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Author SHA1 Message Date
Mervin Praison a8b1948fc0 feat(ag2): add AG2 framework backend integration (#1143)
feat(ag2): add AG2 framework backend integration
2026-03-26 17:48:37 +00:00
Faridun Mirzoev 56f815c1b1 fix(ag2): address Qodo review issues - YAML config resolution, OPENAI_API_BASE, patch create=True
- Add _resolve() helper to read LLM config from YAML top-level, per-role,
  config_list, and env vars in priority order (fixes Bedrock YAML config ignored)
- Include OPENAI_API_BASE env var in base_url resolution chain
- Add create=True to all patch("autogen.*") calls so tests pass without ag2 installed
2026-03-26 09:48:14 -07:00
Faridun Mirzoev 6b30977d9e feat(ag2): add AG2 framework backend integration
Integrates AG2 (community fork of AutoGen, PyPI package: ag2) as a new
framework option alongside praisonai, crewai, and autogen.

Changes:
- pyproject.toml: add [ag2] optional dependency extra (ag2>=0.11.0)
- agents_generator.py: AG2 detection via importlib.metadata + _run_ag2()
  with LLMConfig dict pattern, GroupChat orchestration, Bedrock support,
  ChatResult.summary extraction, and TERMINATE cleanup
- auto.py: AG2_AVAILABLE flag + validation in AutoGenerator.__init__
- .env.example: add AG2 and AWS Bedrock environment variable templates
- examples/ag2/: basic, multi-agent, and Bedrock YAML examples
- tests: 16 unit tests + 9 mock integration tests (25/25 passing)

Detection uses importlib.metadata.distribution('ag2') + LLMConfig check.
All changes are purely additive — existing code paths unaffected.

E2E tested against OpenAI gpt-4o-mini: single-agent and multi-agent
GroupChat flows both produce correct output.
2026-03-24 16:47:46 -04:00
3381 changed files with 37139 additions and 779797 deletions

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---
name: Bug report
about: Create a report to help us improve
title: '[BUG] '
labels: bug
assignees: ''
---
## Environment
- Provider (select one):
- [ ] Anthropic
- [ ] OpenAI
- [ ] Google Vertex AI
- [ ] AWS Bedrock
- [ ] Other: <!-- specify -->
- PraisonAI version: <!-- if known -->
- Operating System: <!-- e.g. macOS 14.3, Windows 11, Ubuntu 22.04 -->
## Full Code
<!-- Full code that reproduces the bug -->
## Steps to Reproduce
1. <!-- First step -->
2. <!-- Second step -->
3. <!-- And so on... -->
## Expected Behavior
<!-- What you expected to happen -->
## Actual Behavior
<!-- What actually happened -->
## Additional Context
<!-- Add any other context about the problem here, such as screenshots, logs, etc. -->
@@ -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"]
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@@ -1,256 +1,20 @@
name: "Claude Code Action"
description: "Run Claude Code in GitHub Actions workflows"
description: "Custom Claude Code runner for PraisonAI"
inputs:
anthropic_api_key:
required: true
description: "Anthropic API key"
required: false
github_token:
description: "GitHub token for Claude to operate with"
required: false
default: ${{ github.token }}
trigger_phrase:
description: "The trigger phrase to look for in comments, issue/PR bodies, and issue titles"
required: false
default: "@claude"
assignee_trigger:
description: "The assignee username that triggers the action (e.g. @claude). Only used for issue assignment"
required: false
max_turns:
description: "Maximum number of conversation turns Claude can take"
required: false
timeout_minutes:
description: "Timeout in minutes for execution"
required: false
default: "30"
model:
description: "Model to use (provider-specific format required for Bedrock/Vertex)"
required: false
use_bedrock:
description: "Use Amazon Bedrock with OIDC authentication instead of direct Anthropic API"
required: false
default: "false"
use_vertex:
description: "Use Google Vertex AI with OIDC authentication instead of direct Anthropic API"
required: false
default: "false"
allowed_tools:
description: "Additional tools for Claude to use (the base GitHub tools will always be included)"
required: false
default: ""
disallowed_tools:
description: "Tools that Claude should never use"
required: false
default: ""
custom_instructions:
description: "Additional custom instructions to include in the prompt for Claude"
required: false
default: ""
mcp_config:
description: "Additional MCP configuration (JSON string) that merges with the built-in GitHub MCP servers"
required: false
default: ""
claude_env:
description: "Custom environment variables to pass to Claude Code execution (YAML format)"
required: false
default: ""
direct_prompt:
description: "Direct prompt for Claude to execute automatically without needing a trigger (for automated workflows)"
required: false
required: true
description: "GitHub token for repo access"
runs:
using: "composite"
steps:
- name: Install Claude Code
shell: bash
run: npm install -g @anthropic-ai/claude-code
- name: Install GitHub MCP Server
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: Extract GitHub Context and Create Prompt
shell: bash
id: prepare_context
env:
# Pass user-controlled inputs via environment variables to prevent script injection (GHSL-2025-093)
EVENT_COMMENT_BODY: ${{ github.event.comment.body }}
EVENT_ISSUE_TITLE: ${{ github.event.issue.title }}
EVENT_ISSUE_BODY: ${{ github.event.issue.body }}
EVENT_ISSUE_NUMBER: ${{ github.event.issue.number }}
EVENT_PR_TITLE: ${{ github.event.pull_request.title }}
EVENT_PR_NUMBER: ${{ github.event.pull_request.number }}
EVENT_REVIEW_BODY: ${{ github.event.review.body }}
EVENT_ASSIGNEE_LOGIN: ${{ github.event.assignee.login }}
INPUT_TRIGGER_PHRASE: ${{ inputs.trigger_phrase }}
INPUT_ASSIGNEE_TRIGGER: ${{ inputs.assignee_trigger }}
INPUT_DIRECT_PROMPT: ${{ inputs.direct_prompt }}
EVENT_ACTION: ${{ github.event.action }}
EVENT_NAME: ${{ github.event_name }}
REPO_NAME: ${{ github.repository }}
run: |
echo "🔍 Extracting GitHub context from event: $EVENT_NAME"
# Function to check for trigger phrase
check_trigger() {
local text="$1"
local trigger="$INPUT_TRIGGER_PHRASE"
if [[ "$text" == *"$trigger"* ]]; then
return 0
fi
return 1
}
# Extract context based on event type
TRIGGER_FOUND="false"
USER_REQUEST=""
CONTEXT_INFO=""
case "$EVENT_NAME" in
"issue_comment")
COMMENT_BODY="$EVENT_COMMENT_BODY"
ISSUE_TITLE="$EVENT_ISSUE_TITLE"
ISSUE_NUMBER="$EVENT_ISSUE_NUMBER"
if check_trigger "$COMMENT_BODY"; then
TRIGGER_FOUND="true"
USER_REQUEST="$COMMENT_BODY"
CONTEXT_INFO="Issue Comment on #$ISSUE_NUMBER: $ISSUE_TITLE"
fi
;;
"pull_request_review_comment")
COMMENT_BODY="$EVENT_COMMENT_BODY"
PR_TITLE="$EVENT_PR_TITLE"
PR_NUMBER="$EVENT_PR_NUMBER"
if check_trigger "$COMMENT_BODY"; then
TRIGGER_FOUND="true"
USER_REQUEST="$COMMENT_BODY"
CONTEXT_INFO="PR Comment on #$PR_NUMBER: $PR_TITLE"
fi
;;
"pull_request_review")
REVIEW_BODY="$EVENT_REVIEW_BODY"
PR_TITLE="$EVENT_PR_TITLE"
PR_NUMBER="$EVENT_PR_NUMBER"
if check_trigger "$REVIEW_BODY"; then
TRIGGER_FOUND="true"
USER_REQUEST="$REVIEW_BODY"
CONTEXT_INFO="PR Review on #$PR_NUMBER: $PR_TITLE"
fi
;;
"issues")
ISSUE_BODY="$EVENT_ISSUE_BODY"
ISSUE_TITLE="$EVENT_ISSUE_TITLE"
ISSUE_NUMBER="$EVENT_ISSUE_NUMBER"
if check_trigger "$ISSUE_TITLE" || check_trigger "$ISSUE_BODY"; then
TRIGGER_FOUND="true"
USER_REQUEST="$ISSUE_BODY"
CONTEXT_INFO="Issue #$ISSUE_NUMBER: $ISSUE_TITLE"
elif [[ "$EVENT_ACTION" == "assigned" && -n "$INPUT_ASSIGNEE_TRIGGER" ]]; then
ASSIGNEE="$EVENT_ASSIGNEE_LOGIN"
if [[ "$ASSIGNEE" == "$INPUT_ASSIGNEE_TRIGGER" ]]; then
TRIGGER_FOUND="true"
USER_REQUEST="$ISSUE_BODY"
CONTEXT_INFO="Issue #$ISSUE_NUMBER assigned to $ASSIGNEE: $ISSUE_TITLE"
fi
fi
;;
esac
# Check for direct prompt override
if [[ -n "$INPUT_DIRECT_PROMPT" ]]; then
TRIGGER_FOUND="true"
USER_REQUEST="$INPUT_DIRECT_PROMPT"
CONTEXT_INFO="Automated GitHub workflow"
fi
if [[ "$TRIGGER_FOUND" != "true" ]]; then
echo "❌ No trigger phrase found or direct prompt provided. Exiting gracefully."
echo "SKIP_EXECUTION=true" >> $GITHUB_ENV
exit 0
fi
echo "✅ Trigger found! Context: $CONTEXT_INFO"
# Create comprehensive prompt
mkdir -p /tmp/claude-action
cat > /tmp/claude-action/github-context-prompt.txt << EOF
You are Claude Code, an AI assistant helping with GitHub workflows and code.
Repository: $REPO_NAME
Context: $CONTEXT_INFO
Event: $EVENT_NAME
User Request:
$USER_REQUEST
Please analyze the request and provide helpful assistance. You have access to repository tools and can help with:
- Code analysis and implementation
- GitHub workflows and automation
- Pull request reviews and feedback
- Issue resolution and bug fixes
- Documentation updates
- Testing and deployment
Respond naturally and helpfully to the user's request using the available tools.
EOF
echo "PROMPT_FILE=/tmp/claude-action/github-context-prompt.txt" >> $GITHUB_ENV
echo "SKIP_EXECUTION=false" >> $GITHUB_ENV
- name: Run Claude Code
if: env.SKIP_EXECUTION != 'true'
shell: bash
run: |
echo "🚀 Running Claude Code with GitHub context..."
# Build command arguments
CMD_ARGS=("-p" "--verbose" "--output-format" "stream-json")
# Add max turns if specified
if [[ -n "${{ inputs.max_turns }}" ]]; then
CMD_ARGS+=("--max-turns" "${{ inputs.max_turns }}")
fi
# Add allowed tools (include GitHub tools by default)
TOOLS="mcp__github__get_issue,mcp__github__get_issue_comments,mcp__github__update_issue,mcp__github__search_issues,mcp__github__list_issues,mcp__github__create_comment,Read,Write,Edit,Bash"
if [[ -n "${{ inputs.allowed_tools }}" ]]; then
TOOLS="$TOOLS,${{ inputs.allowed_tools }}"
fi
CMD_ARGS+=("--allowedTools" "$TOOLS")
# Add disallowed tools
if [[ -n "${{ inputs.disallowed_tools }}" ]]; then
CMD_ARGS+=("--disallowedTools" "${{ inputs.disallowed_tools }}")
fi
echo "📝 Executing Claude Code with prompt from file..."
# Execute Claude Code with timeout, using stdin for the prompt
TIMEOUT_SECONDS=$((${{ inputs.timeout_minutes }} * 60))
timeout $TIMEOUT_SECONDS claude "${CMD_ARGS[@]}" < "${{ env.PROMPT_FILE }}"
echo "✅ Claude Code execution completed"
env:
ANTHROPIC_API_KEY: ${{ inputs.anthropic_api_key }}
GITHUB_TOKEN: ${{ inputs.github_token }}
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=${{ inputs.github_token }}"
@@ -0,0 +1,21 @@
#!/bin/sh
set -e
echo "Running Claude Code in CI mode..."
# Extract GitHub context and create a smart prompt
PROMPT="Analyse the GitHub issue or PR context and generate a smart response based on the repository context."
# Set environment variables from arguments
export ANTHROPIC_API_KEY="${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"
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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 }}
@@ -1,87 +0,0 @@
name: "Claude Issue Triage Action"
description: "Automatically triage GitHub issues using Claude Code"
inputs:
timeout_minutes:
description: "Timeout in minutes for execution"
required: false
default: "5"
anthropic_api_key:
description: "Anthropic API key"
required: true
github_token:
description: "GitHub token with repo and issues permissions"
required: true
runs:
using: "composite"
steps:
- name: Checkout repository code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Create prompt file
shell: bash
run: |
mkdir -p /tmp/claude-prompts
cat > /tmp/claude-prompts/claude-issue-triage-prompt.txt << 'EOF'
You're an issue triage assistant for GitHub issues. Your task is to analyze the issue and select appropriate labels from the provided list.
IMPORTANT: Don't post any comments or messages to the issue. Your only action should be to apply labels.
Issue Information:
- REPO: ${{ github.repository }}
- ISSUE_NUMBER: ${{ github.event.issue.number }}
TASK OVERVIEW:
1. First, fetch the list of labels available in this repository by running: `gh label list`. Run exactly this command with nothing else.
2. Next, use the GitHub tools to get context about the issue:
- You have access to these tools:
- mcp__github__get_issue: Use this to retrieve the current issue's details including title, description, and existing labels
- mcp__github__get_issue_comments: Use this to read any discussion or additional context provided in the comments
- mcp__github__update_issue: Use this to apply labels to the issue (do not use this for commenting)
- mcp__github__search_issues: Use this to find similar issues that might provide context for proper categorization and to identify potential duplicate issues
- mcp__github__list_issues: Use this to understand patterns in how other issues are labeled
- Start by using mcp__github__get_issue to get the issue details
3. Analyze the issue content, considering:
- The issue title and description
- The type of issue (bug report, feature request, question, etc.)
- Technical areas mentioned
- Severity or priority indicators
- User impact
- Components affected
4. Select appropriate labels from the available labels list provided above:
- Choose labels that accurately reflect the issue's nature
- Be specific but comprehensive
- Select priority labels if you can determine urgency (high-priority, med-priority, or low-priority)
- Consider platform labels (android, ios) if applicable
- If you find similar issues using mcp__github__search_issues, consider using a "duplicate" label if appropriate. Only do so if the issue is a duplicate of another OPEN issue.
5. Apply the selected labels:
- Use mcp__github__update_issue to apply your selected labels
- DO NOT post any comments explaining your decision
- DO NOT communicate directly with users
- If no labels are clearly applicable, do not apply any labels
IMPORTANT GUIDELINES:
- Be thorough in your analysis
- Only select labels from the provided list above
- DO NOT post any comments to the issue
- Your ONLY action should be to apply labels using mcp__github__update_issue
- It's okay to not add any labels if none are clearly applicable
EOF
- name: Run Claude Code
uses: ./.github/actions/claude-code-action
with:
prompt_file: /tmp/claude-prompts/claude-issue-triage-prompt.txt
allowed_tools: "Bash(gh label list),mcp__github__get_issue,mcp__github__get_issue_comments,mcp__github__update_issue,mcp__github__search_issues,mcp__github__list_issues"
install_github_mcp: "true"
timeout_minutes: ${{ inputs.timeout_minutes }}
anthropic_api_key: ${{ inputs.anthropic_api_key }}
github_token: ${{ inputs.github_token }}
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name: 'Gemini CLI Action'
description: 'Uses the Gemini CLI to automate software development tasks.'
author: 'Gemini'
inputs:
prompt:
description: 'A specific prompt to guide Gemini.'
required: false
default: 'You are a helpful assistant.'
GEMINI_API_KEY:
description: 'Your Gemini API key.'
required: true
OTLP_GCP_WIF_PROVIDER:
description: 'The workload identity provider for GCP authentication.'
required: false
OTLP_GOOGLE_CLOUD_PROJECT:
description: 'The Google Cloud project for telemetry.'
required: false
settings_json:
description: |
A JSON string to configure the Gemini CLI. This will be written to .gemini/settings.json.
To enable observability with Google Cloud, include:
{"telemetry": {"enabled": true, "target": "gcp"}, "sandbox": false}
required: false
version:
description: |
The version of @google/gemini-cli to execute.
Can be a specific version from npm (e.g., '0.1.0', 'latest'), a branch name (e.g., 'main'), or a commit hash.
required: false
default: 'latest'
runs:
using: "composite"
steps:
- name: Configure Gemini CLI
if: inputs.settings_json != ''
run: |
mkdir -p .gemini
echo "$SETTINGS_JSON" > .gemini/settings.json
shell: bash
env:
SETTINGS_JSON: ${{ inputs.settings_json }}
- name: Authenticate to Google Cloud for Telemetry
if: inputs.OTLP_GCP_WIF_PROVIDER != ''
id: auth
uses: 'google-github-actions/auth@v2'
with:
project_id: ${{ inputs.OTLP_GOOGLE_CLOUD_PROJECT }}
workload_identity_provider: ${{ inputs.OTLP_GCP_WIF_PROVIDER }}
- name: Install Gemini CLI
id: install
run: |
if [[ "${{ inputs.version }}" == "latest" || "${{ inputs.version }}" =~ ^[0-9]+\.[0-9]+\.[0-9]+(-[a-zA-Z0-9\.-]+)?(\+[a-zA-Z0-9\.-]+)?$ ]]; then
echo "Installing Gemini CLI from npm: @google/gemini-cli@${{ inputs.version }}"
npm install -g @google/gemini-cli@${{ inputs.version }}
else
echo "Installing Gemini CLI from GitHub: github:google-gemini/gemini-cli#${{ inputs.version }}"
npm install -g github:google-gemini/gemini-cli#${{ inputs.version }}
fi
echo "Verifying installation:"
if command -v gemini >/dev/null 2>&1; then
gemini --version || echo "Gemini CLI installed successfully (version command not available)"
else
echo "Error: Gemini CLI not found in PATH"
exit 1
fi
shell: bash
- name: Run Gemini CLI
run: |
gemini --yolo --prompt "$PROMPT"
shell: bash
env:
GEMINI_API_KEY: ${{ inputs.GEMINI_API_KEY }}
PROMPT: ${{ inputs.prompt }}
branding:
icon: 'terminal'
color: 'blue'
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name: Auto Issue Comment with PR Creation
on:
issues:
types: [opened]
jobs:
add-comment:
runs-on: ubuntu-latest
permissions:
issues: write
contents: write
pull-requests: write
actions: read
steps:
- name: Add automated comment
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: '@claude review this issue and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. MAINLY IT SHOULD NOT IMPACT ON THE CURRENT SPEED OF EXECUTION of existing features, not increasing the overhead. **Please create a PR using gh tool with your changes**.'
})
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name: Auto PR Comment
on:
pull_request:
types: [opened]
jobs:
add-comment:
runs-on: ubuntu-latest
permissions:
pull-requests: write
steps:
- name: Add automated comment
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: '@claude review this pull request and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. MAINLY IT SHOULD NOT IMPACT ON THE CURRENT SPEED OF EXECUTION of existing features, not increasing the overhead.'
})
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name: Performance Benchmarks
on:
workflow_dispatch:
inputs:
iterations:
description: 'Number of benchmark iterations'
required: false
default: '100'
fail_on_regression:
description: 'Fail if performance regresses'
required: false
default: 'true'
jobs:
benchmark:
runs-on: ubuntu-latest
timeout-minutes: 30
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Python 3.12
uses: actions/setup-python@v5
with:
python-version: '3.12'
cache: 'pip'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -e src/praisonai-agents[all]
pip install agno crewai langgraph
- name: Run Import Time Benchmark
id: import_time
run: |
cd src/praisonai-agents
python benchmarks/import_time.py 2>&1 | tee import_time_output.txt
# Extract import time
IMPORT_TIME=$(grep -oP 'Median:\s+\K[\d.]+' import_time_output.txt || echo "0")
echo "import_time=$IMPORT_TIME" >> $GITHUB_OUTPUT
# Check threshold (200ms)
if (( $(echo "$IMPORT_TIME > 200" | bc -l) )); then
echo "::warning::Import time ${IMPORT_TIME}ms exceeds 200ms threshold"
if [ "${{ inputs.fail_on_regression }}" == "true" ]; then
echo "import_passed=false" >> $GITHUB_OUTPUT
fi
else
echo "import_passed=true" >> $GITHUB_OUTPUT
fi
- name: Run Memory Usage Benchmark
id: memory
run: |
cd src/praisonai-agents
python benchmarks/memory_usage.py 2>&1 | tee memory_output.txt
# Extract peak memory
PEAK_MEMORY=$(grep -oP 'Peak:\s+\K[\d.]+' memory_output.txt || echo "0")
echo "peak_memory=$PEAK_MEMORY" >> $GITHUB_OUTPUT
# Check threshold (30MB)
if (( $(echo "$PEAK_MEMORY > 30" | bc -l) )); then
echo "::warning::Memory usage ${PEAK_MEMORY}MB exceeds 30MB threshold"
if [ "${{ inputs.fail_on_regression }}" == "true" ]; then
echo "memory_passed=false" >> $GITHUB_OUTPUT
fi
else
echo "memory_passed=true" >> $GITHUB_OUTPUT
fi
- name: Run Simple Benchmark
id: simple
run: |
cd src/praisonai-agents
python benchmarks/simple_benchmark.py 2>&1 | tee simple_output.txt
# Extract PraisonAI time
PRAISON_TIME=$(grep -oP 'PraisonAI\s+\K[\d.]+' simple_output.txt | head -1 || echo "0")
echo "praison_time=$PRAISON_TIME" >> $GITHUB_OUTPUT
- name: Run Tools Benchmark
id: tools
run: |
cd src/praisonai-agents
python benchmarks/tools_benchmark.py 2>&1 | tee tools_output.txt
# Extract PraisonAI time with tools
PRAISON_TOOLS_TIME=$(grep -oP 'PraisonAI\s+\K[\d.]+' tools_output.txt | head -1 || echo "0")
echo "praison_tools_time=$PRAISON_TOOLS_TIME" >> $GITHUB_OUTPUT
- name: Run Parameter Impact Benchmark
id: param_impact
run: |
cd src/praisonai-agents
python benchmarks/param_impact_benchmark.py --no-save --iterations ${{ inputs.iterations }} 2>&1 | tee param_impact_output.txt
- name: Check Lazy Imports
id: lazy_imports
run: |
cd src/praisonai-agents
python -c "
import sys
from praisonaiagents import Agent
agent = Agent(name='Test', output='silent')
# Check litellm is NOT loaded
if 'litellm' in sys.modules:
print('FAIL: litellm loaded during silent agent creation')
exit(1)
else:
print('PASS: litellm not loaded (lazy import working)')
"
- name: Generate Benchmark Summary
if: always()
run: |
echo "## Performance Benchmark Results" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "| Metric | Value | Threshold | Status |" >> $GITHUB_STEP_SUMMARY
echo "|--------|-------|-----------|--------|" >> $GITHUB_STEP_SUMMARY
echo "| Import Time | ${{ steps.import_time.outputs.import_time }}ms | <200ms | ${{ steps.import_time.outputs.import_passed == 'true' && '✅' || '❌' }} |" >> $GITHUB_STEP_SUMMARY
echo "| Peak Memory | ${{ steps.memory.outputs.peak_memory }}MB | <30MB | ${{ steps.memory.outputs.memory_passed == 'true' && '✅' || '❌' }} |" >> $GITHUB_STEP_SUMMARY
echo "| Agent Instantiation | ${{ steps.simple.outputs.praison_time }}μs | - | ℹ️ |" >> $GITHUB_STEP_SUMMARY
echo "| Agent with Tools | ${{ steps.tools.outputs.praison_tools_time }}μs | - | ℹ️ |" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### Lazy Import Check" >> $GITHUB_STEP_SUMMARY
echo "✅ litellm not loaded during silent agent creation" >> $GITHUB_STEP_SUMMARY
- name: Fail on Regression
if: inputs.fail_on_regression == 'true'
run: |
if [ "${{ steps.import_time.outputs.import_passed }}" == "false" ]; then
echo "::error::Import time regression detected"
exit 1
fi
if [ "${{ steps.memory.outputs.memory_passed }}" == "false" ]; then
echo "::error::Memory usage regression detected"
exit 1
fi
echo "All performance checks passed!"
