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patreon: MervinPraison
ko_fi: MervinPraison
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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. -->
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name: "Claude Code Action"
description: "Run Claude Code in GitHub Actions workflows"
inputs:
anthropic_api_key:
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
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 }}
@@ -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: Build Claude Code Image
on:
workflow_dispatch:
inputs:
version:
description: 'Image version tag'
required: true
default: 'latest'
jobs:
build:
runs-on: ubuntu-latest
timeout-minutes: 15
permissions:
contents: read
packages: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Login to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GH_TOKEN }}
- name: Build and push Docker image
uses: docker/build-push-action@v5
with:
context: ./.github/actions/claude-code-action
push: true
platforms: linux/amd64
tags: ghcr.io/mervinpraison/praisonai-claudecode:${{ inputs.version }}
cache-from: type=gha
cache-to: type=gha,mode=max
provenance: false
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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 }}
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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 }}
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name: Claude Assistant
on:
issue_comment:
types: [created]
pull_request_review_comment:
types: [created]
issues:
types: [opened, assigned, labeled]
pull_request_review:
types: [submitted]
jobs:
claude-response:
# Prevent certain bots from triggering this action (but allow github-actions[bot] for auto-comments)
if: |
github.actor != 'dependabot[bot]' &&
github.actor != 'cursor[bot]' &&
github.actor != 'renovate[bot]' &&
!(contains(github.actor, '[bot]') && github.actor != 'github-actions[bot]')
runs-on: ubuntu-latest
permissions:
contents: write
pull-requests: write
issues: write
actions: read
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: anthropics/claude-code-action@beta
with:
claude_code_oauth_token: ${{ secrets.CLAUDE_ACCESS_TOKEN }}
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 #${issueNumber}`);
}
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name: Run tests and upload coverage
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main, develop ]
jobs:
test:
name: Run tests and collect coverage
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout
uses: actions/checkout@v4
with:
fetch-depth: 2
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: 3.11
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Install dependencies
run: |
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system duckduckgo_search
uv pip install --system pytest pytest-cov pytest-asyncio
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
- name: Set environment variables
run: |
echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $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"
- name: Upload results to Codecov
uses: codecov/codecov-action@v5
with:
token: ${{ secrets.CODECOV_TOKEN }}
file: src/praisonai/coverage.xml
flags: unit-tests
name: unit-tests-coverage
fail_ci_if_error: false
verbose: true
-54
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@@ -1,54 +0,0 @@
name: Publish Docker
on:
release:
types: [published]
workflow_dispatch:
inputs:
tag:
description: 'Docker image tag (default: latest)'
required: false
default: 'latest'
jobs:
build-and-push:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: 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: Build and push Docker image
uses: docker/build-push-action@v5
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
@@ -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
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@@ -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
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@@ -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*
-68
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@@ -1,68 +0,0 @@
# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
name: Python package
on:
push:
branches: [ "develop" ]
pull_request:
branches: [ "develop" ]
jobs:
build:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.10", "3.11"]
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Set environment variables
run: |
echo "OPENAI_API_KEY=${{ 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 "LOGLEVEL=DEBUG" >> $GITHUB_ENV
- name: Install dependencies
run: |
cd src/praisonai
python -m pip install --upgrade pip
python -m pip install flake8 pytest
python -m pip install ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
python -m pip install duckduckgo_search
python -m pip install pytest-asyncio pytest-cov
# Install knowledge dependencies from praisonai-agents
python -m pip install "praisonaiagents[knowledge]"
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- name: Debug API Key Status
run: |
echo "🔍 Checking API key availability..."
if [ -n "${{ secrets.OPENAI_API_KEY }}" ]; then
echo "✅ GitHub secret OPENAI_API_KEY is available"
echo "🔑 API key starts with: $(echo "$OPENAI_API_KEY" | cut -c1-7)..."
else
echo "⚠️ GitHub secret OPENAI_API_KEY is NOT set - using fallback"
echo "🔑 Using fallback key: sk-test-key..."
fi
echo "🌐 API Base: $OPENAI_API_BASE"
echo "🤖 Model: $OPENAI_MODEL_NAME"
# - name: Lint with flake8
# run: |
# # stop the build if there are Python syntax errors or undefined names
# flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
# # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
# flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
- name: Test with pytest
run: |
cd src/praisonai && python tests/test_runner.py --pattern fast
-40
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@@ -1,40 +0,0 @@
# This workflow will upload a Python Package using Twine when a release is created
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
# This workflow uses actions that are not certified by GitHub.
# They are provided by a third-party and are governed by
# separate terms of service, privacy policy, and support
# documentation.
name: Upload Python Package
on:
release:
types: [published]
permissions:
contents: read
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.x'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install build
- name: Build package
run: python -m build src/praisonai
- name: Publish package
uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
with:
user: __token__
password: ${{ secrets.PYPI_API_TOKEN }}
packages_dir: src/praisonai/dist/
-524
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@@ -1,524 +0,0 @@
name: Release Workflow
on:
workflow_dispatch:
inputs:
tag:
description: 'Tag to create release with (e.g., v2.0.10)'
required: true
fork_owner:
description: 'GitHub username of the fork owner'
required: true
default: 'tosin2013'
jobs:
test-ubuntu:
strategy:
matrix:
os: [ubuntu-22.04, ubuntu-24.04]
framework: ['crewai', 'autogen']
fail-fast: false
runs-on: ${{ matrix.os }}
timeout-minutes: 90
steps:
# First checkout the upstream repo at the specified tag
- name: Checkout upstream repo
uses: actions/checkout@v4
with:
repository: MervinPraison/PraisonAI
ref: ${{ github.event.inputs.tag }}
fetch-depth: 0
# Get the commit hash from the tag
- name: Get commit hash
id: get_commit
run: |
COMMIT_HASH=$(git rev-parse HEAD)
echo "commit_hash=$COMMIT_HASH" >> $GITHUB_OUTPUT
echo "Found commit: $COMMIT_HASH for tag ${{ github.event.inputs.tag }}"
# Now checkout the fork at main branch
- name: Checkout fork
uses: actions/checkout@v4
with:
repository: ${{ github.event.inputs.fork_owner }}/PraisonAI
ref: main
fetch-depth: 0
clean: true
# Sync with upstream commit
- name: Sync with upstream
run: |
git config --global user.name "GitHub Actions"
git config --global user.email "[email protected]"
# Add upstream remote
git remote add upstream https://github.com/MervinPraison/PraisonAI.git
# Fetch from upstream
git fetch upstream --tags --force
# Reset to upstream commit
git reset --hard ${{ steps.get_commit.outputs.commit_hash }}
- name: Setup Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
cache: 'pip'
- name: Install Poetry
run: |
curl -sSL https://install.python-poetry.org | python3 -
echo "$HOME/.local/bin" >> $GITHUB_PATH
poetry config virtualenvs.in-project true
poetry config virtualenvs.create true
poetry --version
poetry config --list
- name: Install Dependencies
run: |
poetry lock #--no-update
poetry install --all-extras
poetry env info
# Verify venv exists
test -d .venv || { echo "Virtual environment not created!"; exit 1; }
test -f .venv/bin/activate || { echo "Activation script not found!"; exit 1; }
- name: Run setup script
timeout-minutes: 20
run: |
set -e # Exit on any error
curl -OL https://raw.githubusercontent.com/tosin2013/PraisonAI/refs/heads/main/setup.sh
chmod +x setup.sh
./setup.sh --all --cicd || { echo "Setup script failed"; exit 1; }
env:
OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
PRAISONAI_NON_INTERACTIVE: "true"
- name: Test Basic Functionality
run: |
set -e # Exit on any error
# Random sleep between 60-120 seconds to avoid API rate limits
SLEEP_TIME=$((RANDOM % 61 + 60))
echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
sleep $SLEEP_TIME
lsof -ti:8084 | xargs -r kill -9 || true
source .venv/bin/activate || { echo "Failed to activate virtual environment"; exit 1; }
mkdir -p test_output || { echo "Failed to create test_output directory"; exit 1; }
echo "Testing basic CLI functionality..."
# Test auto generation with explicit framework
praisonai --framework ${{ matrix.framework }} --auto "create a short test script" 2>&1 | tee test_output/auto_test.log
# Verify the output file exists and has content
if [[ ! -s test_output/auto_test.log ]]; then
echo "Error: No output generated from auto test"
exit 1
fi
# Test auto generation without framework
echo "Basic functionality tests passed!"
env:
OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
LOGLEVEL: "debug"
PRAISONAI_NON_INTERACTIVE: "true"
- name: Test Framework-specific Features
continue-on-error: false
run: |
source .venv/bin/activate
# Random sleep between 60-120 seconds to avoid API rate limits
SLEEP_TIME=$((RANDOM % 61 + 60))
echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
sleep $SLEEP_TIME
lsof -ti:8084 | xargs -r kill -9 || true
praisonai --framework ${{ matrix.framework }} --auto "create a movie script about Robots in Mars" 2>&1 | tee test_output/framework_output.txt || echo "Framework-specific test failed" >> test_output/errors.log
env:
OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
PRAISONAI_NON_INTERACTIVE: "true"
- name: Test Core Functionality
continue-on-error: false
run: |
source .venv/bin/activate
# Random sleep between 60-120 seconds to avoid API rate limits
SLEEP_TIME=$((RANDOM % 61 + 60))
echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
sleep $SLEEP_TIME
echo "Testing core functionality..."
# Test package imports
python3 -c "from praisonai.cli import PraisonAI; from praisonai.auto import AutoGenerator; from praisonai.agents_generator import AgentsGenerator" || exit 1
# Test configuration loading
python3 -c "from praisonai.inc.config import generate_config; print(generate_config())" || exit 1
# Test tools loading
python3 -c "from praisonai.inbuilt_tools import *" || exit 1
echo "Core functionality tests passed!"
env:
OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
LOGLEVEL: "debug"
PRAISONAI_NON_INTERACTIVE: "true"
- name: Test Agent Generation
continue-on-error: false
run: |
source .venv/bin/activate
# Random sleep between 60-120 seconds to avoid API rate limits
SLEEP_TIME=$((RANDOM % 61 + 60))
echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
sleep $SLEEP_TIME
echo "Testing agent generation..."
# Create a test YAML file
cat > test_agents.yaml <<EOF
topic: "Test automation task"
framework: "${{ matrix.framework }} "
roles:
- name: "Tester"
goal: "Test the system"
backstory: "Expert system tester"
tasks:
- description: "Verify system functionality"
expected_output: "Test results"
EOF
# Test YAML loading and agent generation
python3 -c '
from praisonai.agents_generator import AgentsGenerator
config = [{"model": "llama-3.3-70b-versatile", "base_url": "https://api.groq.com/openai/v1/"}]
generator = AgentsGenerator("test_agents.yaml", "${{ matrix.framework }} ", config)
print("Agent generation test passed!")
' || exit 1
echo "Agent generation tests passed!"
env:
OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
LOGLEVEL: "debug"
PRAISONAI_NON_INTERACTIVE: "true"
- name: Test Custom Tools Integration
continue-on-error: false
run: |
source .venv/bin/activate
# Random sleep between 60-120 seconds to avoid API rate limits
SLEEP_TIME=$((RANDOM % 61 + 60))
echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
sleep $SLEEP_TIME
echo "Testing custom tools integration..."
# Create a test tool
cat > test_tool.py <<EOF
from duckduckgo_search import DDGS
from praisonai_tools import BaseTool
class InternetSearchTool(BaseTool):
name: str = "Internet Search Tool"
description: str = "Search Internet for relevant information"
def _run(self, query: str):
return {"status": "success", "query": query}
def test(self):
"""Test method to verify tool setup"""
test_query = "test query"
result = self._run(test_query)
assert result["status"] == "success"
assert result["query"] == test_query
return True
EOF
# Test tool loading and initialization
python3 -c '
import importlib.util
import sys
# Load the test tool
spec = importlib.util.spec_from_file_location("test_tool", "test_tool.py")
module = importlib.util.module_from_spec(spec)
sys.modules["test_tool"] = module
spec.loader.exec_module(module)
# Initialize and test the tool
tool = module.InternetSearchTool()
assert tool.test(), "Tool test failed"
print("Custom tool test passed!")
' || exit 1
echo "Custom tools integration test passed!"
env:
OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
LOGLEVEL: "debug"
PRAISONAI_NON_INTERACTIVE: "true"
- name: Test PraisonAI Agents Configuration
continue-on-error: false
run: |
source .venv/bin/activate
# Random sleep between 60-120 seconds to avoid API rate limits
SLEEP_TIME=$((RANDOM % 61 + 60))
echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
sleep $SLEEP_TIME
echo "Testing agents configuration..."
mkdir -p test_output || { echo "Failed to create test_output directory"; exit 1; }
# Create test script
cat > test_agents.py <<EOF
from langchain_openai import ChatOpenAI
from crewai import Agent, Task, Crew
import os
import logging
import time
import random
logging.basicConfig(level="DEBUG")
logger = logging.getLogger(__name__)
def random_sleep(min_seconds=15, max_seconds=30):
"""Sleep for a random duration to prevent API rate limiting"""
duration = random.uniform(min_seconds, max_seconds)
logger.info(f"Rate limit protection: Sleeping for {duration:.2f} seconds...")
time.sleep(duration)
class RateLimitedChatOpenAI(ChatOpenAI):
"""ChatOpenAI with built-in rate limiting"""
def __call__(self, *args, **kwargs):
random_sleep()
return super().__call__(*args, **kwargs)
def run_test():
logger.info("Initializing test components...")
# Initialize the LLM with rate limiting
llm = RateLimitedChatOpenAI(
model=os.getenv('OPENAI_MODEL_NAME', 'llama-3.3-70b-versatile'),
base_url=os.getenv('OPENAI_API_BASE', 'https://api.groq.com/openai/v1/'),
api_key=os.getenv('OPENAI_API_KEY'),
temperature=0.7
)
random_sleep()
logger.info("Creating researcher agent...")
researcher = Agent(
role="Test Automation Expert",
goal="Verify system functionality and performance",
backstory="""You are an expert in test automation and system verification,
skilled in identifying potential issues and validating functionality.""",
verbose=True,
allow_delegation=False,
llm=llm
)
random_sleep()
logger.info("Creating validator agent...")
validator = Agent(
role="Quality Assurance Specialist",
goal="Validate test results and ensure system reliability",
backstory="""You are a QA specialist focused on ensuring
system reliability and proper functionality.""",
verbose=True,
allow_delegation=False,
llm=llm
)
random_sleep()
logger.info("Creating test tasks...")
test_task = Task(
description="Run a basic system verification test",
expected_output="A report on system functionality verification",
agent=researcher
)
random_sleep()
validation_task = Task(
description="Validate the test results and compile findings",
expected_output="A validation report of test findings",
agent=validator
)
random_sleep()
logger.info("Initializing crew...")
crew = Crew(
agents=[researcher, validator],
tasks=[test_task, validation_task],
verbose=2
)
logger.info("Starting crew execution...")
result = crew.kickoff()
logger.info("Test execution completed.")
logger.info(f"Result: {result}")
return result
if __name__ == "__main__":
try:
run_test()
except Exception as e:
logger.error(f"Test failed with error: {str(e)}", exc_info=True)
raise
EOF
# Run the test script with increased timeout
echo "Testing with Python API..."
timeout 1800 python3 test_agents.py 2>&1 | tee test_output/agents_test.log
# Verify the output
if [[ ! -s test_output/agents_test.log ]]; then
echo "Error: No output generated from agents test"
exit 1
fi
echo "Agents configuration tests passed!"
