple Example
-from praisonaiagents import Agent
-
-research_agent = Agent(
-    instructions="You are a research assistant specializing in climate science. Your goal is to find the latest research on rising sea levels and summarize key findings in bullet points."
-)
-
-research_agent.start()
-```
-
-```python Detailed Example
-from praisonaiagents import Agent
-
-content_agent = Agent(
-    name="ContentCreator",
-    instructions="""
-    You are a content creation specialist with expertise in digital marketing.
-    
-    Your goal is to create engaging blog content that drives traffic and conversions.
-    
-    When creating content:
-    1. Research the target keyword thoroughly
-    2. Create an attention-grabbing headline
-    3. Develop well-structured content with clear sections
-    4. Include actionable takeaways for readers
-    
-    Present content in markdown format with proper headings, bullet points, and emphasis where appropriate.
-    
-    Additional guidelines:
-    - Maintain a conversational but professional tone
-    - Include 2-3 relevant statistics to support key points
-    - Add a call-to-action at the end of each piece
-    """
-)
-
-content_agent.start("Create a blog post about sustainable living tips")
-```
-</CodeGroup>
-
-## Common Instruction Mistakes
-
-<CardGroup cols={2}>
-  <Card title="Being Too Vague" icon="cloud">
-    Vague instructions lead to unpredictable outputs
-  </Card>
-  <Card title="Information Overload" icon="bomb">
-    Too many details can confuse the agent's priorities
-  </Card>
-  <Card title="Contradictory Guidelines" icon="arrows-split-up-and-left">
-    Conflicting instructions create confusion
-  </Card>
-  <Card title="Missing Context" icon="puzzle-piece-missing">
-    Insufficient background information leads to generic outputs
-  </Card>
-</CardGroup>
-
-## Refining Your Instructions
-
-<Tip>
-Good instructions often require iteration. Start with basic instructions, review the agent's performance, and refine as needed.
-</Tip>
-
-Effective instruction improvement follows this cycle:
-
-```mermaid
-graph TD
-    A[Write Initial Instructions] --> B[Test Agent Performance]
-    B --> C[Identify Issues/Gaps]
-    C --> D[Refine Instructions]
-    D --> B
-```
-
-In the next lesson, we'll explore how agents use tools to extend their capabilities.

docs/course/agents/05-agent-tools.mdx

@@ -1,183 +0,0 @@
----
-title: "Agent Tools"
-description: "Understanding how tools extend agent capabilities"
-icon: "screwdriver-wrench"
----
-
-# Agent Tools
-
-Tools allow AI agents to interact with the world and perform specific actions. Think of tools as the "hands" of your agent that let it accomplish tasks beyond just generating text.
-
-## What Are Agent Tools?
-
-<CardGroup cols={1}>
-  <Card title="Definition" icon="toolbox">
-    Tools are functions or capabilities that agents can access to perform specific actions, retrieve information, or interact with external systems.
-  </Card>
-</CardGroup>
-
-Just as humans use tools to extend their physical abilities, AI agents use digital tools to extend their capabilities.
-
-## Why Tools Matter
-
-Tools transform agents from simple chat interfaces into powerful assistants that can:
-
-<CardGroup cols={2}>
-  <Card title="Access Information" icon="database">
-    Retrieve data from various sources
-  </Card>
-  <Card title="Perform Calculations" icon="calculator">
-    Analyze numbers and data accurately
-  </Card>
-  <Card title="Create Content" icon="pen-to-square">
-    Generate images, code, or other media
-  </Card>
-  <Card title="Control Systems" icon="gears">
-    Interact with other software or hardware
-  </Card>
-</CardGroup>
-
-## Common Types of Tools
-
-### 1. Information Retrieval Tools
-
-These tools help agents access information beyond their training data.
-
-**Examples:**
-- Web search tools
-- Database query tools
-- Document retrieval tools
-
-```python
-# Example of a simple web search tool
-from praisonaiagents import Agent
-
-def web_search(query):
-    # Simplified example
-    return f"Results for: {query}"
-
-agent = Agent(
-    instructions="Research assistant",
-    tools=[web_search]
-)
-```
-
-### 2. Computation Tools
-
-These tools perform calculations or data processing.
-
-**Examples:**
-- Calculator tools
-- Data analysis tools
-- Conversion tools
-
-### 3. Content Creation Tools
-
-These tools help generate or modify various types of content.
-
-**Examples:**
-- Image generation tools
-- Code writing tools
-- Document formatting tools
-
-### 4. External API Tools
-
-These tools connect to third-party services.
-
-**Examples:**
-- Weather API tools
-- Translation API tools
-- E-commerce API tools
-
-## How Tools Work in PraisonAI
-
-In the PraisonAI framework, tools follow a simple pattern:
-
-1. Tools are defined as functions
-2. Tools are registered with an agent
-3. The agent decides when to use appropriate tools
-4. Tool results are incorporated into the agent's reasoning
-
-```mermaid
-graph TD
-    A[Agent Receives Task] --> B{Needs a Tool?}
-    B -->|Yes| C[Select Appropriate Tool]
-    C --> D[Execute Tool]
-    D --> E[Process Tool Results]
-    E --> F[Continue Task]
-    B -->|No| F
-```
-
-## Creating Basic Tools
-
-Here's a simple example of creating and using tools in PraisonAI:
-
-```python
-from praisonaiagents import Agent
-
-# Define a simple calculator tool
-def calculator(expression):
-    try:
-        return str(eval(expression))
-    except:
-        return "Error: Could not calculate"
-
-# Create an agent with the tool
-math_agent = Agent(
-    instructions="You are a math tutor helping students solve problems",
-    tools=[calculator]
-)
-
-# Use the agent
-math_agent.start("Calculate 24 * 17 and explain the steps")
-```
-
-## Tool Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Clear Descriptions" icon="comment">
-    Provide detailed descriptions so the agent knows when to use each tool
-  </Card>
-  <Card title="Error Handling" icon="shield">
-    Make tools robust with proper error handling
-  </Card>
-  <Card title="Focused Purpose" icon="bullseye">
-    Each tool should do one thing well
-  </Card>
-  <Card title="Input Validation" icon="check-double">
-    Validate inputs to prevent misuse
-  </Card>
-</CardGroup>
-
-## Building a Toolbox
-
-As you advance, you'll want to create a collection of tools your agent can use:
-
-```python
-from praisonaiagents import Agent, Tool
-
-# Define multiple tools
-def weather(location):
-    return f"Weather in {location}: Sunny, 75°F"
-
-def translator(text, target_language):
-    return f"Translated to {target_language}: {text} (placeholder)"
-
-def reminder(message, time):
-    return f"Reminder set for {time}: {message}"
-
-# Create tool collection
-tools = [
-    Tool(name="weather", function=weather, description="Get weather for a location"),
-    Tool(name="translator", function=translator, description="Translate text"),
-    Tool(name="reminder", function=reminder, description="Set a reminder")
-]
-
-# Create agent with multiple tools
-assistant = Agent(
-    instructions="You are a helpful personal assistant",
-    tools=tools
-)
-```
-
-In the next lesson, we'll explore how agents use memory to maintain context across interactions.

docs/course/agents/06-agent-memory.mdx

@@ -1,167 +0,0 @@
----
-title: "Agent Memory"
-description: "Understanding how agents maintain context and learn"
-icon: "brain"
----
-
-# Agent Memory
-
-Memory enables AI agents to remember past interactions, maintain context, and provide more coherent responses over time. Without memory, agents would treat each interaction as if it was their first.
-
-## Why Memory Matters
-
-<CardGroup cols={2}>
-  <Card title="Context Awareness" icon="comments">
-    Remembers previous parts of a conversation
-  </Card>
-  <Card title="Personalization" icon="user-gear">
-    Recalls user preferences and history
-  </Card>
-  <Card title="Continuous Learning" icon="graduation-cap">
-    Builds on past experiences
-  </Card>
-  <Card title="Task Persistence" icon="list-check">
-    Maintains progress on long-running tasks
-  </Card>
-</CardGroup>
-
-## Types of Agent Memory
-
-### 1. Short-Term Memory (Conversation Memory)
-
-This type of memory stores recent interactions within a session.
-
-```mermaid
-graph LR
-    A[User Input 1] --> B[Agent Response 1]
-    B --> C[User Input 2]
-    C --> D[Agent Response 2]
-    D --> E[User Input 3]
-```
-
-The agent can refer back to earlier messages in the same conversation.
-
-### 2. Long-Term Memory
-
-This allows agents to remember information across different sessions.
-
-<CardGroup cols={1}>
-  <Card title="Use Cases" icon="clock">
-    - Remembering user preferences
-    - Recalling previous tasks completed
-    - Storing learned information
-    - Maintaining relationship history
-  </Card>
-</CardGroup>
-
-### 3. Working Memory
-
-This is where agents process current information and combine it with retrieved memories.
-
-```mermaid
-graph TD
-    A[Current Input] --> D[Working Memory]
-    B[Short-Term Memory] --> D
-    C[Long-Term Memory] --> D
-    D --> E[Agent Response]
-```
-
-## How Memory Works in PraisonAI
-
-In the PraisonAI framework, memory is handled automatically for basic cases and can be customized for more advanced needs:
-
-```python
-from praisonaiagents import Agent
-
-# Basic agent with default memory handling
-agent = Agent(
-    instructions="You are a helpful assistant that remembers our conversations"
-)
-
-# The conversation history is maintained automatically
-agent.start("My name is Alex")
-agent.continue("What's my name?")# Note: TODO: This Feature yet to be developed  # Agent will remember "Alex"
-```
-
-## Memory Limitations
-
-<Tip>
-All AI memories have limitations. They aren't perfect and may occasionally forget or misremember information.
-</Tip>
-
-Common limitations include:
-
-<CardGroup cols={2}>
-  <Card title="Memory Capacity" icon="database">
-    Limit to how much information can be stored
-  </Card>
-  <Card title="Context Window" icon="window">
-    Only a portion of memory fits in the active context
-  </Card>
-  <Card title="Memory Decay" icon="hourglass-half">
-    Older memories may become less accessible
-  </Card>
-  <Card title="Memory Retrieval" icon="magnifying-glass">
-    Finding the right memory at the right time
-  </Card>
-</CardGroup>
-
-## Implementing Persistent Memory
-
-For more advanced applications, you can implement custom memory systems:
-
-```python
-# Note: TODO: This Feature yet to be tested
-from praisonaiagents import Agent, Memory
-
-# Create a simple memory store
-memory = Memory()
-
-# Add information to memory
-memory.add("user_name", "Alex")
-memory.add("favorite_color", "blue")
-
-# Create agent with this memory
-agent = Agent(
-    instructions="You are a personal assistant",
-    memory=memory
-)
-
-# The agent can now access these memories
-agent.start("What's my favorite color?")  # Agent should respond "blue"
-```
-
-## Memory Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Prioritize Important Info" icon="star">
-    Not everything needs to be remembered
-  </Card>
-  <Card title="Verify When Uncertain" icon="question">
-    Have agents confirm uncertain memories
-  </Card>
-  <Card title="Structured Storage" icon="folder-tree">
-    Organize memories by categories
-  </Card>
-  <Card title="Regular Updates" icon="arrows-rotate">
-    Update memories when information changes
-  </Card>
-</CardGroup>
-
-## Memory in Multi-Agent Systems
-
-In systems with multiple agents, memory can be:
-
-1. **Private**: Each agent has its own memories
-2. **Shared**: Agents have access to a common memory store
-3. **Hybrid**: Some memories are private, others are shared
-
-```mermaid
-graph TD
-    A[Agent 1] --> B[Private Memory 1]
-    C[Agent 2] --> D[Private Memory 2]
-    A -.-> E[Shared Memory]
-    C -.-> E
-```
-
-In the next lesson, we'll learn about how to set up multiple agents to work together effectively.

docs/course/agents/07-multi-agent-systems.mdx

@@ -1,171 +0,0 @@
----
-title: "Multi-Agent Systems"
-description: "How multiple agents can work together"
-icon: "users"
----
-
-# Multi-Agent Systems
-
-Multi-agent systems allow multiple AI agents to work together, each handling different parts of a complex task. This approach mirrors how human teams collaborate, with each member contributing their specialized skills.
-
-## Why Use Multiple Agents?
-
-<CardGroup cols={2}>
-  <Card title="Specialization" icon="star">
-    Each agent can focus on its area of expertise
-  </Card>
-  <Card title="Complex Tasks" icon="puzzle-piece">
-    Break down difficult problems into manageable pieces
-  </Card>
-  <Card title="Scalability" icon="arrows-up-down">
-    Add more agents as tasks become more complex
-  </Card>
-  <Card title="Diverse Perspectives" icon="lightbulb">
-    Different agents can approach problems differently
-  </Card>
-</CardGroup>
-
-## Basic Multi-Agent Architecture
-
-```mermaid
-graph LR
-    A[Research Agent] --> B[Analysis Agent]
-    B --> C[Writing Agent]
-    C --> D[Final Output]
-```
-
-In this example:
-1. The Research Agent gathers information
-2. The Analysis Agent processes and interprets the data
-3. The Writing Agent creates the final content
-
-## Multi-Agent Patterns
-
-### 1. Pipeline Pattern
-
-Agents work in sequence, with each agent handling a specific stage of the process.
-
-```mermaid
-graph LR
-    A[Agent 1] --> B[Agent 2]
-    B --> C[Agent 3]
-```
-
-**Example Use Case**: Content creation where one agent researches, another drafts, and a third edits.
-
-### 2. Expert Panel Pattern
-
-Multiple specialist agents work in parallel on the same problem, then their outputs are combined.
-
-```mermaid
-graph TD
-    Task --> A[Expert Agent 1]
-    Task --> B[Expert Agent 2]
-    Task --> C[Expert Agent 3]
-    A --> D[Coordination Agent]
-    B --> D
-    C --> D
-    D --> Result
-```
-
-**Example Use Case**: Financial analysis where different experts analyze market trends, economic indicators, and company performance.
-
-### 3. Hierarchical Pattern
-
-A manager agent delegates tasks to worker agents and coordinates their efforts.
-
-```mermaid
-graph TD
-    A[Manager Agent] --> B[Worker Agent 1]
-    A --> C[Worker Agent 2]
-    A --> D[Worker Agent 3]
-    B --> A
-    C --> A
-    D --> A
-```
-
-**Example Use Case**: Project management where a coordinator assigns tasks and integrates results.
-
-## Implementing Multi-Agent Systems in PraisonAI
-
-Here's a simple example of creating a multi-agent system:
-
-```python
-from praisonaiagents import Agent, PraisonAIAgents
-
-# Create individual agents
-research_agent = Agent(
-    name="Researcher",
-    instructions="Research the latest trends in renewable energy"
-)
-
-analysis_agent = Agent(
-    name="Analyst",
-    instructions="Analyze the research findings and identify key insights"
-)
-
-writing_agent = Agent(
-    name="Writer", 
-    instructions="Create a clear, engaging report based on the analysis"
-)
-
-# Create a multi-agent system
-agents = PraisonAIAgents(
-    agents=[research_agent, analysis_agent, writing_agent]
-)
-
-# Start the agents
-agents.start()
-```
-
-## Agent Communication
-
-For agents to work together effectively, they need to communicate. This happens through:
-
-<CardGroup cols={2}>
-  <Card title="Output Passing" icon="arrow-right">
-    One agent's output becomes another's input
-  </Card>
-  <Card title="Shared Memory" icon="database">
-    Agents can access common information
-  </Card>
-  <Card title="Message Exchange" icon="comments">
-    Agents can send specific messages to each other
-  </Card>
-  <Card title="Coordination Protocols" icon="traffic-light">
-    Rules that determine how agents interact
-  </Card>
-</CardGroup>
-
-## Challenges in Multi-Agent Systems
-
-<CardGroup cols={2}>
-  <Card title="Coordination" icon="shuffle">
-    Ensuring agents work together smoothly
-  </Card>
-  <Card title="Conflicting Goals" icon="arrows-split-up-and-left">
-    Managing agents with different objectives
-  </Card>
-  <Card title="Information Transfer" icon="exchange">
-    Passing information effectively between agents
-  </Card>
-  <Card title="Resource Management" icon="microchip">
-    Allocating computational resources efficiently
-  </Card>
-</CardGroup>
-
-## Designing Effective Multi-Agent Systems
-
-<Tip>
-Start simple with just 2-3 agents and a clear workflow before scaling to more complex systems.
-</Tip>
-
-Key principles for effective design:
-
-1. **Clear Role Definition**: Each agent should have a specific and well-defined role
-2. **Minimized Dependencies**: Reduce complex interdependencies between agents
-3. **Standardized Communication**: Use consistent formats for information exchange
-4. **Failure Handling**: Plan for cases where an agent fails to complete its task
-5. **Performance Monitoring**: Track how well each agent and the overall system performs
-
-In the next lesson, we'll explore how to create effective agent workflows using the Process component.