+1 -3
View File
@@ -11,7 +11,6 @@ on:
jobs:
build:
runs-on: ubuntu-latest
timeout-minutes: 15
permissions:
contents: read
packages: write
@@ -27,14 +26,13 @@ jobs:
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GH_TOKEN }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push Docker image
uses: docker/build-push-action@v5
with:
context: ./.github/actions/claude-code-action
push: true
platforms: linux/amd64
tags: ghcr.io/mervinpraison/praisonai-claudecode:${{ inputs.version }}
cache-from: type=gha
cache-to: type=gha,mode=max
-23
View File
@@ -1,23 +0,0 @@
name: Claude Issue Triage
description: "Automatically triage GitHub issues using Claude Code"
on:
issues:
types: [opened]
jobs:
triage-issue:
runs-on: ubuntu-latest
timeout-minutes: 10
permissions:
contents: read
issues: write
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4
- name: Run Claude Issue Triage
uses: ./.github/actions/claude-issue-triage-action
with:
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
github_token: ${{ secrets.GITHUB_TOKEN }}
-21
View File
@@ -1,21 +0,0 @@
name: Claude OAuth
on:
workflow_dispatch:
inputs:
code:
description: 'Authorization code (leave empty for step 1)'
required: false
permissions:
actions: write # Required for cache management
contents: read # Required for basic repository access
jobs:
auth:
runs-on: ubuntu-latest
steps:
- uses: grll/claude-code-login@v1
with:
code: ${{ inputs.code }}
secrets_admin_pat: ${{ secrets.SECRETS_ADMIN_PAT }}
+61 -139
View File
@@ -1,4 +1,4 @@
name: Claude Assistant
name: Claude Code
on:
issue_comment:
@@ -6,155 +6,77 @@ on:
pull_request_review_comment:
types: [created]
issues:
types: [opened, assigned, labeled]
types: [opened, assigned]
pull_request_review:
types: [submitted]
jobs:
claude-response:
# Prevent certain bots from triggering this action (but allow github-actions[bot] for auto-comments)
claude:
if: |
github.actor != 'dependabot[bot]' &&
github.actor != 'cursor[bot]' &&
github.actor != 'renovate[bot]' &&
!(contains(github.actor, '[bot]') && github.actor != 'github-actions[bot]')
(github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) ||
(github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) ||
(github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) ||
(github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')))
runs-on: ubuntu-latest
permissions:
contents: write
pull-requests: write
issues: write
actions: read
contents: read
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: 0
- uses: anthropics/claude-code-action@beta
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:
claude_code_oauth_token: ${{ secrets.CLAUDE_ACCESS_TOKEN }}
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: ./.github/actions/claude-code-action
with:
anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
github_token: ${{ secrets.GITHUB_TOKEN }}
trigger_phrase: "@claude"
allowed_tools: |
Bash(git:*)
Bash(python:*)
Bash(pip:*)
Bash(conda:*)
Bash(pytest:*)
Bash(gh:*)
Bash(python -m pytest:*)
Bash(python -m pip:*)
Bash(poetry:*)
View
GlobTool
GrepTool
BatchTool
Edit
Replace
mcp__github__get_issue
mcp__github__get_issue_comments
mcp__github__update_issue
timeout_minutes: 30
post-review-comment:
needs: claude-response
if: success() && needs.claude-response.result == 'success'
runs-on: ubuntu-latest
permissions:
contents: read
pull-requests: write
issues: write
steps:
- name: Check for existing review comment
id: check-comment
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const reviewText = '@claude review if the changes made in the above branch are as per the requirements and make changes to the above branch if issues still exist.';
let comments = [];
let issueNumber;
try {
// Determine issue/PR number based on event type
if (context.eventName === 'pull_request_review_comment' || context.eventName === 'pull_request_review') {
issueNumber = context.payload.pull_request?.number || context.issue.number;
} else if (context.eventName === 'issue_comment' && context.payload.issue.pull_request) {
// This is a comment on a PR (not an issue)
issueNumber = context.payload.issue.number;
} else {
// This is a regular issue
issueNumber = context.issue.number;
}
comments = await github.rest.issues.listComments({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: issueNumber
});
// Look for Claude's completed review comments
const claudeReviewComments = comments.data.filter(comment =>
(comment.user.login === 'claude[bot]' || comment.body.includes('✅') || comment.body.includes('## Summary')) &&
!comment.body.includes('⏳') &&
!comment.body.includes('🔄') &&
!comment.body.includes('Loading...')
);
// Check if our review request comment already exists
const existingReviewRequest = comments.data.find(comment =>
comment.body.includes(reviewText)
);
// Post if Claude has completed a review AND we haven't posted the review request yet
const shouldPost = claudeReviewComments.length > 0 && !existingReviewRequest;
core.setOutput('should-post', shouldPost);
console.log(`Claude review completed: ${claudeReviewComments.length > 0}`);
console.log(`Review request exists: ${!!existingReviewRequest}`);
console.log(`Should post comment: ${shouldPost}`);
} catch (error) {
console.log('Error checking comments:', error);
core.setOutput('should-post', false); // If we can't check, don't post
}
- name: Wait 30 seconds before posting comment
if: steps.check-comment.outputs.should-post == 'true'
run: sleep 30
- name: Post review comment
if: steps.check-comment.outputs.should-post == 'true'
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
let issueNumber;
let isPR = false;
// Determine issue/PR number and type based on event type
if (context.eventName === 'pull_request_review_comment' || context.eventName === 'pull_request_review') {
issueNumber = context.payload.pull_request?.number || context.issue.number;
isPR = true;
} else if (context.eventName === 'issue_comment' && context.payload.issue.pull_request) {
// This is a comment on a PR (not an issue)
issueNumber = context.payload.issue.number;
isPR = true;
} else {
// This is a regular issue - don't post review comment
issueNumber = context.issue.number;
isPR = false;
}
// Only post review comment on PRs, not regular issues
if (isPR) {
console.log(`Posting review comment on PR #${issueNumber}`);
await github.rest.issues.createComment({
issue_number: issueNumber,
owner: context.repo.owner,
repo: context.repo.repo,
body: '@claude review if the changes made in the above branch are as per the requirements and make changes to the above branch if issues still exist. MAINLY IT SHOULD NOT IMPACT ON THE CURRENT SPEED OF EXECUTION of existing features, not increasing the overhead.'
});
} else {
console.log(`Skipping review comment for regular issue #${issueNumber}`);
}
+2 -4
View File
@@ -10,7 +10,6 @@ jobs:
test:
name: Run tests and collect coverage
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout
@@ -41,15 +40,14 @@ jobs:
run: |
echo "OPENAI_API_KEY=${{ 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-5-nano' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
- name: Run tests
run: |
cd src/praisonai
# Run tests - allow some failures for now while we fix remaining issues
pytest --cov=praisonai --cov-branch --cov-report=xml tests/unit/ --tb=no -q || echo "Some tests failed, but coverage was collected"
pytest --cov=praisonai --cov-branch --cov-report=xml tests/unit/
- name: Upload results to Codecov
uses: codecov/codecov-action@v5
+28 -38
View File
@@ -1,54 +1,44 @@
name: Publish Docker
name: Daily Publish Docker
on:
release:
types: [published]
workflow_dispatch:
inputs:
tag:
description: 'Docker image tag (default: latest)'
required: false
default: 'latest'
push:
branches:
- main
schedule:
- cron: '0 0 * * *'
jobs:
build-and-push:
check-and-build:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
uses: actions/checkout@v2
- name: Check last commit time
id: check_commit
run: |
last_commit_time=$(git log -1 --format=%ct)
current_time=$(date +%s)
time_diff=$((current_time - last_commit_time))
if [ $time_diff -ge 120 ] && [ $time_diff -lt 240 ]; then
echo "should_run=true" >> $GITHUB_OUTPUT
else
echo "should_run=false" >> $GITHUB_OUTPUT
fi
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
if: steps.check_commit.outputs.should_run == 'true'
uses: docker/setup-buildx-action@v1
- name: Log in to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract metadata for Docker
id: meta
uses: docker/metadata-action@v5
with:
images: ghcr.io/mervinpraison/praisonai
tags: |
type=raw,value=latest,enable=${{ github.event_name == 'release' }}
type=semver,pattern={{version}},enable=${{ github.event_name == 'release' }}
type=semver,pattern={{major}}.{{minor}},enable=${{ github.event_name == 'release' }}
type=raw,value=${{ github.event.inputs.tag || 'latest' }},enable=${{ github.event_name == 'workflow_dispatch' }}
- name: Log in to the GitHub Container Registry
if: steps.check_commit.outputs.should_run == 'true'
run: echo ${{ secrets.GH_TOKEN }} | docker login ghcr.io -u USERNAME --password-stdin
- name: Build and push Docker image
uses: docker/build-push-action@v5
if: steps.check_commit.outputs.should_run == 'true'
uses: docker/build-push-action@v2
with:
context: .
file: ./docker/Dockerfile
push: true
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
cache-from: type=gha
cache-to: type=gha,mode=max
tags: ghcr.io/mervinpraison/praisonai:latest
@@ -1,76 +0,0 @@
name: Gemini Automated Issue Triage
on:
issue_comment:
types: [created]
jobs:
triage-issue:
if: >
github.event_name == 'issue_comment' &&
!github.event.issue.pull_request &&
contains(github.event.comment.body, '@gemini triage') &&
(github.event.comment.author_association == 'OWNER' ||
github.event.comment.author_association == 'MEMBER' ||
github.event.comment.author_association == 'COLLABORATOR')
timeout-minutes: 5
permissions:
issues: write
contents: read
id-token: write
concurrency:
group: ${{ github.workflow }}-${{ github.event.issue.number }}
cancel-in-progress: true
runs-on: ubuntu-latest
steps:
- name: Generate GitHub App Token
id: generate_token
uses: actions/create-github-app-token@v1
with:
app-id: ${{ secrets.APP_ID }}
private-key: ${{ secrets.PRIVATE_KEY }}
- name: Checkout repository
uses: actions/checkout@v4
with:
token: ${{ steps.generate_token.outputs.token }}
- name: Run Gemini Issue Triage
uses: google-gemini/gemini-cli-action@main
env:
GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
ISSUE_TITLE: ${{ github.event.issue.title }}
ISSUE_BODY: ${{ github.event.issue.body }}
ISSUE_NUMBER: ${{ github.event.issue.number }}
REPOSITORY: ${{ github.repository }}
with:
version: 0.1.8-rc.0
GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
OTLP_GCP_WIF_PROVIDER: ${{ secrets.OTLP_GCP_WIF_PROVIDER }}
OTLP_GOOGLE_CLOUD_PROJECT: ${{ secrets.OTLP_GOOGLE_CLOUD_PROJECT }}
settings_json: |
{
"coreTools": [
"run_shell_command(gh label list)",
"run_shell_command(gh issue edit)"
],
"telemetry": {
"enabled": true,
"target": "gcp"
},
"sandbox": false
}
prompt: |
You are an issue triage assistant. Analyze the current GitHub issue and apply the most appropriate existing labels.
Steps:
1. Run: `gh label list --limit 100` to get all available labels.
2. Review the issue title and body provided in the environment variables.
3. Select the most relevant labels from the existing labels, focusing on kind/*, area/*, and priority/*.
4. Apply the selected labels to this issue using: `gh issue edit ISSUE_NUMBER --add-label "label1,label2"`
Guidelines:
- Only use labels that already exist in the repository.
- Do not add comments or modify the issue content.
- Triage only the current issue.
- Assign all applicable kind/*, area/*, and priority/* labels based on the issue content.
-202
View File
@@ -1,202 +0,0 @@
name: Gemini Issue Review
on:
workflow_dispatch:
issue_comment:
types: [created]
permissions:
contents: write
issues: write
pull-requests: read
jobs:
review-issue:
if: >
github.event_name == 'workflow_dispatch' ||
(github.event_name == 'issue_comment' &&
!github.event.issue.pull_request &&
contains(github.event.comment.body, '@gemini') &&
!contains(github.event.comment.body, '@gemini triage') &&
(github.event.comment.author_association == 'OWNER' ||
github.event.comment.author_association == 'MEMBER' ||
github.event.comment.author_association == 'COLLABORATOR'))
runs-on: ubuntu-latest
env:
GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
steps:
- name: Generate token
id: generate_token
uses: actions/create-github-app-token@v1
with:
app-id: ${{ secrets.APP_ID }}
private-key: ${{ secrets.PRIVATE_KEY }}
- name: Checkout repository
uses: actions/checkout@v4
with:
token: ${{ steps.generate_token.outputs.token }}
- name: Set up Git
run: |
git config --local user.email "[email protected]"
git config --local user.name "Gemini Bot"
- name: Get issue data
id: issue_data
env:
GH_TOKEN: ${{ steps.generate_token.outputs.token }}
# Pass user-controlled inputs via environment variables to prevent script injection (GHSL-2025-093)
EVENT_NAME: ${{ github.event_name }}
EVENT_ISSUE_NUMBER: ${{ github.event.issue.number }}
EVENT_COMMENT_BODY: ${{ github.event.comment.body }}
run: |
# Get issue number from event or context
if [ "$EVENT_NAME" = "issue_comment" ]; then
ISSUE_NUMBER="$EVENT_ISSUE_NUMBER"
elif [ "$EVENT_NAME" = "issues" ]; then
ISSUE_NUMBER="$EVENT_ISSUE_NUMBER"
else
echo "Unable to determine issue number"
exit 1
fi
# Get issue details
gh issue view $ISSUE_NUMBER --json title,body,labels,assignees > issue_details.json
# Extract additional instructions from comment if triggered by comment
ADDITIONAL_INSTRUCTIONS=""
if [ "$EVENT_NAME" = "issue_comment" ]; then
COMMENT_BODY="$EVENT_COMMENT_BODY"
ADDITIONAL_INSTRUCTIONS=$(echo "$COMMENT_BODY" | sed -n 's/.*@gemini[[:space:]]*\(.*\)/\1/p' | head -1)
fi
echo "ISSUE_NUMBER=$ISSUE_NUMBER" >> $GITHUB_OUTPUT
echo "ADDITIONAL_INSTRUCTIONS=$ADDITIONAL_INSTRUCTIONS" >> $GITHUB_OUTPUT
- name: Run Gemini CLI
uses: ./.github/actions/gemini
env:
GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
ISSUE_NUMBER: ${{ steps.issue_data.outputs.ISSUE_NUMBER }}
ADDITIONAL_INSTRUCTIONS: ${{ steps.issue_data.outputs.ADDITIONAL_INSTRUCTIONS }}
REPOSITORY: ${{ github.repository }}
with:
GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
OTLP_GCP_WIF_PROVIDER: ${{ secrets.OTLP_GCP_WIF_PROVIDER }}
OTLP_GOOGLE_CLOUD_PROJECT: ${{ secrets.OTLP_GOOGLE_CLOUD_PROJECT }}
prompt: |
You are a helpful GitHub issue assistant. Your task is to analyze and help with GitHub issues.
**Issue Details:**
- Issue Number: #${ISSUE_NUMBER}
- Repository: ${REPOSITORY}
- Issue URL: ${{ github.event.issue.html_url }}
**Additional Instructions:** ${ADDITIONAL_INSTRUCTIONS}
**CRITICAL: When making code modifications, follow this EXACT workflow:**
**STEP 1: Analyze the Issue**
- Use: `gh issue view ${ISSUE_NUMBER} --json title,body,labels,assignees`
- Analyze issue content thoroughly
- Search codebase for related files using `glob`, `search_file_content`, `list_directory`
**STEP 2: If Code Changes Are Needed**
Create a new branch with 'gemini/' prefix:
- Use: `git checkout -b gemini/issue-${ISSUE_NUMBER}-description`
- Example: `git checkout -b gemini/issue-123-fix-sequential-tools`
**STEP 3: Make Code Modifications**
- Use `read_file` to examine existing code
- Use `write_file` or `replace` to make changes
- Use `search_file_content` to find related code
- Make focused, targeted changes to fix the issue
**STEP 4: Test Changes (if applicable)**
- Run relevant tests if test files exist
- Use `run_shell_command` to execute test commands
**STEP 5: Commit and Push Changes**
- Use: `git add .` (or specific files)
- Use: `git commit -m "Gemini: [Issue #${ISSUE_NUMBER}] Brief description of fix"`
- Use: `git push -u origin gemini/issue-${ISSUE_NUMBER}-description`
**STEP 6: Create Pull Request**
- Use: `gh pr create --title "Fix #${ISSUE_NUMBER}: Issue description" --body "Fixes #${ISSUE_NUMBER}\n\nSummary of changes:\n- Change 1\n- Change 2" --head gemini/issue-${ISSUE_NUMBER}-description --base main`
- This will return a PR URL like: https://github.com/repo/pull/123
**STEP 7: Comment on Issue with Results**
Use: `gh issue comment ${ISSUE_NUMBER} -b "MESSAGE"`
**Your final comment MUST include:**
- Summary of what was analyzed
- What changes were made (if any)
- Link to the created PR (if applicable)
- Brief explanation of the solution
**Example final comment:**
```
Hi! I've analyzed and fixed this issue.
**Problem:** Sequential tool calling was getting stuck after first tool call.
**Solution:** Added proper prompt handling after each tool call in the Ollama model integration.
**Changes Made:**
- Modified `src/praisonai-agents/ollama-sequential.py`
- Added continuation prompts for multi-step tool execution
**Pull Request:** https://github.com/MervinPraison/PraisonAI/pull/856
The fix is ready for review. Please test and let me know if you encounter any issues!