env:
OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
LOGLEVEL: "debug"
PRAISONAI_NON_INTERACTIVE: "true"
- name: Upload test artifacts
if: always()
uses: actions/upload-artifact@v4
with:
name: praisonai-output-${{ matrix.os }}-${{ matrix.framework }}
path: test_output/
retention-days: 14
create-release:
needs: [test-ubuntu]
runs-on: ubuntu-latest
if: success()
steps:
# First checkout the fork's main branch
- name: Checkout fork repo
uses: actions/checkout@v4
with:
fetch-depth: 0
token: ${{ secrets.PAT_TOKEN }}
repository: ${{ github.event.inputs.fork_owner }}/PraisonAI
ref: main
# Add upstream and get commit from tag
- name: Get upstream tag commit
id: get_upstream_commit
run: |
# Add upstream remote
git remote add upstream https://github.com/MervinPraison/PraisonAI.git
# Delete local tag if it exists
git tag -d ${{ github.event.inputs.tag }} || true
# Fetch from upstream including tags with force
git fetch upstream --tags --force
# Verify tag exists in upstream
if ! git ls-remote --tags upstream | grep -q "refs/tags/${{ github.event.inputs.tag }}$"; then
echo "::error::Tag ${{ github.event.inputs.tag }} not found in upstream repository"
exit 1
fi
# Get the commit hash from the upstream tag
UPSTREAM_COMMIT=$(git rev-list -n 1 refs/tags/${{ github.event.inputs.tag }})
if [ -z "$UPSTREAM_COMMIT" ]; then
echo "::error::Could not find commit for tag ${{ github.event.inputs.tag }}"
exit 1
fi
echo "upstream_commit=$UPSTREAM_COMMIT" >> $GITHUB_OUTPUT
echo "Found commit: $UPSTREAM_COMMIT for tag ${{ github.event.inputs.tag }}"
# Create tag at the same commit
- name: Create tag at commit
run: |
git config --global user.name "GitHub Actions"
git config --global user.email "[email protected]"
# Remove tag if it exists locally
git tag -d ${{ github.event.inputs.tag }} || true
# Remove tag if it exists remotely
git push origin :refs/tags/${{ github.event.inputs.tag }} || true
# Fetch the specific commit from upstream
git fetch upstream ${{ steps.get_upstream_commit.outputs.upstream_commit }} --force
# Reset to upstream commit
git reset --hard ${{ steps.get_upstream_commit.outputs.upstream_commit }}
# Create and push tag
git tag -a ${{ github.event.inputs.tag }} -m "Release ${{ github.event.inputs.tag }} (synced from upstream MervinPraison/PraisonAI@${{ github.event.inputs.tag }})"
git push origin ${{ github.event.inputs.tag }}
env:
GIT_ASKPASS: /bin/echo
PAT_TOKEN: ${{ secrets.PAT_TOKEN }}
# Create the release
- name: Create Release
uses: actions/create-release@v1
env:
GITHUB_TOKEN: ${{ secrets.PAT_TOKEN }}
with:
tag_name: ${{ github.event.inputs.tag }}
release_name: Release ${{ github.event.inputs.tag }}
draft: false
prerelease: false
body: |
Release ${{ github.event.inputs.tag }} (synced from upstream)
This release is synchronized with MervinPraison/PraisonAI@${{ github.event.inputs.tag }}
Commit: ${{ steps.get_upstream_commit.outputs.upstream_commit }}
For installation:
```bash
pip install praisonai
```
Changes from upstream v2.0.10:
- Updated agent configuration tests
- Added rate limiting protection
- Improved test reliability
Forked from: MervinPraison/PraisonAI
-196
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@@ -1,196 +0,0 @@
name: Comprehensive Test Suite
on:
workflow_dispatch: # Allow manual triggering
inputs:
test_type:
description: 'Type of tests to run'
required: true
default: 'all'
type: choice
options:
- all
- unit
- integration
- fast
- performance
- frameworks
- autogen
- crewai
release:
types: [published, prereleased]
schedule:
# Run comprehensive tests weekly on Sundays at 3 AM UTC
- cron: '0 3 * * 0'
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"]
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Install dependencies
run: |
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system duckduckgo_search
uv pip install --system pytest pytest-asyncio pytest-cov pytest-benchmark pytest-timeout
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
- name: Verify environment variables
run: |
if [ -n "$OPENAI_API_KEY" ]; then
echo "✅ OPENAI_API_KEY is set (length: ${#OPENAI_API_KEY})"
else
echo "⚠️ OPENAI_API_KEY is NOT set"
fi
- name: Run Comprehensive Test Suite
run: |
# Determine test type from input or default to 'all'
TEST_TYPE="${{ github.event.inputs.test_type || 'all' }}"
echo "🧪 Running comprehensive test suite (type: $TEST_TYPE)"
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"
;;
"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"
;;
"fast")
cd src/praisonai && python tests/test_runner.py --pattern fast || echo "Some fast tests failed"
;;
"performance")
cd src/praisonai && python tests/test_runner.py --pattern "performance" || echo "Some performance tests failed"
;;
"frameworks")
cd src/praisonai && python tests/test_runner.py --pattern frameworks || echo "Some framework tests failed"
;;
"autogen")
cd src/praisonai && python tests/test_runner.py --pattern autogen || echo "Some autogen tests failed"
;;
"crewai")
cd src/praisonai && python tests/test_runner.py --pattern crewai || echo "Some crewai tests failed"
;;
"all"|*)
cd src/praisonai && python tests/test_runner.py --pattern all || echo "Some tests failed"
;;
esac
- name: Generate Comprehensive Test Report
if: always()
run: |
echo "# 📋 Comprehensive Test Report" > comprehensive_report.md
echo "" >> comprehensive_report.md
echo "**Python Version:** ${{ matrix.python-version }}" >> comprehensive_report.md
echo "**Test Type:** ${{ github.event.inputs.test_type || 'all' }}" >> comprehensive_report.md
echo "**Trigger:** ${{ github.event_name }}" >> comprehensive_report.md
echo "**Date:** $(date -u)" >> comprehensive_report.md
echo "" >> comprehensive_report.md
echo "## 🧪 Test Categories Covered:" >> comprehensive_report.md
echo "" >> comprehensive_report.md
echo "### Unit Tests:" >> comprehensive_report.md
echo "- ✅ Core agent functionality" >> comprehensive_report.md
echo "- ✅ Async operations" >> comprehensive_report.md
echo "- ✅ Tool integrations" >> comprehensive_report.md
echo "- ✅ UI components" >> comprehensive_report.md
echo "" >> comprehensive_report.md
echo "### Integration Tests:" >> comprehensive_report.md
echo "- ✅ MCP (Model Context Protocol)" >> comprehensive_report.md
echo "- ✅ RAG (Retrieval Augmented Generation)" >> comprehensive_report.md
echo "- ✅ Base URL API mapping" >> comprehensive_report.md
echo "- ✅ Multi-agent workflows" >> comprehensive_report.md
echo "- ✅ AutoGen framework integration" >> comprehensive_report.md
echo "- ✅ CrewAI framework integration" >> comprehensive_report.md
echo "- 💬 LLM integrations (OpenAI, Anthropic, etc.)" >> comprehensive_report.md
echo "- 🖥️ UI frameworks (Gradio, Streamlit)" >> comprehensive_report.md
echo "- 📊 Memory and persistence" >> comprehensive_report.md
echo "- 🌐 Multi-modal capabilities" >> comprehensive_report.md
echo "- ✅ AutoGen framework integration" >> comprehensive_report.md
echo "- ✅ CrewAI framework integration" >> comprehensive_report.md
echo "" >> comprehensive_report.md
echo "### Key Features Tested:" >> comprehensive_report.md
echo "- 🤖 Agent creation and configuration" >> comprehensive_report.md
echo "- 📋 Task management and execution" >> comprehensive_report.md
echo "- 🔄 Sync/async workflows" >> comprehensive_report.md
echo "- 🛠️ Custom tools and error handling" >> comprehensive_report.md
echo "- 🧠 Knowledge bases and RAG" >> comprehensive_report.md
echo "- 🔌 MCP server connections" >> comprehensive_report.md
echo "- 💬 LLM integrations (OpenAI, Anthropic, etc.)" >> comprehensive_report.md
- name: Upload coverage reports to Codecov
uses: codecov/codecov-action@v5
with:
token: ${{ secrets.CODECOV_TOKEN }}
file: src/praisonai/coverage.xml
flags: comprehensive-tests
name: comprehensive-tests-coverage
fail_ci_if_error: false
verbose: true
- name: Upload Comprehensive Test Results
uses: actions/upload-artifact@v4
if: always()
with:
name: comprehensive-test-results-python-${{ matrix.python-version }}
path: |
comprehensive_report.md
src/praisonai/htmlcov/
src/praisonai/coverage.xml
src/praisonai/.coverage
retention-days: 30
test-matrix-summary:
runs-on: ubuntu-latest
needs: comprehensive-test
if: always()
steps:
- name: Generate Matrix Summary
run: |
echo "# 🎯 Test Matrix Summary" > matrix_summary.md
echo "" >> matrix_summary.md
echo "## Python Version Results:" >> matrix_summary.md
echo "- Python 3.10: ${{ needs.comprehensive-test.result }}" >> matrix_summary.md
echo "- Python 3.11: ${{ needs.comprehensive-test.result }}" >> matrix_summary.md
echo "" >> matrix_summary.md
echo "## Overall Status:" >> matrix_summary.md
if [ "${{ needs.comprehensive-test.result }}" == "success" ]; then
echo "✅ **All tests passed across all Python versions!**" >> matrix_summary.md
else
echo "❌ **Some tests failed. Check individual job logs for details.**" >> matrix_summary.md
fi
- name: Upload Matrix Summary
uses: actions/upload-artifact@v4
with:
name: test-matrix-summary
path: matrix_summary.md
retention-days: 30
-288
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@@ -1,288 +0,0 @@
name: Core Tests
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main, develop ]
jobs:
test-core:
runs-on: ubuntu-latest
timeout-minutes: 15
strategy:
matrix:
python-version: [3.11]
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Install dependencies
run: |
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system duckduckgo_search
uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
- name: Set environment variables
run: |
echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $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 LOGLEVEL=DEBUG
# Verify immediate availability
echo "🔧 Immediate verification in same step:"
echo " OPENAI_API_KEY length in current session: ${#OPENAI_API_KEY}"
echo " OPENAI_API_KEY starts with sk-: $(echo "$OPENAI_API_KEY" | grep -q '^sk-' && echo 'YES' || echo 'NO')"
- name: Debug API Key Status
run: |
echo "🔍 Checking API key availability..."
if [ -n "${{ secrets.OPENAI_API_KEY }}" ]; then
echo "✅ GitHub secret OPENAI_API_KEY is available"
echo "🔑 API key starts with: $(echo "$OPENAI_API_KEY" | cut -c1-7)..."
else
echo "⚠️ GitHub secret OPENAI_API_KEY is NOT set - using fallback"
echo "🔑 Using fallback key: sk-test-key..."
fi
echo "🌐 API Base: $OPENAI_API_BASE"
echo "🤖 Model: $OPENAI_MODEL_NAME"
echo "🐛 Log Level: $LOGLEVEL"
echo "📊 Environment Check:"
echo " - OPENAI_API_KEY length: ${#OPENAI_API_KEY}"
echo " - OPENAI_API_BASE: $OPENAI_API_BASE"
echo " - OPENAI_MODEL_NAME: $OPENAI_MODEL_NAME"
echo " - LOGLEVEL: $LOGLEVEL"
echo "🔍 Is API key actually set?"
echo " - API key starts with sk-: $(echo "$OPENAI_API_KEY" | grep -q '^sk-' && echo 'YES' || echo 'NO')"
echo " - API key is not test key: $([ "$OPENAI_API_KEY" != 'sk-test-key-for-github-actions-testing-only-not-real' ] && echo 'YES' || echo 'NO')"
- name: Debug Environment Variables Raw
run: |
echo "🔧 Raw environment variable check:"
echo "OPENAI_API_KEY set: $(if [ -n "$OPENAI_API_KEY" ]; then echo 'YES'; else echo 'NO'; fi)"
echo "OPENAI_API_KEY length: ${#OPENAI_API_KEY}"
echo "OPENAI_API_KEY first 10 chars: ${OPENAI_API_KEY:0:10}"
echo "OPENAI_API_KEY last 5 chars: ${OPENAI_API_KEY: -5}"
printenv | grep OPENAI || echo "No OPENAI env vars found"
- name: Debug Python Environment Variables
run: |
python -c "
import os
print('🐍 Python Environment Variable Check:')
api_key = os.environ.get('OPENAI_API_KEY', 'NOT_SET')
if api_key != 'NOT_SET':
print(f' ✅ OPENAI_API_KEY: {api_key[:7]}... (length: {len(api_key)})')
else:
print(' ❌ OPENAI_API_KEY: NOT_SET')
print(f' 🌐 OPENAI_API_BASE: {os.environ.get(\"OPENAI_API_BASE\", \"NOT_SET\")}')
print(f' 🤖 OPENAI_MODEL_NAME: {os.environ.get(\"OPENAI_MODEL_NAME\", \"NOT_SET\")}')
print(f' 📋 All OPENAI env vars:')
for key, value in os.environ.items():
if key.startswith('OPENAI'):
print(f' {key}: {value[:10] if len(value) > 10 else value}...')
"
- name: Validate API Key
run: |
echo "🔑 Testing API key validity with minimal OpenAI call..."
python -c "
import os
try:
from openai import OpenAI
client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))
response = client.models.list()
print('✅ API Key is VALID - OpenAI responded successfully')
print(f'📊 Available models: {len(list(response.data))} models found')
except Exception as e:
print(f'❌ API Key is INVALID - Error: {e}')
print('🔍 This explains why all API-dependent tests are failing')
print('💡 The GitHub secret OPENAI_API_KEY needs to be updated with a valid key')
"
continue-on-error: true
- name: Debug PraisonAI API Key Usage
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' }}
LOGLEVEL: DEBUG
run: |
echo "🔍 Testing PraisonAI API key usage directly..."
cd src/praisonai
python -c "
import os
import sys
sys.path.insert(0, '.')
# Attempt to import SecretStr, otherwise use a dummy class
try:
from pydantic.types import SecretStr
except ImportError:
class SecretStr: # Dummy class if pydantic is not available in this minimal context
def __init__(self, value): self._value = value
def get_secret_value(self): return self._value
def get_key_display_value(key_value):
if isinstance(key_value, SecretStr):
return key_value.get_secret_value()[:10] if key_value.get_secret_value() else 'EMPTY_SECRET'
elif isinstance(key_value, str):
return key_value[:10] if key_value != 'nokey' and key_value != 'NOT_SET' else key_value
return 'INVALID_TYPE'
print('🔧 Direct PraisonAI API Key Check:')
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
praisonai = PraisonAI()
print(f'PraisonAI config_list: {praisonai.config_list}')
api_key_from_config = praisonai.config_list[0].get('api_key', 'NOT_SET')
print(f'API key from PraisonAI config: {get_key_display_value(api_key_from_config)}...')
from praisonai.inc.models import PraisonAIModel
print('\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
model_with_explicit_key = PraisonAIModel(
model='openai/gpt-5-nano',
base_url=praisonai.config_list[0].get('base_url'),
api_key=api_key_from_config # This will be a string from praisonai.config_list
)
print(f' Model: {model_with_explicit_key.model}')
print(f' Model name: {model_with_explicit_key.model_name}')
print(f' API key var: {model_with_explicit_key.api_key_var}')
# model_with_explicit_key.api_key is now a string, or 'nokey'
print(f' API key (explicitly passed to PraisonAIModel): {get_key_display_value(model_with_explicit_key.api_key)}...')
print(f' Base URL: {model_with_explicit_key.base_url}')
try:
llm_instance = model_with_explicit_key.get_model()
print(f' ✅ LLM instance created successfully: {type(llm_instance).__name__}')
# langchain_openai.ChatOpenAI stores the key in openai_api_key as SecretStr
llm_api_key_attr = getattr(llm_instance, 'openai_api_key', 'NOT_FOUND')
if llm_api_key_attr != 'NOT_FOUND':
print(f' LLM instance API key: {get_key_display_value(llm_api_key_attr)}...')
else:
print(f' LLM instance API key attribute not found.')
except Exception as e:
print(f' ❌ Failed to create LLM instance: {e}')
import traceback
traceback.print_exc()
"
continue-on-error: true
- 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
- name: Run Integration Tests
run: |
cd src/praisonai && python -m pytest tests/integration/ -v --tb=short --disable-warnings
- name: Debug Directory Structure
run: |
echo "🔍 Debugging directory structure for CrewAI tests..."
cd src/praisonai
echo "Current working directory: $(pwd)"
echo "📁 Contents of current directory:"
ls -la
echo ""
echo "📁 Contents of tests directory:"
ls -la tests/ || echo "❌ tests/ directory not found"
echo ""
echo "📁 Contents of tests/integration:"
ls -la tests/integration/ || echo "❌ tests/integration/ directory not found"
echo ""
echo "📁 Looking for crewai directory:"
find . -name "crewai" -type d 2>/dev/null || echo "❌ No crewai directories found"
echo ""
echo "📁 Full directory tree of tests:"
tree tests/ || find tests/ -type d 2>/dev/null || echo "❌ Cannot explore tests directory"
- name: Test AutoGen Framework
run: |
echo "🤖 Testing AutoGen Framework Integration..."
cd src/praisonai && python tests/test_runner.py --pattern autogen --verbose
- name: Test CrewAI Framework
run: |
echo "⛵ Testing CrewAI Framework Integration..."
cd src/praisonai
echo "🔍 Trying test runner first..."
python tests/test_runner.py --pattern crewai --verbose || {
echo "❌ Test runner failed, trying direct pytest..."
echo "📁 Current directory: $(pwd)"
echo "📁 Looking for CrewAI tests..."
find . -name "*crewai*" -type f 2>/dev/null
echo "🧪 Trying direct pytest on integration/crewai..."
python -m pytest tests/integration/crewai/ -v --tb=short --disable-warnings || {
echo "❌ Direct path failed, trying relative path..."
python -m pytest integration/crewai/ -v --tb=short --disable-warnings || {
echo "❌ All CrewAI test attempts failed"
exit 1
}
}
}
- name: Run Legacy Tests with API Key
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' }}
LOGLEVEL: DEBUG
run: |
echo "🧪 Running legacy tests with real API key..."
echo "🔧 Final environment check before pytest:"
echo " OPENAI_API_KEY set: $([ -n "$OPENAI_API_KEY" ] && echo 'YES' || echo 'NO')"
echo " OPENAI_API_KEY length: ${#OPENAI_API_KEY}"
echo " OPENAI_API_KEY starts with sk-: $(echo "$OPENAI_API_KEY" | grep -q '^sk-' && echo 'YES' || echo 'NO')"
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
- name: Upload coverage reports to Codecov
if: matrix.python-version == '3.11'
uses: codecov/codecov-action@v5
with:
token: ${{ secrets.CODECOV_TOKEN }}
file: src/praisonai/coverage.xml
flags: core-tests
name: core-tests-coverage
fail_ci_if_error: false
verbose: true
- name: Upload Coverage Reports (Artifacts)
if: matrix.python-version == '3.11'
uses: actions/upload-artifact@v4
with:
name: coverage-reports
path: |
src/praisonai/.coverage
src/praisonai/htmlcov/
src/praisonai/coverage.xml
retention-days: 7
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@@ -1,175 +0,0 @@
name: Extended Tests
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
schedule:
# Run nightly at 2 AM UTC
- cron: '0 2 * * *'
workflow_dispatch: # Allow manual triggering
jobs:
test-examples:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: 3.11
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Install dependencies
run: |
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system duckduckgo_search
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
- name: Set environment variables
run: |
echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
echo "PYTHONPATH=${{ 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" >> $GITHUB_ENV
- name: Test Key Example Scripts
run: |
echo "🧪 Testing key example scripts from praisonai-agents..."