docs/course/agents/08-agent-process.mdx

@@ -1,152 +0,0 @@
----
-title: "Agent Process"
-description: "Understanding agent workflows and processes"
-icon: "diagram-project"
----
-
-# Agent Process
-
-The Process component in AI agent systems defines how agents work together and the flow of information between them. Think of it as the "playbook" that coordinates how agents complete tasks.
-
-## What is an Agent Process?
-
-<CardGroup cols={1}>
-  <Card title="Definition" icon="route">
-    A process is a structured workflow that defines the sequence of steps, decision points, and information flow between agents to complete a task.
-  </Card>
-</CardGroup>
-
-## Process Flow Basics
-
-A simple process flow might look like this:
-
-```mermaid
-graph LR
-    A[Start] --> B[Gather Information]
-    B --> C[Process Information]
-    C --> D[Make Decision]
-    D --> E[Take Action]
-    E --> F[Report Results]
-```
-
-Each step could be handled by a different agent or by the same agent in different stages.
-
-## Types of Process Flows
-
-### 1. Sequential Process
-
-Agents work in a straight line, one after another.
-
-```mermaid
-graph LR
-    A[Agent 1] --> B[Agent 2]
-    B --> C[Agent 3]
-    C --> D[Complete]
-```
-
-**Best for**: Tasks with clear, linear stages
-
-### 2. Parallel Process
-
-Multiple agents work simultaneously on different parts of a task.
-
-```mermaid
-graph TD
-    A[Start] --> B[Agent 1]
-    A --> C[Agent 2]
-    A --> D[Agent 3]
-    B --> E[Combine Results]
-    C --> E
-    D --> E
-    E --> F[Complete]
-```
-
-**Best for**: Tasks that can be broken into independent pieces
-
-### 3. Conditional Process
-
-The workflow changes based on decisions or conditions.
-
-```mermaid
-graph TD
-    A[Start] --> B{Decision Point}
-    B -->|Condition A| C[Agent 1]
-    B -->|Condition B| D[Agent 2]
-    C --> E[Complete]
-    D --> E
-```
-
-**Best for**: Tasks requiring different approaches based on circumstances
-
-## Implementing Process in PraisonAI
-
-Here's a simple example of creating a process in PraisonAI:
-
-```python
-from praisonaiagents import Agent, Process, PraisonAIAgents
-
-# Create agents
-research_agent = Agent(
-    name="Researcher",
-    instructions="Research the topic and gather key information about AI"
-)
-
-writing_agent = Agent(
-    name="Writer",
-    instructions="Create content based on the research"
-)
-
-editing_agent = Agent(
-    name="Editor",
-    instructions="Review and improve the content"
-)
-
-# Create the multi-agent system with the process
-agents = PraisonAIAgents(
-    agents=[research_agent, writing_agent, editing_agent],
-    process="hierarchical"
-)
-
-# Start the process
-agents.start()
-```
-
-## Process Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Clear Step Definitions" icon="list-check">
-    Define exactly what each step should accomplish
-  </Card>
-  <Card title="Error Handling" icon="bug">
-    Include steps for handling unexpected situations
-  </Card>
-  <Card title="Feedback Loops" icon="repeat">
-    Allow agents to request clarification when needed
-  </Card>
-  <Card title="Modular Design" icon="cubes">
-    Create reusable process components
-  </Card>
-</CardGroup>
-
-<Tip>
-Start with simple processes and gradually add complexity as you become more comfortable with the system.
-</Tip>
-
-## Monitoring Process Execution
-
-It's important to track how your processes are running:
-
-```python
-agents = PraisonAIAgents(
-    agents=[research_agent, writing_agent, editing_agent],
-    process=content_process,
-    verbose=True  # Enables detailed logging
-)
-```
-
-This will provide information about:
-- Which step is currently running
-- The inputs and outputs of each step
-- Any errors or issues encountered
-
-In the next lesson, we'll explore how to use knowledge bases to enhance agent capabilities.

docs/course/agents/09-knowledge-bases.mdx

@@ -1,190 +0,0 @@
----
-title: "Knowledge Bases"
-description: "Enhancing agents with specialized knowledge"
-icon: "book"
----
-
-# Knowledge Bases
-
-Knowledge bases provide agents with specialized information beyond their training data. They help agents answer specific questions, follow guidelines, or work within defined domains.
-
-## What is a Knowledge Base?
-
-<CardGroup cols={1}>
-  <Card title="Definition" icon="database">
-    A knowledge base is a structured collection of information that an agent can access to answer questions or make decisions.
-  </Card>
-</CardGroup>
-
-Think of a knowledge base as a specialized reference library for your agent.
-
-## Why Use Knowledge Bases?
-
-<CardGroup cols={2}>
-  <Card title="Domain Expertise" icon="user-graduate">
-    Provide specialized information for specific fields
-  </Card>
-  <Card title="Current Information" icon="clock">
-    Include data beyond the agent's training cutoff
-  </Card>
-  <Card title="Custom Guidelines" icon="list">
-    Define rules and policies for the agent to follow
-  </Card>
-  <Card title="Consistent Responses" icon="equals">
-    Ensure agents provide standardized answers
-  </Card>
-</CardGroup>
-
-## Types of Knowledge Bases
-
-### 1. Document Collections
-
-Text documents, articles, guides, or manuals that agents can reference.
-
-```mermaid
-graph TD
-    A[Query] --> B[Search Documents]
-    B --> C[Find Relevant Content]
-    C --> D[Generate Response]
-```
-
-### 2. Structured Data
-
-Databases, tables, or other structured formats that organize information systematically.
-
-<CardGroup cols={1}>
-  <Card title="Examples" icon="table">
-    - Product catalogs with specifications
-    - Customer information databases
-    - Statistical data collections
-    - Reference tables for compliance information
-  </Card>
-</CardGroup>
-
-### 3. Vector Databases
-
-Special databases that store information as numerical representations (vectors) for semantic search.
-
-```mermaid
-graph TD
-    A[User Question] --> B[Convert to Vector]
-    B --> C[Find Similar Vectors]
-    C --> D[Retrieve Related Information]
-    D --> E[Generate Answer]
-```
-
-## Implementing Knowledge Bases in PraisonAI
-
-Here's an example of how to use a knowledge base with an agent:
-
-```python
-from praisonaiagents import Agent
-
-agent = Agent(
-    name="Knowledge Agent",
-    instructions="You answer questions based on the provided knowledge.",
-    knowledge=["small.pdf"]
-)
-
-agent.start("What is KAG in one line?")
-```
-
-## Advanced Configuration
-
-For more control over the knowledge base, you can specify a configuration:
-
-```python
-from praisonaiagents import Agent
-
-config = {
-    "vector_store": {
-        "provider": "chroma",
-        "config": {
-            "collection_name": "custom_knowledge",
-            "path": ".praison",
-        }
-    }
-}
-
-agent = Agent(
-    name="Knowledge Agent",
-    instructions="You answer questions based on the provided knowledge.",
-    knowledge=["small.pdf"],
-    knowledge_config=config
-)
-
-agent.start("What is KAG in one line?")
-```
-
-## Knowledge Retrieval Process
-
-When an agent uses a knowledge base, this typical process occurs:
-
-```mermaid
-graph LR
-    A[User Question] --> B[Query Processing]
-    B --> C[Search Knowledge Base]
-    C --> D[Rank Results]
-    D --> E[Synthesize Answer]
-```
-
-1. **Query Processing**: The user's question is analyzed
-2. **Search**: The system searches the knowledge base for relevant information
-3. **Ranking**: Results are ranked by relevance
-4. **Synthesis**: The agent creates an answer using the retrieved information
-
-## Best Practices for Knowledge Bases
-
-<CardGroup cols={2}>
-  <Card title="Keep Information Current" icon="arrows-rotate">
-    Regularly update your knowledge base
-  </Card>
-  <Card title="Organize Logically" icon="folder-tree">
-    Structure information in intuitive categories
-  </Card>
-  <Card title="Prioritize Quality" icon="star">
-    Focus on accurate, high-quality information
-  </Card>
-  <Card title="Include Examples" icon="list">
-    Add examples to illustrate complex concepts
-  </Card>
-</CardGroup>
-
-## When to Use Knowledge Bases
-
-Knowledge bases are particularly valuable when:
-
-1. Your agent needs to reference specific information that may change over time
-2. You need to ensure consistent answers to common questions
-3. Your agent needs to follow specific guidelines or protocols
-4. You want to provide expertise in specialized domains
-
-<Tip>
-Start with a small knowledge base focusing on the most important information, then expand as needed.
-</Tip>
-
-## Creating a Simple Knowledge Base
-
-For beginners, you can start with a simple text-based knowledge base:
-
-```
-# Company FAQ Knowledge Base
-
-## Return Policy
-Our return policy allows customers to return products within 30 days of purchase for a full refund.
-
-## Shipping Information
-Standard shipping takes 3-5 business days. Express shipping takes 1-2 business days.
-
-## Product Warranty
-All products come with a 1-year limited warranty covering manufacturing defects.
-```
-
-Save this as a text file and add it to your knowledge base:
-
-```python
-kb = KnowledgeBase()
-kb.add_document("company_faq.txt")
-```
-
-In the next lesson, we'll explore how agents handle tasks and the task management process.

docs/course/agents/10-agent-tasks.mdx

@@ -1,274 +0,0 @@
----
-title: "Agent Tasks"
-description: "Understanding how agents handle and manage tasks"
-icon: "clipboard-list"
----
-
-# Agent Tasks
-
-Tasks are the specific units of work that AI agents perform. Understanding how to define and manage tasks is essential for creating effective agent systems.
-
-## What is an Agent Task?
-
-<CardGroup cols={1}>
-  <Card title="Definition" icon="list-check">
-    A task is a specific, well-defined piece of work assigned to an agent with clear inputs, expected outputs, and success criteria.
-  </Card>
-</CardGroup>
-
-Just as human jobs are broken into specific responsibilities, agent systems work best when complex goals are divided into manageable tasks.
-
-## Anatomy of a Task
-
-A well-defined task includes:
-
-<CardGroup cols={2}>
-  <Card title="Objective" icon="bullseye">
-    What the task should accomplish
-  </Card>
-  <Card title="Inputs" icon="arrow-right-to-bracket">
-    Information provided to complete the task
-  </Card>
-  <Card title="Process" icon="gears">
-    Steps to follow or approach to take
-  </Card>
-  <Card title="Outputs" icon="arrow-right-from-bracket">
-    Expected results or deliverables
-  </Card>
-</CardGroup>
-
-## Task Types
-
-### 1. Information Tasks
-
-These tasks involve finding, analyzing, or summarizing information.
-
-**Examples:**
-- Research a topic
-- Summarize a document
-- Extract data from text
-- Answer questions
-
-### 2. Creation Tasks
-
-These tasks involve generating new content.
-
-**Examples:**
-- Write an article
-- Create a marketing plan
-- Generate images (with appropriate tools)
-- Design a workflow
-
-### 3. Analysis Tasks
-
-These tasks involve evaluating information and drawing conclusions.
-
-**Examples:**
-- Analyze data trends
-- Review content for quality
-- Evaluate options
-- Identify patterns
-
-### 4. Interaction Tasks
-
-These tasks involve communicating or working with users or other systems.
-
-**Examples:**
-- Answer customer questions
-- Guide users through processes
-- Collect information from users
-- Notify about events
-
-## Creating Tasks in PraisonAI
-
-### Task Flow
-
-The typical lifecycle of a task follows this pattern:
-
-```mermaid
-graph LR
-    A[Define Task] --> B[Assign to Agent]
-    B --> C[Execute Task]
-    C --> D[Return Results]
-    D --> E[Evaluate Success]
-```
-
-### Task Configuration
-
-<CodeGroup>
-  ```python Basic
-  task = Task(
-      description="Research AI trends",
-      expected_output="Summary report",
-      agent=research_agent
-  )
-  ```
-  ```python Advanced
-  task = Task(
-      description="Analyze market data",
-      expected_output="Detailed analysis",
-      agent=analyst_agent,
-      tools=[AnalysisTool()],
-      output_file="analysis.md",
-      async_execution=True
-  )
-  ```
-</CodeGroup>
-
-# Understanding Tasks
-<br />
-```mermaid
-graph LR
-    %% Task Flow Steps
-    Start([▶ Start]) --> TaskDef
-    TaskDef --> AgentAssign
-    AgentAssign --> Exec
-    Exec --> Output([✓ Output])
-    
-    %% Define subgraphs for each step
-    subgraph TaskDef[" "]
-        TD_Icon[📋 Task Definition]
-        TD_Desc[Description & Requirements]
-        
-        TD_Icon --- TD_Desc
-        
-        style TD_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
-        style TD_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
-    end
-    
-    subgraph AgentAssign[" "]
-        AA_Icon[🤖 Agent Assignment]
-        AA_Desc[Matching & Delegation]
-        
-        AA_Icon --- AA_Desc
-        
-        style AA_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
-        style AA_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
-    end
-    
-    subgraph Exec[" "]
-        E_Icon[⚙️ Execution]
-        E_Desc[Processing & Tools]
-        
-        E_Icon --- E_Desc
-        
-        style E_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
-        style E_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
-    end
-    
-    %% Styling for main elements
-    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
-    
-    %% Style for containers
-    style TaskDef fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    style AgentAssign fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    style Exec fill:#1B1F3B,stroke:#7C90A0,color:#fff
-    
-    %% Global styles
-    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
-    linkStyle default stroke:#7C90A0,stroke-width:2px
-```
-<br /><br />
-```mermaid
-graph LR
-    %% Task States
-    Pending([📥 Pending]) --> InProgress([⚡ In Progress])
-    InProgress --> Completed([✅ Completed])
-    InProgress --> Failed([❌ Failed])
-    
-    %% Styling
-    style Pending fill:#2D3047,stroke:#7C90A0,color:#fff
-    style InProgress fill:#5C80BC,stroke:#7C90A0,color:#fff
-    style Completed fill:#2D3047,stroke:#7C90A0,color:#fff
-    style Failed fill:#2D3047,stroke:#7C90A0,color:#fff
-    
-    %% Global styles
-    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
-    linkStyle default stroke:#7C90A0,stroke-width:2px
-```
-
-## Task Types
-
-<Steps>
-  <Step title="Basic Task">
-    Simple, single-operation tasks with clear inputs and outputs
-  </Step>
-  <Step title="Decision Task">
-    Tasks involving choices and conditional paths
-    ```python
-    decision_task = Task(
-        type="decision",
-        conditions={
-            "success": ["next_task"],
-            "failure": ["error_task"]
-        }
-    )
-    ```
-  </Step>
-  <Step title="Loop Task">
-    Repetitive operations over collections
-    ```python
-    loop_task = Task(
-        type="loop",
-        items=data_list,
-        operation="process_item"
-    )
-    ```
-  </Step>
-</Steps>
-
-## Task Relationships
-
-<Warning>
-  Properly managing task dependencies is crucial for complex workflows. Always ensure proper context sharing and error handling.
-</Warning>
-
-### Context Sharing
-```python
-task_a = Task(name="research")
-task_b = Task(
-    name="analyze",
-    context=[task_a]  # Uses task_a's output
-)
-```
-
-## Task Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Be Specific" icon="magnifying-glass">
-    Clearly define what the task should accomplish
-  </Card>
-  <Card title="One Goal Per Task" icon="check-double">
-    Each task should focus on a single objective
-  </Card>
-  <Card title="Provide Context" icon="circle-info">
-    Include necessary background information
-  </Card>
-  <Card title="Define Success" icon="trophy">
-    Specify what a successful outcome looks like
-  </Card>
-</CardGroup>
-
-<Tip>
-Break complex goals into multiple smaller tasks rather than creating one large, complex task.
-</Tip>
-
-## Common Task Mistakes
-
-<CardGroup cols={2}>
-  <Card title="Vague Instructions" icon="question">
-    Unclear about what the agent should do
-  </Card>
-  <Card title="Too Many Objectives" icon="list-ol">
-    Asking for too many things in one task
-  </Card>
-  <Card title="Missing Context" icon="puzzle-piece-missing">
-    Not providing necessary background information
-  </Card>
-  <Card title="Unrealistic Expectations" icon="mountain-sun">
-    Asking for tasks beyond agent capabilities
-  </Card>
-</CardGroup>
-
-In the next lesson, we'll learn about creating your first complete agent application.