```
**IMPORTANT NOTES:**
- Always create branches with 'gemini/' prefix
- Always include issue number in branch name and commit message
- Always create PR that references the issue
- Always provide PR link in your final response
- Use the GITHUB_TOKEN environment variable for authentication
Please proceed with helping resolve this issue following this exact workflow.
settings_json: |
{
"coreTools": [
"run_shell_command(echo)",
"run_shell_command(gh issue view)",
"run_shell_command(gh issue comment)",
"run_shell_command(gh issue list)",
"run_shell_command(gh issue edit)",
"run_shell_command(gh pr create)",
"run_shell_command(gh pr view)",
"run_shell_command(gh pr list)",
"run_shell_command(cat)",
"run_shell_command(head)",
"run_shell_command(tail)",
"run_shell_command(grep)",
"run_shell_command(find)",
"run_shell_command(git checkout)",
"run_shell_command(git add)",
"run_shell_command(git commit)",
"run_shell_command(git push)",
"run_shell_command(git status)",
"run_shell_command(git branch)",
"list_directory",
"read_file",
"read_many_files",
"write_file",
"replace",
"glob",
"search_file_content",
"web_fetch",
"google_web_search",
"save_memory"
],
"telemetry": {
"enabled": false,
"otlpGcpWifProvider": "${{ secrets.OTLP_GCP_WIF_PROVIDER }}",
"googleCloudProject": "${{ secrets.OTLP_GOOGLE_CLOUD_PROJECT }}"
}
}
-248
View File
@@ -1,248 +0,0 @@
name: Gemini PR Review
on:
issue_comment:
types: [created]
workflow_dispatch:
inputs:
pr_number:
description: 'PR number to review'
required: true
type: number
jobs:
review-pr:
if: >
github.event_name == 'workflow_dispatch' ||
(github.event_name == 'issue_comment' &&
github.event.issue.pull_request &&
contains(github.event.comment.body, '@gemini') &&
(github.event.comment.author_association == 'OWNER' ||
github.event.comment.author_association == 'MEMBER' ||
github.event.comment.author_association == 'COLLABORATOR'))
timeout-minutes: 15
runs-on: ubuntu-latest
permissions:
contents: read
id-token: write
pull-requests: write
issues: write
steps:
- name: Generate GitHub App Token
id: generate_token
uses: actions/create-github-app-token@v1
with:
app-id: ${{ secrets.APP_ID }}
private-key: ${{ secrets.PRIVATE_KEY }}
- name: Determine checkout ref
id: checkout_ref
run: |
if [ "${{ github.event_name }}" = "issue_comment" ]; then
echo "ref=refs/pull/${{ github.event.issue.number }}/head" >> "$GITHUB_OUTPUT"
else
echo "ref=${{ github.event.pull_request.head.sha }}" >> "$GITHUB_OUTPUT"
fi
- name: Checkout PR code
uses: actions/checkout@v4
with:
token: ${{ steps.generate_token.outputs.token }}
ref: ${{ steps.checkout_ref.outputs.ref }}
fetch-depth: 0
- name: Get PR details
id: get_pr
env:
GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
# Pass user-controlled inputs via environment variables to prevent script injection (GHSL-2025-093)
EVENT_NAME: ${{ github.event_name }}
EVENT_INPUTS_PR_NUMBER: ${{ github.event.inputs.pr_number }}
EVENT_ISSUE_NUMBER: ${{ github.event.issue.number }}
EVENT_PR_NUMBER: ${{ github.event.pull_request.number }}
EVENT_COMMENT_BODY: ${{ github.event.comment.body }}
run: |
if [ "$EVENT_NAME" = "workflow_dispatch" ]; then
PR_NUMBER=$EVENT_INPUTS_PR_NUMBER
elif [ "$EVENT_NAME" = "issue_comment" ]; then
PR_NUMBER=$EVENT_ISSUE_NUMBER
else
PR_NUMBER=$EVENT_PR_NUMBER
fi
echo "pr_number=$PR_NUMBER" >> "$GITHUB_OUTPUT"
# Extract additional instructions from comment (if triggered by comment)
ADDITIONAL_INSTRUCTIONS=""
if [ "$EVENT_NAME" = "issue_comment" ]; then
COMMENT_BODY="$EVENT_COMMENT_BODY"
ADDITIONAL_INSTRUCTIONS=$(echo "$COMMENT_BODY" | sed 's/.*@gemini//' | xargs)
fi
echo "additional_instructions=$ADDITIONAL_INSTRUCTIONS" >> "$GITHUB_OUTPUT"
# Get PR details
PR_DATA=$(gh pr view $PR_NUMBER --json title,body,additions,deletions,changedFiles,baseRefName,headRefName)
echo "pr_data=$PR_DATA" >> "$GITHUB_OUTPUT"
# Get file changes
CHANGED_FILES=$(gh pr diff $PR_NUMBER --name-only)
echo "changed_files<<EOF" >> "$GITHUB_OUTPUT"
echo "$CHANGED_FILES" >> "$GITHUB_OUTPUT"
echo "EOF" >> "$GITHUB_OUTPUT"
- name: Run Gemini PR Review
uses: ./.github/actions/gemini
env:
GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
PR_NUMBER: ${{ steps.get_pr.outputs.pr_number }}
PR_DATA: ${{ steps.get_pr.outputs.pr_data }}
CHANGED_FILES: ${{ steps.get_pr.outputs.changed_files }}
ADDITIONAL_INSTRUCTIONS: ${{ steps.get_pr.outputs.additional_instructions }}
REPOSITORY: ${{ github.repository }}
with:
version: 0.1.8-rc.0
GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
OTLP_GCP_WIF_PROVIDER: ${{ secrets.OTLP_GCP_WIF_PROVIDER }}
OTLP_GOOGLE_CLOUD_PROJECT: ${{ secrets.OTLP_GOOGLE_CLOUD_PROJECT }}
settings_json: |
{
"coreTools": [
"run_shell_command(echo)",
"run_shell_command(gh pr view)",
"run_shell_command(gh pr diff)",
"run_shell_command(gh pr comment)",
"run_shell_command(gh pr list)",
"run_shell_command(cat)",
"run_shell_command(head)",
"run_shell_command(tail)",
"run_shell_command(grep)",
"run_shell_command(find)",
"run_shell_command(git checkout)",
"run_shell_command(git add)",
"run_shell_command(git commit)",
"run_shell_command(git push)",
"run_shell_command(git status)",
"run_shell_command(git branch)",
"run_shell_command(git fetch)",
"list_directory",
"read_file",
"read_many_files",
"write_file",
"replace",
"glob",
"search_file_content",
"web_fetch",
"google_web_search",
"save_memory"
],
"telemetry": {
"enabled": false,
"target": "gcp"
},
"sandbox": false
}
prompt: |
You are an expert code reviewer and AI assistant for Pull Requests. You can review code AND make modifications to improve the PR.
**PR Details:**
- PR Number: #$PR_NUMBER
- Repository: $REPOSITORY
- Additional Instructions: $ADDITIONAL_INSTRUCTIONS
**CRITICAL: When making code modifications, follow this EXACT workflow:**
**STEP 1: Analyze the PR**
- Use: `echo "$PR_DATA"` to get PR details (JSON format)
- Use: `echo "$CHANGED_FILES"` to get changed files list
- Use: `gh pr diff ${PR_NUMBER}` to see the full diff
- Use: `gh pr view ${PR_NUMBER} --json title,body,headRefName` to get PR info
- Analyze the code changes thoroughly
**STEP 2: If Code Improvements Are Needed**
**IMPORTANT:** DO NOT create a new branch! Work on the EXISTING PR branch.
- Use: `git fetch origin` to get latest changes
- Get PR branch name: `gh pr view ${PR_NUMBER} --json headRefName`
- Switch to PR branch: `git checkout BRANCH_NAME` (use actual branch name from PR)
- Use: `git pull origin BRANCH_NAME` to get latest PR changes
**STEP 3: Make Code Modifications**
- Use `read_file` to examine existing code
- Use `write_file` or `replace` to make improvements
- Use `search_file_content` to find related code
- Make focused, targeted improvements to the PR
**STEP 4: Test Changes (if applicable)**
- Run relevant tests if test files exist
- Use `run_shell_command` to execute test commands
**STEP 5: Commit and Push Changes to EXISTING PR Branch**
- Use: `git add .` (or specific files)
- Use: `git commit -m "Gemini: [PR #${PR_NUMBER}] Brief description of improvements"`
- Use: `git push origin BRANCH_NAME` (push to EXISTING PR branch)
**STEP 6: Review and Comment on PR**
Write comprehensive review and post comment with your analysis and any improvements made.
Use: `gh pr comment ${PR_NUMBER} -b "MESSAGE"`
Review Guidelines:
- Focus on code quality, security, performance, and maintainability
- Check for common issues: potential bugs, security vulnerabilities, performance bottlenecks
- Verify error handling and edge cases
- Look for code style and best practices
- Comment on architecture and design decisions if significant
- Be constructive and specific in feedback
- Highlight both issues and positive aspects
- Suggest improvements with examples when possible
Review Areas:
- **Security**: Authentication, authorization, input validation, data sanitization
- **Performance**: Algorithms, database queries, caching, resource usage
- **Reliability**: Error handling, logging, testing coverage, edge cases
- **Maintainability**: Code structure, documentation, naming conventions
- **Functionality**: Logic correctness, requirements fulfillment
Output Format:
Structure your review using this exact format with markdown:
## 📋 Review Summary
Provide a brief 2-3 sentence overview of the PR and overall assessment.
## 🔍 General Feedback
- List general observations about code quality
- Mention overall patterns or architectural decisions
- Highlight positive aspects of the implementation
- Note any recurring themes across files
## 🎯 Specific Feedback
Only include sections below that have actual issues. If there are no issues in a priority category, omit that entire section.
### 🔴 Critical
(Only include this section if there are critical issues)
Issues that must be addressed before merging (security vulnerabilities, breaking changes, major bugs):
- **File: `filename:line`** - Description of critical issue with specific recommendation
### 🟡 High
(Only include this section if there are high priority issues)
Important issues that should be addressed (performance problems, design flaws, significant bugs):
- **File: `filename:line`** - Description of high priority issue with suggested fix
### 🟢 Medium
(Only include this section if there are medium priority issues)
Improvements that would enhance code quality (style issues, minor optimizations, better practices):
- **File: `filename:line`** - Description of medium priority improvement
### 🔵 Low
(Only include this section if there are suggestions)
Nice-to-have improvements and suggestions (documentation, naming, minor refactoring):
- **File: `filename:line`** - Description of suggestion or enhancement
**Note**: If no specific issues are found in any category, simply state "No specific issues identified in this review."
## ✅ Highlights
(Only include this section if there are positive aspects to highlight)
- Mention specific good practices or implementations
- Acknowledge well-written code sections
- Note improvements from previous versions
---
*Review completed by Gemini CLI*
+1 -1
View File
@@ -29,7 +29,7 @@ jobs:
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-5-nano' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
- name: Install dependencies
+13 -19
View File
@@ -26,10 +26,6 @@ on:
jobs:
comprehensive-test:
runs-on: ubuntu-latest
timeout-minutes: 15
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_MODEL_NAME: gpt-4o-mini
strategy:
matrix:
python-version: ["3.11"]
@@ -54,17 +50,15 @@ jobs:
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system duckduckgo_search
uv pip install --system pytest pytest-asyncio pytest-cov pytest-benchmark pytest-timeout
uv pip install --system pytest pytest-asyncio pytest-cov pytest-benchmark
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
- name: Verify environment variables
- name: Set environment variables
run: |
if [ -n "$OPENAI_API_KEY" ]; then
echo "✅ OPENAI_API_KEY is set (length: ${#OPENAI_API_KEY})"
else
echo "⚠️ OPENAI_API_KEY is NOT set"
fi
echo "OPENAI_API_KEY=${{ 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
- name: Run Comprehensive Test Suite
run: |
@@ -75,28 +69,28 @@ jobs:
case $TEST_TYPE in
"unit")
cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=xml --cov-branch || echo "Some unit tests failed"
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 -m pytest tests/integration/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=xml --cov-branch || echo "Some integration tests failed"
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 || echo "Some fast tests failed"
cd src/praisonai && python tests/test_runner.py --pattern fast
;;
"performance")
cd src/praisonai && python tests/test_runner.py --pattern "performance" || echo "Some performance tests failed"
cd src/praisonai && python tests/test_runner.py --pattern "performance"
;;
"frameworks")
cd src/praisonai && python tests/test_runner.py --pattern frameworks || echo "Some framework tests failed"
cd src/praisonai && python tests/test_runner.py --pattern frameworks
;;
"autogen")
cd src/praisonai && python tests/test_runner.py --pattern autogen || echo "Some autogen tests failed"
cd src/praisonai && python tests/test_runner.py --pattern autogen
;;
"crewai")
cd src/praisonai && python tests/test_runner.py --pattern crewai || echo "Some crewai tests failed"
cd src/praisonai && python tests/test_runner.py --pattern crewai
;;
"all"|*)
cd src/praisonai && python tests/test_runner.py --pattern all || echo "Some tests failed"
cd src/praisonai && python tests/test_runner.py --pattern all
;;
esac
+10 -12
View File
@@ -9,7 +9,6 @@ on:
jobs:
test-core:
runs-on: ubuntu-latest
timeout-minutes: 15
strategy:
matrix:
python-version: [3.11]
@@ -34,7 +33,7 @@ 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
uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
uv pip install --system pytest pytest-asyncio pytest-cov
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
@@ -42,13 +41,13 @@ jobs:
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-5-nano' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
# Also export to current shell session for immediate availability
export OPENAI_API_KEY="${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}"
export OPENAI_API_BASE="${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}"
export OPENAI_MODEL_NAME="${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}"
export OPENAI_MODEL_NAME="${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}"
export LOGLEVEL=DEBUG
# Verify immediate availability
echo "🔧 Immediate verification in same step:"
@@ -126,7 +125,7 @@ jobs:
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}
OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
LOGLEVEL: DEBUG
run: |
echo "🔍 Testing PraisonAI API key usage directly..."
@@ -152,7 +151,7 @@ jobs:
return 'INVALID_TYPE'
print('🔧 Direct PraisonAI API Key Check:')
env_api_key = os.environ.get(\"OPENAI_API_KEY\", \"NOT_SET\")
env_api_key = os.environ.get(\\"OPENAI_API_KEY\\", \\"NOT_SET\\")
print(f'Environment OPENAI_API_KEY: {get_key_display_value(env_api_key)}...')
from praisonai import PraisonAI
@@ -164,9 +163,9 @@ jobs:
from praisonai.inc.models import PraisonAIModel
print('\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
print('\\\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
model_with_explicit_key = PraisonAIModel(
model='openai/gpt-5-nano',
model='openai/gpt-4o-mini',
base_url=praisonai.config_list[0].get('base_url'),
api_key=api_key_from_config # This will be a string from praisonai.config_list
)
@@ -196,8 +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 --cov-report=xml --cov-branch || echo "Some tests failed - see output above"
continue-on-error: true
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: |
@@ -252,7 +250,7 @@ jobs:
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}
OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
LOGLEVEL: DEBUG
run: |
echo "🧪 Running legacy tests with real API key..."
@@ -263,7 +261,7 @@ jobs:
export OPENAI_API_KEY="$OPENAI_API_KEY"
export OPENAI_API_BASE="$OPENAI_API_BASE"
export OPENAI_MODEL_NAME="$OPENAI_MODEL_NAME"
cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings -m "not real" --timeout=60
cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
- name: Upload coverage reports to Codecov
if: matrix.python-version == '3.11'
+2 -13
View File
@@ -13,7 +13,6 @@ on:
jobs:
test-examples:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout code
@@ -42,11 +41,8 @@ jobs:
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-5-nano' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
echo "PRAISONAI_TELEMETRY_DISABLED=true" >> $GITHUB_ENV
echo "PRAISONAI_DISABLE_TELEMETRY=true" >> $GITHUB_ENV
echo "DO_NOT_TRACK=true" >> $GITHUB_ENV
- name: Test Key Example Scripts
run: |
@@ -77,7 +73,6 @@ jobs:
performance-test:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout code
@@ -102,12 +97,6 @@ jobs:
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
- name: Set environment variables
run: |
echo "PRAISONAI_TELEMETRY_DISABLED=true" >> $GITHUB_ENV
echo "PRAISONAI_DISABLE_TELEMETRY=true" >> $GITHUB_ENV
echo "DO_NOT_TRACK=true" >> $GITHUB_ENV
- name: Run Performance Benchmarks
run: |
echo "🏃 Running performance benchmarks..."
@@ -172,4 +161,4 @@ jobs:
with:
name: performance-report
path: performance_report.md
retention-days: 5
retention-days: 30
+2 -3
View File
@@ -24,7 +24,6 @@ on:
jobs:
framework-tests:
runs-on: ubuntu-latest
timeout-minutes: 15
strategy:
matrix:
python-version: [3.11]
@@ -50,13 +49,13 @@ jobs:
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system duckduckgo_search
uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
uv pip install --system pytest pytest-asyncio pytest-cov
- name: Set environment variables
run: |
echo "OPENAI_API_KEY=${{ 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-5-nano' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
- name: Test ${{ matrix.framework }} Framework
run: |
-530
View File
@@ -1,530 +0,0 @@
name: Optimized Test Suite
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main, develop ]
schedule:
# Nightly extended tests at 3 AM UTC
- cron: '0 3 * * *'
workflow_dispatch:
inputs:
tier:
description: 'Test tier to run'
required: true
default: 'main'
type: choice
options:
- smoke
- main
- extended
- nightly
env:
PYTHONPATH: ${{ github.workspace }}/src/praisonai-agents
# Test gating environment variables
PRAISONAI_ALLOW_NETWORK: '0'
PRAISONAI_LIVE_TESTS: '0'
jobs:
# ============================================
# SMOKE TESTS - Fast unit tests, no network
# Target: <= 2 minutes
# ============================================
smoke:
runs-on: ubuntu-latest
timeout-minutes: 5
steps:
- uses: actions/checkout@v4
- name: Set up Python 3.11
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Cache pip packages
uses: actions/cache@v4
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-smoke-${{ hashFiles('**/pyproject.toml') }}
restore-keys: |
${{ runner.os }}-pip-smoke-
${{ runner.os }}-pip-
- name: Install minimal dependencies
run: |
pip install --upgrade pip
# Install praisonai-agents with all deps (required for tests)
cd src/praisonai-agents
pip install -e ".[all]"
cd ../praisonai
pip install -e ".[chat,code]"
pip install pytest pytest-asyncio pytest-timeout prompt_toolkit
- name: Run Smoke Tests
env:
PRAISONAI_TEST_TIER: smoke
PRAISONAI_ALLOW_NETWORK: '0'
run: |
cd src/praisonai
# Run only CLI tests for smoke - fast and critical path
python -m pytest tests/unit/cli/ \
-m "not slow and not network" \
-q --tb=line \
--timeout=30 \
--ignore=tests/fixtures \
--ignore=tests/unit/cli/test_message_queue.py \
--maxfail=5 \
|| echo "Some smoke tests failed"
- name: Smoke Test Summary
if: always()
run: echo "✅ Smoke tests completed"
# ============================================
# MAIN TESTS - Unit + safe integration
# Target: <= 5 minutes
# ============================================
main:
needs: smoke
runs-on: ubuntu-latest
timeout-minutes: 10
strategy:
matrix:
python-version: ['3.11']
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Cache pip packages
uses: actions/cache@v4
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-main-${{ hashFiles('**/pyproject.toml') }}
restore-keys: |
${{ runner.os }}-pip-main-
${{ runner.os }}-pip-
- name: Install dependencies
run: |
pip install --upgrade pip
# Install praisonai-agents with all deps (required for tests)
cd src/praisonai-agents
pip install -e ".[all]"
cd ../praisonai
pip install -e ".[crewai,autogen]"
pip install pytest pytest-asyncio pytest-timeout pytest-cov pytest-xdist
- name: Run Main Tests
env:
PRAISONAI_TEST_TIER: main
PRAISONAI_ALLOW_NETWORK: '0'
OPENAI_API_KEY: 'test-key'
ANTHROPIC_API_KEY: 'test-key'
GOOGLE_API_KEY: 'test-key'
run: |
cd src/praisonai
python -m pytest tests/unit/ tests/integration/ \
-m "not provider_anthropic and not provider_google and not provider_ollama and not provider_grok_xai and not provider_groq and not provider_cohere" \
-q --tb=line \
--timeout=60 \
--ignore=tests/fixtures \
--ignore=tests/e2e \
--ignore=tests/live \
--ignore=tests/unit/jobs \
--ignore=tests/unit/api \
--cov=praisonai \
--cov-report=xml \
-n 4 \
--maxfail=10 \
|| echo "Some tests failed"
- name: Upload coverage
if: matrix.python-version == '3.11'
uses: codecov/codecov-action@v5
with:
token: ${{ secrets.CODECOV_TOKEN }}
file: src/praisonai/coverage.xml
flags: main-tests
fail_ci_if_error: false
# ============================================
# OPENAI LIVE TESTS - Real OpenAI API calls
# Only runs if OPENAI_API_KEY secret is set
# Safe for forks (skips gracefully if no key)
# ============================================
openai-live:
needs: smoke
runs-on: ubuntu-latest
timeout-minutes: 8
# This job always runs but steps are conditional on secret availability
steps:
- name: Check if OpenAI key is available
id: check-key
run: |
if [ -n "${{ secrets.OPENAI_API_KEY }}" ]; then
echo "has_key=true" >> $GITHUB_OUTPUT
else
echo "has_key=false" >> $GITHUB_OUTPUT
echo "⚠️ OPENAI_API_KEY not set - skipping live tests"
fi
- uses: actions/checkout@v4
if: steps.check-key.outputs.has_key == 'true'
- name: Set up Python 3.11
if: steps.check-key.outputs.has_key == 'true'
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Cache pip packages
if: steps.check-key.outputs.has_key == 'true'
uses: actions/cache@v4
with:
path: ~/.cache/pip
key: ${{ runner.os }}-pip-openai-${{ hashFiles('**/pyproject.toml') }}
restore-keys: |
${{ runner.os }}-pip-
- name: Install dependencies
if: steps.check-key.outputs.has_key == 'true'
run: |
pip install --upgrade pip
cd src/praisonai-agents
pip install -e ".[all]"
cd ../praisonai
pip install -e ".[openai]"
pip install pytest pytest-asyncio pytest-timeout
- name: Run OpenAI Live Tests
if: steps.check-key.outputs.has_key == 'true'
env:
PRAISONAI_TEST_TIER: extended
PRAISONAI_ALLOW_NETWORK: '1'
PRAISONAI_LIVE_TESTS: '1'
PRAISONAI_TEST_PROVIDERS: 'openai'
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
run: |
cd src/praisonai
python -m pytest tests/ \
-m "provider_openai or real" \
-v --tb=short \
--timeout=120 \
--ignore=tests/fixtures \
|| echo "Some OpenAI tests failed"
- name: Skip notice
if: steps.check-key.outputs.has_key == 'false'
run: echo "✅ OpenAI live tests skipped (no API key available - this is expected for forks)"
# ============================================
# EXTENDED PROVIDER TESTS - Nightly only
# Runs all provider-specific tests
# ============================================
extended-anthropic:
if: github.event_name == 'schedule' || github.event.inputs.tier == 'extended' || github.event.inputs.tier == 'nightly'
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Check if Anthropic key is available
id: check-key
run: |
if [ -n "${{ secrets.ANTHROPIC_API_KEY }}" ]; then
echo "has_key=true" >> $GITHUB_OUTPUT
else
echo "has_key=false" >> $GITHUB_OUTPUT
echo "⚠️ ANTHROPIC_API_KEY not set - skipping tests"
fi
- uses: actions/checkout@v4
if: steps.check-key.outputs.has_key == 'true'
- name: Set up Python 3.11
if: steps.check-key.outputs.has_key == 'true'
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
if: steps.check-key.outputs.has_key == 'true'
run: |
pip install --upgrade pip
cd src/praisonai-agents
pip install -e ".[all]"
cd ../praisonai
pip install -e ".[anthropic]"
pip install pytest pytest-asyncio pytest-timeout
- name: Run Anthropic Tests
if: steps.check-key.outputs.has_key == 'true'
env:
PRAISONAI_ALLOW_NETWORK: '1'
PRAISONAI_LIVE_TESTS: '1'
PRAISONAI_TEST_PROVIDERS: 'anthropic'
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
run: |
cd src/praisonai
python -m pytest tests/ \
-m "provider_anthropic" \
-v --tb=short \
--timeout=120 \
--ignore=tests/fixtures \
|| echo "Anthropic tests completed"
- name: Skip notice
if: steps.check-key.outputs.has_key == 'false'
run: echo "✅ Anthropic tests skipped (no API key available)"
extended-google:
if: github.event_name == 'schedule' || github.event.inputs.tier == 'extended' || github.event.inputs.tier == 'nightly'
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Check if Google key is available
id: check-key
run: |
if [ -n "${{ secrets.GOOGLE_API_KEY }}" ]; then
echo "has_key=true" >> $GITHUB_OUTPUT
else
echo "has_key=false" >> $GITHUB_OUTPUT
echo "⚠️ GOOGLE_API_KEY not set - skipping tests"
fi
- uses: actions/checkout@v4
if: steps.check-key.outputs.has_key == 'true'
- name: Set up Python 3.11
if: steps.check-key.outputs.has_key == 'true'
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
if: steps.check-key.outputs.has_key == 'true'
run: |
pip install --upgrade pip
cd src/praisonai-agents
pip install -e ".[all]"
cd ../praisonai
pip install -e ".[google]"
pip install pytest pytest-asyncio pytest-timeout
- name: Run Google/Gemini Tests
if: steps.check-key.outputs.has_key == 'true'
env:
PRAISONAI_ALLOW_NETWORK: '1'
PRAISONAI_LIVE_TESTS: '1'
PRAISONAI_TEST_PROVIDERS: 'google'
GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
run: |
cd src/praisonai
python -m pytest tests/ \
-m "provider_google" \
-v --tb=short \
--timeout=120 \
--ignore=tests/fixtures \
|| echo "Google tests completed"
- name: Skip notice
if: steps.check-key.outputs.has_key == 'false'
run: echo "✅ Google tests skipped (no API key available)"
extended-groq:
if: github.event_name == 'schedule' || github.event.inputs.tier == 'extended' || github.event.inputs.tier == 'nightly'
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Check if Groq key is available
id: check-key
run: |
if [ -n "${{ secrets.GROQ_API_KEY }}" ]; then
echo "has_key=true" >> $GITHUB_OUTPUT
else
echo "has_key=false" >> $GITHUB_OUTPUT
echo "⚠️ GROQ_API_KEY not set - skipping tests"
fi
- uses: actions/checkout@v4
if: steps.check-key.outputs.has_key == 'true'
- name: Set up Python 3.11
if: steps.check-key.outputs.has_key == 'true'
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
if: steps.check-key.outputs.has_key == 'true'
run: |
pip install --upgrade pip
cd src/praisonai-agents
pip install -e ".[all]"
cd ../praisonai
pip install -e .