# Create a timeout function for consistent handling
timeout_run() {
timeout 30s "$@" || echo "⏱️ $1 test completed/timed out"
}
# Test basic agent functionality
timeout_run python ${{ github.workspace }}/src/praisonai-agents/basic-agents.py
# Test async functionality
timeout_run python ${{ github.workspace }}/src/praisonai-agents/async_example.py
# Test knowledge/RAG functionality
timeout_run python ${{ github.workspace }}/src/praisonai-agents/knowledge-agents.py
# Test MCP functionality
timeout_run python ${{ github.workspace }}/src/praisonai-agents/mcp-basic.py
# Test UI functionality
timeout_run python ${{ github.workspace }}/src/praisonai-agents/ui.py
echo "✅ Example script testing completed"
continue-on-error: true
performance-test:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: 3.11
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Install dependencies
run: |
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system pytest pytest-benchmark
# 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..."
python -c "
import time
import sys
import statistics
sys.path.insert(0, 'src/praisonai')
print('🏃 Testing agent creation performance...')
times = []
try:
from praisonaiagents import Agent
for i in range(5):
start_time = time.time()
agent = Agent(name=f'PerfAgent{i}')
times.append(time.time() - start_time)
avg_time = statistics.mean(times)
print(f'✅ Average agent creation time: {avg_time:.3f}s')
print(f'📊 Min: {min(times):.3f}s, Max: {max(times):.3f}s')
except Exception as e:
print(f'❌ Agent creation benchmark failed: {e}')
print('🏃 Testing import performance...')
start_time = time.time()
try:
import praisonaiagents
import_time = time.time() - start_time
print(f'✅ Import completed in {import_time:.3f}s')
except Exception as e:
print(f'❌ Import benchmark failed: {e}')
print('🏃 Testing memory usage...')
try:
import psutil
import os
process = psutil.Process(os.getpid())
memory_mb = process.memory_info().rss / 1024 / 1024
print(f'📊 Memory usage: {memory_mb:.1f} MB')
except ImportError:
print('⚠️ psutil not available for memory testing')
except Exception as e:
print(f'❌ Memory benchmark failed: {e}')
"
continue-on-error: true
- name: Generate Performance Report
run: |
echo "## 📊 Performance Test Results" > performance_report.md
echo "" >> performance_report.md
echo "### Benchmarks Run:" >> performance_report.md
echo "- ⚡ Agent creation speed" >> performance_report.md
echo "- 📦 Import performance" >> performance_report.md
echo "- 💾 Memory usage" >> performance_report.md
echo "- 🧪 Example script execution" >> performance_report.md
echo "" >> performance_report.md
echo "_Performance results are logged in the CI output above._" >> performance_report.md
- name: Upload Performance Report
uses: actions/upload-artifact@v4
with:
name: performance-report
path: performance_report.md
retention-days: 5
-154
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@@ -1,154 +0,0 @@
name: Framework Integration Tests
on:
workflow_dispatch: # Allow manual triggering
inputs:
framework:
description: 'Framework to test'
required: true
default: 'all'
type: choice
options:
- all
- autogen
- crewai
schedule:
# Run framework tests daily at 6 AM UTC
- cron: '0 6 * * *'
push:
paths:
- 'tests/integration/autogen/**'
- 'tests/integration/crewai/**'
- '.github/workflows/test-frameworks.yml'
jobs:
framework-tests:
runs-on: ubuntu-latest
timeout-minutes: 15
strategy:
matrix:
python-version: [3.11]
framework: [autogen, crewai]
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Install dependencies
run: |
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system duckduckgo_search
uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
- name: Set environment variables
run: |
echo "OPENAI_API_KEY=${{ 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
- name: Test ${{ matrix.framework }} Framework
run: |
echo "🧪 Testing ${{ matrix.framework }} framework integration with Python ${{ matrix.python-version }}"
cd src/praisonai && python tests/test_runner.py --pattern ${{ matrix.framework }} --verbose --coverage
continue-on-error: false
- name: Generate Framework Test Report
if: always()
run: |
echo "# 🤖 ${{ matrix.framework }} Framework Test Report" > ${{ matrix.framework }}_report.md
echo "" >> ${{ matrix.framework }}_report.md
echo "**Framework:** ${{ matrix.framework }}" >> ${{ matrix.framework }}_report.md
echo "**Python Version:** ${{ matrix.python-version }}" >> ${{ matrix.framework }}_report.md
echo "**Date:** $(date -u)" >> ${{ matrix.framework }}_report.md
echo "**Trigger:** ${{ github.event_name }}" >> ${{ matrix.framework }}_report.md
echo "" >> ${{ matrix.framework }}_report.md
if [ "${{ matrix.framework }}" == "autogen" ]; then
echo "## AutoGen Integration Tests" >> ${{ matrix.framework }}_report.md
echo "- ✅ AutoGen import verification" >> ${{ matrix.framework }}_report.md
echo "- ✅ Basic agent creation through PraisonAI" >> ${{ matrix.framework }}_report.md
echo "- ✅ Conversation flow testing" >> ${{ matrix.framework }}_report.md
echo "- ✅ Configuration validation" >> ${{ matrix.framework }}_report.md
elif [ "${{ matrix.framework }}" == "crewai" ]; then
echo "## CrewAI Integration Tests" >> ${{ matrix.framework }}_report.md
echo "- ✅ CrewAI import verification" >> ${{ matrix.framework }}_report.md
echo "- ✅ Basic crew creation through PraisonAI" >> ${{ matrix.framework }}_report.md
echo "- ✅ Multi-agent workflow testing" >> ${{ matrix.framework }}_report.md
echo "- ✅ Agent collaboration verification" >> ${{ matrix.framework }}_report.md
echo "- ✅ Configuration validation" >> ${{ matrix.framework }}_report.md
fi
echo "" >> ${{ matrix.framework }}_report.md
echo "## Test Commands" >> ${{ matrix.framework }}_report.md
echo '```bash' >> ${{ matrix.framework }}_report.md
echo "# Run ${{ matrix.framework }} tests locally:" >> ${{ matrix.framework }}_report.md
echo "python tests/test_runner.py --pattern ${{ matrix.framework }} --verbose" >> ${{ matrix.framework }}_report.md
echo '```' >> ${{ matrix.framework }}_report.md
- name: Upload Framework Test Results
uses: actions/upload-artifact@v4
if: always()
with:
name: ${{ matrix.framework }}-test-results-python-${{ matrix.python-version }}
path: |
${{ matrix.framework }}_report.md
htmlcov/
coverage.xml
.coverage
retention-days: 14
framework-summary:
runs-on: ubuntu-latest
needs: framework-tests
if: always()
steps:
- name: Generate Framework Summary
run: |
echo "# 🚀 Framework Integration Test Summary" > framework_summary.md
echo "" >> framework_summary.md
echo "## Test Results by Framework and Python Version:" >> framework_summary.md
echo "" >> framework_summary.md
echo "### AutoGen Framework:" >> framework_summary.md
echo "- Python 3.11: ${{ needs.framework-tests.result }}" >> framework_summary.md
echo "" >> framework_summary.md
echo "### CrewAI Framework:" >> framework_summary.md
echo "- Python 3.11: ${{ needs.framework-tests.result }}" >> framework_summary.md
echo "" >> framework_summary.md
echo "## Overall Status:" >> framework_summary.md
if [ "${{ needs.framework-tests.result }}" == "success" ]; then
echo "✅ **All framework integration tests passed!**" >> framework_summary.md
else
echo "❌ **Some framework tests failed. Check individual job logs for details.**" >> framework_summary.md
fi
echo "" >> framework_summary.md
echo "## Frameworks Tested:" >> framework_summary.md
echo "- **AutoGen**: Microsoft's conversational AI framework" >> framework_summary.md
echo "- **CrewAI**: Multi-agent collaboration framework" >> framework_summary.md
echo "" >> framework_summary.md
echo "## Test Coverage:" >> framework_summary.md
echo "- Import verification" >> framework_summary.md
echo "- Agent/crew creation" >> framework_summary.md
echo "- Workflow execution" >> framework_summary.md
echo "- Configuration validation" >> framework_summary.md
echo "- Integration with PraisonAI" >> framework_summary.md
- name: Upload Framework Summary
uses: actions/upload-artifact@v4
with:
name: framework-test-summary
path: framework_summary.md
retention-days: 30
-530
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@@ -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
-168
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@@ -1,168 +0,0 @@
name: Real End-to-End Tests
# ⚠️ WARNING: This workflow makes real API calls and incurs costs!
# Only runs when manually triggered to prevent accidental charges
on:
workflow_dispatch: # Manual trigger only
inputs:
framework:
description: 'Framework to test (⚠️ Will incur API costs!)'
required: true
default: 'none'
type: choice
options:
- none
- autogen
- crewai
- all
confirm_costs:
description: 'I understand this will make real API calls and may incur costs'
required: true
type: boolean
default: false
jobs:
real-tests:
runs-on: ubuntu-latest
if: ${{ github.event.inputs.confirm_costs == 'true' && github.event.inputs.framework != 'none' }}
timeout-minutes: 15
strategy:
matrix:
python-version: [3.11] # Single version to minimize costs
steps:
- name: 🚨 Cost Warning
run: |
echo "⚠️ WARNING: This workflow will make real API calls!"
echo "💰 This may incur charges on your API accounts"
echo "🎯 Framework: ${{ github.event.inputs.framework }}"
echo "✅ Cost confirmation: ${{ github.event.inputs.confirm_costs }}"
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Install dependencies
run: |
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system pytest pytest-asyncio pytest-cov
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
- name: Set environment variables
run: |
echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV
echo "ANTHROPIC_API_KEY=${{ secrets.ANTHROPIC_API_KEY }}" >> $GITHUB_ENV
echo "GOOGLE_API_KEY=${{ secrets.GOOGLE_API_KEY }}" >> $GITHUB_ENV
- name: Verify API Keys
run: |
if [ -z "${{ secrets.OPENAI_API_KEY }}" ]; then
echo "❌ OPENAI_API_KEY not set in secrets"
echo "🔧 Add your API key to repository secrets"
exit 1
fi
echo "✅ API keys configured"
- name: Run Real AutoGen Tests
if: ${{ github.event.inputs.framework == 'autogen' || github.event.inputs.framework == 'all' }}
run: |
echo "🤖 Running REAL AutoGen tests (⚠️ API costs may apply)"
cd src/praisonai && python -m pytest tests/e2e/autogen/ -v -m real --tb=short
continue-on-error: false
- name: Run Real CrewAI Tests
if: ${{ github.event.inputs.framework == 'crewai' || github.event.inputs.framework == 'all' }}
run: |
echo "⛵ Running REAL CrewAI tests (⚠️ API costs may apply)"
cd src/praisonai && python -m pytest tests/e2e/crewai/ -v -m real --tb=short
continue-on-error: false
- name: Generate Real Test Report
if: always()
run: |
echo "# 🔥 Real End-to-End Test Report" > real_test_report.md
echo "" >> real_test_report.md
echo "⚠️ **WARNING: This report represents tests that made real API calls**" >> real_test_report.md
echo "" >> real_test_report.md
echo "**Framework Tested:** ${{ github.event.inputs.framework }}" >> real_test_report.md
echo "**Python Version:** ${{ matrix.python-version }}" >> real_test_report.md
echo "**Date:** $(date -u)" >> real_test_report.md
echo "**Triggered by:** ${{ github.actor }}" >> real_test_report.md
echo "" >> real_test_report.md
echo "## 🧪 Test Results" >> real_test_report.md
echo "" >> real_test_report.md
if [ "${{ github.event.inputs.framework }}" == "autogen" ] || [ "${{ github.event.inputs.framework }}" == "all" ]; then
echo "### AutoGen Real Tests:" >> real_test_report.md
echo "- Environment verification" >> real_test_report.md
echo "- Agent creation with real API calls" >> real_test_report.md
echo "- Configuration validation" >> real_test_report.md
echo "" >> real_test_report.md
fi
if [ "${{ github.event.inputs.framework }}" == "crewai" ] || [ "${{ github.event.inputs.framework }}" == "all" ]; then
echo "### CrewAI Real Tests:" >> real_test_report.md
echo "- Environment verification" >> real_test_report.md
echo "- Crew creation with real API calls" >> real_test_report.md
echo "- Multi-agent setup validation" >> real_test_report.md
echo "" >> real_test_report.md
fi
echo "## 💰 Cost Considerations" >> real_test_report.md
echo "- These tests made actual API calls to LLM providers" >> real_test_report.md
echo "- Costs depend on your API pricing tier" >> real_test_report.md
echo "- Tests are designed to be minimal to reduce costs" >> real_test_report.md
echo "- Check your API provider dashboard for actual usage" >> real_test_report.md
echo "## 📋 Next Steps" >> real_test_report.md
echo "- Review test results for any failures" >> real_test_report.md
echo "- Check API usage in your provider dashboard" >> real_test_report.md
echo "- Use mock tests (tests/integration/) for routine testing" >> real_test_report.md
- name: Upload Real Test Results
uses: actions/upload-artifact@v4
if: always()
with:
name: real-test-results-${{ github.event.inputs.framework }}-python-${{ matrix.python-version }}
path: |
real_test_report.md
retention-days: 30
# Safety job that runs when costs not confirmed
safety-check:
runs-on: ubuntu-latest
if: ${{ github.event.inputs.confirm_costs != 'true' || github.event.inputs.framework == 'none' }}
steps:
- name: 🛡️ Safety Check Failed
run: |
echo "🚨 Real tests not executed due to safety checks:"
echo ""
echo "✅ Costs confirmed: ${{ github.event.inputs.confirm_costs }}"
echo "✅ Framework selected: ${{ github.event.inputs.framework }}"
echo ""
echo "To run real tests:"
echo "1. Select a framework (autogen, crewai, or all)"
echo "2. Check 'I understand this will make real API calls and may incur costs'"
echo "3. Ensure API keys are set in repository secrets"
echo ""
echo "💡 For cost-free testing, use mock tests instead:"
echo " - Run 'python -m pytest tests/integration/' locally"
echo " - Or trigger other workflows that use mock tests"
exit 1
-516
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@@ -1,516 +0,0 @@
name: Quick Validation Tests
on: [push, pull_request]
jobs:
quick-test:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: 3.11
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.local/bin" >> $GITHUB_PATH
- name: Install dependencies
run: |
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system duckduckgo_search
uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
# Install knowledge dependencies from praisonai-agents
uv pip install --system "praisonaiagents[knowledge]"
- name: Set environment variables
run: |
echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
- name: Backup Root Config Files
run: |
echo "🔄 Backing up root configuration files to prevent default file resolution interference..."
echo "ℹ️ PraisonAI automatically uses 'agents.yaml' in working directory when no file specified"
echo "ℹ️ This can interfere with test files that specify their own YAML files"
if [ -f "agents.yaml" ]; then
mv agents.yaml agents.yaml.backup
echo "✅ Moved root agents.yaml to agents.yaml.backup"
echo " - This prevents default file fallback during tests"
fi
if [ -f "tools.py" ]; then
mv tools.py tools.py.backup
echo "✅ Moved tools.py to tools.py.backup"
fi
- name: Debug API Key Status
run: |
echo "🔍 Checking API key availability..."
if [ -n "${{ secrets.OPENAI_API_KEY }}" ]; then
echo "✅ GitHub secret OPENAI_API_KEY is available"
echo "🔑 API key starts with: $(echo "$OPENAI_API_KEY" | cut -c1-7)..."
else
echo "⚠️ GitHub secret OPENAI_API_KEY is NOT set - using fallback"
echo "🔑 Using fallback key: sk-test-key..."
fi
echo "🌐 API Base: $OPENAI_API_BASE"
echo "🤖 Model: $OPENAI_MODEL_NAME"
echo "🐛 Log Level: $LOGLEVEL"
echo "📊 Environment Check:"
echo " - OPENAI_API_KEY length: ${#OPENAI_API_KEY}"
echo " - OPENAI_API_BASE: $OPENAI_API_BASE"
echo " - OPENAI_MODEL_NAME: $OPENAI_MODEL_NAME"
echo " - LOGLEVEL: $LOGLEVEL"
- name: Debug Python Environment Variables
run: |
python -c "
import os
print('🐍 Python Environment Variable Check:')
api_key = os.environ.get('OPENAI_API_KEY', 'NOT_SET')
if api_key != 'NOT_SET':
print(f' ✅ OPENAI_API_KEY: {api_key[:7]}... (length: {len(api_key)})')
else:
print(' ❌ OPENAI_API_KEY: NOT_SET')
print(f' 🌐 OPENAI_API_BASE: {os.environ.get(\"OPENAI_API_BASE\", \"NOT_SET\")}')
print(f' 🤖 OPENAI_MODEL_NAME: {os.environ.get(\"OPENAI_MODEL_NAME\", \"NOT_SET\")}')
print(f' 📋 All OPENAI env vars:')
for key, value in os.environ.items():
if key.startswith('OPENAI'):
print(f' {key}: {value[:10] if len(value) > 10 else value}...')
"
- name: Find Researcher Role Source
run: |
echo "🔍 Hunting for the mysterious 'Researcher' role..."
cd src/praisonai
python -c "
import os
import yaml
import glob
print('📋 Searching for Researcher role in all YAML files:')
yaml_files = glob.glob('src/praisonai/tests/*.yaml')
for yaml_file in yaml_files:
try:
with open(yaml_file, 'r') as f:
config = yaml.safe_load(f)
# Check if any role contains 'researcher'
roles = config.get('roles', {})
for role_key, role_data in roles.items():
role_name = role_data.get('role', '')
if 'researcher' in role_key.lower() or 'researcher' in role_name.lower():
print(f' 🎯 FOUND in {yaml_file}:')
print(f' Framework: {config.get(\"framework\", \"NOT_SET\")}')
print(f' Role key: {role_key}')
print(f' Role name: {role_name}')
print(f' All roles: {list(roles.keys())}')
print()
except Exception as e:
print(f' ❌ Error reading {yaml_file}: {e}')
print('🔍 Checking for default configurations...')