docs/course/agents/11-creating-your-first-agent.mdx

@@ -1,172 +0,0 @@
----
-title: "Creating Your First Agent"
-description: "A step-by-step guide to building your first AI agent"
-icon: "robot"
----
-
-# Creating Your First Agent
-
-In this lesson, we'll walk through the process of building a simple AI agent. We'll focus on practical implementation without complex programming.
-
-## What We'll Build
-
-We'll create a basic research assistant agent that can:
-- Understand simple instructions
-- Respond to questions
-- Provide helpful information
-
-## Prerequisites
-
-Before you begin, make sure you have:
-
-1. A basic understanding of the concepts from previous lessons
-2. Python installed on your computer (if following the code examples)
-3. An OpenAI API key (or access to another supported language model)
-
-## Step 1: Installation
-
-First, install the PraisonAI package:
-
-```bash
-pip install praisonaiagents
-```
-
-## Step 2: Setup Your Environment
-
-Create a file called `first_agent.py` and set up your API key:
-
-```python
-import os
-from praisonaiagents import Agent
-
-# Set your API key
-# Option 1: Set it in your environment (recommended)
-# export OPENAI_API_KEY=your_key_here
-
-# Option 2: Set it in your code (not recommended for production)
-# os.environ["OPENAI_API_KEY"] = "your_key_here"
-```
-
-## Step 3: Define Your Agent
-
-Now, let's create a simple agent:
-
-```python
-# Create a research assistant agent
-research_assistant = Agent(
-    name="ResearchAssistant",
-    instructions="""
-    You are a helpful research assistant that provides clear, 
-    accurate information on various topics. When answering:
-    
-    1. Be concise and to the point
-    2. Explain complex concepts in simple terms
-    3. When appropriate, organize information with bullet points
-    4. If you don't know something, admit it instead of guessing
-    """
-)
-```
-
-## Step 4: Start Your Agent
-
-Now let's use our agent to answer a question:
-
-```python
-# Start the agent with a specific query
-response = research_assistant.start("Explain how solar panels work in simple terms")
-
-# Print the response
-print(response)
-```
-
-## Step 5: Run Your Agent
-
-Save the file and run it from your terminal:
-
-```bash
-python first_agent.py
-```
-
-You should see the agent's response explaining how solar panels work in simple terms.
-
-## Experimenting with Your Agent
-
-Try changing the question to see how your agent responds to different topics:
-
-```python
-# Try different questions
-response = research_assistant.start("What are the benefits of exercise?")
-# or
-response = research_assistant.start("How does artificial intelligence work?")
-```
-
-## Modifying Agent Behavior
-
-You can change how your agent behaves by adjusting its instructions:
-
-```python
-# Create an agent with different instructions
-creative_writer = Agent(
-    name="CreativeWriter",
-    instructions="""
-    You are a creative writer that specializes in short, engaging stories.
-    Your writing should:
-    
-    1. Include vivid descriptions
-    2. Have interesting characters
-    3. Include some dialogue
-    4. Have a clear beginning, middle, and end
-    """
-)
-
-# Ask for a story
-story = creative_writer.start("Write a short story about a lost dog finding its way home")
-print(story)
-```
-
-## Examining How It Works
-
-What's happening behind the scenes:
-
-1. The agent receives your instructions and query
-2. The language model (like GPT-4) processes the information
-3. The model generates a response based on the instructions and query
-4. The response is returned to you
-
-```mermaid
-graph TD
-    A[Your Instructions] --> B[Agent]
-    C[Your Query] --> B
-    B --> D[Language Model]
-    D --> E[Generated Response]
-    E --> F[Output to You]
-```
-
-## Common Challenges
-
-If you encounter issues, check these common problems:
-
-<CardGroup cols={2}>
-  <Card title="API Key Issues" icon="key">
-    Make sure your API key is set correctly
-  </Card>
-  <Card title="Connection Problems" icon="wifi">
-    Check your internet connection
-  </Card>
-  <Card title="Unclear Instructions" icon="circle-question">
-    Try making your instructions more specific
-  </Card>
-  <Card title="Rate Limits" icon="hand">
-    You might be making too many API calls
-  </Card>
-</CardGroup>
-
-## Next Steps
-
-Now that you've created your first agent, you can:
-- Experiment with different instructions
-- Try more complex queries
-- Add specialized capabilities (we'll cover this in upcoming lessons)
-- Create agents for specific use cases
-
-Congratulations on creating your first AI agent! In the next lesson, we'll explore how to add tools to enhance your agent's capabilities.

docs/course/agents/12-adding-tools-to-agents.mdx

@@ -1,275 +0,0 @@
----
-title: "Adding Tools to Agents"
-description: "Enhancing your agents with specialized capabilities"
-icon: "screwdriver-wrench"
----
-
-# Adding Tools to Agents
-
-In this lesson, we'll learn how to enhance your agents by adding tools. Tools allow agents to perform specific actions beyond just generating text, making them much more useful for real-world tasks.
-
-## What You'll Learn
-
-By the end of this lesson, you'll understand:
-- What agent tools are and why they're useful
-- How to create simple tools for your agents
-- How to integrate tools with your agents
-- Best practices for tool design
-
-## Understanding Agent Tools
-
-Tools give your agents the ability to:
-- Perform calculations
-- Access specific information
-- Interact with external systems
-- Generate different types of content
-
-<Tabs>
-
-<Tab title="Code">
-<Steps>
-
-  <Step title="Install PraisonAI">
-Install the core package:
-```bash Terminal
-pip install praisonaiagents duckduckgo-search
-```
-  </Step>
-
-  <Step title="Configure Environment">
-  ```bash Terminal
-  export OPENAI_API_KEY=your_openai_key
-  ```
-  Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-  Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
-
-  <Step title="Create Agent with Tool">
-    Create `app.py`
-<CodeGroup>
-```python Single Agent
-from praisonaiagents import Agent
-from duckduckgo_search import DDGS
-
-# 1. Define the tool
-def internet_search_tool(query: str):
-    results = []
-    ddgs = DDGS()
-    for result in ddgs.text(keywords=query, max_results=5):
-        results.append({
-            "title": result.get("title", ""),
-            "url": result.get("href", ""),
-            "snippet": result.get("body", "")
-        })
-    return results
-
-# 2. Assign the tool to an agent
-search_agent = Agent(
-    instructions="Perform internet searches to collect relevant information.",
-    tools=[internet_search_tool] # <--- Tool Assignment
-)
-
-# 3. Start Agent
-search_agent.start("Search about AI job trends in 2025")
-```
-
-```python Multiple Agents
-from praisonaiagents import Agent, PraisonAIAgents
-from duckduckgo_search import DDGS
-
-# 1. Define the tool
-def internet_search_tool(query: str):
-    results = []
-    ddgs = DDGS()
-    for result in ddgs.text(keywords=query, max_results=5):
-        results.append({
-            "title": result.get("title", ""),
-            "url": result.get("href", ""),
-            "snippet": result.get("body", "")
-        })
-    return results
-
-# 2. Assign the tool to an agent
-search_agent = Agent(
-    instructions="Search about AI job trends in 2025",
-    tools=[internet_search_tool] # <--- Tool Assignment
-)
-
-blog_agent = Agent(
-    instructions="Write a blog article based on the previous agent's search results."
-)
-
-# 3. Start Agents
-agents = PraisonAIAgents(agents=[search_agent, blog_agent])
-agents.start()
-```
-</CodeGroup>
-  </Step>
-
-  <Step title="Start Agents">
-    Execute your script:
-```bash Terminal
-python app.py
-```
-  </Step>
-</Steps>
-
-</Tab>
-<Tab title="No Code">
-<Steps>
-<Step title="Install PraisonAI">
-Install the core package and duckduckgo_search package:
-```bash Terminal
-pip install praisonai duckduckgo_search
-```
-</Step>
-<Step title="Create Custom Tool">
-<Info>
-To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM
-</Info>
-Create a new file `tools.py` with the following content:
-```python
-from duckduckgo_search import DDGS
-from typing import List, Dict
-
-# 1. Tool
-def internet_search_tool(query: str) -> List[Dict]:
-    """
-    Perform Internet Search
-    """
-    results = []
-    ddgs = DDGS()
-    for result in ddgs.text(keywords=query, max_results=5):
-        results.append({
-            "title": result.get("title", ""),
-            "url": result.get("href", ""),
-            "snippet": result.get("body", "")
-        })
-    return results  
-```
-</Step>
-<Step title="Create Agent">
-
-Create a new file `agents.yaml` with the following content:
-```yaml
-framework: praisonai
-topic: create movie script about cat in mars
-roles:
-  scriptwriter:
-    backstory: Expert in dialogue and script structure, translating concepts into
-      scripts.
-    goal: Write a movie script about a cat in Mars
-    role: Scriptwriter
-    tools:
-      - internet_search_tool # <-- Tool assigned to Agent here
-    tasks:
-      scriptwriting_task:
-        description: Turn the story concept into a production-ready movie script,
-          including dialogue and scene details.
-        expected_output: Final movie script with dialogue and scene details.
-```
-</Step>
-
-<Step title="Start Agents">
-
-Execute your script:
-```bash Terminal
-praisonai agents.yaml
-```
-</Step>
-</Steps>
-</Tab>
-</Tabs>
-
-
-## Creating Custom Tool
-<Steps>
-<Step>
-Create any function that you want to use as a tool, that performs a specific task.
-```python
-from duckduckgo_search import DDGS
-
-def internet_search_tool(query: str):
-    results = []
-    ddgs = DDGS()
-    for result in ddgs.text(keywords=query, max_results=5):
-        results.append({
-            "title": result.get("title", ""),
-            "url": result.get("href", ""),
-            "snippet": result.get("body", "")
-        })
-    return results
-```
-</Step>
-<Step>
-Assign the tool to an agent
-```python
-    data_agent = Agent(
-        instructions="Search about AI job trends in 2025",
-        tools=[internet_search_tool], # <-- Tool Assignment
-    )
-```
-</Step>
-</Steps>
-
-<Card title="That's it!">
-<Check>You have created a custom tool and assigned it to an agent.</Check>
-</Card>
-
-## When Does the Agent Use Tools?
-
-The agent decides when to use tools based on:
-1. The nature of the user's request
-2. The tools available to it
-3. The instructions you've provided
-
-If your agent isn't using tools when expected, you might need to:
-- Make your instructions more explicit about when to use tools
-- Provide more descriptive tool definitions
-- Ensure the tools are appropriate for the task
-
-## Tool Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Specific Purpose" icon="bullseye">
-    Each tool should have a clear, specific purpose
-  </Card>
-  <Card title="Error Handling" icon="shield-check">
-    Tools should handle errors gracefully
-  </Card>
-  <Card title="Descriptive Names" icon="tag">
-    Use clear names that indicate functionality
-  </Card>
-  <Card title="Clear Documentation" icon="file-lines">
-    Include detailed descriptions and examples
-  </Card>
-</CardGroup>
-
-## Real-World Tool Examples
-
-In production systems, you might create tools for:
-
-<CardGroup cols={2}>
-  <Card title="Database Access" icon="database">
-    Retrieve or store information in databases
-  </Card>
-  <Card title="API Integration" icon="link">
-    Connect to external services
-  </Card>
-  <Card title="File Operations" icon="folder">
-    Read from or write to files
-  </Card>
-  <Card title="Image Generation" icon="image">
-    Create images based on descriptions
-  </Card>
-</CardGroup>
-
-## Advanced Tool Concepts
-
-As you become more comfortable with tools, you can explore:
-- Tools that call other tools
-- Tools with complex parameters
-- Tools that maintain state
-- Dynamically generated tools
-
-In the next lesson, we'll explore how to create a multi-agent system for more complex tasks.

docs/course/agents/13-building-multi-agent-system.mdx

@@ -1,218 +0,0 @@
----
-title: "Building a Multi-Agent System"
-description: "Creating a system with multiple cooperative agents"
-icon: "users"
----
-
-# Building a Multi-Agent System
-
-In this lesson, we'll create a simple multi-agent system where several agents work together to accomplish a task. This approach allows you to divide complex problems into smaller, more manageable pieces.
-
-## What We'll Build
-
-We'll create a content creation system with three specialized agents:
-1. A research agent to gather information
-2. A writing agent to create content
-3. An editing agent to improve and refine the content
-
-## Step 1: Setting Up
-
-First, let's import the necessary modules and set up our environment:
-
-```python
-from praisonaiagents import Agent, PraisonAIAgents
-
-# Make sure your API key is set in your environment:
-# export OPENAI_API_KEY=your_openai_key
-```
-
-## Step 2: Creating Specialized Agents
-
-Now, let's create our three specialized agents:
-
-```python
-# Research Agent
-research_agent = Agent(
-    name="Researcher",
-    instructions="""
-    You are a research specialist who excels at finding relevant information.
-    Your job is to:
-    1. Thoroughly research the given topic
-    2. Identify key facts, statistics, and insights
-    3. Organize the information in a clear, structured format
-    4. Provide accurate information with no fabrications
-    5. Focus on the most relevant and current information
-    """
-)
-
-# Writing Agent
-writing_agent = Agent(
-    name="Writer",
-    instructions="""
-    You are a content writer who creates engaging, reader-friendly content.
-    Your job is to:
-    1. Use the research provided to create well-structured content
-    2. Write in a clear, engaging style
-    3. Include an attention-grabbing introduction
-    4. Organize the content with appropriate headings
-    5. Conclude with a summary and call-to-action when appropriate
-    """
-)
-
-# Editing Agent
-editing_agent = Agent(
-    name="Editor",
-    instructions="""
-    You are an editor who refines and improves content.
-    Your job is to:
-    1. Correct any grammatical or spelling errors
-    2. Improve clarity and flow
-    3. Ensure consistency in tone and style
-    4. Check for logical structure and organization
-    5. Enhance readability for the target audience
-    """
-)
-```
-
-## Step 3: Creating the Multi-Agent System
-
-Now, let's combine these agents into a multi-agent system:
-
-```python
-# Create a multi-agent system
-content_team = PraisonAIAgents(
-    agents=[research_agent, writing_agent, editing_agent]
-)
-```
-
-## Step 4: Setting Up the Workflow
-
-Next, we need to define how our agents will work together:
-
-```python
-# Define a topic
-topic = "The Benefits of Regular Exercise"
-
-# Start the multi-agent process
-results = content_team.start(
-    f"""
-    Task: Create a comprehensive blog post about {topic}
-    
-    Process:
-    1. Researcher: Research {topic} and provide key information including:
-       - Main benefits of exercise (physical and mental)
-       - Recommended exercise guidelines
-       - Scientific studies supporting the benefits
-       - Common misconceptions
-    
-    2. Writer: Use the research to create a 500-word blog post about {topic} with:
-       - An engaging introduction
-       - Clear sections with headings
-       - Practical advice for readers
-       - A compelling conclusion
-    
-    3. Editor: Review and improve the blog post by:
-       - Correcting any errors
-       - Enhancing clarity and flow
-       - Ensuring a consistent tone
-       - Making the content more engaging
-    """
-)
-
-# Print the final result
-print(results)
-```
-
-## Understanding the Workflow
-
-Let's examine how the multi-agent system works:
-
-```mermaid
-graph TD
-    A[Start] --> B[Research Agent]
-    B --> C[Writing Agent]
-    C --> D[Editing Agent]
-    D --> E[Final Output]
-```
-
-1. The research agent gathers and organizes relevant information
-2. The writing agent uses that research to create the initial content
-3. The editing agent refines and improves the content
-4. The final polished content is returned
-
-## Customizing Agent Interactions
-
-You can customize how agents interact by modifying the process instructions. For example, you could add feedback loops:
-
-```python
-process_with_feedback = """
-Task: Create a comprehensive blog post about {topic}
-
-Process:
-1. Researcher: Research {topic} and provide key information
-
-2. Writer: Use the research to create a blog post
-
-3. Editor: Review the blog post and provide feedback
-
-4. Writer: Revise the blog post based on the editor's feedback
-
-5. Editor: Make final improvements to the revised blog post
-"""
-```
-
-## Alternative Agent Configurations
-
-You can organize multi-agent systems in different ways:
-
-### Parallel Processing
-
-```python
-parallel_process = """
-Task: Create a marketing campaign for a new product
-
-Process:
-1. Market Researcher: Research the target market and competitors
-   Content Researcher: Research the product features and benefits
-   [These tasks happen in parallel]
-
-2. Strategy Agent: Use both research results to create a marketing strategy
-
-3. Content Creator: Create the marketing materials based on the strategy
-"""
-```
-
-### Expert Panel
-
-```python
-expert_panel = """
-Task: Evaluate a business idea
-
-Process:
-1. Financial Expert: Analyze financial viability
-   Market Expert: Analyze market potential
-   Technical Expert: Analyze technical feasibility
-   [These assessments happen in parallel]
-
-2. Coordinator: Compile all expert opinions into a final recommendation
-"""
-```
-
-## Best Practices for Multi-Agent Systems
-
-<CardGroup cols={2}>
-  <Card title="Clear Role Definition" icon="user-tag">
-    Define exactly what each agent is responsible for
-  </Card>
-  <Card title="Explicit Handoffs" icon="arrow-right-arrow-left">
-    Specify how information passes between agents
-  </Card>
-  <Card title="Avoid Redundancy" icon="copy">
-    Don't have multiple agents do the same work
-  </Card>
-  <Card title="Include Error Handling" icon="triangle-exclamation">
-    Plan for cases where an agent might fail
-  </Card>
-</CardGroup>
-
-In the next lesson, we'll learn how to create a conversational agent that can maintain context across multiple interactions.