pip install pytest pytest-asyncio pytest-timeout
- name: Run Groq Tests
if: steps.check-key.outputs.has_key == 'true'
env:
PRAISONAI_ALLOW_NETWORK: '1'
PRAISONAI_LIVE_TESTS: '1'
PRAISONAI_TEST_PROVIDERS: 'groq'
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
run: |
cd src/praisonai
python -m pytest tests/ \
-m "provider_groq" \
-v --tb=short \
--timeout=120 \
--ignore=tests/fixtures \
|| echo "Groq tests completed"
- name: Skip notice
if: steps.check-key.outputs.has_key == 'false'
run: echo "✅ Groq tests skipped (no API key available)"
extended-xai:
if: github.event_name == 'schedule' || github.event.inputs.tier == 'extended' || github.event.inputs.tier == 'nightly'
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Check if xAI key is available
id: check-key
run: |
if [ -n "${{ secrets.XAI_API_KEY }}" ]; then
echo "has_key=true" >> $GITHUB_OUTPUT
else
echo "has_key=false" >> $GITHUB_OUTPUT
echo "⚠️ XAI_API_KEY not set - skipping tests"
fi
- uses: actions/checkout@v4
if: steps.check-key.outputs.has_key == 'true'
- name: Set up Python 3.11
if: steps.check-key.outputs.has_key == 'true'
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
if: steps.check-key.outputs.has_key == 'true'
run: |
pip install --upgrade pip
cd src/praisonai-agents
pip install -e ".[all]"
cd ../praisonai
pip install -e .
pip install pytest pytest-asyncio pytest-timeout
- name: Run xAI/Grok Tests
if: steps.check-key.outputs.has_key == 'true'
env:
PRAISONAI_ALLOW_NETWORK: '1'
PRAISONAI_LIVE_TESTS: '1'
PRAISONAI_TEST_PROVIDERS: 'grok_xai'
XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
run: |
cd src/praisonai
python -m pytest tests/ \
-m "provider_grok_xai" \
-v --tb=short \
--timeout=120 \
--ignore=tests/fixtures \
|| echo "xAI tests completed"
- name: Skip notice
if: steps.check-key.outputs.has_key == 'false'
run: echo "✅ xAI tests skipped (no API key available)"
extended-cohere:
if: github.event_name == 'schedule' || github.event.inputs.tier == 'extended' || github.event.inputs.tier == 'nightly'
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Check if Cohere key is available
id: check-key
run: |
if [ -n "${{ secrets.COHERE_API_KEY }}" ]; then
echo "has_key=true" >> $GITHUB_OUTPUT
else
echo "has_key=false" >> $GITHUB_OUTPUT
echo "⚠️ COHERE_API_KEY not set - skipping tests"
fi
- uses: actions/checkout@v4
if: steps.check-key.outputs.has_key == 'true'
- name: Set up Python 3.11
if: steps.check-key.outputs.has_key == 'true'
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
if: steps.check-key.outputs.has_key == 'true'
run: |
pip install --upgrade pip
cd src/praisonai-agents
pip install -e ".[all]"
cd ../praisonai
pip install -e ".[cohere]"
pip install pytest pytest-asyncio pytest-timeout
- name: Run Cohere Tests
if: steps.check-key.outputs.has_key == 'true'
env:
PRAISONAI_ALLOW_NETWORK: '1'
PRAISONAI_LIVE_TESTS: '1'
PRAISONAI_TEST_PROVIDERS: 'cohere'
COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
run: |
cd src/praisonai
python -m pytest tests/ \
-m "provider_cohere" \
-v --tb=short \
--timeout=120 \
--ignore=tests/fixtures \
|| echo "Cohere tests completed"
- name: Skip notice
if: steps.check-key.outputs.has_key == 'false'
run: echo "✅ Cohere tests skipped (no API key available)"
# ============================================
# TEST SUMMARY
# ============================================
test-summary:
needs: [smoke, main]
if: always()
runs-on: ubuntu-latest
steps:
- name: Test Summary
run: |
echo "# 📊 Test Suite Summary" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "| Job | Status |" >> $GITHUB_STEP_SUMMARY
echo "|-----|--------|" >> $GITHUB_STEP_SUMMARY
echo "| Smoke | ${{ needs.smoke.result }} |" >> $GITHUB_STEP_SUMMARY
echo "| Main | ${{ needs.main.result }} |" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "## Test Tiers" >> $GITHUB_STEP_SUMMARY
echo "- **smoke**: Fast unit tests, no network (≤2 min)" >> $GITHUB_STEP_SUMMARY
echo "- **main**: Unit + integration, no network (≤5 min)" >> $GITHUB_STEP_SUMMARY
echo "- **openai-live**: OpenAI API tests (when key available)" >> $GITHUB_STEP_SUMMARY
echo "- **extended-***: Provider-specific tests (nightly)" >> $GITHUB_STEP_SUMMARY
-1
View File
@@ -26,7 +26,6 @@ jobs:
real-tests:
runs-on: ubuntu-latest
if: ${{ github.event.inputs.confirm_costs == 'true' && github.event.inputs.framework != 'none' }}
timeout-minutes: 15
strategy:
matrix:
-204
View File
@@ -1,204 +0,0 @@
name: Windows Tests
on:
push:
branches: [main, develop]
pull_request:
branches: [main, develop]
workflow_dispatch:
jobs:
test-windows:
runs-on: windows-latest
timeout-minutes: 5
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_MODEL_NAME: gpt-4o-mini
steps:
- uses: actions/checkout@v4
- name: Set up Python 3.11
uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install praisonaiagents
run: |
python -m pip install --upgrade pip
cd src/praisonai-agents
pip install .
shell: bash
- name: Install praisonai wrapper
run: |
cd src/praisonai
pip install .[dev]
shell: bash
# ==================== CORE IMPORTS ====================
- name: Test core imports
run: |
python -c "from praisonaiagents import Agent, Agents, Workflow; print('Core imports OK')"
python -c "from praisonaiagents import tool, Tools; print('Tool imports OK')"
python -c "from praisonaiagents import Task, WorkflowStep; print('Task/Step imports OK')"
# ==================== AGENT FEATURES ====================
- name: Test Agent instantiation
run: |
python -c "
from praisonaiagents import Agent
a = Agent(name='Test', instructions='You are helpful')
print('Agent created:', a.name)
a2 = Agent(name='ToolAgent', instructions='Use tools', tools=[])
print('Agent with tools:', a2.name)
"
- name: Test Agents orchestration
run: |
python -c "
from praisonaiagents import Agent, Agents, Task
a1 = Agent(name='Agent1', instructions='First agent')
a2 = Agent(name='Agent2', instructions='Second agent')
t = Task(description='Do something', agent=a1)
agents = Agents(agents=[a1, a2], tasks=[t])
print('Agents:', len(agents.agents), 'Tasks:', len(agents.tasks))
"
# ==================== WORKFLOW FEATURES ====================
- name: Test Workflow and WorkflowStep
run: |
python -c "
from praisonaiagents import Workflow, WorkflowStep
from praisonaiagents.workflows import WorkflowOutputConfig
w = Workflow(name='test')
s1 = WorkflowStep(name='step1')
s2 = WorkflowStep(name='step2', context=['step1'])
print('Workflow:', w.name)
print('Steps:', s1.name, s2.name)
cfg = WorkflowOutputConfig(verbose=True, stream=False)
w2 = Workflow(name='configured', output=cfg)
print('Configured workflow verbose:', w2.verbose)
"
# ==================== MEMORY FEATURES ====================
- name: Test Memory classes
run: |
python -c "
from praisonaiagents.memory import FileMemory
from praisonaiagents.config import MemoryConfig
cfg = MemoryConfig(backend='file', user_id='test_user')
print('MemoryConfig backend:', cfg.backend)
mem = FileMemory(user_id='test_user', base_path='.praison/test_memory')
print('FileMemory user:', mem.user_id)
"
# ==================== TOOL DECORATOR ====================
- name: Test tool decorator
run: |
python -c "
from praisonaiagents import tool
@tool
def my_tool(query: str) -> str:
'''A test tool'''
return f'Echo: {query}'
result = my_tool('hello')
print('Tool result:', result)
"
# ==================== CONFIG CLASSES ====================
- name: Test config classes
run: |
python -c "
from praisonaiagents.config import OutputConfig, ExecutionConfig, PlanningConfig
out_cfg = OutputConfig(verbose=True, stream=True)
print('OutputConfig verbose:', out_cfg.verbose)
exec_cfg = ExecutionConfig(max_iter=50, max_retry_limit=5)
print('ExecutionConfig max_iter:', exec_cfg.max_iter)
plan_cfg = PlanningConfig(reasoning=True, auto_approve=False)
print('PlanningConfig reasoning:', plan_cfg.reasoning)
"
# ==================== HANDOFF ====================
- name: Test Handoff
run: |
python -c "
from praisonaiagents import Handoff, handoff, Agent
a1 = Agent(name='Agent1', instructions='First')
a2 = Agent(name='Agent2', instructions='Second')
# Using Handoff class directly
h = Handoff(agent=a2)
print('Handoff tool name:', h.tool_name)
# Using handoff function
h2 = handoff(a1, tool_name_override='custom_handoff')
print('Custom handoff name:', h2.tool_name)
"
# ==================== ASYNC FEATURES ====================
- name: Test async imports
run: |
python -c "
import asyncio
from praisonaiagents import Agent
async def test_async():
agent = Agent(name='AsyncAgent', instructions='Test')
assert hasattr(agent, 'arun'), 'Missing arun'
assert hasattr(agent, 'astart'), 'Missing astart'
print('Async methods available')
asyncio.run(test_async())
"
# ==================== CLI COMMANDS ====================
- name: Test CLI help
run: praisonai --help
- name: Test CLI session list
run: praisonai session list
continue-on-error: true
- name: Test CLI queue list
run: praisonai queue list
continue-on-error: true
# ==================== REAL API TESTS ====================
- name: Real API test - Agent run
if: env.OPENAI_API_KEY != ''
run: |
python -c "
from praisonaiagents import Agent
agent = Agent(name='TestAgent', instructions='Reply with exactly: WINDOWS_OK', llm='gpt-4o-mini')
result = agent.run('Say WINDOWS_OK')
print('Result:', result)
print('Real API test passed')
"
- name: Real API test - Multi-agent
if: env.OPENAI_API_KEY != ''
run: |
python -c "
from praisonaiagents import Agent, Agents, Task
researcher = Agent(name='Researcher', instructions='You research topics', llm='gpt-4o-mini')
writer = Agent(name='Writer', instructions='You write content', llm='gpt-4o-mini')
task = Task(description='Say hello world', agent=researcher)
agents = Agents(agents=[researcher, writer], tasks=[task])
result = agents.start()
print('Multi-agent result type:', type(result))
print('Multi-agent test passed')
"
+5 -6
View File
@@ -5,7 +5,6 @@ on: [push, pull_request]
jobs:
quick-test:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout code
@@ -27,7 +26,7 @@ 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
uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
uv pip install --system pytest pytest-asyncio pytest-cov
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
@@ -35,7 +34,7 @@ jobs:
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-5-nano' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
@@ -316,8 +315,8 @@ jobs:
try:
from praisonai.inc.models import PraisonAIModel
# Test PraisonAIModel with openai/gpt-5-nano (from YAML)
model = PraisonAIModel(model='openai/gpt-5-nano')
# Test PraisonAIModel with openai/gpt-4o-mini (from YAML)
model = PraisonAIModel(model='openai/gpt-4o-mini')
print('🤖 PraisonAIModel Configuration:')
print(f' model: {model.model}')
@@ -489,7 +488,7 @@ jobs:
- name: Run Legacy Example Tests
run: |
cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings -m "not real" --timeout=60
cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
continue-on-error: true
- name: Upload coverage reports to Codecov
-57
View File
@@ -1,57 +0,0 @@
name: Update API Reference
on:
push:
branches: [ "main" ]
paths:
- 'src/praisonai-agents/praisonaiagents/**/*.py'
- 'src/praisonai/praisonai/**/*.py'
- 'src/praisonai-ts/src/**/*.ts'
- '.github/workflows/update-api-md.yml'
permissions:
contents: write
jobs:
update-api-md:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
ref: main
persist-credentials: true
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Generate api.md
run: |
cd $GITHUB_WORKSPACE
python3 src/praisonai/scripts/generate_api_md.py
- name: Check for changes
id: check_changes
run: |
if git diff --quiet api.md; then
echo "changes=false" >> $GITHUB_OUTPUT
echo "No changes to api.md"
else
echo "changes=true" >> $GITHUB_OUTPUT
echo "api.md has been updated"
git diff api.md | head -50
fi
- name: Commit and push changes
if: steps.check_changes.outputs.changes == 'true'
uses: stefanzweifel/git-auto-commit-action@v5
with:
commit_message: "docs: auto-update api.md [skip ci]"
file_pattern: 'api.md'
commit_user_name: "github-actions[bot]"
commit_user_email: "41898282+github-actions[bot]@users.noreply.github.com"
commit_author: "github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>"
-80
View File
@@ -1,80 +0,0 @@
name: Update Documentation Parity Tracker
on:
push:
branches: [ "main" ]
paths:
- 'src/praisonai-agents/praisonaiagents/**/*.py'
- 'src/praisonai-ts/src/**/*.ts'
- 'src/praisonai-rust/src/**/*.rs'
- 'src/praisonai/praisonai/_dev/parity/**/*.py'
- '.github/workflows/update-docs-parity.yml'
workflow_dispatch: # Allow manual trigger
permissions:
contents: write
jobs:
update-docs-parity:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
ref: main
persist-credentials: true
fetch-depth: 0
- name: Checkout PraisonAIDocs
uses: actions/checkout@v4
with:
repository: MervinPraison/PraisonAIDocs
path: PraisonAIDocs
token: ${{ secrets.DOCS_PAT }}
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
run: |
pip install pyyaml
- name: Generate docs parity trackers
run: |
cd $GITHUB_WORKSPACE
python3 src/praisonai/scripts/generate_docs_parity.py --docs-root PraisonAIDocs/docs --copy-docs
- name: Check for changes
id: check_changes
run: |
if git diff --quiet src/praisonai-agents/DOCS_PARITY.md src/praisonai-ts/DOCS_PARITY.md src/praisonai-rust/DOCS_PARITY.md 2>/dev/null; then
echo "changes=false" >> $GITHUB_OUTPUT
echo "No changes to docs parity trackers"
else
echo "changes=true" >> $GITHUB_OUTPUT
echo "Docs parity trackers have been updated"
git diff --stat src/praisonai-agents/DOCS_PARITY.md src/praisonai-ts/DOCS_PARITY.md src/praisonai-rust/DOCS_PARITY.md
fi
- name: Commit and push changes to praisonai-package
if: steps.check_changes.outputs.changes == 'true'
uses: stefanzweifel/git-auto-commit-action@v5
with:
commit_message: "docs: auto-update documentation parity trackers [skip ci]"
file_pattern: 'src/praisonai-agents/DOCS_PARITY.md src/praisonai-ts/DOCS_PARITY.md src/praisonai-rust/DOCS_PARITY.md'
commit_user_name: "github-actions[bot]"
commit_user_email: "41898282+github-actions[bot]@users.noreply.github.com"
commit_author: "github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>"
- name: Commit and push changes to PraisonAIDocs
if: steps.check_changes.outputs.changes == 'true'
run: |
cd PraisonAIDocs
git config user.name "github-actions[bot]"
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
git add docs/features/DOCS_PARITY.md docs/js/DOCS_PARITY.md docs/rust/DOCS_PARITY.md
git diff --staged --quiet || git commit -m "docs: auto-update documentation parity trackers [skip ci]"
git push
@@ -1,58 +0,0 @@
name: Update Feature Parity Tracker
on:
push:
branches: [ "main" ]
paths:
- 'src/praisonai-agents/praisonaiagents/**/*.py'
- 'src/praisonai-ts/src/**/*.ts'
- 'src/praisonai-rust/src/**/*.rs'
- '.github/workflows/update-parity-tracker.yml'
permissions:
contents: write
jobs:
update-parity-tracker:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
ref: main
persist-credentials: true
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Generate parity trackers
run: |
cd $GITHUB_WORKSPACE
python3 src/praisonai/scripts/generate_parity_tracker.py
- name: Check for changes
id: check_changes
run: |
if git diff --quiet src/praisonai-ts/FEATURE_PARITY_TRACKER.json src/praisonai-ts/PARITY.md src/praisonai-rust/FEATURE_PARITY_TRACKER.json src/praisonai-rust/PARITY.md 2>/dev/null; then
echo "changes=false" >> $GITHUB_OUTPUT
echo "No changes to parity trackers"
else
echo "changes=true" >> $GITHUB_OUTPUT
echo "Parity trackers have been updated"
git diff src/praisonai-ts/FEATURE_PARITY_TRACKER.json | head -50
fi
- name: Commit and push changes
if: steps.check_changes.outputs.changes == 'true'
uses: stefanzweifel/git-auto-commit-action@v5
with:
commit_message: "docs: auto-update feature parity trackers [skip ci]"
file_pattern: 'src/praisonai-ts/FEATURE_PARITY_TRACKER.json src/praisonai-ts/PARITY.md src/praisonai-rust/FEATURE_PARITY_TRACKER.json src/praisonai-rust/PARITY.md'
commit_user_name: "github-actions[bot]"
commit_user_email: "41898282+github-actions[bot]@users.noreply.github.com"
commit_author: "github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>"
+1 -19
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@@ -20,7 +20,6 @@ watch.sh
docs.sh
other
output
!src/praisonai/praisonai/cli/output/
.files
threads.db
@@ -76,21 +75,4 @@ agents/praisonaiagents/praisonaiagents.egg-info
# Local Netlify folder
.netlify
.qodo
CopilotKit*
.gemini
issue_details.json
# MCP Registry tokens
.mcpregistry_*
*.log
.internal
.praisonai
# Rust / Cargo
target/
Cargo.lock
**/*.rs.bk
*.pdb
.windsurf
.agent
CopilotKit*
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-6
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@@ -13,12 +13,6 @@ GOOGLE_API_KEY=your_google_api_key_here
GROQ_API_KEY=your_groq_api_key_here
COHERE_API_KEY=your_cohere_api_key_here
# Bot Tokens (for messaging bot services)
SLACK_BOT_TOKEN=xoxb-your-slack-bot-token
SLACK_APP_TOKEN=xapp-your-slack-app-token
DISCORD_BOT_TOKEN=your-discord-bot-token
TELEGRAM_BOT_TOKEN=your-telegram-bot-token
# Optional: Service Configuration
FLASK_ENV=development
CHAINLIT_HOST=0.0.0.0
+1 -2
View File
@@ -14,10 +14,9 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
RUN mkdir -p /root/.praison
# Install Python packages (using latest versions)
# Note: praisonai is installed from PyPI - remove version pin to use latest available
RUN pip install --no-cache-dir \
flask \
praisonai \
"praisonai>=2.2.33" \
"praisonai[api]" \
gunicorn \
markdown
+1 -1
View File
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
# Install Python packages (using latest versions)
RUN pip install --no-cache-dir \
praisonai_tools \
"praisonai>=4.5.24" \
"praisonai>=2.2.33" \
"praisonai[chat]" \
"embedchain[github,youtube]"
+1 -1
View File
@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
# Install Python packages (using latest versions)
RUN pip install --no-cache-dir \
praisonai_tools \
"praisonai>=4.5.24" \
"praisonai>=2.2.33" \
"praisonai[ui]" \
"praisonai[chat]" \
"praisonai[realtime]" \
-25
View File
@@ -1,25 +0,0 @@
# Dockerfile for PraisonAI Serve API
# Deploy to Railway, Render, Fly.io, or any container platform
#
# Build: docker build -f Dockerfile.serve -t praisonai-serve .
# Run: docker run -p 8005:8005 -e OPENAI_API_KEY=sk-xxx praisonai-serve
FROM python:3.11-slim
WORKDIR /app
# Install dependencies
RUN pip install --no-cache-dir praisonai praisonaiagents
# Copy agents configuration
COPY agents.yaml .
# Expose the API port
EXPOSE 8005
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD curl -f http://localhost:8005/health || exit 1
# Start the API server
CMD ["praisonai", "serve", "agents.yaml", "--host", "0.0.0.0", "--port", "8005"]
+1 -1
View File
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
# Install Python packages (using latest versions)
RUN pip install --no-cache-dir \
praisonai_tools \
"praisonai>=4.5.24" \
"praisonai>=2.2.33" \
"praisonai[ui]" \
"praisonai[crewai]"
+2 -2
View File
@@ -121,7 +121,7 @@ healthcheck:
## 📦 Package Versions
All Docker images use consistent, up-to-date versions:
- PraisonAI: `>=2.2.95`
- 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==2.2.95" "praisonaiagents==0.0.92"
RUN pip install "praisonai==2.2.33" "praisonaiagents==0.0.92"
```
## 🌐 Production Deployment
-15
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@@ -1,15 +0,0 @@
# Bot Environment Variables
# Copy to .env and fill in your values
# Required: LLM API Key
OPENAI_API_KEY=your_openai_api_key_here
# Slack Bot (Socket Mode)
SLACK_BOT_TOKEN=xoxb-your-slack-bot-token
SLACK_APP_TOKEN=xapp-your-slack-app-token
# Discord Bot
DISCORD_BOT_TOKEN=your-discord-bot-token
# Telegram Bot
TELEGRAM_BOT_TOKEN=your-telegram-bot-token
-42
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@@ -1,42 +0,0 @@
# Dockerfile for PraisonAI Slack Bot
# Secrets are passed at runtime via environment variables
FROM python:3.11-slim
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
gcc \
python3-dev \
curl \
git \
&& rm -rf /var/lib/apt/lists/*
# Create praison config directory
RUN mkdir -p /root/.praison
# Install praisonai with bot dependencies
RUN pip install --no-cache-dir \
praisonai \
slack-bolt \
slack-sdk \
"discord.py>=2.0.0" \
"python-telegram-bot>=20.0"
# Set environment variables for directory management
ENV PRAISON_CONFIG_DIR=/root/.praison
ENV DOCKER_CONTAINER=true
# Bot tokens should be passed at runtime (not baked into image)
# SLACK_BOT_TOKEN=xoxb-...
# SLACK_APP_TOKEN=xapp-...
# Optional: Copy custom agent configuration
# COPY agent.yaml /app/agent.yaml
# Expose no ports - Socket Mode doesn't need them
# For webhook mode, expose port 3000
# Default command - tokens from environment
CMD ["praisonai", "bot", "slack"]
-66
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@@ -1,66 +0,0 @@
# PraisonAI Bot Docker Deployment
Deploy Slack, Discord, or Telegram bots using Docker.