# Check if there are any default configs or hardcoded roles
try:
import praisonai
print(f' PraisonAI package location: {praisonai.__file__}')
# Check if there are any example YAML files in the package
package_dir = os.path.dirname(praisonai.__file__)
for root, dirs, files in os.walk(package_dir):
for file in files:
if file.endswith(('.yaml', '.yml')):
file_path = os.path.join(root, file)
print(f' 📁 Found YAML in package: {file_path}')
except Exception as e:
print(f' ❌ Error checking package: {e}')
"
continue-on-error: true
- name: Trace AutoGen Execution Path
run: |
echo "🔍 Tracing AutoGen execution to find where it diverges..."
cd src/praisonai
python -c "
import os
import sys
sys.path.insert(0, '.')
try:
from praisonai import PraisonAI
from praisonai.agents_generator import AgentsGenerator
# Test the exact execution path
print('🎯 Testing AutoGen execution path:')
praisonai = PraisonAI(agent_file='tests/autogen-agents.yaml')
print(f' 1. PraisonAI framework: \"{praisonai.framework}\"')
agents_gen = AgentsGenerator(
agent_file='tests/autogen-agents.yaml',
framework=praisonai.framework,
config_list=praisonai.config_list
)
print(f' 2. AgentsGenerator framework: \"{agents_gen.framework}\"')
# Load the YAML to check what it contains
import yaml
with open('src/praisonai/tests/autogen-agents.yaml', 'r') as f:
config = yaml.safe_load(f)
framework = agents_gen.framework or config.get('framework')
print(f' 3. Final framework decision: \"{framework}\"')
print(f' 4. Available frameworks:')
# Check framework availability
try:
import autogen
print(f' ✅ AutoGen available')
except ImportError:
print(f' ❌ AutoGen NOT available')
try:
from praisonaiagents import Agent
print(f' ✅ PraisonAI agents available')
except ImportError:
print(f' ❌ PraisonAI agents NOT available')
try:
from crewai import Agent
print(f' ✅ CrewAI available')
except ImportError:
print(f' ❌ CrewAI NOT available')
print(f' 5. Roles in YAML: {list(config.get(\"roles\", {}).keys())}')
# Now test the actual framework execution
if framework == 'autogen':
print(f' 6. ✅ Should execute _run_autogen')
elif framework == 'praisonai':
print(f' 6. ❌ Would execute _run_praisonai (WRONG!)')
else:
print(f' 6. ❌ Would execute _run_crewai (DEFAULT FALLBACK)')
except Exception as e:
print(f'❌ Error tracing execution: {e}')
import traceback
traceback.print_exc()
"
continue-on-error: true
- name: Debug YAML Loading and Roles
run: |
echo "🔍 Tracing YAML file loading and role creation..."
cd src/praisonai
python -c "
import os
import sys
import yaml
sys.path.insert(0, '.')
print('📁 Available YAML files in src/praisonai/tests/:')
import glob
yaml_files = glob.glob('tests/*.yaml')
for f in yaml_files:
print(f' {f}')
print()
print('📋 Content of autogen-agents.yaml:')
with open('tests/autogen-agents.yaml', 'r') as f:
config = yaml.safe_load(f)
print(f' Framework: {config.get(\"framework\")}')
print(f' Topic: {config.get(\"topic\")}')
print(f' Roles: {list(config.get(\"roles\", {}).keys())}')
for role_key, role_data in config.get('roles', {}).items():
print(f' {role_key} -> {role_data.get(\"role\", \"NO_ROLE\")}')
print()
print('🔍 Checking if execution uses a different YAML:')
# Check other YAML files for 'Researcher' role
for yaml_file in yaml_files:
try:
with open(yaml_file, 'r') as f:
test_config = yaml.safe_load(f)
roles = test_config.get('roles', {})
for role_key, role_data in roles.items():
if 'researcher' in role_key.lower() or 'researcher' in role_data.get('role', '').lower():
print(f' 🎯 FOUND Researcher in {yaml_file}!')
print(f' Framework: {test_config.get(\"framework\")}')
print(f' Role key: {role_key} -> {role_data.get(\"role\")}')
except:
pass
"
continue-on-error: true
- name: Debug Framework Detection
run: |
echo "🔍 Testing framework detection and config flow..."
python -c "
import os
import sys
import yaml
sys.path.insert(0, '.')
print('🔧 Testing framework detection:')
# Load the YAML file
with open('tests/autogen-agents.yaml', 'r') as f:
config = yaml.safe_load(f)
print(f' 📋 YAML framework: {config.get(\"framework\", \"NOT_SET\")}')
print(f' 📋 YAML topic: {config.get(\"topic\", \"NOT_SET\")}')
try:
from praisonai import PraisonAI
from praisonai.agents_generator import AgentsGenerator
# Test PraisonAI initialization
praisonai = PraisonAI(agent_file='tests/autogen-agents.yaml')
print(f' 🎯 PraisonAI framework: {praisonai.framework}')
# Test AgentsGenerator initialization
agents_gen = AgentsGenerator(
agent_file='tests/autogen-agents.yaml',
framework=praisonai.framework,
config_list=praisonai.config_list
)
print(f' ⚙️ AgentsGenerator framework: {agents_gen.framework}')
print(f' ⚙️ Final framework decision: {agents_gen.framework or config.get(\"framework\")}')
# Check config_list
print(f' 🔑 Config list model: {praisonai.config_list[0].get(\"model\")}')
print(f' 🔑 Config list API key: {praisonai.config_list[0].get(\"api_key\", \"NOT_SET\")[:10]}...')
except Exception as e:
print(f'❌ Error in framework detection: {e}')
"
continue-on-error: true
- name: Debug PraisonAIModel API Key Flow
run: |
echo "🔍 Testing PraisonAIModel API key handling..."
cd src/praisonai
python -c "
import os
import sys
sys.path.insert(0, '.')
print('🔑 Environment API Key Check:')
env_key = os.environ.get('OPENAI_API_KEY', 'NOT_FOUND')
print(f' OPENAI_API_KEY: {env_key[:10] if env_key != \"NOT_FOUND\" else \"NOT_FOUND\"}...')
try:
from praisonai.inc.models import PraisonAIModel
# Test PraisonAIModel with openai/gpt-5-nano (from YAML)
model = PraisonAIModel(model='openai/gpt-5-nano')
print('🤖 PraisonAIModel Configuration:')
print(f' model: {model.model}')
print(f' model_name: {model.model_name}')
print(f' api_key_var: {model.api_key_var}')
print(f' api_key: {model.api_key[:10] if model.api_key != \"nokey\" else \"DEFAULT_NOKEY\"}...')
print(f' base_url: {model.base_url}')
if model.api_key == 'nokey':
print('❌ FOUND THE ISSUE: PraisonAIModel is using default \"nokey\" instead of environment variable!')
else:
print('✅ PraisonAIModel has valid API key from environment')
except Exception as e:
print(f'❌ Error testing PraisonAIModel: {e}')
"
continue-on-error: true
- name: Validate API Key
run: |
echo "🔑 Testing API key validity with minimal OpenAI call..."
python -c "
import os
try:
from openai import OpenAI
client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))
# Make a minimal API call to test key validity
response = client.models.list()
print('✅ API Key is VALID - OpenAI responded successfully')
print(f'📊 Available models: {len(list(response.data))} models found')
except Exception as e:
print(f'❌ API Key is INVALID - Error: {e}')
print('🔍 This explains why all API-dependent tests are failing')
print('💡 The GitHub secret OPENAI_API_KEY needs to be updated with a valid key')
"
continue-on-error: true
- name: Test Direct PraisonAI Execution
run: |
echo "🧪 Testing direct PraisonAI execution (what works locally)..."
python -m praisonai src/praisonai/tests/autogen-agents.yaml
continue-on-error: true
- name: Comprehensive Execution Debug
run: |
echo "🔍 Comprehensive debugging of PraisonAI execution path..."
cd src/praisonai
python -c "
import os
import sys
import yaml
sys.path.insert(0, '.')
print('=' * 60)
print('🔍 COMPREHENSIVE EXECUTION DEBUG')
print('=' * 60)
# Check current working directory
print(f'📁 Current working directory: {os.getcwd()}')
# List files in current directory
print('📋 Files in current directory:')
for f in os.listdir('.'):
print(f' {f}')
print()
print('📋 Files in src/praisonai/tests/ directory:')
for f in os.listdir('tests'):
if f.endswith('.yaml'):
print(f' src/praisonai/tests/{f}')
# Check if root agents.yaml exists
print()
if os.path.exists('agents.yaml'):
print('❌ ROOT agents.yaml EXISTS (this is the problem!)')
with open('agents.yaml', 'r') as f:
root_config = yaml.safe_load(f)
print(f' Root framework: {root_config.get(\"framework\")}')
print(f' Root roles: {list(root_config.get(\"roles\", {}).keys())}')
else:
print('✅ ROOT agents.yaml does NOT exist (good!)')
# Test the actual execution path
print()
print('🎯 Testing EXACT execution path:')
try:
from praisonai import PraisonAI
from praisonai.agents_generator import AgentsGenerator
# Test with full path
test_file = 'src/praisonai/tests/autogen-agents.yaml'
print(f' Using test file: {test_file}')
print(f' File exists: {os.path.exists(test_file)}')
# Load the test file directly
with open(test_file, 'r') as f:
test_config = yaml.safe_load(f)
print(f' Test file framework: {test_config.get(\"framework\")}')
print(f' Test file roles: {list(test_config.get(\"roles\", {}).keys())}')
# Create PraisonAI instance
praisonai = PraisonAI(agent_file=test_file)
print(f' PraisonAI.agent_file: {praisonai.agent_file}')
print(f' PraisonAI.framework: {praisonai.framework}')
# Create AgentsGenerator
agents_gen = AgentsGenerator(
agent_file=test_file,
framework=praisonai.framework,
config_list=praisonai.config_list
)
print(f' AgentsGenerator.agent_file: {agents_gen.agent_file}')
print(f' AgentsGenerator.framework: {agents_gen.framework}')
# Test what would happen in generate_crew_and_kickoff
print()
print('🔧 Testing generate_crew_and_kickoff logic:')
# Simulate the loading logic
if agents_gen.agent_yaml:
loaded_config = yaml.safe_load(agents_gen.agent_yaml)
print(' Would load from agent_yaml')
else:
if agents_gen.agent_file == '/app/api:app' or agents_gen.agent_file == 'api:app':
agents_gen.agent_file = 'agents.yaml'
print(f' Would change agent_file to: {agents_gen.agent_file}')
try:
with open(agents_gen.agent_file, 'r') as f:
loaded_config = yaml.safe_load(f)
print(f' Successfully loaded: {agents_gen.agent_file}')
except FileNotFoundError:
print(f' FileNotFoundError: {agents_gen.agent_file}')
loaded_config = None
if loaded_config:
final_framework = agents_gen.framework or loaded_config.get('framework')
print(f' Final framework decision: {final_framework}')
print(f' Loaded roles: {list(loaded_config.get(\"roles\", {}).keys())}')
if 'researcher' in loaded_config.get('roles', {}):
print(' ❌ FOUND Researcher role in loaded config!')
else:
print(' ✅ No Researcher role in loaded config')
except Exception as e:
print(f'❌ Error during execution debug: {e}')
import traceback
traceback.print_exc()
"
continue-on-error: true
- name: Run Fast Tests
run: |
# Run the fastest, most essential tests with coverage
cd src/praisonai && python -m pytest tests/unit/test_core_agents.py -v --tb=short --disable-warnings --cov=praisonai --cov-report=xml --cov-branch
- name: Run Real API Tests
run: |
echo "🔑 Running real API tests with actual OpenAI API key..."
cd src/praisonai && python -m pytest tests/test_agents_playbook.py -v --tb=short --disable-warnings -m real
continue-on-error: true
- name: Run E2E Real Tests
run: |
echo "🧪 Running E2E real tests..."
cd src/praisonai && python -m pytest tests/e2e/ -v --tb=short --disable-warnings -m real
continue-on-error: true
- 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
continue-on-error: true
- name: Upload coverage reports to Codecov
uses: codecov/codecov-action@v5
with:
token: ${{ secrets.CODECOV_TOKEN }}
file: src/praisonai/coverage.xml
flags: quick-validation
name: quick-validation-coverage
fail_ci_if_error: false
verbose: true
- name: Restore Root Config Files
run: |
echo "🔄 Restoring root configuration files..."
if [ -f "agents.yaml.backup" ]; then
mv agents.yaml.backup agents.yaml
echo "✅ Restored agents.yaml"
fi
if [ -f "tools.py.backup" ]; then
mv tools.py.backup tools.py
echo "✅ Restored tools.py"
fi
if: always() # Always run this step, even if previous steps failed
-57
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@@ -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>"
+21 -84
View File
@@ -1,86 +1,23 @@
workspace.code-workspace
.vscode
crewai
.cache
__pycache__
chroma.sqlite3
test
dist
.env
assets
.idea
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.js
# testing
/coverage
# production
/build
# misc
.DS_Store
.pytest_cache
praisonAI.egg-info
flagged
test.yaml
db
praisonai_prompt.txt
watch.sh
docs.sh
other
output
!src/praisonai/praisonai/cli/output/
.env.local
.env.development.local
.env.test.local
.env.production.local
.files
threads.db
threads.db-journal
.chainlit
!praisonai/ui/config/.chainlit
site
# Ignore Sphinx build directory
docs/_build/
docs/source/
docs/build/
# Ignore Sphinx cache
docs/_build/doctrees/
docs/_build/doctrees/*
docs/_build/doctrees/.doctrees
# Ignore Sphinx logs and local build files
docs/_build/.buildinfo
docs/_build/.buildinfo/*
docs/_build/.buildinfo/.buildinfo
praisonai/doc
# agentops
trained_agents_data.pkl
agentops.log
# crewAI
crewAI
!tests/integration/crewai
!tests/e2e/crewai
!src/praisonai/tests/integration/crewai
!src/praisonai/tests/e2e/crewai
# virtualenv
.venv
# Credentials
credentials.json
# node_modules
node_modules
outputs
agents/praisonaiagents/build
agents/praisonaiagents/dist
agents/praisonaiagents/praisonaiagents.egg-info
.codegpt
.praison
# Local Netlify folder
.netlify
.qodo
CopilotKit*
.gemini
issue_details.json
# MCP Registry tokens
.mcpregistry_*
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
-20
View File
@@ -1,20 +0,0 @@
MIT License
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+38 -3393
View File
File diff suppressed because it is too large. Load diff
-867
View File
@@ -1,867 +0,0 @@
# PraisonAI API Reference
This file is auto-generated. Do not edit manually.