docs/course/agents/14-conversational-agents.mdx

@@ -1,193 +0,0 @@
----
-title: "Building Conversational Agents"
-description: "Creating agents that maintain context in conversations"
-icon: "comments"
----
-
-# Building Conversational Agents
-
-In this lesson, we'll learn how to create conversational agents that can maintain context across multiple interactions. This is essential for creating assistants that feel natural to interact with.
-
-## What Makes a Good Conversational Agent?
-
-A good conversational agent should:
-- Remember previous parts of the conversation
-- Understand context and references
-- Provide coherent, relevant responses
-- Maintain a consistent personality
-
-## Creating a Simple Conversational Agent
-
-Let's build a basic conversational agent:
-
-```python
-# Note: TODO: This Feature yet to be developed
-from praisonaiagents import Agent
-
-# Create a conversational agent
-chat_agent = Agent(
-    name="Conversational Assistant",
-    instructions="""
-    You are a friendly, helpful conversational assistant.
-    
-    When chatting with users:
-    1. Maintain a warm, friendly tone
-    2. Remember information shared earlier in the conversation
-    3. Ask clarifying questions when needed
-    4. Keep responses concise but informative
-    5. Be helpful while respecting boundaries
-    """
-)
-
-# Start a conversation
-response = chat_agent.start("Hi there! My name is Jamie.")
-print(response)
-
-# Continue the conversation
-response = chat_agent.continue("What can you help me with today?")
-print(response)
-
-# The agent should remember the user's name
-response = chat_agent.continue("Can you remember my name?")
-print(response)
-```
-
-## How Context Works
-
-PraisonAI automatically manages conversation context for you:
-
-```mermaid
-graph TD
-    A[User Message 1] --> B[Agent]
-    B --> C[Response 1]
-    D[User Message 2] --> E[Agent + Context]
-    E --> F[Response 2]
-    G[User Message 3] --> H[Agent + More Context]
-    H --> I[Response 3]
-```
-
-The agent maintains a memory of the conversation, allowing it to refer back to previous messages.
-
-## Conversation Settings
-
-You can customize how your conversational agent behaves:
-
-```python
-# Create an agent with specific conversation settings
-custom_chat_agent = Agent(
-    name="Custom Chat Assistant",
-    instructions="You are a helpful assistant",
-    memory_size=10,  # Remember the last 10 messages
-    temperature=0.7  # Slightly more creative in responses
-)
-```
-
-## Handling Different Types of Conversations
-
-Different use cases require different conversation styles:
-
-<CardGroup cols={2}>
-  <Card title="Customer Support" icon="headset">
-    - Professional and helpful tone
-    - Focus on problem resolution
-    - Remember customer details
-  </Card>
-  <Card title="Educational Tutor" icon="graduation-cap">
-    - Patient and explanatory
-    - Builds on previous lessons
-    - Adjusts to learning pace
-  </Card>
-  <Card title="Personal Assistant" icon="calendar">
-    - Remembers preferences
-    - Provides reminders
-    - Handles scheduling
-  </Card>
-  <Card title="Companion Chat" icon="user-group">
-    - More casual tone
-    - Focuses on engagement
-    - Shows personality
-  </Card>
-</CardGroup>
-
-## Example: Customer Support Agent
-
-Let's create a specialized customer support agent:
-
-```python
-support_agent = Agent(
-    name="Support Agent",
-    instructions="""
-    You are a customer support specialist for a tech company.
-    
-    When helping customers:
-    1. Greet them professionally
-    2. Show empathy for their issues
-    3. Ask for necessary information to troubleshoot
-    4. Provide clear step-by-step solutions
-    5. Confirm if the issue is resolved
-    6. End with an offer for additional help
-    
-    Remember product details and customer information throughout the conversation.
-    """
-)
-
-# Example conversation
-print(support_agent.start("Hi, I'm having trouble logging into my account."))
-print(support_agent.continue("My username is user123."))
-print(support_agent.continue("I've tried resetting my password but I'm not receiving the email."))
-```
-
-## Building Multi-Turn Conversations
-
-To create engaging multi-turn conversations, consider:
-
-1. **Appropriate follow-up questions**: Keep the conversation flowing
-2. **Acknowledgment**: Reference what the user has said
-3. **Progressive disclosure**: Don't overwhelm with information at once
-4. **Conversation repair**: Gracefully handle misunderstandings
-
-## Example: Multi-Turn Educational Conversation
-
-```python
-tutor_agent = Agent(
-    name="Math Tutor",
-    instructions="""
-    You are a patient, encouraging math tutor.
-    
-    When teaching:
-    1. Gauge the student's current understanding
-    2. Explain concepts using clear, simple language
-    3. Provide examples to illustrate points
-    4. Ask questions to check understanding
-    5. Give positive reinforcement for progress
-    
-    Adapt your explanations based on the student's responses.
-    """
-)
-
-# Example tutoring session
-print(tutor_agent.start("Can you help me understand algebra?"))
-print(tutor_agent.continue("I'm struggling with equations like 2x + 5 = 13"))
-print(tutor_agent.continue("So I subtract 5 from both sides first?"))
-print(tutor_agent.continue("Then I divide by 2 to get x = 4?"))
-print(tutor_agent.continue("Could you give me another example to practice?"))
-```
-
-## Conversation Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Clear Transitions" icon="arrow-right">
-    Signal when changing topics
-  </Card>
-  <Card title="Manageable Chunks" icon="layer-group">
-    Break complex information into smaller pieces
-  </Card>
-  <Card title="Engagement Checks" icon="hand">
-    Periodically confirm understanding
-  </Card>
-  <Card title="Personality Consistency" icon="masks-theater">
-    Maintain a consistent tone and style
-  </Card>
-</CardGroup>
-
-In the next lesson, we'll explore how to build agents that can search and retrieve information from external sources.

docs/course/agents/15-research-agents.mdx

@@ -1,216 +0,0 @@
----
-title: "Building Research Agents"
-description: "Creating agents that can gather and analyze information"
-icon: "magnifying-glass"
----
-
-# Building Research Agents
-
-In this lesson, we'll build agents specialized in gathering, analyzing, and summarizing information. Research agents are valuable for quickly obtaining insights on various topics.
-
-## What is a Research Agent?
-
-A research agent is designed to:
-- Find relevant information on specific topics
-- Analyze and synthesize data
-- Present findings in a clear, structured format
-- Answer questions based on gathered information
-
-## Creating a Basic Research Agent
-
-Let's start by building a simple research agent:
-
-```python
-from praisonaiagents import Agent
-
-# Create a basic research agent
-research_agent = Agent(
-    name="ResearchAgent",
-    instructions="""
-    You are a research specialist who finds and summarizes information.
-    
-    When researching topics:
-    1. Focus on finding accurate, current information
-    2. Organize findings in a logical structure
-    3. Identify key points and insights
-    4. Cite sources when available
-    5. Present information in a clear, concise format
-    """
-)
-
-# Use the research agent
-research_results = research_agent.start("Research the impact of renewable energy on reducing carbon emissions")
-print(research_results)
-```
-
-## Adding Web Search Capability
-
-To make our research agent more powerful, let's add web search capability:
-
-```python
-from praisonaiagents import Agent, Tool
-
-# Define a web search function (simulated for this example)
-def web_search(query):
-    """
-    Simulated web search function.
-    In a real application, this would connect to a search API.
-    
-    Args:
-        query (str): The search query
-        
-    Returns:
-        str: Search results
-    """
-    # This is just a simulation - in a real application, you'd call a search API
-    if "renewable energy" in query.lower():
-        return """
-        Search results for 'renewable energy carbon emissions':
-        
-        1. According to the IEA, renewable energy prevented 2.1 billion tonnes of CO2 emissions in 2022.
-        
-        2. Solar and wind power generate electricity with 95-98% lower carbon emissions than coal-based electricity.
-        
-        3. The IPCC reports that renewable energy could deliver more than half of the emission reductions needed by 2030.
-        """
-    else:
-        return f"Search results for '{query}' (simulated data)"
-
-# Create the web search tool
-search_tool = Tool(
-    name="web_search",
-    function=web_search,
-    description="Search the web for current information on a topic"
-)
-
-# Create a research agent with web search capability
-enhanced_research_agent = Agent(
-    name="EnhancedResearchAgent",
-    instructions="""
-    You are a research specialist who finds and summarizes information.
-    
-    When researching topics:
-    1. Use the web_search tool to find current information
-    2. Analyze and synthesize the search results
-    3. Organize findings in a logical structure
-    4. Identify key points and insights
-    5. Present information in a clear, concise format
-    """,
-    tools=[search_tool]
-)
-
-# Use the enhanced research agent
-enhanced_results = enhanced_research_agent.start("What is the impact of renewable energy on carbon emissions?")
-print(enhanced_results)
-```
-
-## Research Agent with Structured Output
-
-For more organized research results, we can instruct the agent to provide structured output:
-
-```python
-structured_research_agent = Agent(
-    name="StructuredResearchAgent",
-    instructions="""
-    You are a research specialist who finds and organizes information.
-    
-    When researching topics:
-    1. Use available tools to find current information
-    2. Analyze and synthesize the findings
-    
-    Always structure your response in this format:
-    
-    ## Summary
-    [Brief 2-3 sentence overview of the topic]
-    
-    ## Key Facts
-    - [Important fact 1]
-    - [Important fact 2]
-    - [Important fact 3]
-    
-    ## Analysis
-    [Deeper analysis of the information, including implications]
-    
-    ## Sources
-    [List of sources if available]
-    """,
-    tools=[search_tool]
-)
-
-# Use the structured research agent
-structured_results = structured_research_agent.start("Research the latest advancements in quantum computing")
-print(structured_results)
-```
-
-## Specialized Research Agents
-
-Different research tasks require different approaches. Here are some specialized research agents:
-
-### Comparison Research Agent
-
-```python
-comparison_agent = Agent(
-    name="ComparisonAgent",
-    instructions="""
-    You are a research specialist who compares different options or topics.
-    
-    When comparing items:
-    1. Identify the key criteria for comparison
-    2. Research each option thoroughly
-    3. Create a clear side-by-side comparison
-    4. Highlight advantages and disadvantages of each option
-    5. Provide a balanced conclusion
-    
-    Present your findings in a structured table format when appropriate.
-    """,
-    tools=[search_tool]
-)
-
-# Use the comparison agent
-comparison_results = comparison_agent.start("Compare solar and wind energy for residential use")
-print(comparison_results)
-```
-
-### Literature Review Agent
-
-```python
-literature_agent = Agent(
-    name="LiteratureAgent",
-    instructions="""
-    You are a research specialist who reviews academic and professional literature.
-    
-    When reviewing literature:
-    1. Identify key papers, articles, and studies on the topic
-    2. Summarize the main findings and methodologies
-    3. Identify trends, agreements, and contradictions
-    4. Evaluate the strength of evidence
-    5. Highlight gaps in current research
-    
-    Structure your review in a clear, academic format.
-    """,
-    tools=[search_tool]
-)
-
-# Use the literature review agent
-literature_results = literature_agent.start("Review recent research on artificial intelligence in healthcare")
-print(literature_results)
-```
-
-## Research Process Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Define Scope" icon="bullseye">
-    Set clear boundaries for the research
-  </Card>
-  <Card title="Verify Information" icon="check-double">
-    Cross-check facts from multiple sources
-  </Card>
-  <Card title="Consider Bias" icon="scale-balanced">
-    Be aware of potential biases in sources
-  </Card>
-  <Card title="Prioritize Relevance" icon="filter">
-    Focus on information most relevant to the query
-  </Card>
-</CardGroup>
-
-In the next lesson, we'll explore how to build agents that can assist with creative tasks like content creation.

docs/course/agents/16-content-creation-agents.mdx

@@ -1,252 +0,0 @@
----
-title: "Content Creation Agents"
-description: "Building agents that generate various types of content"
-icon: "pen-to-square"
----
-
-# Content Creation Agents
-
-Content creation agents can help generate various types of content, from blog posts to marketing materials. In this lesson, we'll learn how to build effective content creation agents.
-
-## What is a Content Creation Agent?
-
-A content creation agent is designed to:
-- Generate written content based on specific requirements
-- Structure content in appropriate formats
-- Adapt tone and style to match the target audience
-- Create content that achieves specific goals (inform, persuade, entertain)
-
-## Creating a Basic Content Creation Agent
-
-Let's start with a simple content creation agent:
-
-```python
-from praisonaiagents import Agent
-
-# Create a basic content creation agent
-content_agent = Agent(
-    name="ContentCreator",
-    instructions="""
-    You are a versatile content creator who specializes in writing engaging material.
-    
-    When creating content:
-    1. Understand the purpose and target audience
-    2. Create a logical structure with clear sections
-    3. Use an engaging, appropriate writing style
-    4. Include a strong introduction and conclusion
-    5. Format content for easy readability
-    """
-)
-
-# Use the content creation agent
-blog_post = content_agent.start(
-    """
-    Create a blog post about the benefits of meditation.
-    Target audience: Busy professionals
-    Length: Approximately 500 words
-    Tone: Informative but conversational
-    """
-)
-print(blog_post)
-```
-
-## Specialized Content Creation Agents
-
-Different types of content require different approaches. Let's create specialized agents for various content needs:
-
-### Blog Post Agent
-
-```python
-blog_agent = Agent(
-    name="BlogWriter",
-    instructions="""
-    You are a blog post writer who creates engaging, informative articles.
-    
-    When writing blog posts:
-    1. Create attention-grabbing headlines
-    2. Start with a compelling introduction
-    3. Use subheadings to organize content
-    4. Include relevant examples and evidence
-    5. Incorporate a clear call-to-action at the end
-    
-    Format your content with proper Markdown formatting including:
-    - Headings (## for main sections, ### for subsections)
-    - Bullet points for lists
-    - *Italic* for emphasis
-    - **Bold** for important points
-    - > Blockquotes for notable quotes
-    """
-)
-
-# Use the blog agent
-blog_content = blog_agent.start(
-    """
-    Topic: 5 Ways to Improve Your Productivity
-    Target audience: Remote workers
-    Style: Practical, actionable advice
-    Length: 800 words
-    Include: Tips, examples, and research-backed information
-    """
-)
-print(blog_content)
-```
-
-### Social Media Content Agent
-
-```python
-social_media_agent = Agent(
-    name="SocialMediaCreator",
-    instructions="""
-    You are a social media content creator who crafts engaging posts.
-    
-    When creating social media content:
-    1. Create attention-grabbing, concise content
-    2. Adapt to the specific platform requirements
-    3. Include relevant hashtags when appropriate
-    4. Craft content that encourages engagement
-    5. Consider visual elements that would pair well with the text
-    
-    For each platform, follow these guidelines:
-    - Twitter/X: 280 characters max, punchy, relevant hashtags
-    - LinkedIn: Professional tone, industry insights, 1-3 paragraphs
-    - Instagram: Visual-focused descriptions, emoji-friendly, hashtags
-    - Facebook: Conversational, can be longer, question-based engagement
-    """
-)
-
-# Use the social media agent
-social_content = social_media_agent.start(
-    """
-    Create posts for Twitter, LinkedIn, and Facebook announcing:
-    
-    Event: Free Webinar on "Future of AI in Business"
-    Date: March 15, 2025
-    Time: 2:00 PM EST
-    Registration link: example.com/webinar
-    Key speakers: Dr. Jane Smith, AI Ethics Expert & Tom Johnson, Business AI Implementation Specialist
-    """
-)
-print(social_content)
-```
-
-### Email Campaign Agent
-
-```python
-email_agent = Agent(
-    name="EmailWriter",
-    instructions="""
-    You are an email marketing specialist who creates effective email campaigns.
-    
-    When writing marketing emails:
-    1. Create compelling subject lines that increase open rates
-    2. Write engaging opening lines that hook the reader
-    3. Keep content concise and focused on a single main message
-    4. Include clear calls-to-action
-    5. Use a tone appropriate for the brand and audience
-    
-    Structure your emails with:
-    - Subject line
-    - Greeting
-    - Opening hook
-    - Main content (2-3 paragraphs maximum)
-    - Call-to-action
-    - Sign-off
-    """
-)
-
-# Use the email agent
-email_content = email_agent.start(
-    """
-    Create a promotional email for:
-    
-    Product: Premium Fitness Subscription
-    Key benefit: Personalized workout plans and nutrition advice
-    Special offer: 30% off first 3 months
-    Target audience: Health-conscious professionals aged 25-45
-    CTA: Sign up for the discounted offer
-    Tone: Motivational but not aggressive
-    """
-)
-print(email_content)
-```
-
-## Content Creation with Templates
-
-Templates can help create more consistent content. Let's build an agent that works with templates:
-
-```python
-template_agent = Agent(
-    name="TemplateWriter",
-    instructions="""
-    You are a content creator who works with templates to create consistent content.
-    
-    When filling a template:
-    1. Maintain the overall structure of the template
-    2. Replace placeholder text with appropriate content
-    3. Ensure consistency in tone and style
-    4. Adapt content to the specific requirements
-    5. Review for coherence and flow
-    
-    Always preserve formatting elements like headings, lists, and emphasis.
-    """
-)
-
-# Example template
-product_review_template = """
-# {Product Name} Review: {Brief Opinion}
-
-## Quick Summary
-**Rating:** {Rating}/10
-**Price:** {Price Range}
-**Recommended For:** {Target User}
-
-## What We Liked
-- {Positive Point 1}
-- {Positive Point 2}
-- {Positive Point 3}
-
-## What Could Be Better
-- {Negative Point 1}
-- {Negative Point 2}
-
-## Bottom Line
-{Conclusion Paragraph}
-
-"""
-
-# Use the template agent
-filled_template = template_agent.start(
-    f"""
-    Fill the following template for a product review:
-    
-    {product_review_template}
-    
-    Product: Wireless Noise-Cancelling Headphones XZ200
-    Key features: 30-hour battery life, active noise cancellation, voice assistant integration
-    Price: $249.99
-    Target audience: Commuters and office workers
-    Notable pros: Sound quality, comfort, battery life
-    Notable cons: Price, occasional Bluetooth connectivity issues
-    """
-)
-print(filled_template)
-```
-
-## Content Creation Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Know Your Audience" icon="users">
-    Understand who will consume the content
-  </Card>
-  <Card title="Clear Purpose" icon="bullseye">
-    Define what the content should achieve
-  </Card>
-  <Card title="Consistent Voice" icon="comment">
-    Maintain a consistent tone and style
-  </Card>
-  <Card title="Readability" icon="eye">
-    Make content easy to scan and understand
-  </Card>
-</CardGroup>
-
-In the next lesson, we'll explore how to build AI agents that can analyze and work with data.