## Quick Start
```bash
# 1. Copy environment template
cp .env.template .env
# 2. Edit .env with your tokens
nano .env
# 3. Run specific bot
docker compose up slack-bot -d
# Or run all bots
docker compose up -d
```
## Environment Variables
| Variable | Required | Description |
|----------|----------|-------------|
| `OPENAI_API_KEY` | Yes | LLM API key |
| `SLACK_BOT_TOKEN` | For Slack | Bot token (xoxb-...) |
| `SLACK_APP_TOKEN` | For Slack | App token (xapp-...) |
| `DISCORD_BOT_TOKEN` | For Discord | Bot token |
| `TELEGRAM_BOT_TOKEN` | For Telegram | Bot token |
## Slack Setup
1. Create app at https://api.slack.com/apps
2. Enable **Socket Mode** → Generate App-Level Token with `connections:write`
3. Add **OAuth Scopes**: `chat:write`, `app_mentions:read`, `im:history`
4. Enable **Event Subscriptions** → Subscribe to `app_mention`, `message.im`
5. **Install to Workspace**
6. Copy Bot Token and App Token to `.env`
## Commands
```bash
# Start Slack bot
docker compose up slack-bot -d
# View logs
docker compose logs -f slack-bot
# Stop bot
docker compose down slack-bot
# Rebuild after updates
docker compose build --no-cache slack-bot
```
## Custom Agent Configuration
Mount your agent config:
```yaml
services:
slack-bot:
volumes:
- ./agent.yaml:/app/agent.yaml
command: ["praisonai", "bot", "slack", "--agent", "/app/agent.yaml"]
```
-53
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@@ -1,53 +0,0 @@
version: '3.8'
# PraisonAI Bot Services
# Run specific bots: docker compose up slack-bot
# Run all bots: docker compose up
services:
# Slack Bot
slack-bot:
build:
context: ../..
dockerfile: docker/bots/Dockerfile
volumes:
- praison_data:/root/.praison
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
SLACK_BOT_TOKEN: ${SLACK_BOT_TOKEN}
SLACK_APP_TOKEN: ${SLACK_APP_TOKEN}
PRAISON_CONFIG_DIR: /root/.praison
command: [ "praisonai", "bot", "slack" ]
restart: unless-stopped
# Discord Bot
discord-bot:
build:
context: ../..
dockerfile: docker/bots/Dockerfile
volumes:
- praison_data:/root/.praison
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
DISCORD_BOT_TOKEN: ${DISCORD_BOT_TOKEN}
PRAISON_CONFIG_DIR: /root/.praison
command: [ "praisonai", "bot", "discord" ]
restart: unless-stopped
# Telegram Bot
telegram-bot:
build:
context: ../..
dockerfile: docker/bots/Dockerfile
volumes:
- praison_data:/root/.praison
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
TELEGRAM_BOT_TOKEN: ${TELEGRAM_BOT_TOKEN}
PRAISON_CONFIG_DIR: /root/.praison
command: [ "praisonai", "bot", "telegram" ]
restart: unless-stopped
volumes:
praison_data:
driver: local
+15 -4
View File
@@ -137,11 +137,22 @@ services:
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
read_only: true
tmpfs:
- /tmp
- /var/tmp
ports:
- "5432:5432"
networks:
+4 -5
View File
@@ -22,7 +22,7 @@ services:
CHAINLIT_DB_DIR: /root/.praison
command: praisonai ui --host 0.0.0.0
healthcheck:
test: [ "CMD", "curl", "-f", "http://localhost:8082" ]
test: ["CMD", "curl", "-f", "http://localhost:8082"]
interval: 10s
timeout: 5s
retries: 3
@@ -49,7 +49,7 @@ services:
CHAINLIT_DB_DIR: /root/.praison
command: praisonai chat --host 0.0.0.0 --port 8083
healthcheck:
test: [ "CMD", "curl", "-f", "http://localhost:8083" ]
test: ["CMD", "curl", "-f", "http://localhost:8083"]
interval: 10s
timeout: 5s
retries: 3
@@ -69,7 +69,7 @@ services:
OPENAI_API_KEY: ${OPENAI_API_KEY}
PRAISON_CONFIG_DIR: /root/.praison
healthcheck:
test: [ "CMD", "curl", "-f", "http://localhost:8080/health" ]
test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
interval: 10s
timeout: 5s
retries: 3
@@ -89,7 +89,6 @@ services:
PRAISON_CONFIG_DIR: /root/.praison
networks:
- praison_network
# For bot services (Slack, Discord, Telegram), see docker/bots/
volumes:
praison_data:
@@ -97,4 +96,4 @@ volumes:
networks:
praison_network:
driver: bridge
driver: bridge
-83
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@@ -1,83 +0,0 @@
# PraisonAI Examples
This folder contains examples for PraisonAI. For detailed documentation, visit [docs.praison.ai](https://docs.praison.ai).
## Structure
```
examples/
├── python/ # Python examples
│ ├── agents/ # Agent examples (single, multi, router, etc.)
│ ├── workflows/ # Workflow patterns (routing, parallel, loop)
│ ├── tools/ # Custom tools examples
│ ├── mcp/ # MCP protocol examples
│ ├── memory/ # Memory and sessions
│ ├── code/ # Code editing and external CLI tools
│ └── ...
├── serve/ # Server and endpoints examples
├── yaml/ # YAML workflow examples
└── cookbooks/ # Complete use-case examples
```
## Quick Links
| Category | Examples | Docs |
|----------|----------|------|
| **Consolidated Params** | [consolidated_params/](consolidated_params/) | [📖](https://docs.praison.ai/api/consolidated-params) |
| **Agents** | [python/agents/](python/agents/) | [📖](https://docs.praison.ai/concepts/agents) |
| **Workflows** | [python/workflows/](python/workflows/) | [📖](https://docs.praison.ai/features/workflows) |
| **Model Router** | [python/agents/router-agent-cost-optimization.py](python/agents/router-agent-cost-optimization.py) | [📖](https://docs.praison.ai/features/model-router) |
| **MCP** | [python/mcp/](python/mcp/) | [📖](https://docs.praison.ai/mcp) |
| **Memory** | [python/memory/](python/memory/) | [📖](https://docs.praison.ai/concepts/memory) |
| **Tools** | [python/tools/](python/tools/) | [📖](https://docs.praison.ai/tools) |
| **Code** | [python/code/](python/code/) | [📖](https://docs.praison.ai/code) |
| **YAML** | [yaml/](yaml/) | [📖](https://docs.praison.ai/features/yaml-workflows) |
| **Doctor** | [doctor/](doctor/) | [📖](https://docs.praison.ai/cli/doctor) |
| **Serve** | [serve/](serve/) | [📖](https://docs.praison.ai/cli/serve) |
| **Endpoints** | [serve/](serve/) | [📖](https://docs.praison.ai/cli/endpoints) |
## Consolidated Params Examples
Agent-centric API with unified parameter resolution. Precedence: Instance > Config > Array > Dict > String > Bool > Default
| Example | Description |
|---------|-------------|
| [basic_agent.py](consolidated_params/basic_agent.py) | Minimal agent with memory |
| [basic_agents.py](consolidated_params/basic_agents.py) | Multi-agent with memory+planning |
| [basic_workflow.py](consolidated_params/basic_workflow.py) | Workflow with consolidated params |
| [basic_memory.py](consolidated_params/basic_memory.py) | Memory presets (file, redis, postgres) |
| [basic_guardrails.py](consolidated_params/basic_guardrails.py) | Guardrails with callable or config |
| [basic_workflow_agentlike.py](consolidated_params/basic_workflow_agentlike.py) | Workflow with agent-like params |
| [basic_step_override.py](consolidated_params/basic_step_override.py) | Step-level override of workflow defaults |
| [advanced_workflow_full_features.py](consolidated_params/advanced_workflow_full_features.py) | All consolidated params |
## Serve Examples
| Example | Description | CLI Command |
|---------|-------------|-------------|
| [unified_server.py](serve/unified_server.py) | All providers in one server | `praisonai serve unified` |
| [agent_as_api_single.py](serve/agent_as_api_single.py) | Single agent HTTP API | `praisonai serve agents` |
| [agents_as_api_router.py](serve/agents_as_api_router.py) | Multi-agent router API | `praisonai serve agents` |
| [a2a_server_client.py](serve/a2a_server_client.py) | A2A protocol server | `praisonai serve a2a` |
| [a2u_events_stream.py](serve/a2u_events_stream.py) | A2U event stream | `praisonai serve a2u` |
| [mcp_http_server.py](serve/mcp_http_server.py) | MCP HTTP server | `praisonai serve mcp` |
| [tools_as_mcp_server.py](serve/tools_as_mcp_server.py) | Tools as MCP server | `praisonai serve tools` |
| [agent_launch_modes.py](serve/agent_launch_modes.py) | Agent.launch() API | Python only |
| [endpoints_unified_client.py](serve/endpoints_unified_client.py) | Unified client | `praisonai endpoints` |
## Running Examples
```bash
# Install PraisonAI
pip install praisonai
# Set API key
export OPENAI_API_KEY=your_key_here
# Run an example
python examples/python/agents/single-agent.py
```
## CLI Commands
See the main [README.md](../README.md#-cli--no-code-interface) for all CLI commands.
-137
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@@ -1,137 +0,0 @@
# ACP (Agent Client Protocol) Examples
This directory contains examples for using PraisonAI with the Agent Client Protocol (ACP).
## What is ACP?
The Agent Client Protocol (ACP) is a standardized JSON-RPC 2.0 protocol that allows code editors and IDEs to communicate with AI coding agents. It enables seamless integration between PraisonAI and editors like:
- **Zed**
- **JetBrains IDEs** (IntelliJ, PyCharm, WebStorm, etc.)
- **VSCode** (via extensions)
- **Toad**
## Quick Start
### CLI Usage
```bash
# Start ACP server with defaults
praisonai acp
# Enable debug logging
praisonai acp --debug
# Allow file writes
praisonai acp --allow-write
# Use a specific model
praisonai acp -m gpt-4o
# Resume last session
praisonai acp --resume --last
```
### Python API
```python
from praisonai.acp import serve
serve(
workspace=".",
debug=True,
allow_write=True,
)
```
## Editor Configuration
### Zed
Add to `~/.config/zed/settings.json`:
```json
{
"agent_servers": {
"PraisonAI": {
"command": "praisonai",
"args": ["acp"],
"env": {}
}
}
}
```
### JetBrains
Add to `~/.jetbrains/acp.json`:
```json
{
"agent_servers": {
"PraisonAI": {
"command": "praisonai",
"args": ["acp"],
"env": {}
}
}
}
```
### Toad
```bash
toad acp "praisonai acp"
```
## Examples
- `basic_acp_server.py` - Simple ACP server with default settings
- `custom_agent_acp.py` - ACP server with a custom agent
## CLI Options
| Option | Description |
|--------|-------------|
| `-w, --workspace` | Workspace root directory |
| `-a, --agent` | Agent name or config file |
| `-m, --model` | LLM model to use |
| `-r, --resume` | Resume session by ID |
| `--last` | Resume the last session |
| `--approve` | Approval mode: manual, auto, scoped |
| `--read-only` | Read-only mode (default) |
| `--allow-write` | Allow file writes |
| `--allow-shell` | Allow shell commands |
| `--debug` | Enable debug logging |
## Installation
```bash
pip install praisonai[acp]
```
## Protocol Details
ACP uses JSON-RPC 2.0 over stdio:
- **stdin**: Receives JSON-RPC requests from the client
- **stdout**: Sends JSON-RPC responses to the client
- **stderr**: Debug/log output (never pollutes stdout)
### Supported Methods
**Agent Methods (client → agent):**
- `initialize` - Negotiate protocol version and capabilities
- `authenticate` - Optional authentication
- `session/new` - Create a new session
- `session/load` - Load an existing session
- `session/prompt` - Send user message
- `session/cancel` - Cancel ongoing operations
- `session/set_mode` - Change operating mode
**Client Methods (agent → client):**
- `session/update` - Send progress updates
- `session/request_permission` - Request user approval
- `fs/read_text_file` - Read file contents
- `fs/write_text_file` - Write file contents
- `terminal/create` - Create terminal
- `terminal/output` - Get terminal output
-27
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@@ -1,27 +0,0 @@
#!/usr/bin/env python3
"""
Basic ACP Server Example
This example demonstrates how to run PraisonAI as an ACP server
that can be connected to by IDEs like Zed, JetBrains, VSCode, or Toad.
Usage:
# Run from command line
praisonai acp
# Or run this script directly
python basic_acp_server.py
"""
from praisonai.acp import serve
if __name__ == "__main__":
# Start ACP server with default settings
# This will listen on stdin/stdout for JSON-RPC messages
serve(
workspace=".", # Current directory as workspace
agent="default", # Use default agent
debug=False, # Set to True for debug logging to stderr
read_only=True, # Safe by default
approval_mode="manual", # Require approval for actions
)
-45
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@@ -1,45 +0,0 @@
#!/usr/bin/env python3
"""
Custom Agent ACP Server Example
This example shows how to run a custom PraisonAI agent as an ACP server.
Usage:
python custom_agent_acp.py
"""
from praisonai.acp import serve, ACPServer, ACPConfig
from praisonaiagents import Agent
# Create a custom agent
agent = Agent(
name="CodeAssistant",
instructions="""You are an expert coding assistant. You help users:
- Write clean, efficient code
- Debug issues
- Explain complex concepts
- Review code for best practices
Always provide clear explanations with your code suggestions.""",
model="gpt-4o-mini",
)
if __name__ == "__main__":
# Create config
config = ACPConfig(
workspace=".",
debug=True,
allow_write=True, # Allow file modifications
approval_mode="manual",
)
# Create server with custom agent
server = ACPServer(config=config, agent=agent)
# Run the server
serve(
workspace=".",
debug=True,
allow_write=True,
approval_mode="manual",
)
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framework: ag2
topic: "Research the latest developments in AI agents"
# Install: pip install "praisonai[ag2]"
# Run: praisonai --framework ag2 examples/ag2/ag2_basic.yaml
# or praisonai run examples/ag2/ag2_basic.yaml --framework ag2
roles:
research_agent:
role: "AI Research Specialist"
goal: "Research and summarise the latest developments in AI agent frameworks"
backstory: |
You are an experienced AI researcher with deep knowledge of multi-agent
systems, large language models, and the latest trends in AI tooling.
You excel at synthesising complex technical topics into clear summaries.
tasks:
research_task:
description: |
Research and summarise the latest developments in AI agent frameworks
for the topic: {topic}
Focus on:
1. Key frameworks and their unique capabilities
2. Recent innovations and improvements
3. Community adoption and ecosystem growth
4. Practical use cases and success stories
expected_output: |
A concise research summary covering the key developments,
major frameworks, and practical insights. Include 3-5 bullet
points of the most important findings.
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framework: ag2
topic: "Cloud-native AI deployment strategies on AWS"
# AG2 exclusive feature: native AWS Bedrock support via LLMConfig(api_type="bedrock")
#
# Prerequisites:
# pip install "praisonai[ag2]"
# aws configure (or set AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION)
#
# Run:
# praisonai --framework ag2 examples/ag2/ag2_bedrock.yaml
#
# The AG2 adapter detects api_type="bedrock" from the llm config and uses
# LLMConfig(api_type="bedrock", model=...) — no OPENAI_API_KEY required.
# AWS credentials are sourced from boto3 (env vars, ~/.aws/credentials, IAM role).
roles:
cloud_architect:
role: "AWS Cloud Architect"
goal: "Design and explain cloud-native AI deployment strategies on AWS"
backstory: |
You are an AWS Solutions Architect specialising in AI/ML workloads.
You have deep expertise in Amazon Bedrock, SageMaker, ECS, and Lambda,
and you help organisations deploy AI agents at scale securely and cost-effectively.
llm:
model: "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0"
api_type: "bedrock"
aws_region: "us-east-1"
tasks:
architecture_task:
description: |
Design a cloud-native deployment strategy for AI agents on AWS for: {topic}
Cover:
1. Recommended AWS services (Bedrock, ECS, Lambda, etc.)
2. Scalability and cost optimisation patterns
3. Security and compliance considerations
4. A simple reference architecture overview
expected_output: |
A concise architecture guide with service recommendations,
a high-level deployment diagram description, and key
best practices for production AI agent deployments on AWS.
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framework: ag2
topic: "The impact of open-source AI on enterprise software development"
# Install: pip install "praisonai[ag2]"
# Run: praisonai --framework ag2 examples/ag2/ag2_multi_agent.yaml
# or praisonai run examples/ag2/ag2_multi_agent.yaml --framework ag2
#
# This example demonstrates AG2's GroupChat multi-agent coordination.
# Both agents participate in a collaborative conversation managed by
# a GroupChatManager until the task is complete.
roles:
researcher:
role: "Research Specialist"
goal: "Gather and analyse information on the given topic"
backstory: |
You are a meticulous researcher who excels at finding relevant
information, analysing trends, and presenting data-backed insights.
You always cite your reasoning and structure your findings clearly.
tasks:
research_task:
description: |
Research the topic: {topic}
Investigate:
1. Current state and adoption rates
2. Key players and projects driving the trend
3. Technical advantages and challenges
4. Business impact and cost implications
expected_output: |
A structured research briefing with findings on the topic,
including key data points, trends, and technical observations.
writer:
role: "Technical Content Writer"
goal: "Transform research findings into clear, engaging written content"
backstory: |
You are a skilled technical writer who turns complex research into
accessible, well-structured articles. You focus on clarity, logical
flow, and actionable takeaways for a professional audience.
tasks:
writing_task:
description: |
Using the research findings provided by the Research Specialist,
write a concise article on: {topic}
The article should:
1. Open with a compelling hook
2. Present key findings logically
3. Include practical implications for developers
4. Close with a forward-looking conclusion
expected_output: |
A 400-500 word article suitable for a technical blog,
with clear sections, professional tone, and concrete takeaways.
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"""
Agent-Centric API Example
Demonstrates the consolidated feature parameters for PraisonAI Agents.
These params provide a cleaner, more intuitive API with progressive disclosure.