Regenerate with: `praisonai docs api-md --write`
# Shared Types
Types:
```python
from praisonaiagents import ContextPolicy, GuardrailResult, HandoffConfig, HandoffCycleError, HandoffDepthError, HandoffError, HandoffInputData, HandoffResult, HandoffTimeoutError, ReflectionOutput, StepResult, TaskOutput, ToolResult, ToolValidationError, WorkflowContext
```
Methods:
* <code title="class GuardrailResult">GuardrailResult.<a href="./src/praisonai-agents/praisonaiagents/guardrails/guardrail_result.py">from_tuple</a>(result: Tuple[bool, Any]) -> 'GuardrailResult'</code>
* <code title="class HandoffConfig">HandoffConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">from_dict</a>(data: Dict[str, Any]) -> 'HandoffConfig'</code>
* <code title="class HandoffConfig">HandoffConfig.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">to_dict</a>() -> Dict[str, Any]</code>
* <code title="class TaskOutput">TaskOutput.<a href="./src/praisonai-agents/praisonaiagents/main.py">json</a>() -> Optional[str]</code>
* <code title="class TaskOutput">TaskOutput.<a href="./src/praisonai-agents/praisonaiagents/main.py">to_dict</a>() -> dict</code>
* <code title="class ToolResult">ToolResult.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">to_dict</a>() -> Dict[str, Any]</code>
# Agents
Types:
```python
from praisonaiagents import Agent, Agents, AutoAgents, AutoRagAgent, ContextAgent, DeepResearchAgent, ImageAgent, PraisonAIAgents, PromptExpanderAgent, QueryRewriterAgent, create_context_agent
```
Methods:
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">achat</a>(prompt: str, temperature = 1.0, tools = None, output_json = None, output_pydantic = None, reasoning_steps = False, task_name = None, task_description = None, task_id = None)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">aexecute</a>(task, context = None)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">agent_id</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">analyze_prompt</a>(prompt: str) -> set</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">arun</a>(prompt: str, **kwargs)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">astart</a>(prompt: str, **kwargs)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">auto_memory</a>(value)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">background</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">background</a>(value)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat</a>(prompt, temperature = 1.0, tools = None, output_json = None, output_pydantic = None, reasoning_steps = False, stream = None, task_name = None, task_description = None, task_id = None, config = None, force_retrieval = False, skip_retrieval = False)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">chat_with_context</a>(message: str, context: 'ContextPack', **kwargs) -> str</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">checkpoints</a>(value)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">clean_json_output</a>(output: str) -> str</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">clear_history</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">console</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">context_manager</a>(value)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">display_generating</a>(content: str, start_time: float)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">execute</a>(task, context = None)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">execute_tool</a>(function_name, arguments)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">execute_tool_async</a>(function_name: str, arguments: Dict[str, Any]) -> Any</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">from_template</a>(uri: str, config: Optional[Dict[str, Any]] = None, offline: bool = False, **kwargs) -> 'Agent'</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">generate_task</a>() -> 'Task'</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_available_tools</a>() -> List[Any]</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_memory_context</a>(query: Optional[str] = None) -> str</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_recommended_stage</a>(prompt: str) -> str</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_rules_context</a>(file_path: Optional[str] = None, include_manual: Optional[List[str]] = None) -> str</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">get_skills_prompt</a>() -> str</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">handoff_to_async</a>(target_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None, config: Optional['HandoffConfig'] = None) -> 'HandoffResult'</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">launch</a>(path: str = '/', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = 'http')</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">llm_model</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">output_style</a>(value)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">policy</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">policy</a>(value)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">query</a>(question: str, **kwargs) -> 'RAGResult'</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rag</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rag_query</a>(question: str, **kwargs) -> 'RAGResult'</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">retrieval_config</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">retrieve</a>(query: str, **kwargs) -> 'ContextPack'</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">rules_manager</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run</a>(prompt: str, **kwargs)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">run_autonomous</a>(prompt: str, max_iterations: Optional[int] = None, timeout_seconds: Optional[float] = None)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">session_id</a>() -> Optional[str]</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">skill_manager</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">start</a>(prompt: str, **kwargs)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">store_memory</a>(content: str, memory_type: str = 'short_term', **kwargs)</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">switch_model</a>(new_model: str) -> None</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>()</code>
* <code title="class Agent">Agent.<a href="./src/praisonai-agents/praisonaiagents/agent/agent.py">thinking_budget</a>(value)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">add_task</a>(task)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aexecute_task</a>(task_id)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">append_to_state</a>(key: str, value: Any, max_length: Optional[int] = None) -> List[Any]</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_all_tasks</a>()</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_task</a>(task_id)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">astart</a>(content = None, return_dict = False, **kwargs)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clean_json_output</a>(output: str) -> str</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clear_state</a>() -> None</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">context_manager</a>()</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">current_plan</a>()</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">default_completion_checker</a>(task, agent_output)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">delete_state</a>(key: str) -> bool</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">display_token_usage</a>()</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">execute_task</a>(task_id)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_agent_details</a>(agent_name)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_state</a>() -> Dict[str, Any]</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_tasks_status</a>()</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_detailed_token_report</a>() -> Dict[str, Any]</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_plan_markdown</a>() -> str</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_state</a>(key: str, default: Any = None) -> Any</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_details</a>(task_id)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_result</a>(task_id)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_status</a>(task_id)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_todo_markdown</a>() -> str</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_token_usage_summary</a>() -> Dict[str, Any]</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">has_state</a>(key: str) -> bool</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">increment_state</a>(key: str, amount: float = 1, default: float = 0) -> float</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">launch</a>(path: str = '/agents', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = 'http')</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">restore_session_state</a>(session_id: str) -> bool</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run</a>(content = None, return_dict = False, **kwargs)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_all_tasks</a>()</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_task</a>(task_id)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, **kwargs)</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
* <code title="class Agents">Agents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
* <code title="class AutoAgents">AutoAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/autoagents.py">astart</a>()</code>
* <code title="class AutoAgents">AutoAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/autoagents.py">start</a>()</code>
* <code title="class AutoRagAgent">AutoRagAgent.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">achat</a>(message: str, **kwargs) -> str</code>
* <code title="class AutoRagAgent">AutoRagAgent.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">chat</a>(message: str, **kwargs) -> str</code>
* <code title="class AutoRagAgent">AutoRagAgent.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">name</a>() -> str</code>
* <code title="class AutoRagAgent">AutoRagAgent.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">rag</a>() -> Optional['RAG']</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">analyze_codebase_with_gitingest</a>(project_path: str) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">analyze_integration_points</a>(project_path: str) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">analyze_test_patterns</a>(project_path: str) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">build_implementation_blueprint</a>(feature_request: str, context_analysis: Dict[str, Any] = None) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">compile_context_documentation</a>(project_path: str) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">create_quality_gates</a>(requirements: List[str]) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">create_validation_framework</a>(project_path: str) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">execute_prp</a>(prp_file_path: str) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">extract_implementation_patterns</a>(project_path: str, ast_analysis: Dict[str, Any] = None) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">generate_comprehensive_prp</a>(feature_request: str, context_analysis: Dict[str, Any] = None) -> str</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">generate_feature_prp</a>(feature_request: str) -> str</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">get_agent_interaction_summary</a>() -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">log_debug</a>(message: str, **kwargs)</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">perform_ast_analysis</a>(project_path: str) -> Dict[str, Any]</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">save_comprehensive_session_report</a>()</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">save_markdown_output</a>(content: str, filename: str, section_title: str = 'Output')</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">setup_logging</a>()</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">setup_output_directories</a>()</code>
* <code title="class ContextAgent">ContextAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">start</a>(input_text: str) -> str</code>
* <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">aresearch</a>(query: str, instructions: Optional[str] = None, model: Optional[str] = None, summary_mode: Optional[Literal['auto', 'detailed', 'concise']] = None, web_search: Optional[bool] = None, code_interpreter: Optional[bool] = None, mcp_servers: Optional[List[Dict[str, Any]]] = None, file_ids: Optional[List[str]] = None, file_search: Optional[bool] = None, file_search_stores: Optional[List[str]] = None) -> DeepResearchResponse</code>
* <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">async_openai_client</a>()</code>
* <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">clarify</a>(query: str, model: Optional[str] = None) -> str</code>
* <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">follow_up</a>(query: str, previous_interaction_id: str, model: Optional[str] = None) -> DeepResearchResponse</code>
* <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">gemini_client</a>()</code>
* <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">openai_client</a>()</code>
* <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">research</a>(query: str, instructions: Optional[str] = None, model: Optional[str] = None, summary_mode: Optional[Literal['auto', 'detailed', 'concise']] = None, web_search: Optional[bool] = None, code_interpreter: Optional[bool] = None, mcp_servers: Optional[List[Dict[str, Any]]] = None, file_ids: Optional[List[str]] = None, file_search: Optional[bool] = None, file_search_stores: Optional[List[str]] = None, stream: bool = True) -> DeepResearchResponse</code>
* <code title="class DeepResearchAgent">DeepResearchAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">rewrite_query</a>(query: str, model: Optional[str] = None) -> str</code>
* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">achat</a>(prompt: str, temperature: float = 0.2, tools: Optional[List[Any]] = None, output_json: Optional[str] = None, output_pydantic: Optional[Any] = None, reasoning_steps: bool = False, **kwargs) -> Union[str, Dict[str, Any]]</code>
* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">agenerate_image</a>(prompt: str, **kwargs) -> Dict[str, Any]</code>
* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">chat</a>(prompt: str, **kwargs) -> Dict[str, Any]</code>
* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">generate_image</a>(prompt: str, **kwargs) -> Dict[str, Any]</code>
* <code title="class ImageAgent">ImageAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/image_agent.py">litellm</a>()</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">add_task</a>(task)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">aexecute_task</a>(task_id)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">append_to_state</a>(key: str, value: Any, max_length: Optional[int] = None) -> List[Any]</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_all_tasks</a>()</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">arun_task</a>(task_id)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">astart</a>(content = None, return_dict = False, **kwargs)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clean_json_output</a>(output: str) -> str</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">clear_state</a>() -> None</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">context_manager</a>()</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">current_plan</a>()</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">default_completion_checker</a>(task, agent_output)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">delete_state</a>(key: str) -> bool</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">display_token_usage</a>()</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">execute_task</a>(task_id)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_agent_details</a>(agent_name)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_state</a>() -> Dict[str, Any]</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_all_tasks_status</a>()</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_detailed_token_report</a>() -> Dict[str, Any]</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_plan_markdown</a>() -> str</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_state</a>(key: str, default: Any = None) -> Any</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_details</a>(task_id)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_result</a>(task_id)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_task_status</a>(task_id)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_todo_markdown</a>() -> str</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">get_token_usage_summary</a>() -> Dict[str, Any]</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">has_state</a>(key: str) -> bool</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">increment_state</a>(key: str, amount: float = 1, default: float = 0) -> float</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">launch</a>(path: str = '/agents', port: int = 8000, host: str = '0.0.0.0', debug: bool = False, protocol: str = 'http')</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">restore_session_state</a>(session_id: str) -> bool</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run</a>(content = None, return_dict = False, **kwargs)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_all_tasks</a>()</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">run_task</a>(task_id)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_output_to_file</a>(task, task_output)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">save_session_state</a>(session_id: str, include_memory: bool = True) -> None</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">set_state</a>(key: str, value: Any) -> None</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">start</a>(content = None, return_dict = False, **kwargs)</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">todo_list</a>()</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_plan_step_status</a>(step_id: str, status: str) -> bool</code>
* <code title="class PraisonAIAgents">PraisonAIAgents.<a href="./src/praisonai-agents/praisonaiagents/agents/agents.py">update_state</a>(updates: Dict) -> None</code>
* <code title="class PromptExpanderAgent">PromptExpanderAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py">agent</a>()</code>
* <code title="class PromptExpanderAgent">PromptExpanderAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py">expand</a>(prompt: str, strategy: ExpandStrategy = ..., context: Optional[str] = None) -> ExpandResult</code>
* <code title="class PromptExpanderAgent">PromptExpanderAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py">expand_basic</a>(prompt: str, context: Optional[str] = None) -> ExpandResult</code>
* <code title="class PromptExpanderAgent">PromptExpanderAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py">expand_creative</a>(prompt: str, context: Optional[str] = None) -> ExpandResult</code>
* <code title="class PromptExpanderAgent">PromptExpanderAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py">expand_detailed</a>(prompt: str, context: Optional[str] = None) -> ExpandResult</code>
* <code title="class PromptExpanderAgent">PromptExpanderAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py">expand_structured</a>(prompt: str, context: Optional[str] = None) -> ExpandResult</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">add_abbreviation</a>(abbrev: str, expansion: str) -> None</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">add_abbreviations</a>(abbreviations: Dict[str, str]) -> None</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">agent</a>()</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">rewrite</a>(query: str, strategy: RewriteStrategy = ..., chat_history: Optional[List[Dict[str, str]]] = None, context: Optional[str] = None, num_queries: int = None) -> RewriteResult</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">rewrite_basic</a>(query: str) -> RewriteResult</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">rewrite_contextual</a>(query: str, chat_history: List[Dict[str, str]]) -> RewriteResult</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">rewrite_hyde</a>(query: str) -> RewriteResult</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">rewrite_multi_query</a>(query: str, num_queries: int = None) -> RewriteResult</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">rewrite_step_back</a>(query: str) -> RewriteResult</code>
* <code title="class QueryRewriterAgent">QueryRewriterAgent.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">rewrite_sub_queries</a>(query: str) -> RewriteResult</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/context_agent.py">create_context_agent</a>(llm: Optional[Union[str, Any]] = None, **kwargs) -> ContextAgent</code>
# Tools
Types:
```python
from praisonaiagents import BaseTool, FunctionTool, ToolRegistry, Tools, get_registry, get_tool, register_tool, tool, validate_tool
```
Methods:
* <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">__call__</a>(**kwargs) -> Any</code>
* <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">get_schema</a>() -> Dict[str, Any]</code>
* <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">run</a>(**kwargs) -> Any</code>
* <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">safe_run</a>(**kwargs) -> ToolResult</code>
* <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate</a>() -> bool</code>
* <code title="class BaseTool">BaseTool.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_class</a>() -> bool</code>
* <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">__call__</a>(*args, **kwargs) -> Any</code>
* <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">injected_params</a>() -> Dict[str, Any]</code>
* <code title="class FunctionTool">FunctionTool.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">run</a>(**kwargs) -> Any</code>
* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">clear</a>() -> None</code>
* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">discover_plugins</a>() -> int</code>
* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get</a>(name: str) -> Optional[Union[BaseTool, Callable]]</code>
* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_all</a>() -> Dict[str, Union[BaseTool, Callable]]</code>
* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_base_tools</a>() -> List[BaseTool]</code>
* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">list_tools</a>() -> List[str]</code>
* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None, overwrite: bool = False) -> None</code>
* <code title="class ToolRegistry">ToolRegistry.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">unregister</a>(name: str) -> bool</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_registry</a>() -> ToolRegistry</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">get_tool</a>(name: str) -> Optional[Union[BaseTool, Callable]]</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/registry.py">register_tool</a>(tool: Union[BaseTool, Callable], name: Optional[str] = None) -> None</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/decorator.py">tool</a>(func: Optional[Callable] = None) -> Union[FunctionTool, Callable[[Callable], FunctionTool]]</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/tools/base.py">validate_tool</a>(tool: Any) -> bool</code>
# Workflows
Types:
```python
from praisonaiagents import Loop, Parallel, Pipeline, Repeat, Route, Workflow, WorkflowStep, loop, parallel, repeat, route
```
Methods:
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">arun</a>(input: str = '', llm: Optional[str] = None, verbose: bool = False) -> Dict[str, Any]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">astart</a>(input: str = '', llm: Optional[str] = None, verbose: bool = False) -> Dict[str, Any]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">from_template</a>(uri: str, config: Optional[Dict[str, Any]] = None, offline: bool = False, **kwargs) -> 'Workflow'</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">memory_config</a>() -> Optional[Dict[str, Any]]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">on_step_complete</a>() -> Optional[Callable]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">on_step_error</a>() -> Optional[Callable]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">on_step_start</a>() -> Optional[Callable]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">on_workflow_complete</a>() -> Optional[Callable]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">on_workflow_start</a>() -> Optional[Callable]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">planning_llm</a>() -> Optional[str]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">reasoning</a>() -> bool</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">run</a>(input: str = '', llm: Optional[str] = None, verbose: bool = False, stream: bool = None) -> Dict[str, Any]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">start</a>(input: str = '', **kwargs) -> Dict[str, Any]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">stream</a>() -> bool</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">to_dict</a>() -> Dict[str, Any]</code>
* <code title="class Workflow">Workflow.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">verbose</a>() -> bool</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">async_execution</a>() -> bool</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">branch_condition</a>() -> Optional[Dict[str, List[str]]]</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">context_from</a>() -> Optional[List[str]]</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">evaluator</a>()</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">executor</a>()</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">max_retries</a>() -> int</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">next_steps</a>() -> Optional[List[str]]</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">on_error</a>() -> str</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">output_file</a>() -> Optional[str]</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">output_json</a>() -> Optional[Any]</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">output_pydantic</a>() -> Optional[Any]</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">output_variable</a>() -> Optional[str]</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">quality_check</a>() -> bool</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">rerun</a>() -> bool</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">retain_full_context</a>() -> bool</code>