docs/course/agents/17-data-analysis-agents.mdx

@@ -1,333 +0,0 @@
----
-title: "Data Analysis Agents"
-description: "Creating agents that analyze and interpret data"
-icon: "chart-line"
----
-
-# Data Analysis Agents
-
-Data analysis agents can help process, analyze, and extract insights from data. In this lesson, we'll learn how to build agents that can work with data effectively.
-
-## What is a Data Analysis Agent?
-
-A data analysis agent is designed to:
-- Process and interpret data
-- Identify patterns and trends
-- Generate insights and recommendations
-- Present findings in an understandable format
-
-## Creating a Basic Data Analysis Agent
-
-Let's start by building a simple data analysis agent:
-
-```python
-from praisonaiagents import Agent
-
-# Create a basic data analysis agent
-data_analysis_agent = Agent(
-    name="DataAnalyst",
-    instructions="""
-    You are a data analysis specialist who interprets and explains data.
-    
-    When analyzing data:
-    1. First understand what the data represents
-    2. Identify key patterns, trends, or anomalies
-    3. Generate meaningful insights
-    4. Explain findings in simple, clear language
-    5. Provide actionable recommendations when appropriate
-    """
-)
-
-# Use the data analysis agent with sample data
-sample_data = """
-Monthly Sales Data (2024):
-January: $12,500
-February: $13,200
-March: $15,800
-April: $14,300
-May: $16,700
-June: $18,900
-"""
-
-analysis = data_analysis_agent.start(
-    f"""
-    Analyze the following sales data and provide insights:
-    
-    {sample_data}
-    
-    Questions to answer:
-    1. What is the overall sales trend?
-    2. Which month had the highest sales?
-    3. What is the average monthly sales?
-    4. What recommendations would you make based on this data?
-    """
-)
-print(analysis)
-```
-
-## Adding Data Processing Tools
-
-Let's enhance our agent with basic data processing capabilities:
-
-```python
-from praisonaiagents import Agent, Tool
-
-# Define a simple calculation tool
-def calculate_statistics(data_string):
-    """
-    Calculate basic statistics from a string of numbers.
-    
-    Args:
-        data_string (str): A string with numbers separated by commas
-        
-    Returns:
-        str: Statistical summary
-    """
-    try:
-        # Convert string to list of numbers
-        data = [float(x.strip()) for x in data_string.split(',')]
-        
-        # Calculate statistics
-        total = sum(data)
-        average = total / len(data)
-        minimum = min(data)
-        maximum = max(data)
-        
-        return f"""
-        Statistical summary:
-        - Count: {len(data)}
-        - Sum: {total}
-        - Average: {average:.2f}
-        - Minimum: {minimum}
-        - Maximum: {maximum}
-        - Range: {maximum - minimum}
-        """
-    except Exception as e:
-        return f"Error processing data: {str(e)}"
-
-# Create the tool
-stats_tool = Tool(
-    name="calculate_statistics",
-    function=calculate_statistics,
-    description="Calculate basic statistics from a list of numbers (comma-separated)"
-)
-
-# Create an agent with the statistics tool
-enhanced_data_agent = Agent(
-    name="EnhancedDataAnalyst",
-    instructions="""
-    You are a data analysis specialist who interprets and explains data.
-    
-    When analyzing data:
-    1. First understand what the data represents
-    2. Use the calculate_statistics tool for numerical data
-    3. Identify key patterns, trends, or anomalies
-    4. Generate meaningful insights
-    5. Explain findings in simple, clear language
-    6. Provide actionable recommendations when appropriate
-    """,
-    tools=[stats_tool]
-)
-
-# Use the enhanced data agent
-numerical_data = "12500, 13200, 15800, 14300, 16700, 18900"
-enhanced_analysis = enhanced_data_agent.start(
-    f"""
-    Analyze the following monthly sales data for the first half of 2024:
-    
-    {numerical_data}
-    
-    Provide a complete analysis with trends and recommendations.
-    """
-)
-print(enhanced_analysis)
-```
-
-## Specialized Data Analysis Agents
-
-Different analysis tasks require specialized approaches. Let's create some specialized agents:
-
-### Market Trend Analysis Agent
-
-```python
-market_analysis_agent = Agent(
-    name="MarketAnalyst",
-    instructions="""
-    You are a market trend analyst who specializes in identifying patterns and making predictions.
-    
-    When analyzing market data:
-    1. Identify long-term trends and short-term patterns
-    2. Compare data points to industry benchmarks
-    3. Identify potential causes for changes
-    4. Assess future implications
-    5. Provide strategic recommendations
-    
-    Present your analysis with clear sections for:
-    - Overall Market Assessment
-    - Key Trends Identified
-    - Growth Opportunities
-    - Risk Factors
-    - Strategic Recommendations
-    """,
-    tools=[stats_tool]
-)
-
-# Use the market analysis agent
-market_data = """
-Industry Growth Rates (2021-2024):
-2021: 3.2%
-2022: 2.8%
-2023: 4.5%
-2024: 5.1%
-
-Our Company Growth:
-2021: 2.9%
-2022: 3.1%
-2023: 4.8%
-2024: 6.3%
-
-Competitor A Growth:
-2021: 3.5%
-2022: 3.7%
-2023: 4.2%
-2024: 4.5%
-
-Competitor B Growth:
-2021: 3.0%
-2022: 2.5%
-2023: 5.0%
-2024: 5.8%
-"""
-
-market_analysis = market_analysis_agent.start(
-    f"""
-    Analyze the following market and company growth data:
-    
-    {market_data}
-    
-    Provide a comprehensive market analysis and strategic recommendations.
-    """
-)
-print(market_analysis)
-```
-
-### Customer Data Analysis Agent
-
-```python
-customer_analysis_agent = Agent(
-    name="CustomerAnalyst",
-    instructions="""
-    You are a customer data analyst who specializes in understanding customer behavior.
-    
-    When analyzing customer data:
-    1. Identify customer segments and patterns
-    2. Analyze purchasing behavior
-    3. Evaluate customer satisfaction metrics
-    4. Identify opportunities to improve customer experience
-    5. Recommend strategies for customer retention and growth
-    
-    Present your analysis with sections for:
-    - Customer Segmentation
-    - Behavior Patterns
-    - Key Insights
-    - Recommended Actions
-    """,
-    tools=[stats_tool]
-)
-
-# Use the customer analysis agent
-customer_data = """
-Customer Segments by Age:
-18-24: 15% of customers (Avg. Purchase: $45)
-25-34: 32% of customers (Avg. Purchase: $78)
-35-44: 28% of customers (Avg. Purchase: $92)
-45-54: 18% of customers (Avg. Purchase: $85)
-55+: 7% of customers (Avg. Purchase: $65)
-
-Customer Satisfaction:
-Very Satisfied: 42%
-Satisfied: 35%
-Neutral: 15%
-Dissatisfied: 6%
-Very Dissatisfied: 2%
-
-Repeat Purchase Rate:
-One-time: 40%
-2-5 purchases: 35%
-6-10 purchases: 15%
-10+ purchases: 10%
-"""
-
-customer_analysis = customer_analysis_agent.start(
-    f"""
-    Analyze the following customer data:
-    
-    {customer_data}
-    
-    Provide insights on customer segments, behavior patterns, and recommendations to improve customer retention.
-    """
-)
-print(customer_analysis)
-```
-
-## Data Visualization Recommendations
-
-While our agents can't directly create visualizations, they can recommend appropriate visualization types:
-
-```python
-visualization_agent = Agent(
-    name="VisualizationAdvisor",
-    instructions="""
-    You are a data visualization specialist who helps select the best ways to visually represent data.
-    
-    When recommending visualizations:
-    1. Understand the nature of the data (categorical, numerical, time-series, etc.)
-    2. Consider the key relationships or patterns to highlight
-    3. Recommend the most appropriate chart or graph type
-    4. Explain why your recommendation is effective
-    5. Provide suggestions for layout, color scheme, and labeling
-    
-    Common visualization types to consider:
-    - Line charts (for trends over time)
-    - Bar charts (for comparing categories)
-    - Pie charts (for showing composition, use sparingly)
-    - Scatter plots (for showing relationships between variables)
-    - Heatmaps (for showing patterns in complex data)
-    - Box plots (for showing distributions)
-    """
-)
-
-# Use the visualization agent
-viz_recommendations = visualization_agent.start(
-    """
-    I have the following data and need visualization recommendations:
-    
-    1. Monthly sales data for the past year
-    2. Customer satisfaction ratings across 5 product categories
-    3. Market share compared to 3 competitors
-    4. Customer age distribution
-    
-    What visualizations would you recommend for each of these datasets and why?
-    """
-)
-print(viz_recommendations)
-```
-
-## Data Analysis Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Data Quality" icon="shield-check">
-    Ensure data is accurate and complete
-  </Card>
-  <Card title="Context Matters" icon="circle-info">
-    Consider external factors affecting data
-  </Card>
-  <Card title="Clear Communication" icon="comments">
-    Present findings in understandable terms
-  </Card>
-  <Card title="Actionable Insights" icon="bolt">
-    Focus on insights that lead to action
-  </Card>
-</CardGroup>
-
-In the next lesson, we'll explore how to build AI agents that can assist with customer support tasks.

docs/course/agents/18-customer-support-agents.mdx

@@ -1,260 +0,0 @@
----
-title: "Customer Support Agents"
-description: "Building agents that handle customer inquiries and issues"
-icon: "headset"
----
-
-# Customer Support Agents
-
-Customer support agents can handle inquiries, resolve issues, and provide assistance to users. In this lesson, we'll learn how to build effective customer support agents.
-
-## What is a Customer Support Agent?
-
-A customer support agent is designed to:
-- Answer frequently asked questions
-- Troubleshoot common problems
-- Provide information about products or services
-- Collect information for support tickets
-- Guide users through processes or procedures
-
-## Creating a Basic Customer Support Agent
-
-Let's start by building a simple customer support agent:
-
-```python
-from praisonaiagents import Agent
-
-# Create a basic customer support agent
-support_agent = Agent(
-    name="SupportAgent",
-    instructions="""
-    You are a helpful customer support representative for a software company.
-    
-    When helping customers:
-    1. Greet them professionally and warmly
-    2. Understand their issue completely before attempting to solve it
-    3. Provide clear, step-by-step solutions when possible
-    4. Maintain a friendly, patient tone throughout
-    5. If you cannot solve the issue, explain how to escalate it
-    
-    Our software product is a productivity app with features for:
-    - Task management
-    - Calendar scheduling
-    - Note-taking
-    - Team collaboration
-    """
-)
-
-# Use the support agent
-response = support_agent.start("I'm having trouble syncing my calendar with my phone. Can you help?")
-print(response)
-```
-
-## Adding a Knowledge Base
-
-Let's enhance our agent with a simple knowledge base to answer common questions:
-
-```python
-from praisonaiagents import Agent, KnowledgeBase
-
-# Create a simple knowledge base
-faq_kb = KnowledgeBase()
-
-# Add FAQ content (in a real scenario, you would load this from files)
-faq_kb.add_text("""
-# Frequently Asked Questions
-
-## Account Issues
-
-### How do I reset my password?
-To reset your password:
-1. Go to the login page
-2. Click on "Forgot Password"
-3. Enter your email address
-4. Follow the instructions sent to your email
-
-### How do I change my email address?
-To change your email address:
-1. Log in to your account
-2. Go to Settings > Account
-3. Click on "Change Email"
-4. Enter your new email and confirm with your password
-
-## Subscription and Billing
-
-### What subscription plans do you offer?
-We offer three plans:
-- Basic: $9.99/month - Includes core features for individuals
-- Pro: $19.99/month - Includes advanced features and team capabilities for up to 5 users
-- Enterprise: Custom pricing - Full feature set with advanced security and support
-
-### How do I cancel my subscription?
-To cancel your subscription:
-1. Log in to your account
-2. Go to Settings > Billing
-3. Click "Cancel Subscription"
-4. Follow the confirmation steps
-
-## Technical Issues
-
-### Why isn't my calendar syncing?
-Common reasons for calendar sync issues:
-1. Outdated app version - Try updating the app
-2. Account permission issues - Check sync permissions in Settings > Integrations
-3. Internet connectivity problems - Ensure you have a stable connection
-
-### How do I export my data?
-To export your data:
-1. Go to Settings > Data
-2. Click "Export Data"
-3. Choose the data types you want to export
-4. Select your preferred format (CSV or JSON)
-5. Click "Export" and save the file
-""")
-
-# Create an agent with the knowledge base
-kb_support_agent = Agent(
-    name="KnowledgeBaseAgent",
-    instructions="""
-    You are a helpful customer support representative with access to our FAQ knowledge base.
-    
-    When helping customers:
-    1. Greet them professionally and warmly
-    2. Search the knowledge base for relevant information
-    3. Provide clear, accurate answers based on the knowledge base
-    4. If the knowledge base doesn't cover the question, provide general helpful guidance
-    5. Maintain a friendly, supportive tone throughout
-    """,
-    knowledge_base=faq_kb
-)
-
-# Use the knowledge base support agent
-kb_response = kb_support_agent.start("How do I cancel my subscription?")
-print(kb_response)
-```
-
-## Creating a Troubleshooting Agent
-
-Let's create an agent specialized in technical troubleshooting:
-
-```python
-troubleshooting_agent = Agent(
-    name="TroubleshootingAgent",
-    instructions="""
-    You are a technical support specialist who helps users troubleshoot problems.
-    
-    When troubleshooting:
-    1. Ask clarifying questions to understand the exact issue
-    2. Identify the most likely causes of the problem
-    3. Provide step-by-step solutions, starting with the simplest fixes
-    4. Explain what each step does and why it might help
-    5. Confirm if the issue is resolved or suggest next steps
-    
-    Common troubleshooting areas:
-    - Login and account access issues
-    - App installation and updates
-    - Feature functionality problems
-    - Data synchronization
-    - Performance issues
-    """
-)
-
-# Use the troubleshooting agent
-troubleshooting_response = troubleshooting_agent.start("The app keeps crashing when I try to open it on my iPhone")
-print(troubleshooting_response)
-```
-
-## Building a Conversational Support Agent
-
-Let's create a support agent that can handle multi-turn conversations:
-
-```python
-conversational_support = Agent(
-    name="ConversationalSupport",
-    instructions="""
-    You are a customer support representative who handles support conversations.
-    
-    When handling support conversations:
-    1. Maintain context throughout the conversation
-    2. Ask follow-up questions when needed
-    3. Verify understanding before providing solutions
-    4. Be patient with users who may be frustrated
-    5. Summarize the conversation and next steps at the end
-    
-    Our product is a mobile banking app with features for:
-    - Account balance checking
-    - Money transfers
-    - Bill payments
-    - Mobile check deposits
-    - Budget tracking
-    """
-)
-
-# Start a support conversation
-initial_response = conversational_support.start("I can't find where to deposit checks in the app")
-print("Initial Response:", initial_response)
-
-# Continue the conversation
-follow_up_response = conversational_support.continue("I'm using version 2.5 of the app on Android")
-print("Follow-up Response:", follow_up_response)
-
-# Further continuation
-resolution_response = conversational_support.continue("I found the deposit button now, but it's giving me an error when I try to take a photo")
-print("Resolution Response:", resolution_response)
-```
-
-## Support Ticket Creation Agent
-
-Let's create an agent that can gather information for support tickets:
-
-```python
-ticket_agent = Agent(
-    name="TicketCreationAgent",
-    instructions="""
-    You are a support ticket creation specialist who gathers the necessary information to create support tickets.
-    
-    When creating a ticket:
-    1. Collect the customer's name and contact information
-    2. Identify the category of the issue
-    3. Gather a detailed description of the problem
-    4. Determine the severity level
-    5. Collect relevant technical details (device, software version, etc.)
-    6. Organize all information in a structured ticket format
-    
-    Present the final ticket in a clearly formatted structure.
-    """
-)
-
-# Use the ticket creation agent
-ticket = ticket_agent.start(
-    """
-    Create a support ticket for the following issue:
-    
-    Customer: Jane Smith
-    Email: [email protected]
-    Issue: Cannot access premium features after payment
-    Details: Completed payment yesterday, received confirmation email, but premium features are still locked in the app
-    Using: iPhone 13, app version 3.2.1
-    """
-)
-print(ticket)
-```
-
-## Customer Support Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Empathy First" icon="heart">
-    Show understanding before offering solutions
-  </Card>
-  <Card title="Clear Communication" icon="comment">
-    Use simple language, avoid jargon
-  </Card>
-  <Card title="Consistent Responses" icon="equals">
-    Provide consistent answers to similar questions
-  </Card>
-  <Card title="Know Escalation Paths" icon="arrow-up-right">
-    Have clear processes for escalating complex issues
-  </Card>
-</CardGroup>
-
-In the next lesson, we'll explore how to build AI agents that can assist with personal productivity tasks.