Precedence Rule: Instance > Config > Array > String > Bool > Default
Consolidated Params Support:
- memory: bool | str URL | str preset | [preset, overrides] | MemoryConfig | Instance
- knowledge: bool | str path | [sources...] | KnowledgeConfig | Instance
- planning: bool | str LLM | [preset, overrides] | PlanningConfig
- reflection: bool | str preset | [preset, overrides] | ReflectionConfig
- guardrails: bool | callable | str prompt | GuardrailConfig
- web: bool | str provider | [provider, overrides] | WebConfig
- output: str preset | [preset, overrides] | OutputConfig
- execution: str preset | [preset, overrides] | ExecutionConfig
"""
from praisonaiagents import (
Agent,
# Config classes for progressive disclosure
MemoryConfig,
KnowledgeConfig,
PlanningConfig,
ReflectionConfig,
GuardrailConfig,
WebConfig,
)
def example_simple_enable():
"""Example 1: Simple boolean enable (easiest)"""
print("\n=== Example 1: Simple Boolean Enable ===")
agent = Agent(
instructions="You are a helpful assistant",
memory=True, # Enable memory with defaults
reflection=True, # Enable self-reflection
web=True, # Enable web search + fetch
)
print(f"Agent created with memory={agent._memory_instance is not None}")
print(f" self_reflect={agent.self_reflect}")
print(f" web_search={agent.web_search}")
def example_with_config():
"""Example 2: Using config objects (more control)"""
print("\n=== Example 2: Using Config Objects ===")
agent = Agent(
instructions="You are a research assistant",
memory=MemoryConfig(
backend="file",
user_id="researcher_001",
auto_memory=True,
),
reflection=ReflectionConfig(
min_iterations=1,
max_iterations=5,
llm="gpt-4o-mini",
),
web=WebConfig(
search=True,
fetch=False, # Only search, no full page fetch
max_results=3,
),
)
print(f"Agent created with user_id={agent.user_id}")
print(f" {agent.max_reflect}")
print(f" web_search={agent.web_search}, web_fetch={agent.web_fetch}")
def example_guardrails():
"""Example 3: Guardrails with callable or config"""
print("\n=== Example 3: Guardrails ===")
# Option A: Direct callable
def my_validator(output):
"""Simple validator that checks response length."""
is_valid = len(output.raw) < 1000
return (is_valid, output if is_valid else "Response too long")
agent_a = Agent(
instructions="Be concise",
guardrails=my_validator, # Direct callable
)
print(f"Agent A: guardrail is callable = {callable(agent_a.guardrail)}")
# Option B: Using GuardrailConfig
agent_b = Agent(
instructions="Be helpful and safe",
guardrails=GuardrailConfig(
llm_validator="Ensure the response is helpful, accurate, and safe",
max_retries=3,
),
)
print(f"Agent B: guardrail = {agent_b.guardrail}")
print(f" max_retries = {agent_b.max_guardrail_retries}")
def example_planning():
"""Example 4: Planning mode"""
print("\n=== Example 4: Planning Mode ===")
agent = Agent(
instructions="You are a project planner",
planning=PlanningConfig(
reasoning=True,
read_only=True, # Plan mode - only read operations
),
)
print(f"Agent created with planning={agent.planning}")
print(f" plan_mode={agent.plan_mode}")
print(f" planning_reasoning={agent.planning_reasoning}")
def example_backward_compatible():
"""Example 5: Using new consolidated params"""
print("\n=== Example 5: Consolidated Params ===")
# New consolidated params
agent = Agent(
instructions="Test agent",
reflection=True,
web=WebConfig(search=True, fetch=False),
)
print(f"Consolidated params work: self_reflect={agent.self_reflect}")
print(f" web_search={agent.web_search}, web_fetch={agent.web_fetch}")
def example_real_chat():
"""Example 6: Real chat with new API"""
print("\n=== Example 6: Real Chat ===")
agent = Agent(
instructions="You are a helpful math tutor. Be concise.",
reflection=False,
web=False,
)
response = agent.start("What is the square root of 144?")
print(f"Response: {response}")
def example_string_presets():
"""Example 7: String presets (NEW - user-friendly shortcuts)"""
print("\n=== Example 7: String Presets ===")
# Output presets: minimal, normal, verbose, debug, silent
agent = Agent(
instructions="You are a verbose assistant",
output="verbose", # String preset for output
execution="fast", # String preset for execution
web="tavily", # String preset for web provider
)
print(f"Output preset 'verbose': verbose={agent.verbose}, metrics={agent.metrics}")
print(f"Execution preset 'fast': max_iter={agent.max_iter}")
print(f"Web preset 'tavily': web_search={agent.web_search}")
def example_array_overrides():
"""Example 8: Array with overrides (NEW - preset + customization)"""
print("\n=== Example 8: Array Overrides ===")
# Array format: [preset, {overrides}]
agent = Agent(
instructions="You are a streaming assistant",
output=["verbose", {"stream": True}], # Verbose preset + enable streaming
execution=["fast", {"max_iter": 15}], # Fast preset + custom max_iter
)
print(f"Output array override: verbose={agent.verbose}, stream={agent.stream}")
print(f"Execution array override: max_iter={agent.max_iter}")
def example_url_parsing():
"""Example 9: URL parsing for memory (NEW - connection strings)"""
print("\n=== Example 9: URL Parsing ===")
# Memory supports URL connection strings
# Supported: postgresql://, redis://, sqlite:///, mongodb://
agent = Agent(
instructions="You are a database-backed assistant",
memory="postgresql://postgres:password@localhost:5432/praisonai",
)
print("Memory URL parsed: memory enabled")
# Also works with presets
agent2 = Agent(
instructions="You are a redis-backed assistant",
memory="redis", # Preset name
)
print("Memory preset 'redis': configured")
def example_knowledge_sources():
"""Example 10: Knowledge with sources (list and string)"""
print("\n=== Example 10: Knowledge Sources ===")
# Single path string
agent1 = Agent(
instructions="You are a document assistant",
knowledge="docs/", # Single path
)
print(f"Knowledge single path: knowledge={agent1.knowledge}")
# List of sources
agent2 = Agent(
instructions="You are a multi-source assistant",
knowledge=["docs/", "data.pdf", "https://example.com/api"],
)
print(f"Knowledge list: knowledge={agent2.knowledge}")
# With config for advanced settings
agent3 = Agent(
instructions="You are an advanced RAG assistant",
knowledge=KnowledgeConfig(
sources=["docs/"],
retrieval_k=10,
rerank=True,
),
)
print("Knowledge config: configured with rerank")
if __name__ == "__main__":
print("=" * 60)
print("PraisonAI Agent-Centric API Examples")
print("=" * 60)
example_simple_enable()
example_with_config()
example_guardrails()
example_planning()
example_backward_compatible()
example_real_chat()
example_string_presets()
example_array_overrides()
example_url_parsing()
example_knowledge_sources()
print("\n" + "=" * 60)
print("All examples completed!")
print("=" * 60)
@@ -1,135 +0,0 @@
"""
Agent-Centric Tools Example
This example demonstrates how to use LSP/ACP-powered tools that make
the Agent the central orchestrator for file operations.
How to run:
export OPENAI_API_KEY=your_key
python examples/agent_tools/agent_centric_example.py
Prerequisites:
pip install praisonai praisonaiagents
"""
import asyncio
import os
import sys
import tempfile
import shutil
# Add paths for development
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai'))
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai-agents'))
async def main():
print("=" * 60)
print("AGENT-CENTRIC TOOLS EXAMPLE")
print("=" * 60)
print()
# Create temporary workspace
workspace = tempfile.mkdtemp(prefix="praisonai_example_")
print(f"Workspace: {workspace}")
print()
try:
# Import after path setup
from praisonai.cli.features import (
create_agent_centric_tools,
InteractiveRuntime,
RuntimeConfig
)
from praisonaiagents import Agent
# 1. Create runtime with ACP enabled
print("1. Creating InteractiveRuntime with ACP...")
config = RuntimeConfig(
workspace=workspace,
lsp_enabled=False, # Skip LSP for this example
acp_enabled=True,
approval_mode="auto" # Auto-approve for demo
)
runtime = InteractiveRuntime(config)
status = await runtime.start()
print(f" ACP ready: {runtime.acp_ready}")
print(f" Read-only: {runtime.read_only}")
print()
# 2. Create agent-centric tools
print("2. Creating agent-centric tools...")
tools = create_agent_centric_tools(runtime)
print(f" Tools created: {len(tools)}")
for tool in tools:
print(f" - {tool.__name__}")
print()
# 3. Create Agent with ACP-powered tools
print("3. Creating Agent with ACP-powered tools...")
agent = Agent(
name="FileAgent",
instructions="""You help create and manage files.
Use acp_create_file to create files.
Use read_file to read files.
Use list_files to list directory contents.""",
tools=tools,
output="verbose" # Use new consolidated param
)
print()
# 4. Ask agent to create a file
print("4. Asking agent to create a Python file...")
print("-" * 40)
result = agent.start(
"Create a Python file called calculator.py with add and subtract functions"
)
print("-" * 40)
print(f"Agent response: {result[:200]}..." if len(str(result)) > 200 else f"Agent response: {result}")
print()
# 5. Verify file was created
print("5. Verifying file creation...")
calc_path = os.path.join(workspace, "calculator.py")
if os.path.exists(calc_path):
print(f" ✓ File created at {calc_path}")
with open(calc_path) as f:
content = f.read()
print(f" Content preview:")
for line in content.split('\n')[:10]:
print(f" {line}")
else:
# Check what files exist
files = os.listdir(workspace)
print(f" Files in workspace: {files}")
for f in files:
fp = os.path.join(workspace, f)
if os.path.isfile(fp):
print(f" ✓ Found: {f}")
print()
# 6. Ask agent to read the file
print("6. Asking agent to read the file...")
print("-" * 40)
result2 = agent.start("Read the calculator.py file and tell me what functions it has")
print("-" * 40)
print(f"Agent response: {result2[:300]}..." if len(str(result2)) > 300 else f"Agent response: {result2}")
print()
# Cleanup
await runtime.stop()
finally:
# Clean up workspace
shutil.rmtree(workspace, ignore_errors=True)
print(f"Cleaned up workspace: {workspace}")
print()
print("=" * 60)
print("EXAMPLE COMPLETE")
print("=" * 60)
if __name__ == "__main__":
asyncio.run(main())
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# Approval Examples
Minimal examples showing how to route tool-execution approvals through
different backends.
## Quick Start
```bash
pip install praisonaiagents praisonai[bot]
export OPENAI_API_KEY=sk-...
```
| Backend | Script | Extra env vars |
|----------|-----------------------|--------------------------------------------------|
| Slack | `slack_approval.py` | `SLACK_BOT_TOKEN`, `SLACK_CHANNEL` |
| Telegram | `telegram_approval.py`| `TELEGRAM_BOT_TOKEN`, `TELEGRAM_CHAT_ID` |
| Discord | `discord_approval.py` | `DISCORD_BOT_TOKEN`, `DISCORD_CHANNEL_ID` |
| HTTP | `http_approval.py` | *(none — opens a local web dashboard)* |
| Agent | `agent_approval.py` | *(none — AI reviewer agent decides)* |
## SSL Verification
For corporate proxy / CA issues, Discord and Telegram backends support
optional SSL bypass:
```bash
export PRAISONAI_DISCORD_SSL_VERIFY=false
export PRAISONAI_TELEGRAM_SSL_VERIFY=false
```
Default is `true` (secure).
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"""
Agent Approval Example
======================
Uses an AI agent as an automated reviewer to approve/deny tool calls.
Requires:
pip install praisonaiagents
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.approval import AgentApproval
from praisonaiagents.tools.shell_tools import execute_command
reviewer = AgentApproval(
instructions="Only approve read-only commands like 'ls' or 'cat'. Deny destructive commands.",
)
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=reviewer,
)
agent.start("List files in the current directory")
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"""
Discord Approval Example
========================
Routes tool approvals to a Discord channel.
Requires:
pip install praisonaiagents praisonai[bot]
export DISCORD_BOT_TOKEN=MTIz...
export DISCORD_CHANNEL_ID=1234567890
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.tools.shell_tools import execute_command
from praisonai.bots import DiscordApproval
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=DiscordApproval(),
)
agent.start("List files in the current directory")
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"""
HTTP Approval Example
=====================
Opens a local web dashboard for tool approvals.
Requires:
pip install praisonaiagents praisonai[bot]
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.tools.shell_tools import execute_command
from praisonai.bots import HTTPApproval
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=HTTPApproval(),
)
agent.start("List files in the current directory")
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"""
Slack Approval Example
======================
Routes tool approvals to a Slack channel.
Requires:
pip install praisonaiagents praisonai[bot]
export SLACK_BOT_TOKEN=xoxb-...
export SLACK_CHANNEL=C0123456789
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.tools.shell_tools import execute_command
from praisonai.bots import SlackApproval
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=SlackApproval(),
)
agent.start("List files in the current directory")
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"""
Telegram Approval Example
=========================
Routes tool approvals to a Telegram chat.
Requires:
pip install praisonaiagents praisonai[bot]
export TELEGRAM_BOT_TOKEN=123456:ABC-...
export TELEGRAM_CHAT_ID=987654321
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.tools.shell_tools import execute_command
from praisonai.bots import TelegramApproval
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=TelegramApproval(),
)
agent.start("List files in the current directory")
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"""
Async Runs Examples
===================
This directory contains examples for the PraisonAI Async Jobs API.
Examples:
---------
1. sdk_submit_poll_result.py - Python SDK example for submit/poll/result
2. cli_submit_poll_result.sh - Bash/curl example for submit/poll/result
3. idempotency.sh - Demonstrates idempotency key usage
4. cancel.sh - Demonstrates job cancellation
5. sse_stream.sh - Demonstrates SSE streaming
Prerequisites:
--------------
1. Set OPENAI_API_KEY environment variable
2. Start the jobs server:
cd /path/to/praisonai-package/src/praisonai
python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory
3. Run examples:
python sdk_submit_poll_result.py
bash cli_submit_poll_result.sh
bash idempotency.sh
bash cancel.sh
bash sse_stream.sh
Environment Variables:
----------------------
- OPENAI_API_KEY: Required for agent execution
- PRAISONAI_BASE_URL: API server URL (default: http://127.0.0.1:8005)
"""
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#!/bin/bash
# CLI Example: Cancel a Running Job
#
# This example demonstrates how to cancel a running job.
#
# Requirements:
# - Jobs server running on http://127.0.0.1:8005
#
# Usage:
# bash cancel.sh
set -e
API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
echo "============================================================"
echo "PraisonAI Job Cancellation Example"
echo "============================================================"
# Submit a long-running job
echo ""
echo "1. Submitting a job..."
RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
-H "Content-Type: application/json" \
-d '{"prompt": "Write a detailed essay about the history of computing."}')
JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
echo " Job ID: $JOB_ID"
# Wait briefly
echo ""
echo "2. Waiting 1 second..."
sleep 1
# Cancel the job
echo ""
echo "3. Cancelling job..."
CANCEL_RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs/$JOB_ID/cancel")
STATUS=$(echo "$CANCEL_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
echo " Status after cancel: $STATUS"
# Verify cancellation
echo ""
echo "4. Verification:"
if [ "$STATUS" = "cancelled" ]; then
echo " ✓ SUCCESS: Job was cancelled"
else
echo " ℹ Job status: $STATUS (may have completed before cancel)"
fi
echo ""
echo "============================================================"
echo "Cancellation example completed!"
echo "============================================================"
@@ -1,75 +0,0 @@
#!/bin/bash
# CLI Example: Submit, Poll, and Get Result for Async Jobs
#
# This example demonstrates how to use the PraisonAI CLI to:
# 1. Submit an async job
# 2. Check job status
# 3. Get the result
#
# Requirements:
# - OPENAI_API_KEY environment variable set
# - Jobs server running on http://127.0.0.1:8005
#
# Usage:
# bash cli_submit_poll_result.sh
set -e
API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
echo "============================================================"
echo "PraisonAI Async Jobs CLI Example"
echo "============================================================"
# Check if server is running
echo ""
echo "Checking server health..."
if ! curl -s "$API_URL/health" > /dev/null 2>&1; then
echo "✗ Server not available at $API_URL"
echo " Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory"
exit 1
fi
echo "✓ Server is healthy"
# 1. Submit a job
echo ""
echo "1. Submitting job..."
RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
-H "Content-Type: application/json" \
-d '{"prompt": "What is 2+2? Answer with just the number."}')
JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
echo " Job ID: $JOB_ID"
# 2. Poll for status
echo ""
echo "2. Polling for status..."
while true; do
STATUS_RESPONSE=$(curl -s "$API_URL/api/v1/runs/$JOB_ID")
STATUS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
PROGRESS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['progress']['percentage'])")
echo " Status: $STATUS | Progress: ${PROGRESS}%"
if [ "$STATUS" = "succeeded" ] || [ "$STATUS" = "failed" ] || [ "$STATUS" = "cancelled" ]; then
break
fi
sleep 2
done
# 3. Get result
echo ""
echo "3. Getting result..."
if [ "$STATUS" = "succeeded" ]; then
RESULT=$(curl -s "$API_URL/api/v1/runs/$JOB_ID/result")
echo " Result: $(echo "$RESULT" | python3 -c "import sys,json; print(json.load(sys.stdin)['result'])")"
echo " Duration: $(echo "$RESULT" | python3 -c "import sys,json; print(f\"{json.load(sys.stdin)['duration_seconds']:.2f}s\")")"
else
echo " Job did not succeed: $STATUS"
fi
echo ""
echo "============================================================"
echo "Example completed!"
echo "============================================================"
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#!/bin/bash
# CLI Example: Idempotency Key Usage
#
# This example demonstrates how idempotency keys prevent duplicate job submissions.
# Submitting the same request with the same idempotency key returns the existing job.
#
# Requirements:
# - Jobs server running on http://127.0.0.1:8005
#
# Usage:
# bash idempotency.sh
set -e
API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
IDEM_KEY="example-idem-key-$(date +%s)"
echo "============================================================"
echo "PraisonAI Idempotency Key Example"
echo "============================================================"
# First submission
echo ""
echo "1. First submission with Idempotency-Key: $IDEM_KEY"
RESPONSE1=$(curl -s -X POST "$API_URL/api/v1/runs" \
-H "Content-Type: application/json" \
-H "Idempotency-Key: $IDEM_KEY" \
-d '{"prompt": "What is 1+1?"}')
JOB_ID1=$(echo "$RESPONSE1" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
echo " Job ID: $JOB_ID1"
# Second submission with same key
echo ""
echo "2. Second submission with SAME Idempotency-Key: $IDEM_KEY"
RESPONSE2=$(curl -s -X POST "$API_URL/api/v1/runs" \
-H "Content-Type: application/json" \
-H "Idempotency-Key: $IDEM_KEY" \
-d '{"prompt": "Different prompt but same key"}')
JOB_ID2=$(echo "$RESPONSE2" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
echo " Job ID: $JOB_ID2"
# Verify same job ID
echo ""
echo "3. Verification:"
if [ "$JOB_ID1" = "$JOB_ID2" ]; then
echo " ✓ SUCCESS: Same job ID returned (no duplicate created)"
else
echo " ✗ FAILURE: Different job IDs returned"
exit 1
fi
echo ""
echo "============================================================"
echo "Idempotency example completed!"
echo "============================================================"
@@ -1,121 +0,0 @@
#!/usr/bin/env python3
"""
SDK Example: Submit, Poll, and Get Result for Async Jobs
This example demonstrates how to use the PraisonAI Jobs SDK to:
1. Submit an async job
2. Poll for status
3. Get the result
Requirements:
- OPENAI_API_KEY environment variable set
- Jobs server running on http://127.0.0.1:8005
Usage:
python sdk_submit_poll_result.py
"""
import os
import time
import httpx
# Configuration
API_URL = os.getenv("PRAISONAI_BASE_URL", "http://127.0.0.1:8005")
POLL_INTERVAL = 2 # seconds
def submit_job(prompt: str, idempotency_key: str = None) -> dict:
"""Submit a job and return the response."""
headers = {}
if idempotency_key:
headers["Idempotency-Key"] = idempotency_key
with httpx.Client(timeout=30.0) as client:
response = client.post(
f"{API_URL}/api/v1/runs",
json={"prompt": prompt},
headers=headers if headers else None
)
response.raise_for_status()
return response.json()
def get_status(job_id: str) -> dict:
"""Get job status."""
with httpx.Client(timeout=30.0) as client:
response = client.get(f"{API_URL}/api/v1/runs/{job_id}")
response.raise_for_status()
return response.json()
def get_result(job_id: str) -> dict:
"""Get job result."""
with httpx.Client(timeout=30.0) as client:
response = client.get(f"{API_URL}/api/v1/runs/{job_id}/result")
response.raise_for_status()
return response.json()
def wait_for_completion(job_id: str, max_wait: int = 120) -> dict:
"""Poll until job completes."""
start = time.time()
while time.time() - start < max_wait:
status = get_status(job_id)
print(f" Status: {status['status']} | Progress: {status['progress']['percentage']:.0f}%")
if status["status"] in ("succeeded", "failed", "cancelled"):
return status
# Honor retry_after if present
wait_time = status.get("retry_after") or POLL_INTERVAL
time.sleep(wait_time)
raise TimeoutError(f"Job {job_id} did not complete within {max_wait}s")
def main():
print("=" * 60)
print("PraisonAI Async Jobs SDK Example")
print("=" * 60)
# Check if server is running
try:
with httpx.Client(timeout=5.0) as client:
health = client.get(f"{API_URL}/health")
health.raise_for_status()
print(f"✓ Server healthy: {health.json()['status']}")
except Exception as e:
print(f"✗ Server not available at {API_URL}: {e}")
print(" Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory")
return
# 1. Submit a job
print("\n1. Submitting job...")
prompt = "What is the capital of France? Answer in one word."
result = submit_job(prompt)
job_id = result["job_id"]
print(f" Job ID: {job_id}")
print(f" Status: {result['status']}")
print(f" Poll URL: {result['poll_url']}")
# 2. Wait for completion
print("\n2. Waiting for completion...")
final_status = wait_for_completion(job_id)
print(f" Final status: {final_status['status']}")
# 3. Get result
if final_status["status"] == "succeeded":
print("\n3. Getting result...")
result = get_result(job_id)
print(f" Result: {result['result']}")
print(f" Duration: {result['duration_seconds']:.2f}s")
else:
print(f"\n3. Job did not succeed: {final_status.get('error', 'Unknown error')}")
print("\n" + "=" * 60)
print("Example completed successfully!")
print("=" * 60)
if __name__ == "__main__":
main()
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#!/bin/bash
# CLI Example: SSE Streaming
#
# This example demonstrates how to stream job progress via Server-Sent Events.
#
# Requirements:
# - Jobs server running on http://127.0.0.1:8005
#
# Usage:
# bash sse_stream.sh
set -e
API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
echo "============================================================"
echo "PraisonAI SSE Streaming Example"
echo "============================================================"
# Submit a job
echo ""
echo "1. Submitting job..."
RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
-H "Content-Type: application/json" \
-d '{"prompt": "Count from 1 to 5."}')
JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
echo " Job ID: $JOB_ID"
# Stream progress
echo ""
echo "2. Streaming progress (Ctrl+C to stop)..."
echo " Connecting to: $API_URL/api/v1/runs/$JOB_ID/stream"
echo ""
# Use curl to stream SSE events (timeout after 60s)
curl -s -N --max-time 60 "$API_URL/api/v1/runs/$JOB_ID/stream" | while read -r line; do
if [[ "$line" == data:* ]]; then
data="${line#data: }"
if [ "$data" = "[DONE]" ]; then
echo " Stream completed"
break
fi
echo " Event: $data"
fi
done
echo ""
echo "============================================================"
echo "Streaming example completed!"
echo "============================================================"
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#!/usr/bin/env python3
"""
Basic Background Tasks Example for PraisonAI Agents.
This example demonstrates how to use the background task system with Agent:
1. Create an Agent with background task support
2. Run agent tasks asynchronously
3. Track task progress
4. Handle task completion
Usage:
python basic_background.py
"""
import asyncio
from praisonaiagents import Agent
from praisonaiagents.background import BackgroundRunner, BackgroundConfig, TaskStatus
async def main():
print("=" * 60)
print("Agent-Centric Background Tasks Demo")
print("=" * 60)
# Create a background runner with config (used standalone, not passed to Agent)
runner = BackgroundRunner(config=BackgroundConfig(max_concurrent_tasks=3))
# Create an Agent
agent = Agent(
name="AsyncAssistant",
instructions="You are a helpful research assistant.",
)
print("\n--- Agent with Background Support Created ---")
print(f"Agent: {agent.name}")
print(f"Background runner: {runner is not None}")
# Define some example tasks
async def research_task(topic: str) -> str:
"""Simulate a research task."""
print(f" 🔄 Researching '{topic}'...")
await asyncio.sleep(1.5)
return f"Research on '{topic}' completed with 5 key findings"
async def analysis_task(data: str) -> str:
"""Simulate an analysis task."""
print(f" 🔄 Analyzing data...")
await asyncio.sleep(1.0)
return f"Analysis complete: {data}"
# Submit tasks via background runner
print("\n--- Submitting Background Tasks ---")
task1 = await runner.submit(
research_task,
args=("AI trends 2025",),
name="research_ai"
)
print(f"✅ Submitted research task: {task1.id[:8]}")
task2 = await runner.submit(
analysis_task,
args=("market data",),
name="analyze_market"
)
print(f"✅ Submitted analysis task: {task2.id[:8]}")
# List all tasks
print("\n--- Task Status ---")
for task in runner.tasks:
print(f" [{task.id[:8]}] {task.status.value}")
# Wait for tasks to complete
print("\n--- Waiting for Tasks ---")
await task1.wait(timeout=10.0)
await task2.wait(timeout=10.0)
# Final results
print("\n--- Results ---")
for task in [task1, task2]:
status = "✅" if task.status == TaskStatus.COMPLETED else "❌"
print(f" {status} {task.result}")
# Cleanup
runner.clear_completed()
print("\n✅ Cleared completed tasks")
print("\n" + "=" * 60)
print("Demo Complete!")
print("=" * 60)
if __name__ == "__main__":
asyncio.run(main())
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"""Calculator with Tests - Editor Output Example.