* <code title="class WorkflowStep">WorkflowStep.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">to_dict</a>() -> Dict[str, Any]</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">loop</a>(step: Any, over: Optional[str] = None, from_csv: Optional[str] = None, from_file: Optional[str] = None, var_name: str = 'item') -> Loop</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">parallel</a>(steps: List) -> Parallel</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">repeat</a>(step: Any, until: Optional[Callable[[WorkflowContext], bool]] = None, max_iterations: int = 10) -> Repeat</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/workflows/workflows.py">route</a>(routes: Dict[str, List], default: Optional[List] = None) -> Route</code>
# DB
Types:
```python
from praisonaiagents import db
```
# Memory
Types:
```python
from praisonaiagents import Memory
```
Methods:
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">build_context_for_task</a>(task_descr: str, user_id: Optional[str] = None, additional: str = '', max_items: int = 3, include_in_output: Optional[bool] = None) -> str</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">calculate_quality_metrics</a>(output: str, expected_output: str, llm: Optional[str] = None, custom_prompt: Optional[str] = None) -> Dict[str, float]</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">compute_quality_score</a>(completeness: float, relevance: float, clarity: float, accuracy: float, weights: Dict[str, float] = None) -> float</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">finalize_task_output</a>(content: str, agent_name: str, quality_score: float, threshold: float = 0.7, metrics: Dict[str, Any] = None, task_id: str = None)</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">get_all_memories</a>() -> List[Dict[str, Any]]</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">reset_all</a>()</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">reset_entity_only</a>()</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">reset_long_term</a>()</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">reset_short_term</a>()</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">reset_user_memory</a>()</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">search</a>(query: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, limit: int = 5, rerank: bool = False, **kwargs) -> List[Dict[str, Any]]</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">search_entity</a>(query: str, limit: int = 5) -> List[Dict[str, Any]]</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">search_long_term</a>(query: str, limit: int = 5, relevance_cutoff: float = 0.0, min_quality: float = 0.0, rerank: bool = False, **kwargs) -> List[Dict[str, Any]]</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">search_short_term</a>(query: str, limit: int = 5, min_quality: float = 0.0, relevance_cutoff: float = 0.0, rerank: bool = False, **kwargs) -> List[Dict[str, Any]]</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">search_user_memory</a>(user_id: str, query: str, limit: int = 5, rerank: bool = False, **kwargs) -> List[Dict[str, Any]]</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">search_with_quality</a>(query: str, min_quality: float = 0.0, memory_type: Literal['short', 'long'] = 'long', limit: int = 5) -> List[Dict[str, Any]]</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">store_entity</a>(name: str, type_: str, desc: str, relations: str)</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">store_long_term</a>(text: str, metadata: Dict[str, Any] = None, completeness: float = None, relevance: float = None, clarity: float = None, accuracy: float = None, weights: Dict[str, float] = None, evaluator_quality: float = None)</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">store_quality</a>(text: str, quality_score: float, task_id: Optional[str] = None, iteration: Optional[int] = None, metrics: Optional[Dict[str, float]] = None, memory_type: Literal['short', 'long'] = 'long') -> None</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">store_short_term</a>(text: str, metadata: Dict[str, Any] = None, completeness: float = None, relevance: float = None, clarity: float = None, accuracy: float = None, weights: Dict[str, float] = None, evaluator_quality: float = None)</code>
* <code title="class Memory">Memory.<a href="./src/praisonai-agents/praisonaiagents/memory/memory.py">store_user_memory</a>(user_id: str, text: str, extra: Dict[str, Any] = None)</code>
# Knowledge
Types:
```python
from praisonaiagents import Chunking, Knowledge
```
Methods:
* <code title="class Chunking">Chunking.<a href="./src/praisonai-agents/praisonaiagents/knowledge/chunking.py">SUPPORTED_CHUNKERS</a>() -> Dict[str, Any]</code>
* <code title="class Chunking">Chunking.<a href="./src/praisonai-agents/praisonaiagents/knowledge/chunking.py">__call__</a>(text: Union[str, List[str]], **kwargs) -> Union[List[Any], List[List[Any]]]</code>
* <code title="class Chunking">Chunking.<a href="./src/praisonai-agents/praisonaiagents/knowledge/chunking.py">chunk</a>(text: Union[str, List[str]], **kwargs) -> Union[List[Any], List[List[Any]]]</code>
* <code title="class Chunking">Chunking.<a href="./src/praisonai-agents/praisonaiagents/knowledge/chunking.py">chunker</a>()</code>
* <code title="class Chunking">Chunking.<a href="./src/praisonai-agents/praisonaiagents/knowledge/chunking.py">embedding_model</a>()</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">add</a>(file_path, user_id = None, agent_id = None, run_id = None, metadata = None)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">chunker</a>()</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">config</a>()</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">delete</a>(memory_id)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">delete_all</a>(user_id = None, agent_id = None, run_id = None)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">get</a>(memory_id)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">get_all</a>(user_id = None, agent_id = None, run_id = None)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">get_corpus_stats</a>()</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">history</a>(memory_id)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">index</a>(path: str, incremental: bool = True, force: bool = False, include_glob: list = None, exclude_glob: list = None, user_id: str = None, agent_id: str = None, run_id: str = None)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">markdown</a>()</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">memory</a>()</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">normalize_content</a>(content)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">reset</a>()</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">search</a>(query, user_id = None, agent_id = None, run_id = None, rerank = None, **kwargs)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">store</a>(content, user_id = None, agent_id = None, run_id = None, metadata = None)</code>
* <code title="class Knowledge">Knowledge.<a href="./src/praisonai-agents/praisonaiagents/knowledge/knowledge.py">update</a>(memory_id, data)</code>
# RAG
Types:
```python
from praisonaiagents import RAG, RAGCitation, RAGConfig, RAGResult
```
Methods:
* <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">aquery</a>(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> RAGResult</code>
* <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">aretrieve</a>(query: str, **kwargs) -> ContextPack</code>
* <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">astream</a>(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> AsyncIterator[str]</code>
* <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">get_citations</a>(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> List[Citation]</code>
* <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">llm</a>()</code>
* <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">query</a>(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> RAGResult</code>
* <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">retrieve</a>(query: str, **kwargs) -> ContextPack</code>
* <code title="class RAG">RAG.<a href="./src/praisonai-agents/praisonaiagents/rag/pipeline.py">stream</a>(question: str, user_id: Optional[str] = None, agent_id: Optional[str] = None, run_id: Optional[str] = None, **kwargs) -> Iterator[str]</code>
* <code title="class RAGCitation">RAGCitation.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">from_dict</a>(data: Dict[str, Any]) -> 'Citation'</code>
* <code title="class RAGCitation">RAGCitation.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">to_dict</a>() -> Dict[str, Any]</code>
* <code title="class RAGConfig">RAGConfig.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">from_dict</a>(data: Dict[str, Any]) -> 'RAGConfig'</code>
* <code title="class RAGConfig">RAGConfig.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">to_dict</a>() -> Dict[str, Any]</code>
* <code title="class RAGResult">RAGResult.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">format_answer_with_citations</a>() -> str</code>
* <code title="class RAGResult">RAGResult.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">from_dict</a>(data: Dict[str, Any]) -> 'RAGResult'</code>
* <code title="class RAGResult">RAGResult.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">has_citations</a>() -> bool</code>
* <code title="class RAGResult">RAGResult.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">to_dict</a>() -> Dict[str, Any]</code>
# Handoff
Types:
```python
from praisonaiagents import Handoff, RECOMMENDED_PROMPT_PREFIX, handoff, handoff_filters, prompt_with_handoff_instructions
```
Methods:
* <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">default_tool_description</a>() -> str</code>
* <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">default_tool_name</a>() -> str</code>
* <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">execute_async</a>(source_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None) -> HandoffResult</code>
* <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">execute_programmatic</a>(source_agent: 'Agent', prompt: str, context: Optional[Dict[str, Any]] = None) -> HandoffResult</code>
* <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">to_tool_function</a>(source_agent: 'Agent') -> Callable</code>
* <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_description</a>() -> str</code>
* <code title="class Handoff">Handoff.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">tool_name</a>() -> str</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">handoff</a>(agent: 'Agent', tool_name_override: Optional[str] = None, tool_description_override: Optional[str] = None, on_handoff: Optional[Callable] = None, input_type: Optional[type] = None, input_filter: Optional[Callable[[HandoffInputData], HandoffInputData]] = None, config: Optional[HandoffConfig] = None, context_policy: Optional[str] = None, timeout_seconds: Optional[float] = None, max_concurrent: Optional[int] = None, detect_cycles: Optional[bool] = None, max_depth: Optional[int] = None) -> Handoff</code>
* <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">keep_last_n_messages</a>(n: int) -> Callable[[HandoffInputData], HandoffInputData]</code>
* <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">remove_all_tools</a>(data: HandoffInputData) -> HandoffInputData</code>
* <code title="class handoff_filters">handoff_filters.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">remove_system_messages</a>(data: HandoffInputData) -> HandoffInputData</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/agent/handoff.py">prompt_with_handoff_instructions</a>(base_prompt: str, agent: 'Agent') -> str</code>
# Guardrails
Types:
```python
from praisonaiagents import LLMGuardrail
```
Methods:
* <code title="class LLMGuardrail">LLMGuardrail.<a href="./src/praisonai-agents/praisonaiagents/guardrails/llm_guardrail.py">__call__</a>(task_output: TaskOutput) -> Tuple[bool, Union[str, TaskOutput]]</code>
# Skills
Types:
```python
from praisonaiagents import SkillLoader, SkillManager, SkillMetadata, SkillProperties
```
Methods:
* <code title="class SkillLoader">SkillLoader.<a href="./src/praisonai-agents/praisonaiagents/skills/loader.py">activate</a>(skill: LoadedSkill) -> bool</code>
* <code title="class SkillLoader">SkillLoader.<a href="./src/praisonai-agents/praisonaiagents/skills/loader.py">load</a>(skill_path: str, activate: bool = False) -> Optional[LoadedSkill]</code>
* <code title="class SkillLoader">SkillLoader.<a href="./src/praisonai-agents/praisonaiagents/skills/loader.py">load_all_resources</a>(skill: LoadedSkill) -> None</code>
* <code title="class SkillLoader">SkillLoader.<a href="./src/praisonai-agents/praisonaiagents/skills/loader.py">load_assets</a>(skill: LoadedSkill) -> dict</code>
* <code title="class SkillLoader">SkillLoader.<a href="./src/praisonai-agents/praisonaiagents/skills/loader.py">load_metadata</a>(skill_path: str) -> Optional[LoadedSkill]</code>
* <code title="class SkillLoader">SkillLoader.<a href="./src/praisonai-agents/praisonaiagents/skills/loader.py">load_references</a>(skill: LoadedSkill) -> dict</code>
* <code title="class SkillLoader">SkillLoader.<a href="./src/praisonai-agents/praisonaiagents/skills/loader.py">load_scripts</a>(skill: LoadedSkill) -> dict</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">activate</a>(skill: LoadedSkill) -> bool</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">activate_by_name</a>(name: str) -> bool</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">add_skill</a>(skill_path: str) -> Optional[LoadedSkill]</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">clear</a>() -> None</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">discover</a>(skill_dirs: Optional[List[str]] = None, include_defaults: bool = True) -> int</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_available_skills</a>() -> List[SkillMetadata]</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_instructions</a>(name: str) -> Optional[str]</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">get_skill</a>(name: str) -> Optional[LoadedSkill]</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">load_resources</a>(name: str) -> bool</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">skill_names</a>() -> List[str]</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">skills</a>() -> List[LoadedSkill]</code>
* <code title="class SkillManager">SkillManager.<a href="./src/praisonai-agents/praisonaiagents/skills/manager.py">to_prompt</a>() -> str</code>
* <code title="class SkillMetadata">SkillMetadata.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">from_properties</a>(props: SkillProperties) -> 'SkillMetadata'</code>
* <code title="class SkillProperties">SkillProperties.<a href="./src/praisonai-agents/praisonaiagents/skills/models.py">to_dict</a>() -> dict</code>
# Session
Types:
```python
from praisonaiagents import Session
```
Methods:
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">Agent</a>(name: str, role: str = 'Assistant', instructions: Optional[str] = None, tools: Optional[List[Any]] = None, memory: bool = True, knowledge: Optional[List[str]] = None, **kwargs) -> 'Agent'</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">add_knowledge</a>(source: str) -> None</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">add_memory</a>(text: str, memory_type: str = 'long', **metadata) -> None</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">chat</a>(message: str, **kwargs) -> str</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">clear_memory</a>(memory_type: str = 'all') -> None</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">create_agent</a>(*args, **kwargs) -> 'Agent'</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">get_context</a>(query: str, max_items: int = 3) -> str</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">get_state</a>(key: str, default: Any = None) -> Any</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">increment_state</a>(key: str, increment: int = 1, default: int = 0) -> None</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">knowledge</a>() -> 'Knowledge'</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">memory</a>() -> 'Memory'</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">restore_state</a>() -> Dict[str, Any]</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">save_state</a>(state_data: Dict[str, Any]) -> None</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">search_knowledge</a>(query: str, limit: int = 5) -> List[Dict[str, Any]]</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">search_memory</a>(query: str, memory_type: str = 'long', limit: int = 5) -> List[Dict[str, Any]]</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">send_message</a>(message: str, **kwargs) -> str</code>
* <code title="class Session">Session.<a href="./src/praisonai-agents/praisonaiagents/session/api.py">set_state</a>(key: str, value: Any) -> None</code>
# Telemetry
Types:
```python
from praisonaiagents import get_telemetry
```
Methods:
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/telemetry/__init__.py">get_telemetry</a>() -> 'MinimalTelemetry'</code>
# Observability
Types:
```python
from praisonaiagents import FlowDisplay, obs, track_workflow
```
Methods:
* <code title="class FlowDisplay">FlowDisplay.<a href="./src/praisonai-agents/praisonaiagents/flow_display.py">display</a>()</code>
* <code title="class FlowDisplay">FlowDisplay.<a href="./src/praisonai-agents/praisonaiagents/flow_display.py">start</a>()</code>
* <code title="class FlowDisplay">FlowDisplay.<a href="./src/praisonai-agents/praisonaiagents/flow_display.py">stop</a>()</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/flow_display.py">track_workflow</a>()</code>
# Context
Types:
```python
from praisonaiagents import ContextManager, ManagerConfig
```
Methods:
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">capture_llm_boundary</a>(messages: List[Dict[str, Any]], tools: List[Dict[str, Any]]) -> SnapshotHookData</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">estimate_tokens</a>(text: str, validate: bool = False) -> Tuple[int, Optional[EstimationMetrics]]</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">get_history</a>() -> List[Dict[str, Any]]</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">get_last_snapshot_hook</a>() -> Optional[SnapshotHookData]</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">get_resolved_config</a>() -> Dict[str, Any]</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">get_stats</a>() -> Dict[str, Any]</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">get_tool_budget</a>(tool_name: str) -> int</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">process</a>(messages: List[Dict[str, Any]], system_prompt: str = '', tools: Optional[List[Dict[str, Any]]] = None, trigger: Literal['turn', 'tool_call', 'manual', 'overflow'] = 'turn') -> Dict[str, Any]</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">register_snapshot_callback</a>(callback: Callable[[SnapshotHookData], None]) -> None</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">reset</a>() -> None</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">set_tool_budget</a>(tool_name: str, max_tokens: int, protected: bool = False) -> None</code>
* <code title="class ContextManager">ContextManager.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">truncate_tool_output</a>(tool_name: str, output: str) -> str</code>
* <code title="class ManagerConfig">ManagerConfig.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">from_env</a>() -> 'ManagerConfig'</code>
* <code title="class ManagerConfig">ManagerConfig.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">merge</a>(**overrides) -> 'ManagerConfig'</code>
* <code title="class ManagerConfig">ManagerConfig.<a href="./src/praisonai-agents/praisonaiagents/context/manager.py">to_dict</a>() -> Dict[str, Any]</code>
# Display
Types:
```python
from praisonaiagents import async_display_callbacks, clean_triple_backticks, display_error, display_generating, display_instruction, display_interaction, display_self_reflection, display_tool_call, error_logs, register_display_callback, sync_display_callbacks
```
Methods:
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">clean_triple_backticks</a>(text: str) -> str</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_error</a>(message: str, console = None)</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_generating</a>(content: str = '', start_time: Optional[float] = None)</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_instruction</a>(message: str, console = None, agent_name: str = None, agent_role: str = None, agent_tools: List[str] = None)</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_interaction</a>(message, response, markdown = True, generation_time = None, console = None, agent_name = None, agent_role = None, agent_tools = None, task_name = None, task_description = None, task_id = None)</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_self_reflection</a>(message: str, console = None)</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">display_tool_call</a>(message: str, console = None, tool_name: str = None, tool_input: dict = None, tool_output: str = None)</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/main.py">register_display_callback</a>(display_type: str, callback_fn, is_async: bool = False)</code>
# Other
Types:
```python
from praisonaiagents import AutoRagConfig, Citation, CodeExecutionStep, DeepResearchResponse, ExpandResult, ExpandStrategy, FileSearchCall, MCPCall, Provider, RagRetrievalPolicy, ReasoningStep, RewriteResult, RewriteStrategy, Task, WebSearchCall, warmup
```
Methods:
* <code title="class AutoRagConfig">AutoRagConfig.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">from_dict</a>(data: Dict[str, Any]) -> 'AutoRagConfig'</code>
* <code title="class AutoRagConfig">AutoRagConfig.<a href="./src/praisonai-agents/praisonaiagents/agents/auto_rag_agent.py">to_dict</a>() -> Dict[str, Any]</code>
* <code title="class Citation">Citation.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">from_dict</a>(data: Dict[str, Any]) -> 'Citation'</code>
* <code title="class Citation">Citation.<a href="./src/praisonai-agents/praisonaiagents/rag/models.py">to_dict</a>() -> Dict[str, Any]</code>
* <code title="class DeepResearchResponse">DeepResearchResponse.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">get_all_sources</a>() -> List[Dict[str, str]]</code>
* <code title="class DeepResearchResponse">DeepResearchResponse.<a href="./src/praisonai-agents/praisonaiagents/agent/deep_research_agent.py">get_citation_text</a>(citation: Citation) -> str</code>
* <code title="class RewriteResult">RewriteResult.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">all_queries</a>() -> List[str]</code>
* <code title="class RewriteResult">RewriteResult.<a href="./src/praisonai-agents/praisonaiagents/agent/query_rewriter_agent.py">primary_query</a>() -> str</code>
* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback</a>(task_output: TaskOutput) -> None</code>
* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback_sync</a>(task_output: TaskOutput) -> None</code>
* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">initialize_memory</a>()</code>
* <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">store_in_memory</a>(content: str, agent_name: str = None, task_id: str = None)</code>
* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/__init__.py">warmup</a>(include_litellm: bool = False, include_openai: bool = True) -> dict</code>
# Wrapper (praisonai)
Types:
```python
from praisonai import CloudProvider, Deploy, DeployConfig, DeployType, PraisonAI, __version__
```
# CLI
Methods:
* <code title="cli">praisonai <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
* <code title="cli">praisonai acp acp-main <a href="./src/praisonai/praisonai/cli/commands/acp.py">--help</a></code>
* <code title="cli">praisonai agents create <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
* <code title="cli">praisonai agents info <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
* <code title="cli">praisonai agents list <a href="./src/praisonai/praisonai/cli/commands/agents.py">--help</a></code>
* <code title="cli">praisonai audit agent-centric <a href="./src/praisonai/praisonai/cli/commands/audit.py">--help</a></code>
* <code title="cli">praisonai benchmark agent <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
* <code title="cli">praisonai benchmark benchmark-callback <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
* <code title="cli">praisonai benchmark cli <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
* <code title="cli">praisonai benchmark compare <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
* <code title="cli">praisonai benchmark litellm <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
* <code title="cli">praisonai benchmark profile <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
* <code title="cli">praisonai benchmark sdk <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
* <code title="cli">praisonai benchmark workflow <a href="./src/praisonai/praisonai/cli/commands/benchmark.py">--help</a></code>
* <code title="cli">praisonai call call-main <a href="./src/praisonai/praisonai/cli/commands/call.py">--help</a></code>
* <code title="cli">praisonai chat <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
* <code title="cli">praisonai chat chat-main <a href="./src/praisonai/praisonai/cli/commands/chat.py">--help</a></code>
* <code title="cli">praisonai code <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
* <code title="cli">praisonai code code-main <a href="./src/praisonai/praisonai/cli/commands/code.py">--help</a></code>
* <code title="cli">praisonai commit commit-main <a href="./src/praisonai/praisonai/cli/commands/commit.py">--help</a></code>
* <code title="cli">praisonai completion bash <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
* <code title="cli">praisonai completion completion-callback <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
* <code title="cli">praisonai completion fish <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
* <code title="cli">praisonai completion zsh <a href="./src/praisonai/praisonai/cli/commands/completion.py">--help</a></code>
* <code title="cli">praisonai config get <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
* <code title="cli">praisonai config list <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
* <code title="cli">praisonai config path <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
* <code title="cli">praisonai config reset <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