docs/course/agents/19-personal-assistant-agents.mdx

@@ -1,270 +0,0 @@
----
-title: "Personal Assistant Agents"
-description: "Building agents that help with personal tasks and productivity"
-icon: "calendar-check"
----
-
-# Personal Assistant Agents
-
-Personal assistant agents can help users manage tasks, organize information, and increase productivity. In this lesson, we'll learn how to build effective personal assistant agents.
-
-## What is a Personal Assistant Agent?
-
-A personal assistant agent is designed to:
-- Manage tasks and reminders
-- Organize information
-- Answer questions
-- Provide recommendations
-- Help with personal productivity
-
-## Creating a Basic Personal Assistant Agent
-
-Let's start by building a simple personal assistant agent:
-
-```python
-from praisonaiagents import Agent
-
-# Create a basic personal assistant agent
-assistant_agent = Agent(
-    name="PersonalAssistant",
-    instructions="""
-    You are a helpful personal assistant who helps users manage tasks and information.
-    
-    When assisting users:
-    1. Be concise and efficient in your responses
-    2. Prioritize user requests based on importance and urgency
-    3. Maintain a helpful, friendly tone
-    4. Remember important information the user shares
-    5. Proactively suggest relevant information when appropriate
-    """,
-    llm="gpt-4o-mini"  # Using the specified model
-)
-
-# Use the personal assistant agent
-response = assistant_agent.start("I need to plan a meeting with my team next Tuesday at 2 PM")
-print(response)
-```
-
-## Task Management Assistant
-
-Let's create an agent specialized in task management:
-
-```python
-task_agent = Agent(
-    name="TaskManager",
-    instructions="""
-    You are a task management assistant who helps users organize and prioritize tasks.
-    
-    When managing tasks:
-    1. Help identify and clarify tasks
-    2. Assist with prioritization
-    3. Break down complex tasks into manageable steps
-    4. Track deadlines and progress
-    5. Provide reminders and follow-ups
-    
-    For each task, collect:
-    - Task description
-    - Priority (High, Medium, Low)
-    - Deadline (if applicable)
-    - Dependencies (other tasks that must be completed first)
-    - Estimated time required
-    """,
-    llm="gpt-4o-mini"  # Using the specified model
-)
-
-# Use the task management agent
-task_response = task_agent.start(
-    """
-    I need help organizing these tasks for the week:
-    
-    - Prepare quarterly report (due Friday)
-    - Meet with client about new project (Wednesday, 2 PM)
-    - Review team's project proposals
-    - Finish budget planning for next quarter
-    - Send feedback on marketing materials
-    """
-)
-print(task_response)
-```
-
-## Information Organization Assistant
-
-Let's create an agent that helps organize information:
-
-```python
-info_agent = Agent(
-    name="InformationOrganizer",
-    instructions="""
-    You are an information organization assistant who helps users structure and categorize information.
-    
-    When organizing information:
-    1. Identify the main categories and topics
-    2. Create logical groupings
-    3. Suggest clear labels and headers
-    4. Establish relationships between different pieces of information
-    5. Present information in a structured, easily navigable format
-    """,
-    llm="gpt-4o-mini"  # Using the specified model
-)
-
-# Use the information organization agent
-info_response = info_agent.start(
-    """
-    Help me organize my research notes on climate change:
-    
-    - Rising sea levels affecting coastal cities
-    - Carbon dioxide levels increasing annually
-    - Renewable energy adoption statistics by country
-    - Impact of deforestation on carbon sequestration
-    - Climate policy differences between EU and US
-    - Economic impacts of extreme weather events
-    - Public opinion polls on climate action
-    - Arctic ice sheet measurements over 50 years
-    - Agricultural adaptations to changing climate
-    """
-)
-print(info_response)
-```
-
-## Daily Planning Assistant
-
-Let's create an agent that helps with daily planning:
-
-```python
-planning_agent = Agent(
-    name="DailyPlanner",
-    instructions="""
-    You are a daily planning assistant who helps users organize their day efficiently.
-    
-    When helping with daily planning:
-    1. Prioritize tasks based on importance and deadlines
-    2. Consider time constraints and commitments
-    3. Suggest efficient scheduling
-    4. Allow for breaks and transitions
-    5. Balance work tasks with personal needs
-    
-    Create a structured daily plan with time blocks and priorities.
-    """,
-    llm="gpt-4o-mini"  # Using the specified model
-)
-
-# Use the daily planning agent
-planning_response = planning_agent.start(
-    """
-    Help me plan my day tomorrow. I have:
-    
-    - A team meeting from 9:30-10:30 AM
-    - A report due by end of day
-    - Emails to catch up on (about 1 hour's worth)
-    - Lunch with a colleague at 12:30 PM
-    - A dentist appointment at 3:00 PM (will take about 1 hour including travel)
-    - Need to prepare a presentation for Friday
-    
-    I typically work from 8:30 AM to 5:30 PM and prefer to have short breaks between tasks.
-    """
-)
-print(planning_response)
-```
-
-## Decision Support Assistant
-
-Let's create an agent that helps with decision-making:
-
-```python
-decision_agent = Agent(
-    name="DecisionSupport",
-    instructions="""
-    You are a decision support assistant who helps users make informed choices.
-    
-    When supporting decisions:
-    1. Help clarify the decision to be made
-    2. Identify relevant criteria and factors
-    3. Present pros and cons for each option
-    4. Ask questions to understand preferences and priorities
-    5. Provide a structured analysis without making the final decision
-    
-    Present options with balanced information to help the user make their own informed choice.
-    """,
-    llm="gpt-4o-mini"  # Using the specified model
-)
-
-# Use the decision support agent
-decision_response = decision_agent.start(
-    """
-    I'm trying to decide whether to buy a new laptop now or wait 3 months. 
-    
-    Current situation:
-    - My current laptop is 4 years old and running slow
-    - It still works but has battery issues
-    - I use it daily for work (programming and video calls)
-    - I've heard new models are coming out in 3 months
-    - My budget is around $1,500
-    
-    What should I consider in making this decision?
-    """
-)
-print(decision_response)
-```
-
-## Recommendation Assistant
-
-Let's create an agent that provides personalized recommendations:
-
-```python
-recommendation_agent = Agent(
-    name="RecommendationAssistant",
-    instructions="""
-    You are a recommendation assistant who provides personalized suggestions.
-    
-    When making recommendations:
-    1. Understand the user's preferences and requirements
-    2. Consider constraints (budget, time, location, etc.)
-    3. Provide diverse options that match the criteria
-    4. Explain why each recommendation might be suitable
-    5. Give enough detail for the user to evaluate options
-    
-    For each recommendation, include:
-    - A brief description
-    - Key features or highlights
-    - Why it matches the user's needs
-    """,
-    llm="gpt-4o-mini"  # Using the specified model
-)
-
-# Use the recommendation agent
-recommendation_response = recommendation_agent.start(
-    """
-    Can you recommend some books for me? I enjoy:
-    
-    - Science fiction and fantasy
-    - Books with complex, morally gray characters
-    - Intricate world-building
-    - Books that explore philosophical themes
-    
-    I've recently read and enjoyed:
-    - "The Fifth Season" by N.K. Jemisin
-    - "Dune" by Frank Herbert
-    - "The Lies of Locke Lamora" by Scott Lynch
-    """
-)
-print(recommendation_response)
-```
-
-## Personal Assistant Best Practices
-
-<CardGroup cols={2}>
-  <Card title="Respect Privacy" icon="lock">
-    Be careful with personal information
-  </Card>
-  <Card title="Customize Interactions" icon="sliders">
-    Adapt to the user's preferences
-  </Card>
-  <Card title="Clear Communication" icon="comment">
-    Keep responses direct and easy to understand
-  </Card>
-  <Card title="Contextual Awareness" icon="puzzle-piece">
-    Remember relevant context from previous interactions
-  </Card>
-</CardGroup>
-
-In the next lesson, we'll explore how to deploy and share your AI agents with others.

docs/course/agents/20-deploying-agents.mdx

@@ -1,342 +0,0 @@
----
-title: "Deploying Agents"
-description: "How to deploy and share your AI agents with others"
-icon: "rocket"
----
-
-# Deploying Agents
-
-After creating your AI agents, the next step is deploying them for use by yourself or others. In this final lesson, we'll explore different deployment options and best practices.
-
-## Deployment Options
-
-There are several ways to deploy your AI agents:
-
-### 1. Local Deployment
-
-Running your agents locally is the simplest approach:
-
-```python
-from praisonaiagents import Agent
-
-def run_local_agent():
-    agent = Agent(
-        name="SimpleAgent",
-        instructions="You are a helpful assistant that answers questions concisely.",
-        llm="gpt-4o-mini"  # Using the specified model
-    )
-    
-    while True:
-        user_input = input("Ask a question (type 'exit' to quit): ")
-        if user_input.lower() == 'exit':
-            break
-        
-        response = agent.start(user_input)
-        print("\nAgent response:")
-        print(response)
-        print("\n" + "-"*50 + "\n")
-
-if __name__ == "__main__":
-    run_local_agent()
-```
-
-### 2. Web Application Deployment
-
-You can deploy your agents as part of a web application:
-
-```python
-from flask import Flask, request, jsonify
-from praisonaiagents import Agent
-import os
-
-app = Flask(__name__)
-
-# Create agent once at startup
-support_agent = Agent(
-    name="SupportAgent",
-    instructions="You are a customer support agent that helps users with product questions.",
-    llm="gpt-4o-mini"  # Using the specified model
-)
-
-@app.route('/api/support', methods=['POST'])
-def get_support():
-    data = request.json
-    user_query = data.get('query', '')
-    
-    if not user_query:
-        return jsonify({"error": "No query provided"}), 400
-    
-    try:
-        response = support_agent.start(user_query)
-        return jsonify({"response": response})
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == "__main__":
-    app.run(debug=True, port=5000)
-```
-
-### 3. Serverless Function Deployment
-
-For scalable, event-driven deployments, serverless functions work well:
-
-```python
-# Example AWS Lambda function
-import json
-from praisonaiagents import Agent
-
-# Initialize agent outside the handler for reuse across invocations
-agent = Agent(
-    name="ServerlessAgent",
-    instructions="You provide concise, helpful responses to user questions.",
-    llm="gpt-4o-mini"  # Using the specified model
-)
-
-def lambda_handler(event, context):
-    try:
-        # Get the user query from the event
-        body = json.loads(event.get('body', '{}'))
-        user_query = body.get('query', '')
-        
-        if not user_query:
-            return {
-                'statusCode': 400,
-                'body': json.dumps({'error': 'No query provided'})
-            }
-        
-        # Process with the agent
-        response = agent.start(user_query)
-        
-        return {
-            'statusCode': 200,
-            'body': json.dumps({'response': response})
-        }
-    except Exception as e:
-        return {
-            'statusCode': 500,
-            'body': json.dumps({'error': str(e)})
-        }
-```
-
-## Environment Variables and Security
-
-When deploying agents, it's crucial to handle API keys and secrets securely:
-
-```python
-import os
-from dotenv import load_dotenv
-from praisonaiagents import Agent
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Access API key from environment variable
-api_key = os.getenv("OPENAI_API_KEY")
-
-# Create agent with API key
-agent = Agent(
-    name="SecureAgent",
-    instructions="You are a helpful assistant.",
-    llm="gpt-4o-mini",  # Using the specified model
-    api_key=api_key  # Pass API key securely
-)
-```
-
-## Creating a Simple Chat Interface
-
-Here's a simple command-line chat interface for your agent:
-
-```python
-import os
-from praisonaiagents import Agent
-
-def create_chat_interface():
-    # Initialize agent
-    agent = Agent(
-        name="ChatAgent",
-        instructions="""
-        You are a conversational assistant that maintains context throughout the conversation.
-        Respond in a helpful, concise manner.
-        """,
-        llm="gpt-4o-mini"  # Using the specified model
-    )
-    
-    print("Chat with AI Assistant (type 'exit' to quit)")
-    print("-" * 50)
-    
-    # Start conversation
-    conversation_active = True
-    first_message = True
-    
-    while conversation_active:
-        # Get user input
-        user_message = input("You: ")
-        
-        # Check if user wants to exit
-        if user_message.lower() == 'exit':
-            print("Goodbye!")
-            conversation_active = False
-            continue
-        
-        # Get agent response
-        try:
-            if first_message:
-                response = agent.start(user_message)
-                first_message = False
-            else:
-                response = agent.continue(user_message)
-            
-            print("\nAssistant:", response)
-            print("\n" + "-" * 50)
-        except Exception as e:
-            print(f"Error: {str(e)}")
-    
-if __name__ == "__main__":
-    create_chat_interface()
-```
-
-## Scaling Considerations
-
-As you deploy agents for wider use, consider these scaling factors:
-
-<CardGroup cols={2}>
-  <Card title="Rate Limiting" icon="gauge-high">
-    Implement rate limiting to manage API usage and costs
-  </Card>
-  <Card title="Caching" icon="database">
-    Cache common responses to improve performance and reduce API calls
-  </Card>
-  <Card title="Error Handling" icon="triangle-exclamation">
-    Implement robust error handling for API failures
-  </Card>
-  <Card title="Monitoring" icon="chart-line">
-    Set up monitoring for usage patterns and performance issues
-  </Card>
-</CardGroup>
-
-## Best Practices for Deployment
-
-### 1. Testing Before Deployment
-
-Always test your agents thoroughly before deployment:
-
-```python
-def test_agent_functionality():
-    """Test basic agent functionality with various inputs"""
-    agent = Agent(
-        name="TestAgent",
-        instructions="You are a helpful assistant for testing.",
-        llm="gpt-4o-mini"  # Using the specified model
-    )
-    
-    test_cases = [
-        "What is artificial intelligence?",
-        "How do I reset my password?",
-        "Tell me about machine learning"
-    ]
-    
-    for test_case in test_cases:
-        print(f"\nTesting: {test_case}")
-        response = agent.start(test_case)
-        print(f"Response: {response[:100]}...")  # Print first 100 chars
-        
-        # Add assertions or validation logic here
-        assert len(response) > 0, "Response should not be empty"
-```
-
-### 2. Documentation
-
-Create clear documentation for users of your agent:
-
-```python
-"""
-# Customer Support Agent API
-
-This API provides access to an AI customer support agent
-that can answer questions about our products.
-
-## Authentication
-
-Include your API key in the request header:
-```
-Authorization: Bearer YOUR_API_KEY
-```
-
-## Endpoints
-
-POST /api/support
-- Request body: {"query": "Your question here"}
-- Response: {"response": "Agent's answer"}
-
-## Example
-
-```python
-import requests
-
-response = requests.post(
-    "https://api.example.com/api/support",
-    headers={"Authorization": "Bearer YOUR_API_KEY"},
-    json={"query": "How do I reset my password?"}
-)
-
-print(response.json())
-```
-"""
-```
-
-### 3. Version Control
-
-Implement version control for your agents to track changes:
-
-```python
-class VersionedAgent:
-    def __init__(self, name, version, instructions):
-        self.name = name
-        self.version = version
-        self.agent = Agent(
-            name=f"{name}_v{version}",
-            instructions=instructions,
-            llm="gpt-4o-mini"  # Using the specified model
-        )
-    
-    def get_response(self, query):
-        response = self.agent.start(query)
-        return {
-            "agent_name": self.name,
-            "version": self.version,
-            "response": response,
-            "timestamp": datetime.now().isoformat()
-        }
-```
-
-## Conclusion
-
-Congratulations on completing the AI Agents Course! You've learned how to:
-
-1. Understand different types of AI agents and their architectures
-2. Create effective agent instructions and tools
-3. Implement memory and context for your agents
-4. Build specialized agents for various tasks
-5. Create multi-agent systems
-6. Deploy your agents for real-world use
-
-As AI agent technology continues to evolve, keep experimenting with new capabilities and use cases. Remember that the best agents are those that effectively solve real problems for users while being trustworthy, reliable, and helpful.
-
-We hope this course has provided you with the knowledge and skills to build powerful AI agents that enhance productivity and creativity!
-
-## Next Steps
-
-<CardGroup cols={2}>
-  <Card title="Join the Community" icon="users">
-    Participate in discussions and share your agent projects
-  </Card>
-  <Card title="Stay Updated" icon="newspaper">
-    Follow updates in AI agent technology and new features
-  </Card>
-  <Card title="Share Your Feedback" icon="comment">
-    Let us know how you're using agents and what you'd like to learn next
-  </Card>
-  <Card title="Build Real Solutions" icon="lightbulb">
-    Apply your knowledge to build solutions for real-world problems
-  </Card>
-</CardGroup>