Builds a calculator module, writes unit tests, runs them, generates a
coverage-style report, and reads it back.
"""
from praisonaiagents import Agent
from praisonaiagents.tools import (
write_file, read_file, execute_command
)
agent = Agent(
instructions="You are a helpful coding assistant.",
output="editor",
tools=[write_file, read_file, execute_command],
approval=True,
)
agent.start(
"Build a mini calculator - create /tmp/calculator.py with add, subtract, "
"multiply, divide functions, write unit tests in /tmp/test_calculator.py "
"using unittest, run the tests to make sure they pass, then generate a "
"coverage-style report in /tmp/calc_report.md listing which functions were "
"tested and read it back"
)
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"""Fibonacci Generator - Editor Output Example.
Creates a Fibonacci script, runs it, writes a summary, and reads back
both files to verify everything looks good.
"""
from praisonaiagents import Agent
from praisonaiagents.tools import (
write_file, read_file, execute_command, list_directory
)
agent = Agent(
instructions="You are a helpful coding assistant.",
output="editor",
tools=[write_file, read_file, execute_command, list_directory],
approval=True,
)
agent.start(
"Create a Python file at /tmp/fibonacci.py that generates the first 20 "
"Fibonacci numbers, run it to verify the output is correct, then write "
"a summary to /tmp/fib_summary.md explaining the algorithm you used and "
"the output, finally read back both files to confirm everything looks good"
)
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"""Multi-Step Research - Editor Output Example.
Researches Python frameworks, creates comparison code, runs it, writes a
markdown report, reads it back, lists files, and gets system info.
"""
from praisonaiagents import Agent
from praisonaiagents.tools import (
write_file, read_file, execute_command, list_directory, get_system_info
)
try:
from praisonaiagents.tools import web_search
tools = [web_search, write_file, read_file, execute_command,
list_directory, get_system_info]
except ImportError:
tools = [write_file, read_file, execute_command,
list_directory, get_system_info]
agent = Agent(
instructions="You are a helpful research assistant.",
output="editor",
tools=tools,
approval=True,
)
agent.start(
"Research the top 3 Python web frameworks (Django, FastAPI, Flask), then: "
"1) Search the web for each framework's latest version and key features, "
"2) Create a file called /tmp/framework_comparison.py that contains a "
"Python dictionary with the comparison data, "
"3) Execute the code to verify the dictionary is valid, "
"4) Write a markdown report to /tmp/framework_report.md summarizing your "
"findings in a table format, "
"5) Read back the report file to verify it was written correctly, "
"6) List the files in /tmp to confirm both files exist, "
"7) Get system info to note what OS this report was generated on"
)
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"""System Audit - Editor Output Example.
Runs system commands, gathers info, writes a report, and reads it back.
"""
from praisonaiagents import Agent
from praisonaiagents.tools import (
write_file, read_file, execute_command
)
agent = Agent(
instructions="You are a helpful system administration assistant.",
output="editor",
tools=[write_file, read_file, execute_command],
approval=True,
)
agent.start(
"Investigate my system - get the current date, check disk usage, find out "
"what Python version is installed, list running processes sorted by memory, "
"then write all findings to /tmp/system_audit.md as a neat report and read "
"it back to verify"
)
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"""Weather Search - Editor Output Example.
Searches for weather, creates JSON data, writes a parser script, executes
it, and lists created files.
"""
from praisonaiagents import Agent
from praisonaiagents.tools import (
write_file, execute_command, list_directory
)
try:
from praisonaiagents.tools import web_search
tools = [web_search, write_file, execute_command, list_directory]
except ImportError:
tools = [write_file, execute_command, list_directory]
agent = Agent(
instructions="You are a helpful research assistant.",
output="editor",
tools=tools,
approval=True,
)
agent.start(
"Search the web for the current weather in Tokyo, create a JSON file at "
"/tmp/weather_data.json with the findings, then write a Python script at "
"/tmp/parse_weather.py that reads the JSON and prints a formatted weather "
"report, execute the script, and list all files you created in /tmp"
)
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#!/usr/bin/env python3
"""
PraisonAI Benchmark Example
This example demonstrates how to use the benchmark system programmatically
to compare performance across different execution paths.
Usage:
python benchmark_example.py
Requirements:
- OPENAI_API_KEY environment variable set
- praisonai package installed
"""
import os
import sys
# Ensure the package is importable
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai'))
from praisonai.cli.features.benchmark import BenchmarkHandler, BenchmarkReport
def run_quick_comparison():
"""Run a quick comparison of key execution paths."""
print("=" * 60)
print("Quick Benchmark Comparison")
print("=" * 60)
handler = BenchmarkHandler()
# Run benchmark with 2 iterations on key paths
report = handler.run_full_benchmark(
prompt="What is 2+2?",
iterations=2,
paths=["openai_sdk", "praisonai_agent"],
output="verbose"
)
# Print comparison table
print("\n" + handler.create_comparison_table(report))
return report
def run_full_benchmark():
"""Run full benchmark suite across all paths."""
print("=" * 60)
print("Full Benchmark Suite")
print("=" * 60)
handler = BenchmarkHandler()
# Run full benchmark
report = handler.run_full_benchmark(
prompt="Hi",
iterations=3,
output="verbose"
)
# Print full report
handler.print_report(report)
return report
def run_agent_benchmark():
"""Benchmark PraisonAI Agent specifically."""
print("=" * 60)
print("Agent Benchmark")
print("=" * 60)
handler = BenchmarkHandler()
# Benchmark agent vs SDK
report = handler.run_full_benchmark(
prompt="Explain Python in one sentence",
iterations=3,
paths=["openai_sdk", "praisonai_agent"],
output="verbose"
)
# Show timeline diagrams
for name, result in report.results.items():
print(f"\n### {name}")
print(handler.create_timeline_diagram(result))
# Show variance analysis
print(handler.create_variance_table(report))
return report
def save_benchmark_results(report: BenchmarkReport, output_path: str):
"""Save benchmark results to JSON file."""
import json
with open(output_path, 'w') as f:
json.dump(report.to_dict(), f, indent=2)
print(f"\nResults saved to: {output_path}")
def main():
"""Main entry point."""
import argparse
parser = argparse.ArgumentParser(description="PraisonAI Benchmark Example")
parser.add_argument(
"--mode",
choices=["quick", "full", "agent"],
default="quick",
help="Benchmark mode: quick (2 paths), full (all paths), agent (agent focus)"
)
parser.add_argument(
"--output",
type=str,
default=None,
help="Output JSON file path"
)
args = parser.parse_args()
# Check for API key
if not os.environ.get("OPENAI_API_KEY"):
print("Error: OPENAI_API_KEY environment variable not set")
sys.exit(1)
# Run selected benchmark
if args.mode == "quick":
report = run_quick_comparison()
elif args.mode == "full":
report = run_full_benchmark()
elif args.mode == "agent":
report = run_agent_benchmark()
# Save results if requested
if args.output:
save_benchmark_results(report, args.output)
print("\n✅ Benchmark complete!")
if __name__ == "__main__":
main()
@@ -1,43 +0,0 @@
"""
Basic Caching Configuration Example
Demonstrates using caching for response and prompt caching.
"""
import os
from praisonaiagents import Agent, CachingConfig
# Ensure API key is set from environment
assert os.getenv("OPENAI_API_KEY"), "OPENAI_API_KEY must be set"
# Default caching (enabled)
agent_cached = Agent(
instructions="You are a helpful assistant.",
caching=True,
)
# Disable caching
agent_no_cache = Agent(
instructions="You are a helpful assistant.",
caching=False,
)
# Custom caching configuration
agent_custom = Agent(
instructions="You are a helpful assistant.",
caching=CachingConfig(
enabled=True,
prompt_caching=True, # Enable prompt caching for supported providers
),
)
if __name__ == "__main__":
print("Testing CachingConfig...")
print(f"Cached agent cache: {agent_cached.cache}")
print(f"No-cache agent cache: {agent_no_cache.cache}")
print(f"Custom agent cache: {agent_custom.cache}")
result = agent_cached.chat("What is caching?")
print(f"Result: {result}")
print("\nCachingConfig tests passed!")
@@ -1,24 +0,0 @@
"""
Assistants Capabilities Example
Demonstrates OpenAI Assistants API using PraisonAI capabilities.
"""
from praisonai.capabilities import assistant_list
print("=== List Assistants ===")
try:
assistants = assistant_list()
print(f"Assistants found: {len(assistants)}")
for a in assistants[:5]: # Show first 5
print(f" - {a.get('name', 'unnamed')}: {a.get('id', 'unknown')}")
except Exception as e:
print(f"Note: {e}")
print("\n=== Assistant Operations ===")
print("Available functions:")
print(" assistant_create(name, instructions, model='gpt-4o-mini')")
print(" assistant_list()")
print("\nSee CLI: praisonai assistants list")
print("See CLI: praisonai assistants create --name 'My Assistant'")
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"""
Audio Capabilities Example
Demonstrates transcription and text-to-speech using PraisonAI capabilities.
"""
from praisonai.capabilities import transcribe, speech
# Note: Audio transcription requires an audio file
# This example shows the API structure
print("=== Audio Transcription ===")
print("To transcribe audio, use:")
print(" result = transcribe('audio.mp3', model='whisper-1')")
print(" print(result.text)")
print("\n=== Text-to-Speech ===")
print("To generate speech, use:")
print(" audio_data = speech('Hello world', model='tts-1', voice='alloy')")
print(" # Save to file or play")
print("\nNote: These operations require audio files or generate audio output.")
print("See CLI: praisonai audio transcribe <file>")
print("See CLI: praisonai audio speech <text>")
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"""
Batches Capabilities Example
Demonstrates batch processing using PraisonAI capabilities.
"""
from praisonai.capabilities import batch_list
print("=== Batch List ===")
try:
batches = batch_list()
print(f"Batches found: {len(batches)}")
for b in batches[:5]: # Show first 5
print(f" - {b.get('id', 'unknown')}: {b.get('status', 'unknown')}")
except Exception as e:
print(f"Note: {e}")
print("Batch operations require batch jobs to be created first.")
print("\n=== Batch Operations ===")
print("Available functions:")
print(" batch_create(input_file_id, endpoint, completion_window='24h')")
print(" batch_list()")
print(" batch_retrieve(batch_id)")
print(" batch_cancel(batch_id)")
print("\nSee CLI: praisonai batches list")
print("See CLI: praisonai batches create <input_file_id>")
@@ -1,30 +0,0 @@
"""
Completions Capability Example
Demonstrates chat and text completions using PraisonAI capabilities.
"""
from praisonai.capabilities import chat_completion, text_completion
# Chat completion example
print("=== Chat Completion ===")
result = chat_completion(
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is 2 + 2? Answer in one word."}
],
model="gpt-4o-mini",
max_tokens=10
)
print(f"Response: {result.content}")
print(f"Model: {result.model}")
print(f"Usage: {result.usage}")
# Text completion example (legacy)
print("\n=== Text Completion ===")
result = text_completion(
prompt="The capital of France is",
model="gpt-3.5-turbo-instruct",
max_tokens=10
)
print(f"Response: {result.content}")
@@ -1,46 +0,0 @@
"""
Embeddings Capability Example
Demonstrates text embedding generation using PraisonAI capabilities.
Both `embed` and `embedding` work identically - use whichever you prefer.
"""
from praisonai import embed, embedding # Both work from top-level
# Or: from praisonai.capabilities import embed, embedding
# Single text embedding using embed()
print("=== Single Text Embedding (using embed) ===")
result = embed(
input="Hello, world!",
model="text-embedding-3-small"
)
print(f"Embedding dimensions: {len(result.embeddings[0])}")
print(f"First 5 values: {result.embeddings[0][:5]}")
print(f"Usage: {result.usage}")
# Same thing using embedding() alias
print("\n=== Single Text Embedding (using embedding alias) ===")
result = embedding(
input="Hello, world!",
model="text-embedding-3-small"
)
print(f"Embedding dimensions: {len(result.embeddings[0])}")
# Multiple text embeddings
print("\n=== Multiple Text Embeddings ===")
result = embed(
input=["Hello", "World", "AI"],
model="text-embedding-3-small"
)
print(f"Number of embeddings: {len(result.embeddings)}")
for i, emb in enumerate(result.embeddings):
print(f" Text {i+1}: {len(emb)} dimensions")
# With custom dimensions (for models that support it)
print("\n=== Custom Dimensions ===")
result = embed(
input="Hello world",
model="text-embedding-3-large",
dimensions=256
)
print(f"Reduced dimensions: {len(result.embeddings[0])}")
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"""
Files Capabilities Example
Demonstrates file management using PraisonAI capabilities.
"""
from praisonai.capabilities import file_list
print("=== File List ===")
try:
files = file_list()
print(f"Files found: {len(files)}")
for f in files[:5]: # Show first 5
print(f" - {f.get('filename', 'unknown')}: {f.get('id', 'unknown')}")
except Exception as e:
print(f"Note: {e}")
print("File operations require files to be uploaded first.")
print("\n=== File Operations ===")
print("Available functions:")
print(" file_create(file_path, purpose='assistants')")
print(" file_list(purpose=None)")
print(" file_retrieve(file_id)")
print(" file_delete(file_id)")
print(" file_content(file_id)")
print("\nSee CLI: praisonai files list")
print("See CLI: praisonai files upload <file>")
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"""
Images Capability Example
Demonstrates image generation using PraisonAI capabilities.
"""
from praisonai.capabilities import image_generate
# Generate an image with DALL-E 2
print("=== Image Generation (DALL-E 2) ===")
result = image_generate(
prompt="A simple blue circle on a white background",
model="dall-e-2",
size="256x256",
n=1
)
print(f"Images generated: {len(result)}")
if result:
print(f"URL: {result[0].url[:80]}...")
# Generate with DALL-E 3 (higher quality)
print("\n=== Image Generation (DALL-E 3) ===")
result = image_generate(
prompt="A serene mountain landscape at sunset",
model="dall-e-3",
size="1024x1024",
quality="standard"
)
print(f"Images generated: {len(result)}")
if result:
print(f"Revised prompt: {result[0].revised_prompt[:80]}..." if result[0].revised_prompt else "No revised prompt")
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@@ -1,33 +0,0 @@
"""
Messages Capability Example
Demonstrates Anthropic-style messages API and token counting.
"""
from praisonai.capabilities import messages_create, count_tokens
# Create a message (Anthropic-style)
print("=== Messages Create ===")
result = messages_create(
messages=[{"role": "user", "content": "What is the meaning of life? Answer briefly."}],
model="gpt-4o-mini", # Works with any model via LiteLLM
max_tokens=100,
system="You are a philosophical assistant."
)
print(f"Message ID: {result.id}")
if result.content:
for block in result.content:
if block.get("type") == "text":
print(f"Response: {block.get('text')}")
print(f"Usage: {result.usage}")
# Count tokens
print("\n=== Token Counting ===")
result = count_tokens(
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello, how are you today?"}
],
model="gpt-4o-mini"
)
print(f"Token count: {result.input_tokens}")
@@ -1,24 +0,0 @@
"""
Moderations Capability Example
Demonstrates content moderation using PraisonAI capabilities.
"""
from praisonai.capabilities import moderate
# Check safe content
print("=== Safe Content Check ===")
result = moderate(
input="Hello, how are you today?"
)
print(f"Flagged: {result[0].flagged}")
print(f"Categories: {list(result[0].categories.keys())[:5]}...")
# Check multiple texts
print("\n=== Multiple Text Moderation ===")
result = moderate(
input=["Hello world", "Have a nice day", "This is a test"]
)
print(f"Number of results: {len(result)}")
for i, r in enumerate(result):
print(f" Text {i+1}: Flagged = {r.flagged}")
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@@ -1,23 +0,0 @@
"""
Realtime Capability Example
Demonstrates realtime session creation for audio/video streaming.
"""
from praisonai.capabilities import realtime_connect
# Create a realtime session
print("=== Realtime Session ===")
session = realtime_connect(
model="gpt-4o-realtime-preview",
modalities=["text", "audio"],
voice="alloy"
)
print(f"Session ID: {session.id}")
print(f"Status: {session.status}")
print(f"URL: {session.url}")
print(f"Modalities: {session.metadata.get('modalities')}")
print(f"Voice: {session.metadata.get('voice')}")
print("\nNote: To use this session, connect via WebSocket to the URL above.")
print("See OpenAI Realtime API documentation for WebSocket protocol details.")
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@@ -1,32 +0,0 @@
"""
Rerank Capabilities Example
Demonstrates document reranking using PraisonAI capabilities.
"""
from praisonai.capabilities import rerank
print("=== Document Reranking ===")
try:
documents = [
"Python is a programming language",
"Machine learning uses algorithms",
"Python is great for data science",
"JavaScript runs in browsers"
]
results = rerank(
query="Python programming",
documents=documents,
model="cohere/rerank-english-v3.0"
)
print(f"Query: 'Python programming'")
print(f"Reranked results:")
for r in results:
print(f" Score {r.get('relevance_score', 0):.4f}: {r.get('document', {}).get('text', '')[:50]}")
except Exception as e:
print(f"Note: Reranking requires Cohere API key")
print(f"Error: {e}")
print("\nSee CLI: praisonai rerank <query> --documents 'doc1' 'doc2'")
@@ -1,22 +0,0 @@
"""
Vector Stores Capabilities Example
Demonstrates vector store management using PraisonAI capabilities.
"""
from praisonai.capabilities import vector_store_create, vector_store_search
print("=== Vector Store Operations ===")
print("Available functions:")
print(" vector_store_create(name)")
print(" vector_store_search(vector_store_id, query)")
print("\n=== Create Vector Store ===")
try:
store = vector_store_create(name="test-store")
print(f"Created: {store}")
except Exception as e:
print(f"Note: {e}")
print("\nSee CLI: praisonai vector-stores create --name 'my-store'")
print("See CLI: praisonai vector-stores search <store_id> <query>")
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#!/usr/bin/env python3
"""
Example: Fetch and search PraisonAI templates from the catalog.
This example demonstrates how to:
- Fetch templates.json from the deployed catalog
- Search templates by keyword
- Filter by tags and requirements
- Generate CLI commands
"""
import json
import urllib.request
from typing import List, Dict, Any, Optional
CATALOG_URL = "https://mervinpraison.github.io/praisonai-template-catalog/data/templates.json"
def fetch_catalog(url: str = CATALOG_URL) -> Dict[str, Any]:
"""Fetch the template catalog from URL."""
try:
with urllib.request.urlopen(url, timeout=10) as response:
return json.loads(response.read().decode())
except Exception as e:
print(f"Failed to fetch catalog: {e}")
# Return empty catalog on failure
return {"version": "0.0.0", "count": 0, "templates": []}
def search_templates(templates: List[Dict], query: str) -> List[Dict]:
"""Search templates by name, description, or tags."""
query = query.lower()
results = []
for t in templates:
if (query in t.get("name", "").lower() or
query in t.get("description", "").lower() or
any(query in tag.lower() for tag in t.get("tags", []))):
results.append(t)
return results
def filter_by_tags(templates: List[Dict], tags: List[str]) -> List[Dict]:
"""Filter templates that have all specified tags."""
return [
t for t in templates
if all(tag in t.get("tags", []) for tag in tags)
]
def filter_by_tool(templates: List[Dict], tool_name: str) -> List[Dict]:
"""Filter templates that require a specific tool."""
return [
t for t in templates
if tool_name in t.get("requires", {}).get("tools", [])
]
def get_template(templates: List[Dict], name: str) -> Optional[Dict]:
"""Get a template by name."""
for t in templates:
if t.get("name") == name:
return t
return None
def generate_cli_command(template: Dict, action: str = "run") -> str:
"""Generate CLI command for a template."""
name = template.get("name", "unknown")
if action == "run":
return f"praisonai templates run {name}"
elif action == "info":
return f"praisonai templates info {name}"
elif action == "init":
return f"praisonai templates init my-project --template {name}"
return f"praisonai templates {action} {name}"
def print_template_summary(template: Dict):
"""Print a summary of a template."""
print(f"\n{'='*60}")
print(f"Name: {template.get('name')}")
print(f"Version: {template.get('version')}")
print(f"Description: {template.get('description', '')[:100]}...")
print(f"Tags: {', '.join(template.get('tags', []))}")
requires = template.get("requires", {})
if requires.get("tools"):
print(f"Tools: {', '.join(requires['tools'])}")
if requires.get("packages"):
print(f"Packages: {', '.join(requires['packages'])}")
if requires.get("env"):
print(f"Env vars: {', '.join(requires['env'])}")
print(f"\nRun: {generate_cli_command(template, 'run')}")
print(f"{'='*60}")
def main():
print("PraisonAI Template Catalog - Python Example")
print("-" * 45)
# Fetch catalog
print("\n1. Fetching template catalog...")
catalog = fetch_catalog()
templates = catalog.get("templates", [])
print(f" Catalog version: {catalog.get('version', 'unknown')}")
print(f" Total templates: {len(templates)}")
if not templates:
print(" No templates found. The catalog may not be deployed yet.")
print(" Try building locally: praisonai templates catalog build")
return
# List all templates
print("\n2. Available templates:")
for t in templates:
print(f" - {t.get('name')} (v{t.get('version')})")
# Search for video templates
print("\n3. Searching for 'video' templates...")
video_templates = search_templates(templates, "video")
print(f" Found {len(video_templates)} video templates:")
for t in video_templates:
print(f" - {t.get('name')}: {t.get('description', '')[:50]}...")
# Filter by tool
print("\n4. Templates using 'shell_tool':")
shell_templates = filter_by_tool(templates, "shell_tool")
for t in shell_templates:
print(f" - {t.get('name')}")
# Get specific template details
if templates:
first_template = templates[0]
print(f"\n5. Template details for '{first_template.get('name')}':")
print_template_summary(first_template)
# Generate CLI commands
print("\n6. CLI commands for all templates:")
for t in templates[:5]: # First 5 only
print(f" {generate_cli_command(t, 'run')}")
print("\nDone!")
if __name__ == "__main__":
main()
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#!/usr/bin/env npx ts-node
/**
* Example: Fetch and search PraisonAI templates from the catalog.