* <code title="cli">praisonai config set <a href="./src/praisonai/praisonai/cli/commands/config.py">--help</a></code>
* <code title="cli">praisonai context add <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
* <code title="cli">praisonai context clear <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
* <code title="cli">praisonai context compact <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
* <code title="cli">praisonai context export <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
* <code title="cli">praisonai context show <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
* <code title="cli">praisonai context stats <a href="./src/praisonai/praisonai/cli/commands/context.py">--help</a></code>
* <code title="cli">praisonai debug acp <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
* <code title="cli">praisonai debug debug-callback <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
* <code title="cli">praisonai debug interactive <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
* <code title="cli">praisonai debug lsp <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
* <code title="cli">praisonai debug trace <a href="./src/praisonai/praisonai/cli/commands/debug.py">--help</a></code>
* <code title="cli">praisonai deploy aws <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
* <code title="cli">praisonai deploy azure <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
* <code title="cli">praisonai deploy docker <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
* <code title="cli">praisonai deploy gcp <a href="./src/praisonai/praisonai/cli/commands/deploy.py">--help</a></code>
* <code title="cli">praisonai diag diag-callback <a href="./src/praisonai/praisonai/cli/commands/diag.py">--help</a></code>
* <code title="cli">praisonai diag export <a href="./src/praisonai/praisonai/cli/commands/diag.py">--help</a></code>
* <code title="cli">praisonai docs api-md <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
* <code title="cli">praisonai docs generate <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
* <code title="cli">praisonai docs list <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
* <code title="cli">praisonai docs report <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
* <code title="cli">praisonai docs run <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
* <code title="cli">praisonai docs run-all <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
* <code title="cli">praisonai docs serve <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
* <code title="cli">praisonai docs stats <a href="./src/praisonai/praisonai/cli/commands/docs.py">--help</a></code>
* <code title="cli">praisonai doctor config <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
* <code title="cli">praisonai doctor db <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
* <code title="cli">praisonai doctor doctor-callback <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
* <code title="cli">praisonai doctor env <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
* <code title="cli">praisonai doctor mcp <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
* <code title="cli">praisonai doctor network <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
* <code title="cli">praisonai doctor performance <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
* <code title="cli">praisonai doctor selftest <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
* <code title="cli">praisonai doctor tools <a href="./src/praisonai/praisonai/cli/commands/doctor.py">--help</a></code>
* <code title="cli">praisonai endpoints list <a href="./src/praisonai/praisonai/cli/commands/endpoints.py">--help</a></code>
* <code title="cli">praisonai endpoints test <a href="./src/praisonai/praisonai/cli/commands/endpoints.py">--help</a></code>
* <code title="cli">praisonai environment check <a href="./src/praisonai/praisonai/cli/commands/environment.py">--help</a></code>
* <code title="cli">praisonai environment doctor <a href="./src/praisonai/praisonai/cli/commands/environment.py">--help</a></code>
* <code title="cli">praisonai environment view <a href="./src/praisonai/praisonai/cli/commands/environment.py">--help</a></code>
* <code title="cli">praisonai eval accuracy <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
* <code title="cli">praisonai eval performance <a href="./src/praisonai/praisonai/cli/commands/eval.py">--help</a></code>
* <code title="cli">praisonai examples info <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
* <code title="cli">praisonai examples list <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
* <code title="cli">praisonai examples report <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
* <code title="cli">praisonai examples run <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
* <code title="cli">praisonai examples run-all <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
* <code title="cli">praisonai examples stats <a href="./src/praisonai/praisonai/cli/commands/examples.py">--help</a></code>
* <code title="cli">praisonai hooks add <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
* <code title="cli">praisonai hooks list <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
* <code title="cli">praisonai hooks remove <a href="./src/praisonai/praisonai/cli/commands/hooks.py">--help</a></code>
* <code title="cli">praisonai knowledge add <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
* <code title="cli">praisonai knowledge index <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
* <code title="cli">praisonai knowledge list <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
* <code title="cli">praisonai knowledge search <a href="./src/praisonai/praisonai/cli/commands/knowledge.py">--help</a></code>
* <code title="cli">praisonai lsp logs <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
* <code title="cli">praisonai lsp lsp-callback <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
* <code title="cli">praisonai lsp start <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
* <code title="cli">praisonai lsp status <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
* <code title="cli">praisonai lsp stop <a href="./src/praisonai/praisonai/cli/commands/lsp.py">--help</a></code>
* <code title="cli">praisonai mcp add <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
* <code title="cli">praisonai mcp list <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
* <code title="cli">praisonai mcp mcp-callback <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
* <code title="cli">praisonai mcp remove <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
* <code title="cli">praisonai mcp test <a href="./src/praisonai/praisonai/cli/commands/mcp.py">--help</a></code>
* <code title="cli">praisonai memory add <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
* <code title="cli">praisonai memory clear <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
* <code title="cli">praisonai memory search <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
* <code title="cli">praisonai memory show <a href="./src/praisonai/praisonai/cli/commands/memory.py">--help</a></code>
* <code title="cli">praisonai package install <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
* <code title="cli">praisonai package list <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
* <code title="cli">praisonai package uninstall <a href="./src/praisonai/praisonai/cli/commands/package.py">--help</a></code>
* <code title="cli">praisonai profile imports <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
* <code title="cli">praisonai profile optimize <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
* <code title="cli">praisonai profile profile-callback <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
* <code title="cli">praisonai profile query <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
* <code title="cli">praisonai profile snapshot <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
* <code title="cli">praisonai profile startup <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
* <code title="cli">praisonai profile suite <a href="./src/praisonai/praisonai/cli/commands/profile.py">--help</a></code>
* <code title="cli">praisonai rag chat <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
* <code title="cli">praisonai rag eval <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
* <code title="cli">praisonai rag index <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
* <code title="cli">praisonai rag query <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
* <code title="cli">praisonai rag serve <a href="./src/praisonai/praisonai/cli/commands/rag.py">--help</a></code>
* <code title="cli">praisonai realtime realtime-main <a href="./src/praisonai/praisonai/cli/commands/realtime.py">--help</a></code>
* <code title="cli">praisonai recipe install <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
* <code title="cli">praisonai recipe list <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
* <code title="cli">praisonai recipe run <a href="./src/praisonai/praisonai/cli/commands/recipe.py">--help</a></code>
* <code title="cli">praisonai registry list <a href="./src/praisonai/praisonai/cli/commands/registry.py">--help</a></code>
* <code title="cli">praisonai registry serve <a href="./src/praisonai/praisonai/cli/commands/registry.py">--help</a></code>
* <code title="cli">praisonai research research-main <a href="./src/praisonai/praisonai/cli/commands/research.py">--help</a></code>
* <code title="cli">praisonai retrieval index <a href="./src/praisonai/praisonai/cli/commands/retrieval.py">--help</a></code>
* <code title="cli">praisonai retrieval query <a href="./src/praisonai/praisonai/cli/commands/retrieval.py">--help</a></code>
* <code title="cli">praisonai retrieval search <a href="./src/praisonai/praisonai/cli/commands/retrieval.py">--help</a></code>
* <code title="cli">praisonai rules add <a href="./src/praisonai/praisonai/cli/commands/rules.py">--help</a></code>
* <code title="cli">praisonai rules clear <a href="./src/praisonai/praisonai/cli/commands/rules.py">--help</a></code>
* <code title="cli">praisonai rules list <a href="./src/praisonai/praisonai/cli/commands/rules.py">--help</a></code>
* <code title="cli">praisonai run <a href="./src/praisonai/praisonai/cli/main.py">--help</a></code>
* <code title="cli">praisonai run run-main <a href="./src/praisonai/praisonai/cli/commands/run.py">--help</a></code>
* <code title="cli">praisonai schedule delete <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
* <code title="cli">praisonai schedule describe <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
* <code title="cli">praisonai schedule list <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
* <code title="cli">praisonai schedule logs <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
* <code title="cli">praisonai schedule restart <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
* <code title="cli">praisonai schedule schedule-callback <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
* <code title="cli">praisonai schedule start <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
* <code title="cli">praisonai schedule stats <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
* <code title="cli">praisonai schedule stop <a href="./src/praisonai/praisonai/cli/commands/schedule.py">--help</a></code>
* <code title="cli">praisonai serve serve-callback <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
* <code title="cli">praisonai serve start <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
* <code title="cli">praisonai serve status <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
* <code title="cli">praisonai serve stop <a href="./src/praisonai/praisonai/cli/commands/serve.py">--help</a></code>
* <code title="cli">praisonai session delete <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
* <code title="cli">praisonai session export <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
* <code title="cli">praisonai session import <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
* <code title="cli">praisonai session list <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
* <code title="cli">praisonai session resume <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
* <code title="cli">praisonai session show <a href="./src/praisonai/praisonai/cli/commands/session.py">--help</a></code>
* <code title="cli">praisonai skills create <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
* <code title="cli">praisonai skills install <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
* <code title="cli">praisonai skills list <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
* <code title="cli">praisonai skills validate <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
* <code title="cli">praisonai standardise check <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
* <code title="cli">praisonai standardise fix <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
* <code title="cli">praisonai standardise init <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
* <code title="cli">praisonai standardise report <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
* <code title="cli">praisonai templates create <a href="./src/praisonai/praisonai/cli/commands/templates.py">--help</a></code>
* <code title="cli">praisonai templates list <a href="./src/praisonai/praisonai/cli/commands/templates.py">--help</a></code>
* <code title="cli">praisonai test info <a href="./src/praisonai/praisonai/cli/commands/test.py">--help</a></code>
* <code title="cli">praisonai test run <a href="./src/praisonai/praisonai/cli/commands/test.py">--help</a></code>
* <code title="cli">praisonai todo add <a href="./src/praisonai/praisonai/cli/commands/todo.py">--help</a></code>
* <code title="cli">praisonai todo done <a href="./src/praisonai/praisonai/cli/commands/todo.py">--help</a></code>
* <code title="cli">praisonai todo list <a href="./src/praisonai/praisonai/cli/commands/todo.py">--help</a></code>
* <code title="cli">praisonai tools info <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
* <code title="cli">praisonai tools list <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
* <code title="cli">praisonai tools test <a href="./src/praisonai/praisonai/cli/commands/tools.py">--help</a></code>
* <code title="cli">praisonai traces disable <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
* <code title="cli">praisonai traces enable <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
* <code title="cli">praisonai traces list <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
* <code title="cli">praisonai traces status <a href="./src/praisonai/praisonai/cli/commands/traces.py">--help</a></code>
* <code title="cli">praisonai train train-main <a href="./src/praisonai/praisonai/cli/commands/train.py">--help</a></code>
* <code title="cli">praisonai ui chat <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
* <code title="cli">praisonai ui code <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
* <code title="cli">praisonai ui gradio <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
* <code title="cli">praisonai ui realtime <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
* <code title="cli">praisonai ui ui-main <a href="./src/praisonai/praisonai/cli/commands/ui.py">--help</a></code>
* <code title="cli">praisonai version check <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
* <code title="cli">praisonai version show <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
* <code title="cli">praisonai version version-callback <a href="./src/praisonai/praisonai/cli/commands/version.py">--help</a></code>
* <code title="cli">praisonai workflow create <a href="./src/praisonai/praisonai/cli/commands/workflow.py">--help</a></code>
* <code title="cli">praisonai workflow list <a href="./src/praisonai/praisonai/cli/commands/workflow.py">--help</a></code>
* <code title="cli">praisonai workflow run <a href="./src/praisonai/praisonai/cli/commands/workflow.py">--help</a></code>
# TypeScript
Types/Exports:
```ts
export { Agent, Agents, PraisonAIAgents, Router } from "./agent";
export type { PraisonAIAgentsConfig, SimpleAgentConfig, SimpleRouteConfig, SimpleRouterConfig } from "./agent";
export { ContextAgent, createContextAgent } from "./agent/context";
export { Handoff, handoff, handoffFilters } from "./agent/handoff";
export { ImageAgent, createImageAgent } from "./agent/image";
export { PromptExpanderAgent, createPromptExpanderAgent } from "./agent/prompt-expander";
export { QueryRewriterAgent, createQueryRewriterAgent } from "./agent/query-rewriter";
export { DeepResearchAgent, createDeepResearchAgent } from "./agent/research";
export { RouterAgent, createRouter, routeConditions } from "./agent/router";
export { // Agent loop
createAgentLoop, // DevTools
enableDevTools, // MCP
createMCP, // Middleware (renamed to avoid conflicts)
createCachingMiddleware, // Models
createModel, // Multimodal
createImagePart, // Next.js
createRouteHandler, // OAuth for MCP
type OAuthClientProvider, // Server adapters
createHttpHandler, // Speech & Transcription
generateSpeech, // Telemetry (AI SDK v6 parity)
configureTelemetry, // Tool Approval (AI SDK v6 parity)
ApprovalManager, // Tools
defineTool, // UI Message (AI SDK v6 parity)
convertToModelMessages, AIAgentStep, AIEmbedManyResult, AIEmbedOptions, AIEmbedResult, AIFilePart, AIGenerateImageOptions, AIGenerateImageResult, AIGenerateObjectOptions, AIGenerateObjectResult, AIGenerateTextOptions, AIGenerateTextResult, AIImagePart, AIMiddleware, AIMiddlewareConfig, AIModelMessage, AISpan, AISpanKind, AISpanOptions, AISpanStatus, AIStreamObjectOptions, AIStreamObjectResult, AIStreamTextOptions, AIStreamTextResult, AITelemetryEvent, AITelemetrySettings, AITextPart, AIToolDefinition, AITracer, AgentLoop, DANGEROUS_PATTERNS, MODEL_ALIASES, SPEECH_MODELS, TRANSCRIPTION_MODELS, ToolApprovalDeniedError, ToolApprovalTimeoutError, aiEmbed, aiEmbedMany, aiGenerateImage, aiGenerateObject, aiGenerateText, aiStreamObject, aiStreamText, applyMiddleware, autoEnableDevTools, base64ToUint8Array, clearAICache, clearEvents, closeAllMCPClients, closeMCPClient, convertToUIMessages, createAILoggingMiddleware, createAISpan, createApprovalResponse, createDangerousPatternChecker, createDevToolsMiddleware, createExpressHandler, createFastifyHandler, createFilePart, createHonoHandler, createMultimodalMessage, createNestHandler, createPagesHandler, createPdfPart, createSystemMessage, createTelemetryMiddleware, createTelemetrySettings, createTextMessage, createTextPart, createToolSet, disableAITelemetry, disableDevTools, enableAITelemetry, functionToTool, getAICacheStats, getApprovalManager, getDevToolsState, getDevToolsUrl, getEvents, getMCPClient, getModel, getTelemetrySettings, getToolsNeedingApproval, getTracer, hasModelAlias, hasPendingApprovals, initOpenTelemetry, isDangerous, isDataUrl, isDevToolsEnabled, isTelemetryEnabled, isUrl, listModelAliases, mcpToolsToAITools, parseModel, pipeUIMessageStreamToResponse, recordEvent, resolveModelAlias, safeValidateUIMessages, setApprovalManager, stopAfterSteps, stopWhen, stopWhenNoToolCalls, toMessageContent, toUIMessageStreamResponse, transcribe, uint8ArrayToBase64, validateUIMessages, withApproval, withSpan, wrapModel } from "./ai";
export { AutoAgents, createAutoAgents } from "./auto";
export { BaseCache, FileCache, MemoryCache, createFileCache, createMemoryCache } from "./cache";
export { CLI_SPEC_VERSION, executeCommand, parseArgs } from "./cli";
export { // Autonomy Mode
AutonomyManager, // Background Jobs
JobQueue, // Checkpoints
CheckpointManager, // Cost Tracker
CostTracker, // External Agents
BaseExternalAgent, // Fast Context
FastContext, // Flow Display
FlowDisplay, // Git Integration
GitManager, // Interactive TUI
InteractiveTUI, // N8N Integration
N8NIntegration, // Repo Map
RepoMap, // Sandbox Executor
SandboxExecutor, // Scheduler
Scheduler, // Slash Commands
SlashCommandHandler, AiderAgent, ClaudeCodeAgent, CodexCliAgent, CommandValidator, CostTokenUsage, DEFAULT_BLOCKED_COMMANDS, DEFAULT_BLOCKED_PATHS, DEFAULT_IGNORE_PATTERNS, DiffViewer, FileCheckpointStorage, FileJobStorage, GeminiCliAgent, GenericExternalAgent, HistoryManager, MODEL_PRICING, MODE_POLICIES, MemoryCheckpointStorage, MemoryJobStorage, StatusDisplay, cliApprovalPrompt, createAutonomyManager, createCheckpointManager, createCostTracker, createDiffViewer, createExternalAgent, createFastContext, createFileCheckpointStorage, createFileJobStorage, createFlowDisplay, createGitManager, createHistoryManager, createInteractiveTUI, createJobQueue, createN8NIntegration, createRepoMap, createSandboxExecutor, createScheduler, createSlashCommandHandler, createStatusDisplay, cronExpressions, estimateTokens, executeSlashCommand, externalAgentAsTool, formatCost, getExternalAgentRegistry, getQuickContext, getRepoTree, isSlashCommand, parseSlashCommand, registerCommand, renderWorkflow, sandboxExec, triggerN8NWebhook } from "./cli/features";
export { createDbAdapter, db, getDefaultDbAdapter, setDefaultDbAdapter } from "./db";
export type { DbAdapter, DbConfig, DbMessage, DbRun, DbTrace } from "./db";
export { MemoryPostgresAdapter, NeonPostgresAdapter, PostgresSessionStorage, createMemoryPostgres, createNeonPostgres, createPostgresSessionStorage } from "./db/postgres";
export { MemoryRedisAdapter, UpstashRedisAdapter, createMemoryRedis, createUpstashRedis } from "./db/redis";
export { SQLiteAdapter, createSQLiteAdapter } from "./db/sqlite";
export { EvalSuite, accuracyEval, performanceEval, reliabilityEval } from "./eval";
export { AgentEventBus, AgentEvents, EventEmitterPubSub, PubSub, createEventBus, createPubSub } from "./events";
export { LLMGuardrail, createLLMGuardrail } from "./guardrails/llm-guardrail";
export { // Computer Use
createComputerUse, ComputerUseClient, createCLIApprovalPrompt, createComputerUseAgent } from "./integrations/computer-use";
export { BaseObservabilityProvider, ConsoleObservabilityProvider, LangfuseObservabilityProvider, MemoryObservabilityProvider, ObservabilityTraceContext, createConsoleObservability, createLangfuseObservability, createMemoryObservability } from "./integrations/observability";
export { // Natural Language Postgres
createNLPostgres, NLPostgresClient, NLPostgresConfig, createPostgresTool } from "./integrations/postgres";
export { // Slack
createSlackBot, SlackBot, parseSlackMessage, verifySlackSignature } from "./integrations/slack";
export { BaseVectorStore, ChromaVectorStore, MemoryVectorStore, PineconeVectorStore, QdrantVectorStore, VectorQueryResult, WeaviateVectorStore, createChromaStore, createMemoryVectorStore, createPineconeStore, createQdrantStore, createWeaviateStore } from "./integrations/vector";
export { BaseVoiceProvider, ElevenLabsVoiceProvider, OpenAIVoiceProvider, createElevenLabsVoice, createOpenAIVoice } from "./integrations/voice";
export { GraphRAG, GraphStore, createGraphRAG } from "./knowledge/graph-rag";
export { BaseReranker, CohereReranker, CrossEncoderReranker, LLMReranker, createCohereReranker, createCrossEncoderReranker, createLLMReranker } from "./knowledge/reranker";
export { // Provider classes
OpenAIProvider, // Provider factory and utilities
createProvider, // Provider registry (extensibility API)
ProviderRegistry, // Types
type LLMProvider, AnthropicProvider, BaseProvider, GoogleProvider, ProviderMessage, ProviderToolDefinition, createProviderRegistry, getAvailableProviders, getDefaultProvider, getDefaultRegistry, hasProvider, isProviderAvailable, listProviders, parseModelString, registerBuiltinProviders, registerProvider, unregisterProvider } from "./llm/providers";
export { ADAPTERS, AISDK_PROVIDERS, COMMUNITY_PROVIDERS, PROVIDER_ALIASES } from "./llm/providers/ai-sdk/types";
export { AutoMemory, AutoMemoryKnowledgeBase, AutoMemoryVectorStore, DEFAULT_POLICIES, createAutoMemory, createLLMSummarizer } from "./memory/auto-memory";
export { FileMemory, createFileMemory } from "./memory/file-memory";
export { Memory, createMemory } from "./memory/memory";
export type { MemoryConfig, MemoryEntry } from "./memory/memory";
export { // Adapters
NoopObservabilityAdapter, // Constants
OBSERVABILITY_TOOLS, // Global adapter management
setObservabilityAdapter, // Types
type SpanKind, ConsoleObservabilityAdapter, MemoryObservabilityAdapter, clearAdapterCache, createConsoleAdapter, createMemoryAdapter, createObservabilityAdapter, getObservabilityAdapter, getObservabilityToolInfo, hasObservabilityToolEnvVar, listObservabilityTools, noopAdapter, resetObservabilityAdapter, trace } from "./observability";
export { Plan, PlanStep, PlanStorage, PlanningAgent, TaskAgent, TodoItem, TodoList, createPlan, createPlanStorage, createPlanningAgent, createTaskAgent, createTodoList } from "./planning";
export { SkillManager, createSkillManager, parseSkillFile } from "./skills";
export { AgentTelemetry, TelemetryCollector, cleanupTelemetry, createAgentTelemetry, disableTelemetry, enableTelemetry, getTelemetry } from "./telemetry";
export { BaseTool, FunctionTool, ToolRegistry, ToolResult, ToolValidationError, createTool, getRegistry, getTool, registerTool, tool, validateTool } from "./tools";
export { airweaveSearch, bedrockBrowserClick, bedrockBrowserFill, bedrockBrowserNavigate, bedrockCodeInterpreter, codeExecution, codeMode, createCustomTool, exaSearch, firecrawlCrawl, firecrawlScrape, parallelSearch, perplexitySearch, registerCustomTool, registerLocalTool, registerNpmTool, superagentGuard, superagentRedact, superagentVerify, tavilyCrawl, tavilyExtract, tavilySearch, valyuBioSearch, valyuCompanyResearch, valyuEconomicsSearch, valyuFinanceSearch, valyuPaperSearch, valyuPatentSearch, valyuSecSearch, valyuWebSearch } from "./tools/builtins";
export { MissingDependencyError, MissingEnvVarError, ToolsRegistry, composeMiddleware, createLoggingMiddleware, createRateLimitMiddleware, createRedactionMiddleware, createRetryMiddleware, createTimeoutMiddleware, createToolsRegistry, createTracingMiddleware, createValidationMiddleware, getToolsRegistry, resetToolsRegistry } from "./tools/registry";
export type { InstallHints, PraisonTool, RedactionHooks, RegisteredTool, ToolCapabilities, ToolExecutionContext, ToolExecutionResult, ToolFactory, ToolHooks, ToolInstallStatus, ToolLimits, ToolLogger, ToolMetadata, ToolMiddleware, ToolParameterProperty, ToolParameterSchema } from "./tools/registry";
export { registerBuiltinTools, tools } from "./tools/tools";
export { Workflow, loop, parallel, repeat, route } from "./workflows";
export type { StepResult, WorkflowContext, WorkflowStep } from "./workflows";
export { createWorkflowFromYAML, loadWorkflowFromFile, parseYAMLWorkflow, validateWorkflowDefinition } from "./workflows/yaml-parser";
```
# Optional Plugins
External tools are available via `praisonai-tools` package:
```bash
pip install praisonai-tools
```
See [PraisonAI-tools](https://github.com/MervinPraison/PraisonAI-tools) for available tools.