docs/deploy.mdx

@@ -1,35 +0,0 @@
----
-title: "Deploy"
-description: "Guide for deploying PraisonAI to cloud platforms, with detailed instructions for Google Cloud deployment"
-icon: "cloud"
----
-
-## Google Cloud
-
-```bash
-gcloud init
-gcloud services enable run.googleapis.com
-gcloud services enable containerregistry.googleapis.com
-gcloud services enable cloudbuild.googleapis.com
-
-export OPENAI_MODEL_NAME="gpt-4o"
-export OPENAI_API_KEY="Enter your API key"
-export OPENAI_API_BASE="https://api.openai.com/v1"
-
-yes | gcloud auth configure-docker us-central1-docker.pkg.dev 
-gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1
-
-PROJECT_ID=$(gcloud config get-value project)
-TAG="latest"
-docker build --platform linux/amd64 -t gcr.io/${PROJECT_ID}/praisonai-app:${TAG} .
-docker tag gcr.io/${PROJECT_ID}/praisonai-app:${TAG} us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
-docker push us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
-
-gcloud run deploy praisonai-service \
-    --image us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG} \
-    --platform managed \
-    --region us-central1 \
-    --allow-unauthenticated \
-    --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}
-```
-

docs/deploy/aws.mdx

@@ -1,416 +0,0 @@
----
-title: "Deploying to AWS"
-sidebarTitle: "AWS"
-description: "Deploy PraisonAI agents to Amazon Web Services for scalable production environments"
-icon: "aws"
----
-
-# Deploying PraisonAI Agents to AWS
-
-This guide provides step-by-step instructions for deploying PraisonAI agents to Amazon Web Services (AWS), offering multiple deployment options to suit different requirements.
-
-## Prerequisites
-
-- AWS account with appropriate permissions
-- AWS CLI installed and configured
-- Docker installed on your local machine (for container-based deployments)
-- Basic knowledge of AWS services and cloud deployment
-
-## Deployment Options
-
-There are several ways to deploy PraisonAI agents to AWS:
-
-1. **AWS Lambda with API Gateway** (for serverless deployments)
-2. **Amazon ECS/Fargate** (for containerized deployments)
-3. **Amazon EC2** (for traditional VM-based deployments)
-4. **AWS App Runner** (for simplified container deployments)
-
-## Option 1: Deploying to EC2 (Traditional VM)
-
-<Steps>
-    <Step title="Launch an EC2 Instance">
-        1. Go to the AWS Management Console
-        2. Navigate to EC2 and click "Launch Instance"
-        3. Choose Amazon Linux 2 or Ubuntu Server
-        4. Select an instance type (t2.micro for testing, t2.medium or larger for production)
-        5. Configure security groups to allow HTTP/HTTPS traffic
-        6. Launch the instance and connect to it via SSH
-    </Step>
-
-    <Step title="Set Up the Environment">
-        ```bash
-        # Update system packages
-        sudo apt update && sudo apt upgrade -y
-        
-        # Install Python and pip
-        sudo apt install -y python3 python3-pip
-        
-        # Install required packages
-        pip3 install "praisonaiagents[api]>=0.0.79"
-        ```
-    </Step>
-
-    <Step title="Create the API Application">
-        Create a file named `api.py`:
-        
-        ```python
-        from praisonaiagents import Agent
-
-        agent = Agent(instructions="""You are a helpful assistant.""", llm="gpt-4o-mini")
-        agent.launch(path="/ask", port=8080, host="0.0.0.0")
-        ```
-    </Step>
-
-    <Step title="Set Up a Systemd Service">
-        Create a systemd service file to run your application as a background service:
-        
-        ```bash
-        sudo nano /etc/systemd/system/praisonai-agent.service
-        ```
-        
-        Add the following content:
-        
-        ```ini
-        [Unit]
-        Description=PraisonAI Agent API Service
-        After=network.target
-
-        [Service]
-        User=ubuntu
-        WorkingDirectory=/home/ubuntu
-        Environment="OPENAI_API_KEY=your_api_key"
-        ExecStart=/usr/bin/python3 /home/ubuntu/api.py
-        Restart=always
-
-        [Install]
-        WantedBy=multi-user.target
-        ```
-        
-        Enable and start the service:
-        
-        ```bash
-        sudo systemctl daemon-reload
-        sudo systemctl enable praisonai-agent
-        sudo systemctl start praisonai-agent
-        ```
-    </Step>
-
-    <Step title="Set Up Nginx as a Reverse Proxy">
-        ```bash
-        # Install Nginx
-        sudo apt install -y nginx
-        
-        # Create a configuration file
-        sudo nano /etc/nginx/sites-available/praisonai-agent
-        ```
-        
-        Add the following configuration:
-        
-        ```nginx
-        server {
-            listen 80;
-            server_name your_domain.com;
-
-            location / {
-                proxy_pass http://localhost:8080;
-                proxy_set_header Host $host;
-                proxy_set_header X-Real-IP $remote_addr;
-                proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-                proxy_set_header X-Forwarded-Proto $scheme;
-            }
-        }
-        ```
-        
-        Enable the site and restart Nginx:
-        
-        ```bash
-        sudo ln -s /etc/nginx/sites-available/praisonai-agent /etc/nginx/sites-enabled/
-        sudo nginx -t
-        sudo systemctl restart nginx
-        ```
-    </Step>
-</Steps>
-
-## Option 2: Deploying with Docker and ECS/Fargate
-
-<Steps>
-    <Step title="Create a Dockerfile">
-        ```dockerfile
-        FROM python:3.11-slim
-
-        WORKDIR /app
-
-        # Install dependencies
-        COPY requirements.txt .
-        RUN pip install --no-cache-dir -r requirements.txt
-
-        # Copy application code
-        COPY . .
-
-        # Run the application
-        CMD ["python", "api.py"]
-        ```
-        
-        Create a `requirements.txt` file:
-        ```
-        praisonaiagents[api]>=0.0.79
-        ```
-        
-        Create your `api.py` file:
-        ```python
-        from praisonaiagents import Agent
-
-        agent = Agent(instructions="""You are a helpful assistant.""", llm="gpt-4o-mini")
-        agent.launch(path="/ask", port=8080, host="0.0.0.0")
-        ```
-    </Step>
-
-    <Step title="Build and Push the Docker Image to ECR">
-        ```bash
-        # Create an ECR repository
-        aws ecr create-repository --repository-name praisonai-agent
-        
-        # Get the login command
-        aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin YOUR_AWS_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com
-        
-        # Build the Docker image
-        docker build -t praisonai-agent .
-        
-        # Tag the image
-        docker tag praisonai-agent:latest YOUR_AWS_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/praisonai-agent:latest
-        
-        # Push the image to ECR
-        docker push YOUR_AWS_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/praisonai-agent:latest
-        ```
-    </Step>
-
-    <Step title="Create an ECS Cluster and Task Definition">
-        ```bash
-        # Create an ECS cluster
-        aws ecs create-cluster --cluster-name praisonai-cluster
-        
-        # Create a task definition (JSON file)
-        cat > task-definition.json << EOF
-        {
-          "family": "praisonai-agent",
-          "networkMode": "awsvpc",
-          "executionRoleArn": "arn:aws:iam::YOUR_AWS_ACCOUNT_ID:role/ecsTaskExecutionRole",
-          "containerDefinitions": [
-            {
-              "name": "praisonai-agent",
-              "image": "YOUR_AWS_ACCOUNT_ID.dkr.ecr.us-east-1.amazonaws.com/praisonai-agent:latest",
-              "essential": true,
-              "portMappings": [
-                {
-                  "containerPort": 8080,
-                  "hostPort": 8080,
-                  "protocol": "tcp"
-                }
-              ],
-              "environment": [
-                {
-                  "name": "OPENAI_API_KEY",
-                  "value": "your_api_key"
-                }
-              ],
-              "logConfiguration": {
-                "logDriver": "awslogs",
-                "options": {
-                  "awslogs-group": "/ecs/praisonai-agent",
-                  "awslogs-region": "us-east-1",
-                  "awslogs-stream-prefix": "ecs"
-                }
-              }
-            }
-          ],
-          "requiresCompatibilities": [
-            "FARGATE"
-          ],
-          "cpu": "256",
-          "memory": "512"
-        }
-        EOF
-        
-        # Register the task definition
-        aws ecs register-task-definition --cli-input-json file://task-definition.json
-        ```
-    </Step>
-
-    <Step title="Create a Fargate Service">
-        ```bash
-        # Create a service
-        aws ecs create-service \
-          --cluster praisonai-cluster \
-          --service-name praisonai-service \
-          --task-definition praisonai-agent:1 \
-          --desired-count 1 \
-          --launch-type FARGATE \
-          --network-configuration "awsvpcConfiguration={subnets=[subnet-12345678],securityGroups=[sg-12345678],assignPublicIp=ENABLED}" \
-          --load-balancers "targetGroupArn=arn:aws:elasticloadbalancing:us-east-1:YOUR_AWS_ACCOUNT_ID:targetgroup/praisonai-tg/1234567890abcdef,containerName=praisonai-agent,containerPort=8080"
-        ```
-        
-        Note: You'll need to create an Application Load Balancer and target group before running this command.
-    </Step>
-</Steps>
-
-## Multi-Agent Deployment
-
-For deploying multiple agents, you can use a single service with different endpoints:
-
-```python
-# multi-agent-api.py
-from praisonaiagents import Agent
-
-weather_agent = Agent(
-    instructions="""You are a weather agent that can provide weather information for a given city.""",
-    llm="gpt-4o-mini"
-)
-
-stock_agent = Agent(
-    instructions="""You are a stock market agent that can provide information about stock prices and market trends.""",
-    llm="gpt-4o-mini"
-)
-
-travel_agent = Agent(
-    instructions="""You are a travel agent that can provide recommendations for destinations, hotels, and activities.""",
-    llm="gpt-4o-mini"
-)
-
-weather_agent.launch(path="/weather", port=8080, host="0.0.0.0")
-stock_agent.launch(path="/stock", port=8080, host="0.0.0.0")
-travel_agent.launch(path="/travel", port=8080, host="0.0.0.0")
-```
-
-## Option 3: Serverless Deployment with AWS Lambda
-
-For lightweight agents that don't require long-running processes, you can use AWS Lambda with API Gateway:
-
-```python
-# lambda_function.py
-from praisonaiagents import Agent
-import json
-
-# Initialize the agent outside the handler for better cold start performance
-agent = Agent(instructions="""You are a helpful assistant.""", llm="gpt-4o-mini")
-
-def lambda_handler(event, context):
-    try:
-        # Extract the message from the event
-        body = json.loads(event.get('body', '{}'))
-        message = body.get('message', '')
-        
-        # Process the message with the agent
-        response = agent.process(message)
-        
-        # Return the response
-        return {
-            'statusCode': 200,
-            'headers': {
-                'Content-Type': 'application/json',
-                'Access-Control-Allow-Origin': '*'
-            },
-            'body': json.dumps({
-                'response': response
-            })
-        }
-    except Exception as e:
-        return {
-            'statusCode': 500,
-            'headers': {
-                'Content-Type': 'application/json',
-                'Access-Control-Allow-Origin': '*'
-            },
-            'body': json.dumps({
-                'error': str(e)
-            })
-        }
-```
-
-## Scaling and Performance
-
-### Auto Scaling
-
-For EC2 deployments, set up an Auto Scaling group:
-
-```bash
-# Create a launch template
-aws ec2 create-launch-template \
-  --launch-template-name praisonai-template \
-  --version-description "Initial version" \
-  --launch-template-data '{"ImageId":"ami-12345678","InstanceType":"t2.medium","UserData":"#!/bin/bash\ncd /home/ubuntu\npython3 api.py &"}'  
-
-# Create an Auto Scaling group
-aws autoscaling create-auto-scaling-group \
-  --auto-scaling-group-name praisonai-asg \
-  --launch-template "LaunchTemplateName=praisonai-template,Version=1" \
-  --min-size 1 \
-  --max-size 5 \
-  --desired-capacity 2 \
-  --vpc-zone-identifier "subnet-12345678,subnet-87654321" \
-  --target-group-arns "arn:aws:elasticloadbalancing:us-east-1:YOUR_AWS_ACCOUNT_ID:targetgroup/praisonai-tg/1234567890abcdef"
-```
-
-### Load Balancing
-
-Set up an Application Load Balancer to distribute traffic:
-
-```bash
-# Create a load balancer
-aws elbv2 create-load-balancer \
-  --name praisonai-alb \
-  --subnets subnet-12345678 subnet-87654321 \
-  --security-groups sg-12345678
-
-# Create a target group
-aws elbv2 create-target-group \
-  --name praisonai-tg \
-  --protocol HTTP \
-  --port 8080 \
-  --vpc-id vpc-12345678 \
-  --target-type instance
-
-# Create a listener
-aws elbv2 create-listener \
-  --load-balancer-arn arn:aws:elasticloadbalancing:us-east-1:YOUR_AWS_ACCOUNT_ID:loadbalancer/app/praisonai-alb/1234567890abcdef \
-  --protocol HTTP \
-  --port 80 \
-  --default-actions Type=forward,TargetGroupArn=arn:aws:elasticloadbalancing:us-east-1:YOUR_AWS_ACCOUNT_ID:targetgroup/praisonai-tg/1234567890abcdef
-```
-
-## Security Best Practices
-
-1. **Secret Management**: Use AWS Secrets Manager for API keys
-   ```bash
-   # Store a secret
-   aws secretsmanager create-secret \
-     --name OPENAI_API_KEY \
-     --secret-string "your_api_key"
-   ```
-
-2. **IAM Roles**: Use the principle of least privilege
-3. **VPC**: Deploy in a private subnet with controlled access
-4. **WAF**: Set up AWS WAF to protect against common web exploits
-5. **API Gateway**: Implement request validation and throttling
-
-## Monitoring and Logging
-
-1. **CloudWatch**: Set up dashboards and alarms
-   ```bash
-   # Create a CloudWatch alarm for high CPU utilization
-   aws cloudwatch put-metric-alarm \
-     --alarm-name praisonai-high-cpu \
-     --alarm-description "Alarm when CPU exceeds 80%" \
-     --metric-name CPUUtilization \
-     --namespace AWS/EC2 \
-     --statistic Average \
-     --period 300 \
-     --threshold 80 \
-     --comparison-operator GreaterThanThreshold \
-     --dimensions Name=AutoScalingGroupName,Value=praisonai-asg \
-     --evaluation-periods 2 \
-     --alarm-actions arn:aws:sns:us-east-1:YOUR_AWS_ACCOUNT_ID:praisonai-alerts
-   ```
-
-2. **X-Ray**: Enable tracing for detailed request analysis
-3. **CloudTrail**: Monitor API calls for security analysis
-4. **Log Insights**: Query and analyze logs with CloudWatch Log Insights