*
* This TypeScript example demonstrates how to:
* - Fetch templates.json from the deployed catalog
* - Search templates by keyword
* - Filter by tags and requirements
* - Generate CLI commands
*/
const CATALOG_URL = "https://mervinpraison.github.io/praisonai-template-catalog/data/templates.json";
interface Template {
name: string;
version: string;
description: string;
author?: string;
license?: string;
tags?: string[];
category?: string;
difficulty?: "beginner" | "intermediate" | "advanced";
requires?: {
tools?: string[];
packages?: string[];
env?: string[];
};
}
interface Catalog {
version: string;
generated_at: string;
count: number;
templates: Template[];
}
async function fetchCatalog(url: string = CATALOG_URL): Promise<Catalog> {
const response = await fetch(url);
if (!response.ok) {
throw new Error(`Failed to fetch catalog: ${response.statusText}`);
}
return response.json();
}
function searchTemplates(templates: Template[], query: string): Template[] {
const q = query.toLowerCase();
return templates.filter(t =>
t.name.toLowerCase().includes(q) ||
t.description?.toLowerCase().includes(q) ||
t.tags?.some(tag => tag.toLowerCase().includes(q))
);
}
function filterByTags(templates: Template[], tags: string[]): Template[] {
return templates.filter(t =>
tags.every(tag => t.tags?.includes(tag))
);
}
function filterByTool(templates: Template[], toolName: string): Template[] {
return templates.filter(t =>
t.requires?.tools?.includes(toolName)
);
}
function getTemplate(templates: Template[], name: string): Template | undefined {
return templates.find(t => t.name === name);
}
function generateCLICommand(template: Template, action: string = "run"): string {
const name = template.name;
switch (action) {
case "run":
return `praisonai templates run ${name}`;
case "info":
return `praisonai templates info ${name}`;
case "init":
return `praisonai templates init my-project --template ${name}`;
default:
return `praisonai templates ${action} ${name}`;
}
}
function printTemplateSummary(template: Template): void {
console.log("\n" + "=".repeat(60));
console.log(`Name: ${template.name}`);
console.log(`Version: ${template.version}`);
console.log(`Description: ${template.description?.slice(0, 100)}...`);
console.log(`Tags: ${template.tags?.join(", ") || "none"}`);
const requires = template.requires;
if (requires?.tools?.length) {
console.log(`Tools: ${requires.tools.join(", ")}`);
}
if (requires?.packages?.length) {
console.log(`Packages: ${requires.packages.join(", ")}`);
}
if (requires?.env?.length) {
console.log(`Env vars: ${requires.env.join(", ")}`);
}
console.log(`\nRun: ${generateCLICommand(template, "run")}`);
console.log("=".repeat(60));
}
async function main(): Promise<void> {
console.log("PraisonAI Template Catalog - TypeScript Example");
console.log("-".repeat(45));
// Fetch catalog
console.log("\n1. Fetching template catalog...");
let catalog: Catalog;
try {
catalog = await fetchCatalog();
} catch (error) {
console.log(` Failed to fetch: ${error}`);
console.log(" The catalog may not be deployed yet.");
return;
}
const templates = catalog.templates;
console.log(` Catalog version: ${catalog.version}`);
console.log(` Total templates: ${templates.length}`);
if (templates.length === 0) {
console.log(" No templates found.");
return;
}
// List all templates
console.log("\n2. Available templates:");
templates.forEach(t => {
console.log(` - ${t.name} (v${t.version})`);
});
// Search for video templates
console.log("\n3. Searching for 'video' templates...");
const videoTemplates = searchTemplates(templates, "video");
console.log(` Found ${videoTemplates.length} video templates:`);
videoTemplates.forEach(t => {
console.log(` - ${t.name}: ${t.description?.slice(0, 50)}...`);
});
// Filter by tool
console.log("\n4. Templates using 'shell_tool':");
const shellTemplates = filterByTool(templates, "shell_tool");
shellTemplates.forEach(t => {
console.log(` - ${t.name}`);
});
// Get specific template details
if (templates.length > 0) {
const firstTemplate = templates[0];
console.log(`\n5. Template details for '${firstTemplate.name}':`);
printTemplateSummary(firstTemplate);
}
// Generate CLI commands
console.log("\n6. CLI commands for all templates:");
templates.slice(0, 5).forEach(t => {
console.log(` ${generateCLICommand(t, "run")}`);
});
console.log("\nDone!");
}
main().catch(console.error);
@@ -1,121 +0,0 @@
#!/usr/bin/env python3
"""
Run all catalog examples and report pass/fail status.
Usage:
python run_all_catalog_examples.py
Environment variables:
OPENAI_API_KEY or PRAISONAI_OPENAI_API_KEY - Required for agent example
PRAISONAI_RUN_INTEGRATION - Set to "1" to run integration tests
"""
import os
import subprocess
import sys
from pathlib import Path
SCRIPT_DIR = Path(__file__).parent
def run_example(name: str, script: str, requires_api_key: bool = False) -> bool:
"""Run an example script and return success status."""
print(f"\n{'='*60}")
print(f"Running: {name}")
print(f"Script: {script}")
print("="*60)
# Check API key requirement
if requires_api_key:
api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("PRAISONAI_OPENAI_API_KEY")
run_integration = os.environ.get("PRAISONAI_RUN_INTEGRATION") == "1"
if not api_key:
print(f"⏭️ SKIPPED: {name} (no API key)")
print(" Set OPENAI_API_KEY to run this example")
return True # Not a failure, just skipped
if not run_integration:
print(f"⏭️ SKIPPED: {name} (integration tests disabled)")
print(" Set PRAISONAI_RUN_INTEGRATION=1 to run")
return True
script_path = SCRIPT_DIR / script
if not script_path.exists():
print(f"❌ FAILED: Script not found: {script_path}")
return False
try:
result = subprocess.run(
[sys.executable, str(script_path)],
capture_output=True,
text=True,
timeout=60,
cwd=str(SCRIPT_DIR)
)
print(result.stdout)
if result.stderr:
print(f"STDERR: {result.stderr}")
if result.returncode == 0:
print(f"✅ PASSED: {name}")
return True
else:
print(f"❌ FAILED: {name} (exit code {result.returncode})")
return False
except subprocess.TimeoutExpired:
print(f"❌ FAILED: {name} (timeout)")
return False
except Exception as e:
print(f"❌ FAILED: {name} ({e})")
return False
def main():
print("PraisonAI Template Catalog - Example Runner")
print("=" * 50)
# Check environment
api_key = os.environ.get("OPENAI_API_KEY") or os.environ.get("PRAISONAI_OPENAI_API_KEY")
if api_key:
print(f"API key: present (...{api_key[-4:]})")
else:
print("API key: not set")
run_integration = os.environ.get("PRAISONAI_RUN_INTEGRATION") == "1"
print(f"Integration tests: {'enabled' if run_integration else 'disabled'}")
# Define examples
examples = [
("Fetch Templates (Python)", "fetch_templates.py", False),
("Template Finder Agent", "template_finder_agent.py", True),
]
# Run examples
results = []
for name, script, requires_key in examples:
success = run_example(name, script, requires_key)
results.append((name, success))
# Summary
print("\n" + "=" * 60)
print("SUMMARY")
print("=" * 60)
passed = sum(1 for _, s in results if s)
failed = sum(1 for _, s in results if not s)
for name, success in results:
status = "✅ PASS" if success else "❌ FAIL"
print(f" {status}: {name}")
print(f"\nTotal: {passed} passed, {failed} failed")
# Exit with appropriate code
sys.exit(0 if failed == 0 else 1)
if __name__ == "__main__":
main()
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#!/usr/bin/env python3
"""
Example: Template Finder Agent
An Agent()-centric example that uses an AI agent to find templates
based on natural language queries.
Requires: OPENAI_API_KEY environment variable
"""
import json
import os
import urllib.request
CATALOG_URL = "https://mervinpraison.github.io/praisonai-template-catalog/data/templates.json"
def fetch_templates() -> list:
"""Fetch templates from catalog."""
try:
with urllib.request.urlopen(CATALOG_URL, timeout=10) as response:
data = json.loads(response.read().decode())
return data.get("templates", [])
except Exception:
return []
def search_templates_tool(query: str) -> str:
"""
Search templates by keyword.
Args:
query: Search query (e.g., "video editing", "transcript", "data")
Returns:
JSON string with matching templates
"""
templates = fetch_templates()
query_lower = query.lower()
results = []
for t in templates:
if (query_lower in t.get("name", "").lower() or
query_lower in t.get("description", "").lower() or
any(query_lower in tag.lower() for tag in t.get("tags", []))):
results.append({
"name": t.get("name"),
"description": t.get("description", "")[:100],
"tags": t.get("tags", []),
"cli_command": f"praisonai templates run {t.get('name')}"
})
if not results:
return json.dumps({"message": "No templates found matching your query", "templates": []})
return json.dumps({"count": len(results), "templates": results})
def list_all_templates_tool() -> str:
"""
List all available templates.
Returns:
JSON string with all template names and descriptions
"""
templates = fetch_templates()
if not templates:
return json.dumps({"message": "No templates available", "templates": []})
results = [
{"name": t.get("name"), "description": t.get("description", "")[:80]}
for t in templates
]
return json.dumps({"count": len(results), "templates": results})
def get_template_details_tool(name: str) -> str:
"""
Get detailed information about a specific template.
Args:
name: Template name (e.g., "ai-video-editor")
Returns:
JSON string with template details
"""
templates = fetch_templates()
for t in templates:
if t.get("name") == name:
return json.dumps({
"name": t.get("name"),
"version": t.get("version"),
"description": t.get("description"),
"author": t.get("author"),
"tags": t.get("tags", []),
"requires": t.get("requires", {}),
"cli_commands": {
"run": f"praisonai templates run {name}",
"info": f"praisonai templates info {name}",
"init": f"praisonai templates init my-project --template {name}"
}
})
return json.dumps({"error": f"Template '{name}' not found"})
def check_api_key() -> bool:
"""Check if API key is available."""
key = os.environ.get("OPENAI_API_KEY") or os.environ.get("PRAISONAI_OPENAI_API_KEY")
if key:
print(f"API key present: ...{key[-4:]}")
return True
return False
def main():
print("PraisonAI Template Finder Agent")
print("=" * 40)
# Check for API key
if not check_api_key():
print("\n⚠️ No API key found.")
print("Set OPENAI_API_KEY or PRAISONAI_OPENAI_API_KEY to use the agent.")
print("\nFalling back to direct tool usage:\n")
# Demo the tools directly
print("1. Listing all templates:")
result = list_all_templates_tool()
data = json.loads(result)
for t in data.get("templates", [])[:5]:
print(f" - {t['name']}: {t['description'][:50]}...")
print("\n2. Searching for 'video' templates:")
result = search_templates_tool("video")
data = json.loads(result)
for t in data.get("templates", []):
print(f" - {t['name']}")
print(f" Run: {t['cli_command']}")
return
# Import Agent only if API key is available
try:
from praisonaiagents import Agent
except ImportError:
print("praisonaiagents not installed. Install with: pip install praisonaiagents")
return
# Create the Template Finder Agent
agent = Agent(
name="Template Finder",
role="PraisonAI Template Expert",
goal="Help users find the right template for their needs",
backstory="""You are an expert on PraisonAI templates. You help users
discover templates that match their requirements. You can search templates,
list all available templates, and provide detailed information about specific
templates. Always provide the CLI command to run the template.""",
tools=[
search_templates_tool,
list_all_templates_tool,
get_template_details_tool
],
output="verbose" # Use new consolidated param
)
# Example queries
queries = [
"Find me a template for editing videos",
"What templates are available for working with transcripts?",
"Show me details about the ai-video-editor template"
]
print("\nRunning Template Finder Agent...")
print("-" * 40)
for query in queries:
print(f"\n📝 Query: {query}")
print("-" * 40)
try:
result = agent.start(query)
print(f"\n🤖 Response:\n{result}")
except Exception as e:
print(f"Error: {e}")
print("\n" + "=" * 40)
if __name__ == "__main__":
main()
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#!/usr/bin/env python3
"""
Basic Checkpoints Example for PraisonAI Agents.
This example demonstrates how to use the shadow git checkpointing system with Agent:
1. Create an Agent with checkpoint support
2. Save checkpoints before making changes
3. List available checkpoints
4. Restore to a previous checkpoint
Usage:
python basic_checkpoints.py
"""
import asyncio
import tempfile
from pathlib import Path
from praisonaiagents import Agent
from praisonaiagents.checkpoints import CheckpointService
async def main():
# Create a temporary workspace for demonstration
with tempfile.TemporaryDirectory() as workspace:
print("=" * 60)
print("Agent-Centric Checkpointing Demo")
print("=" * 60)
print(f"\nWorkspace: {workspace}")
# Create some initial files
test_file = Path(workspace) / "example.py"
test_file.write_text("# Initial content\nprint('Hello, World!')\n")
print(f"\n✅ Created initial file: {test_file.name}")
# Create checkpoint service (used standalone, not passed to Agent)
checkpoint_service = CheckpointService(workspace_dir=workspace)
# Create an Agent
agent = Agent(
name="RefactorBot",
instructions="You are a code refactoring assistant.",
)
print("\n--- Agent with Checkpoint Support Created ---")
print(f"Agent: {agent.name}")
print(f"Checkpoint service initialized: {checkpoint_service is not None}")
try:
# Initialize the shadow git repository
print("\n--- Initializing Checkpoint Service ---")
await checkpoint_service.initialize()
print("✅ Shadow git repository initialized")
# Save first checkpoint
print("\n--- Saving Checkpoint 1: Initial State ---")
result1 = await checkpoint_service.save("Initial state")
print(f"✅ Checkpoint saved: {result1.checkpoint.id[:8]}")
print(f" Message: {result1.checkpoint.message}")
# Make some changes (simulating agent modifications)
print("\n--- Simulating Agent Changes ---")
test_file.write_text("# Refactored content\nprint('Hello, PraisonAI!')\nprint('Checkpoints are awesome!')\n")
print("✅ Modified example.py")
# Create a new file
new_file = Path(workspace) / "utils.py"
new_file.write_text("def helper():\n return 'I help!'\n")
print("✅ Created utils.py")
# Save second checkpoint
print("\n--- Saving Checkpoint 2: After Refactoring ---")
result2 = await checkpoint_service.save("Added features")
print(f"✅ Checkpoint saved: {result2.checkpoint.id[:8]}")
# List all checkpoints
print("\n--- Listing All Checkpoints ---")
checkpoints_list = await checkpoint_service.list_checkpoints()
for i, cp in enumerate(checkpoints_list, 1):
print(f" {i}. [{cp.id[:8]}] {cp.message}")
# Show diff
print("\n--- Current Diff ---")
diff = await checkpoint_service.diff()
if diff.files:
for f in diff.files:
print(f" {f.status}: {f.path}")
else:
print(" No uncommitted changes")
# Make more changes
test_file.write_text("# Bad changes\nprint('This will be reverted')\n")
print("\n✅ Made problematic changes to example.py")
# Restore to checkpoint 2
print(f"\n--- Restoring to Checkpoint: {result2.checkpoint.id[:8]} ---")
await checkpoint_service.restore(result2.checkpoint.id)
print("✅ Restored successfully!")
# Verify restoration
content = test_file.read_text()
print(f"\n--- Verified Content After Restore ---")
print(content)
except Exception as e:
print(f"\n❌ Error: {e}")
raise
print("\n" + "=" * 60)
print("Demo Complete!")
print("=" * 60)
if __name__ == "__main__":
asyncio.run(main())
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#!/bin/bash
# Calculator with tests: code + unit tests + test execution + coverage report
# Tests: multiple file creation, test execution, report generation, file reading
praisonai "Build a mini calculator - create /tmp/calculator.py with add, subtract, multiply, divide functions, write unit tests in /tmp/test_calculator.py using unittest, run the tests to make sure they pass, then generate a coverage-style report in /tmp/calc_report.md listing which functions were tested and read it back" --trust
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#!/bin/bash
# Fibonacci generator: code creation, execution, documentation, verification
# Tests: file creation, code execution, markdown writing, file reading
praisonai "Create a Python file at /tmp/fibonacci.py that generates the first 20 Fibonacci numbers, run it to verify the output is correct, then write a summary to /tmp/fib_summary.md explaining the algorithm you used and the output, finally read back both files to confirm everything looks good" --trust
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#!/bin/bash
# Multi-step research: web search, file creation, code execution, reporting
# Tests: web search, file write, code execution, markdown report, file read, directory listing, system info
praisonai "Research the top 3 Python web frameworks (Django, FastAPI, Flask), then: 1) Search the web for each framework's latest version and key features, 2) Create a file called /tmp/framework_comparison.py that contains a Python dictionary with the comparison data, 3) Execute the code to verify the dictionary is valid, 4) Write a markdown report to /tmp/framework_report.md summarizing your findings in a table format, 5) Read back the report file to verify it was written correctly, 6) List the files in /tmp to confirm both files exist, 7) Get system info to note what OS this report was generated on, 8) Analyze the Python code you wrote for any issues, 9) Format the Python code properly, 10) Finally search for any recent news about Python 3.13 features" --trust
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# Persistence CLI Commands
## Quick Reference
```bash
# Check database connectivity
praisonai persistence doctor \
--conversation-url postgresql://localhost:5432/praisonai \
--state-url redis://localhost:6379
# Run agent with persistence
praisonai persistence run \
--conversation-url postgresql://localhost:5432/praisonai \
--session-id my-session
# Resume a session
praisonai persistence resume \
--conversation-url postgresql://localhost:5432/praisonai \
--session-id my-session
# Export session to file
praisonai persistence export \
--conversation-url postgresql://localhost:5432/praisonai \
--session-id my-session \
--output session_backup.jsonl
# Import session from file
praisonai persistence import \
--conversation-url postgresql://localhost:5432/praisonai \
--file session_backup.jsonl
# Check schema status
praisonai persistence status \
--conversation-url postgresql://localhost:5432/praisonai \
--state-url redis://localhost:6379
# Apply migrations
praisonai persistence migrate \
--conversation-url postgresql://localhost:5432/praisonai \
--state-url redis://localhost:6379
```
## Environment Variables
Instead of CLI flags, you can use environment variables:
```bash
export PRAISON_CONVERSATION_URL=postgresql://localhost:5432/praisonai
export PRAISON_STATE_URL=redis://localhost:6379
export PRAISON_KNOWLEDGE_URL=http://localhost:6333
# Then run without URL flags
praisonai persistence doctor
praisonai persistence status
```
## Docker Setup
```bash
# Start all services
docker run -d --name praison-postgres \
-e POSTGRES_PASSWORD=praison123 \
-e POSTGRES_DB=praisonai \
-p 5432:5432 postgres:16
docker run -d --name praison-redis \
-p 6379:6379 redis:7
docker run -d --name praison-qdrant \
-p 6333:6333 -p 6334:6334 qdrant/qdrant
# Verify
docker ps
```
## Example Workflow
```bash
# 1. Start services
docker-compose up -d
# 2. Check connectivity
praisonai persistence doctor
# 3. Run agent
praisonai persistence run --session-id demo-session
# 4. Export for backup
praisonai persistence export --session-id demo-session --output backup.jsonl
# 5. Import to another environment
praisonai persistence import --file backup.jsonl
```
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# PraisonAI Persistence CLI - End-to-End Guide
This guide demonstrates the CLI commands for database persistence.
## Prerequisites
```bash
pip install "praisonai[tools]"
# Start Docker containers
docker run -d --name praison-postgres -p 5432:5432 \
-e POSTGRES_PASSWORD=praison123 \
-e POSTGRES_DB=praisonai \
postgres:16
docker run -d --name praison-qdrant -p 6333:6333 qdrant/qdrant
docker run -d --name praison-redis -p 6379:6379 redis:7
```
## 1. Doctor Command - Validate Connectivity
```bash
# Check all stores
praisonai persistence doctor \
--conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
--knowledge-url "http://localhost:6333" \
--state-url "redis://localhost:6379"
```
**Expected Output:**
```
==================================================
PraisonAI Persistence Doctor
==================================================
[Conversation Store] Testing: postgresql://postgres:****@localhost:5432/praisonai
✅ Connected (postgres)
[Knowledge Store] Testing: http://localhost:6333
✅ Connected (qdrant)
[State Store] Testing: redis://localhost:6379
✅ Connected (redis)
==================================================
Results: 3/3 stores connected successfully
==================================================
```
## 2. Run Command - Execute Agent with Persistence
```bash
# Run agent with persistence (dry-run first)
praisonai persistence run \
--session-id "cli-demo-001" \
--conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
--dry-run \
"Hello, my name is Bob"
# Actually run it
praisonai persistence run \
--session-id "cli-demo-001" \
--conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
"Hello, my name is Bob"
```
**Expected Output:**
```
[Session: cli-demo-001]
Agent: Hello Bob! Nice to meet you. How can I help you today?
```
## 3. Resume Command - Continue a Session
```bash
# Show history
praisonai persistence resume \
--session-id "cli-demo-001" \
--conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
--show-history
# Continue conversation
praisonai persistence resume \
--session-id "cli-demo-001" \
--conversation-url "postgresql://postgres:praison123@localhost:5432/praisonai" \
--continue "What's my name?"
```
**Expected Output:**
```
[Session: cli-demo-001]
Messages in history: 2
--- Conversation History ---
[USER] Hello, my name is Bob
[ASSISTANT] Hello Bob! Nice to meet you...
--- End History ---
Agent: Your name is Bob!
```
## Environment Variables
Instead of passing URLs on command line, use environment variables:
```bash
export PRAISON_CONVERSATION_URL="postgresql://postgres:praison123@localhost:5432/praisonai"
export PRAISON_KNOWLEDGE_URL="http://localhost:6333"
export PRAISON_STATE_URL="redis://localhost:6379"
export OPENAI_API_KEY="your-key"
# Now commands are simpler
praisonai persistence doctor --all
praisonai persistence run --session-id "my-session" "Hello!"
```
## Full CLI Reference
```
praisonai persistence --help
praisonai persistence doctor --help
praisonai persistence run --help
praisonai persistence resume --help
```
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#!/bin/bash
# System audit: multiple command executions, report generation
# Tests: command execution (date, disk, python, processes), file creation, file reading
praisonai "Investigate my system - get the current date, check disk usage, find out what Python version is installed, list running processes sorted by memory, then write all findings to /tmp/system_audit.md as a neat report and read it back to verify" --trust
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#!/bin/bash
# Todo app: multi-file app creation, execution, documentation, verification
# Tests: multiple file creation, code execution, markdown docs, directory listing
praisonai "Write a todo list app at /tmp/todo.py with functions to add, remove, and list tasks stored in /tmp/todos.json, run the script with some sample tasks added, then create documentation at /tmp/todo_docs.md explaining usage with examples, and verify all three files exist" --trust
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