docs/deploy/deploy.mdx

@@ -1,223 +0,0 @@
----
-title: "Deploying PraisonAI Agents as APIs"
-sidebarTitle: "API Deployment"
-description: "Learn how to deploy PraisonAI agents as RESTful APIs for production environments"
-icon: "server"
----
-
-PraisonAI agents can be easily deployed as RESTful APIs, allowing you to integrate them into various applications and services. This guide covers how to deploy both single and multiple agents as APIs.
-
-## Quick Start
-
-<Steps>
-    <Step title="Install Dependencies">
-        Make sure you have the required packages installed:
-        ```bash
-        pip install "praisonaiagents[api]"
-        ```
-    </Step>
-    <Step title="Set API Key">
-        ```bash
-        export OPENAI_API_KEY="your_api_key"
-        ```
-    </Step>
-    <Step title="Deploy a Simple Agent API">
-        Create a file named `simple-api.py` with the following code:
-        ```python
-        from praisonaiagents import Agent
-
-        agent = Agent(instructions="""You are a helpful assistant.""", llm="gpt-4o-mini")
-        agent.launch(path="/ask", port=3030)
-        ```
-    </Step>
-    <Step title="Run the API Server">
-        ```bash
-        python simple-api.py
-        ```
-        
-        Your API will be available at `http://localhost:3030/ask`
-    </Step>
-</Steps>
-
-## Making API Requests
-
-Once your agent is deployed, you can make POST requests to interact with it:
-
-```bash
-curl -X POST http://localhost:3030/ask \
-  -H "Content-Type: application/json" \
-  -d '{"message": "What is artificial intelligence?"}'
-```
-
-The response will be in JSON format:
-
-```json
-{
-  "response": "Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It encompasses various technologies including machine learning, natural language processing, computer vision, and robotics. AI systems can perform tasks that typically require human intelligence such as understanding natural language, recognizing patterns, solving problems, and making decisions."
-}
-```
-
-## Multi-Agent API Deployment
-
-You can deploy multiple agents on the same server, each with its own endpoint:
-
-```python
-from praisonaiagents import Agent
-
-weather_agent = Agent(
-    instructions="""You are a weather agent that can provide weather information for a given city.""",
-    llm="gpt-4o-mini"
-)
-
-stock_agent = Agent(
-    instructions="""You are a stock market agent that can provide information about stock prices and market trends.""",
-    llm="gpt-4o-mini"
-)
-
-travel_agent = Agent(
-    instructions="""You are a travel agent that can provide recommendations for destinations, hotels, and activities.""",
-    llm="gpt-4o-mini"
-)
-
-weather_agent.launch(path="/weather", port=3030)
-stock_agent.launch(path="/stock", port=3030)
-travel_agent.launch(path="/travel", port=3030) 
-```
-
-With this setup, you can access:
-- Weather agent at `http://localhost:3030/weather`
-- Stock agent at `http://localhost:3030/stock`
-- Travel agent at `http://localhost:3030/travel`
-
-## Production Deployment Options
-
-For production environments, consider the following deployment options:
-
-### Docker Deployment
-
-<Steps>
-    <Step title="Create a Dockerfile">
-        ```dockerfile
-        FROM python:3.11-slim
-
-        WORKDIR /app
-
-        COPY requirements.txt .
-        RUN pip install --no-cache-dir -r requirements.txt
-
-        COPY . .
-
-        EXPOSE 3030
-
-        CMD ["python", "api.py"]
-        ```
-    </Step>
-    <Step title="Create requirements.txt">
-        ```
-        praisonaiagents[api]>=0.0.79
-        ```
-    </Step>
-    <Step title="Build and Run Docker Container">
-        ```bash
-        docker build -t praisonai-api .
-        docker run -p 3030:3030 -e OPENAI_API_KEY=your_api_key praisonai-api
-        ```
-    </Step>
-</Steps>
-
-### Cloud Deployment
-
-#### Deploying to AWS
-
-<Steps>
-    <Step title="Create an EC2 Instance">
-        Launch an EC2 instance with Ubuntu or Amazon Linux.
-    </Step>
-    <Step title="Install Dependencies">
-        ```bash
-        sudo apt update
-        sudo apt install -y python3-pip
-        pip install "praisonaiagents[api]"
-        ```
-    </Step>
-    <Step title="Configure Security Group">
-        Make sure to open port 3030 in your security group settings.
-    </Step>
-    <Step title="Run with Systemd">
-        Create a systemd service file for automatic startup and management.
-    </Step>
-</Steps>
-
-#### Deploying to Google Cloud Run
-
-<Steps>
-    <Step title="Build Docker Image">
-        ```bash
-        docker build -t gcr.io/your-project/praisonai-api .
-        ```
-    </Step>
-    <Step title="Push to Container Registry">
-        ```bash
-        docker push gcr.io/your-project/praisonai-api
-        ```
-    </Step>
-    <Step title="Deploy to Cloud Run">
-        ```bash
-        gcloud run deploy praisonai-api \
-          --image gcr.io/your-project/praisonai-api \
-          --platform managed \
-          --allow-unauthenticated \
-          --set-env-vars="OPENAI_API_KEY=your_api_key"
-        ```
-    </Step>
-</Steps>
-
-## API Configuration Options
-
-When launching your agent as an API, you can customize various parameters:
-
-```python
-agent.launch(
-    path="/custom-endpoint",  # API endpoint path
-    port=8080,                # Port number
-    host="0.0.0.0",           # Host address (0.0.0.0 for external access)
-    debug=True,               # Enable debug mode
-    cors_origins=["*"],       # CORS configuration
-    api_key="your-api-key"    # Optional API key for authentication
-)
-```
-
-## Securing Your API
-
-For production deployments, consider implementing:
-
-1. **API Key Authentication**: Require API keys for all requests
-2. **Rate Limiting**: Limit the number of requests per client
-3. **HTTPS**: Use SSL/TLS certificates for encrypted communication
-4. **Input Validation**: Validate all input data before processing
-
-## Monitoring and Scaling
-
-For production environments, consider:
-
-1. **Load Balancing**: Distribute traffic across multiple instances
-2. **Auto-Scaling**: Automatically adjust resources based on demand
-3. **Logging**: Implement comprehensive logging for debugging
-4. **Monitoring**: Set up alerts for errors and performance issues
-
-## Features
-
-<CardGroup cols={2}>
-  <Card title="RESTful API" icon="server">
-    Deploy your agents as standard RESTful APIs for easy integration.
-  </Card>
-  <Card title="Multi-Agent Support" icon="users">
-    Deploy multiple agents with different endpoints on the same server.
-  </Card>
-  <Card title="Customizable" icon="gear">
-    Configure ports, paths, CORS settings, and more.
-  </Card>
-  <Card title="Production-Ready" icon="rocket">
-    Easily deploy to Docker, AWS, Google Cloud, or other cloud platforms.
-  </Card>
-</CardGroup>

docs/deploy/googlecloud.mdx

@@ -1,248 +0,0 @@
----
-title: "Deploying to Google Cloud"
-sidebarTitle: "Google Cloud"
-description: "Deploy PraisonAI agents to Google Cloud Platform for scalable production environments"
-icon: "google"
----
-
-# Deploying PraisonAI Agents to Google Cloud
-
-This guide provides step-by-step instructions for deploying PraisonAI agents to Google Cloud Platform using Cloud Run, which offers a serverless environment for containerized applications.
-
-## Prerequisites
-
-- Google Cloud account with billing enabled
-- Google Cloud SDK installed and configured
-- Docker installed on your local machine
-- Basic knowledge of containerization and cloud deployment
-
-## Deployment Options
-
-There are several ways to deploy PraisonAI agents to Google Cloud:
-
-1. **Google Cloud Run** (recommended for most use cases)
-2. **Google Compute Engine** (for custom VM requirements)
-3. **Google Kubernetes Engine** (for complex, scalable deployments)
-
-This guide focuses on Cloud Run as it's the simplest and most cost-effective option for most deployments.
-
-## Deploying to Google Cloud Run
-
-<Steps>
-    <Step title="Prepare Your Application">
-        Create a simple API file named `api.py`:
-
-        ```python
-        from praisonaiagents import Agent
-
-        agent = Agent(instructions="""You are a helpful assistant.""", llm="gpt-4o-mini")
-        agent.launch(path="/ask", port=8080, host="0.0.0.0")
-        ```
-
-        Note: Cloud Run expects your application to listen on the port defined by the `PORT` environment variable, which defaults to 8080.
-    </Step>
-
-    <Step title="Create a Dockerfile">
-        ```dockerfile
-        FROM python:3.11-slim
-
-        WORKDIR /app
-
-        # Install dependencies
-        COPY requirements.txt .
-        RUN pip install --no-cache-dir -r requirements.txt
-
-        # Copy application code
-        COPY . .
-
-        # Set environment variables
-        ENV PORT=8080
-
-        # Run the application
-        CMD exec python api.py
-        ```
-
-        Create a `requirements.txt` file:
-        ```
-        praisonaiagents[api]>=0.0.79
-        ```
-    </Step>
-
-    <Step title="Build and Push the Docker Image">
-        ```bash
-        # Build the Docker image
-        docker build -t gcr.io/YOUR_PROJECT_ID/praisonai-agent .
-
-        # Configure Docker to use Google Cloud credentials
-        gcloud auth configure-docker
-
-        # Push the image to Google Container Registry
-        docker push gcr.io/YOUR_PROJECT_ID/praisonai-agent
-        ```
-    </Step>
-
-    <Step title="Deploy to Cloud Run">
-        ```bash
-        gcloud run deploy praisonai-agent \
-          --image gcr.io/YOUR_PROJECT_ID/praisonai-agent \
-          --platform managed \
-          --region us-central1 \
-          --allow-unauthenticated \
-          --set-env-vars="OPENAI_API_KEY=your_api_key"
-        ```
-
-        Note: For production deployments, it's recommended to use Secret Manager for API keys instead of environment variables.
-    </Step>
-</Steps>
-
-## Multi-Agent Deployment
-
-For deploying multiple agents, you can use a single Cloud Run service with different endpoints:
-
-```python
-# multi-agent-api.py
-from praisonaiagents import Agent
-
-weather_agent = Agent(
-    instructions="""You are a weather agent that can provide weather information for a given city.""",
-    llm="gpt-4o-mini"
-)
-
-stock_agent = Agent(
-    instructions="""You are a stock market agent that can provide information about stock prices and market trends.""",
-    llm="gpt-4o-mini"
-)
-
-travel_agent = Agent(
-    instructions="""You are a travel agent that can provide recommendations for destinations, hotels, and activities.""",
-    llm="gpt-4o-mini"
-)
-
-weather_agent.launch(path="/weather", port=8080, host="0.0.0.0")
-stock_agent.launch(path="/stock", port=8080, host="0.0.0.0")
-travel_agent.launch(path="/travel", port=8080, host="0.0.0.0")
-```
-
-## Scaling and Performance
-
-Google Cloud Run automatically scales based on traffic, from zero to many instances. You can configure:
-
-- **Minimum instances**: Keep a certain number of instances warm to avoid cold starts
-- **Maximum instances**: Limit the number of instances to control costs
-- **Memory allocation**: Allocate more memory for complex agents
-- **CPU allocation**: Allocate dedicated CPUs for compute-intensive workloads
-
-```bash
-gcloud run services update praisonai-agent \
-  --min-instances=1 \
-  --max-instances=10 \
-  --memory=2Gi \
-  --cpu=1
-```
-
-## Continuous Deployment
-
-Set up continuous deployment with Cloud Build:
-
-```yaml
-# cloudbuild.yaml
-steps:
-  # Build the container image
-  - name: 'gcr.io/cloud-builders/docker'
-    args: ['build', '-t', 'gcr.io/$PROJECT_ID/praisonai-agent', '.']
-  # Push the container image to Container Registry
-  - name: 'gcr.io/cloud-builders/docker'
-    args: ['push', 'gcr.io/$PROJECT_ID/praisonai-agent']
-  # Deploy container image to Cloud Run
-  - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
-    entrypoint: gcloud
-    args:
-    - 'run'
-    - 'deploy'
-    - 'praisonai-agent'
-    - '--image'
-    - 'gcr.io/$PROJECT_ID/praisonai-agent'
-    - '--region'
-    - 'us-central1'
-    - '--platform'
-    - 'managed'
-    - '--allow-unauthenticated'
-substitutions:
-  _REGION: us-central1
-images:
-  - 'gcr.io/$PROJECT_ID/praisonai-agent'
-```
-
-## Monitoring and Logging
-
-Google Cloud provides comprehensive monitoring and logging capabilities:
-
-- **Cloud Monitoring**: Set up dashboards and alerts for your deployed agents
-- **Cloud Logging**: View and analyze logs from your applications
-- **Error Reporting**: Automatically detect and group errors
-
-Access logs through the Google Cloud Console or using the gcloud command:
-
-```bash
-gcloud logging read "resource.type=cloud_run_revision AND resource.labels.service_name=praisonai-agent" --limit=10
-```
-
-## Cost Optimization
-
-Cloud Run charges based on the resources your service uses only when it's processing requests. To optimize costs:
-
-1. Set appropriate memory and CPU allocations
-2. Configure minimum instances based on traffic patterns
-3. Use regional deployments closest to your users
-4. Implement caching for frequent requests
-
-## Security Best Practices
-
-1. **Secret Management**: Store API keys and credentials in Secret Manager
-   ```bash
-   # Create a secret
-   echo -n "your_api_key" | gcloud secrets create openai-api-key --data-file=-
-   
-   # Grant access to the Cloud Run service
-   gcloud secrets add-iam-policy-binding openai-api-key \
-     --member=serviceAccount:YOUR_SERVICE_ACCOUNT \
-     --role=roles/secretmanager.secretAccessor
-   
-   # Reference in deployment
-   gcloud run deploy praisonai-agent \
-     --image gcr.io/YOUR_PROJECT_ID/praisonai-agent \
-     --set-secrets=OPENAI_API_KEY=openai-api-key:latest
-   ```
-
-2. **Service Identity**: Use dedicated service accounts with minimal permissions
-3. **Network Security**: Configure VPC Service Controls for sensitive deployments
-4. **API Authentication**: Implement authentication for your API endpoints
-
-## All in One 
-
-```bash
-gcloud init
-gcloud services enable run.googleapis.com
-gcloud services enable containerregistry.googleapis.com
-gcloud services enable cloudbuild.googleapis.com
-
-export OPENAI_MODEL_NAME="gpt-4o"
-export OPENAI_API_KEY="Enter your API key"
-export OPENAI_API_BASE="https://api.openai.com/v1"
-
-yes | gcloud auth configure-docker us-central1-docker.pkg.dev 
-gcloud artifacts repositories create praisonai-repository --repository-format=docker --location=us-central1
-
-PROJECT_ID=$(gcloud config get-value project)
-TAG="latest"
-docker build --platform linux/amd64 -t gcr.io/${PROJECT_ID}/praisonai-app:${TAG} .
-docker tag gcr.io/${PROJECT_ID}/praisonai-app:${TAG} us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
-docker push us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG}
-
-gcloud run deploy praisonai-service \
-    --image us-central1-docker.pkg.dev/${PROJECT_ID}/praisonai-repository/praisonai-app:${TAG} \
-    --platform managed \
-    --region us-central1 \
-    --allow-unauthenticated \
-    --set-env-vars OPENAI_MODEL_NAME=${OPENAI_MODEL_NAME},OPENAI_API_KEY=${OPENAI_API_KEY},OPENAI_API_BASE=${OPENAI_API_BASE}
-```