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# PraisonAI Docker Environment Configuration
# Copy this file to .env and update with your values
# Required: OpenAI API Key
OPENAI_API_KEY=your_openai_api_key_here
# Required: Chainlit Authentication Secret (generate a random string)
CHAINLIT_AUTH_SECRET=your_chainlit_secret_here
# Optional: Additional API Keys
ANTHROPIC_API_KEY=your_anthropic_api_key_here
GOOGLE_API_KEY=your_google_api_key_here
GROQ_API_KEY=your_groq_api_key_here
COHERE_API_KEY=your_cohere_api_key_here
# Optional: Service Configuration
FLASK_ENV=development
CHAINLIT_HOST=0.0.0.0
# Optional: Port Configuration (modify if you have conflicts)
UI_PORT=8082
CHAT_PORT=8083
API_PORT=8080
# Optional: Database Configuration
DATABASE_URL=sqlite:///root/.praison/database.sqlite
# Optional: Logging Configuration
LOG_LEVEL=INFO
PYTHONPATH=/app
# Optional: Development Settings
DEV_MODE=false
DEBUG=false
# Optional: Model Configuration
DEFAULT_MODEL=gpt-4
DEFAULT_TEMPERATURE=0.7
MAX_TOKENS=4000
# Optional: Security Settings
CORS_ORIGINS=http://localhost:3000,http://localhost:8082,http://localhost:8083
RATE_LIMIT=100
# Optional: Feature Flags
ENABLE_CHAT=true
ENABLE_UI=true
ENABLE_API=true
ENABLE_REALTIME=true
# Optional: Volume Paths (for custom mounting)
PRAISON_DATA_PATH=~/.praison
EXAMPLES_PATH=./examples
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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 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[api]" \
gunicorn \
markdown
# Copy application code
COPY . .
# Set environment variables for directory management
ENV PRAISON_CONFIG_DIR=/root/.praison
ENV DOCKER_CONTAINER=true
# Create health check API if api.py doesn't exist
RUN if [ ! -f api.py ]; then \
echo "from flask import Flask\n\
app = Flask(__name__)\n\
\n\
@app.route('/health')\n\
def health():\n\
return {'status': 'healthy'}, 200\n\
\n\
if __name__ == '__main__':\n\
app.run()" > api.py; \
fi
EXPOSE 8080
CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]
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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 Python packages (using latest versions)
RUN pip install --no-cache-dir \
praisonai_tools \
"praisonai>=3.7.4" \
"praisonai[chat]" \
"embedchain[github,youtube]"
# Copy application code
COPY . .
# Set environment variables for directory management
ENV PRAISON_CONFIG_DIR=/root/.praison
ENV CHAINLIT_CONFIG_DIR=/root/.praison
ENV CHAINLIT_DB_DIR=/root/.praison
ENV DOCKER_CONTAINER=true
# Default command (will be overridden by docker-compose)
CMD ["praisonai", "chat"]
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FROM python:3.11-slim
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
gcc \
python3-dev \
portaudio19-dev \
python3-pyaudio \
curl \
git \
vim \
nano \
&& rm -rf /var/lib/apt/lists/*
# Create praison config directory
RUN mkdir -p /root/.praison
# Install Python packages (using latest versions)
RUN pip install --no-cache-dir \
praisonai_tools \
"praisonai>=3.7.4" \
"praisonai[ui]" \
"praisonai[chat]" \
"praisonai[realtime]" \
"praisonai[code]" \
"embedchain[github,youtube]" \
jupyter \
jupyterlab \
notebook \
pytest \
pytest-asyncio \
mkdocs \
mkdocs-material
# Copy application code
COPY . .
# Set environment variables for directory management
ENV PRAISON_CONFIG_DIR=/root/.praison
ENV CHAINLIT_CONFIG_DIR=/root/.praison
ENV CHAINLIT_DB_DIR=/root/.praison
ENV DOCKER_CONTAINER=true
# Default command (will be overridden by docker-compose)
CMD ["praisonai", "ui"]
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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
# Copy praisonaiagents source
COPY src/praisonai-agents/ /app/praisonaiagents/
WORKDIR /app/praisonaiagents
# Install praisonaiagents with all features (non-editable for production)
RUN pip install --no-cache-dir ".[all]"
# Set environment variables for directory management
ENV PRAISON_CONFIG_DIR=/root/.praison
ENV CHAINLIT_CONFIG_DIR=/root/.praison
ENV CHAINLIT_DB_DIR=/root/.praison
ENV DOCKER_CONTAINER=true
WORKDIR /app
# Default command
CMD ["python", "-c", "import praisonaiagents; print('PraisonAI Agents installed successfully')"]
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# 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"]
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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 Python packages (using latest versions)
RUN pip install --no-cache-dir \
praisonai_tools \
"praisonai>=3.7.4" \
"praisonai[ui]" \
"praisonai[crewai]"
# Copy application code
COPY . .
# Set environment variables for directory management
ENV PRAISON_CONFIG_DIR=/root/.praison
ENV CHAINLIT_CONFIG_DIR=/root/.praison
ENV CHAINLIT_DB_DIR=/root/.praison
ENV DOCKER_CONTAINER=true
# Default command (will be overridden by docker-compose)
CMD ["praisonai", "ui"]
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# PraisonAI Docker Setup
This directory contains Docker configurations for running PraisonAI services in containerized environments. The setup addresses directory management issues and provides comprehensive multi-service deployment options.
## 🐳 Available Services
### Core Services
- **UI Service** (`port 8082`) - Chainlit-based web interface
- **Chat Service** (`port 8083`) - Dedicated chat interface
- **API Service** (`port 8080`) - REST API endpoint
- **Agents Service** - Standalone PraisonAI Agents runtime
### Docker Files
- `Dockerfile` - Basic API service
- `Dockerfile.ui` - UI service with web interface
- `Dockerfile.chat` - Chat-focused service
- `Dockerfile.dev` - Development environment with tools
- `Dockerfile.praisonaiagents` - Standalone agents framework
- `docker-compose.yml` - Multi-service orchestration
## 🚀 Quick Start
### Single Service
```bash
# Run UI service
docker run -p 8082:8082 -e OPENAI_API_KEY=your_key ghcr.io/mervinpraison/praisonai:ui
# Run Chat service
docker run -p 8083:8083 -e OPENAI_API_KEY=your_key ghcr.io/mervinpraison/praisonai:chat
# Run API service
docker run -p 8080:8080 -e OPENAI_API_KEY=your_key ghcr.io/mervinpraison/praisonai:api
```
### Multi-Service with Docker Compose
```bash
# Create environment file
cat > .env << EOF
OPENAI_API_KEY=your_openai_api_key_here
CHAINLIT_AUTH_SECRET=your_secret_here
EOF
# Start all services
docker-compose up -d
# View logs
docker-compose logs -f
# Stop services
docker-compose down
```
## 📁 Directory Management
### Problem Solved
The original issue was that files like `chainlit.md`, `.chainlit` directory, and `public` folder were cluttering the root directory.
### Solution Implemented
All PraisonAI configuration and runtime files are now stored in `~/.praison/`:
```bash
~/.praison/
├── database.sqlite # Chainlit database
├── chainlit.md # Chainlit configuration
├── .chainlit/ # Chainlit runtime files
└── config/ # PraisonAI configuration
```
### Environment Variables
```bash
PRAISON_CONFIG_DIR=/root/.praison # Main config directory
CHAINLIT_CONFIG_DIR=/root/.praison # Chainlit config location
CHAINLIT_DB_DIR=/root/.praison # Database location
```
## 🔧 Service Configuration
### UI Service (Port 8082)
```yaml
environment:
- CHAINLIT_PORT=8082
- CHAINLIT_HOST=0.0.0.0
- OPENAI_API_KEY=${OPENAI_API_KEY}
- CHAINLIT_AUTH_SECRET=${CHAINLIT_AUTH_SECRET}
```
### Chat Service (Port 8083)
```yaml
environment:
- CHAINLIT_PORT=8083
- CHAINLIT_HOST=0.0.0.0
- OPENAI_API_KEY=${OPENAI_API_KEY}
```
### API Service (Port 8080)
```yaml
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
```
## 🎯 Service Endpoints
| Service | Port | Endpoint | Description |
|---------|------|----------|-------------|
| UI | 8082 | http://localhost:8082 | Web interface |
| Chat | 8083 | http://localhost:8083 | Chat interface |
| API | 8080 | http://localhost:8080 | REST API |
| API Health | 8080 | http://localhost:8080/health | Health check |
## 🔍 Health Checks
All services include health checks:
```yaml
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:PORT"]
interval: 10s
timeout: 5s
retries: 3
```
## 📦 Package Versions
All Docker images use consistent, up-to-date versions:
- PraisonAI: `>=2.2.95`
- PraisonAI Agents: `>=0.0.92`
- Python: `3.11-slim`
## 🔒 Security Features
- Non-root user execution where possible
- Minimal base image (python:3.11-slim)
- No unnecessary packages installed
- Environment variable-based configuration
- Volume mounting for persistent data
## 🛠 Development
### Development Environment
```bash
# Use development Dockerfile with additional tools
docker build -f Dockerfile.dev -t praisonai:dev .
docker run -it -v $(pwd):/app praisonai:dev bash
```
### Custom Configuration
```bash
# Mount custom config directory
docker run -v ~/.praison:/root/.praison praisonai:ui
```
## 📊 Monitoring
### Docker Compose Monitoring
```bash
# View service status
docker-compose ps
# View resource usage
docker-compose top
# View logs for specific service
docker-compose logs ui
docker-compose logs chat
docker-compose logs api
```
## 🚨 Troubleshooting
### Common Issues
1. **Port conflicts**
```bash
# Check port usage
netstat -tlnp | grep :8082
# Use different ports
docker run -p 9082:8082 praisonai:ui
```
2. **Environment variables not loading**
```bash
# Verify .env file
cat .env
# Set variables directly
docker run -e OPENAI_API_KEY=your_key praisonai:ui
```
3. **Permission issues**
```bash
# Check volume permissions
ls -la ~/.praison/
# Fix permissions
sudo chown -R $(id -u):$(id -g) ~/.praison/
```
4. **Service won't start**
```bash
# Check logs
docker-compose logs service_name
# Restart service
docker-compose restart service_name
```
## 🔄 Updates
### Pulling Latest Images
```bash
# Pull latest images
docker-compose pull
# Restart with new images
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"
```
## 🌐 Production Deployment
### Recommended Production Setup
```yaml
# docker-compose.prod.yml
version: '3.8'
services:
ui:
image: ghcr.io/mervinpraison/praisonai:ui
restart: unless-stopped
environment:
- CHAINLIT_HOST=0.0.0.0
- CHAINLIT_PORT=8082
volumes:
- praison_data:/root/.praison
networks:
- praison_network
deploy:
resources:
limits:
memory: 1G
reservations:
memory: 512M
```
### Load Balancer Configuration
For production environments, consider using nginx or similar:
```nginx
upstream praisonai {
server localhost:8082;
server localhost:8083;
}
server {
listen 80;
location / {
proxy_pass http://praisonai;
}
}
```
This Docker setup provides a clean, organized, and scalable way to deploy PraisonAI services while solving the directory management issues mentioned in the original request.
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# Use an official Python runtime as a parent image
FROM python:3.11-slim
# Set environment variables
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
# Set work directory
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
&& rm -rf /var/lib/apt/lists/*
# Install PraisonAI with the 'call' extra and ensure it's the latest version
RUN pip install --no-cache-dir --upgrade "praisonai[call]"
# Expose the port the app runs on
EXPOSE 8090
# Run the application
CMD ["praisonai", "call"]
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# Praison AI Call Docker
1. Build the Docker image:
```
docker build -t praisonai-call .
```
2. Run the container:
```
docker run -d -p 8090:8090 praisonai-call -e OPENAI_API_KEY=your_api_key_here
```
Make sure to replace your_api_key_here with your actual OpenAI API key.
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version: '3.8'
# Development Docker Compose Configuration
# Extends the main docker-compose.yml with development-specific settings
services:
# Development UI Service with hot reload
ui-dev:
build:
context: ..
dockerfile: docker/Dockerfile.dev
volumes:
- praison_data:/root/.praison
- ../src:/app/src:rw
- ../examples:/app/examples:rw
- ../docs:/app/docs:ro
ports:
- "${UI_PORT:-8082}:8082"
environment:
FLASK_ENV: development
CHAINLIT_PORT: "8082"
CHAINLIT_HOST: "0.0.0.0"
OPENAI_API_KEY: ${OPENAI_API_KEY}
CHAINLIT_AUTH_SECRET: ${CHAINLIT_AUTH_SECRET}
PRAISON_CONFIG_DIR: /root/.praison
CHAINLIT_CONFIG_DIR: /root/.praison
CHAINLIT_DB_DIR: /root/.praison
DEBUG: "true"
DEV_MODE: "true"
PYTHONPATH: /app/src
command: praisonai ui --host 0.0.0.0 --reload
networks:
- praison_network
depends_on:
- database
# Development Chat Service
chat-dev:
build:
context: ..
dockerfile: docker/Dockerfile.dev
volumes:
- praison_data:/root/.praison
- ../src:/app/src:rw
- ../examples:/app/examples:rw
ports:
- "${CHAT_PORT:-8083}:8083"
environment:
CHAINLIT_PORT: "8083"
CHAINLIT_HOST: "0.0.0.0"
OPENAI_API_KEY: ${OPENAI_API_KEY}
CHAINLIT_AUTH_SECRET: ${CHAINLIT_AUTH_SECRET}
PRAISON_CONFIG_DIR: /root/.praison
CHAINLIT_CONFIG_DIR: /root/.praison
CHAINLIT_DB_DIR: /root/.praison
DEBUG: "true"
DEV_MODE: "true"
PYTHONPATH: /app/src
command: praisonai chat --host 0.0.0.0 --port 8083 --reload
networks:
- praison_network
depends_on:
- database
# Development API Service with hot reload
api-dev:
build:
context: ..
dockerfile: docker/Dockerfile.dev
volumes:
- praison_data:/root/.praison
- ../src:/app/src:rw
- ../examples:/app/examples:rw
ports:
- "${API_PORT:-8080}:8080"
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
PRAISON_CONFIG_DIR: /root/.praison
FLASK_ENV: development
DEBUG: "true"
DEV_MODE: "true"
PYTHONPATH: /app/src
command: python -m flask --app api run --host 0.0.0.0 --port 8080 --debug
networks:
- praison_network
depends_on:
- database
# Development Jupyter Notebook Service
jupyter:
build:
context: ..
dockerfile: docker/Dockerfile.dev
volumes:
- praison_data:/root/.praison
- ../src:/app/src:rw
- ../examples:/app/examples:rw
- ../docs:/app/docs:rw
ports:
- "8888:8888"
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
PRAISON_CONFIG_DIR: /root/.praison
JUPYTER_ENABLE_LAB: "yes"
PYTHONPATH: /app/src
command: jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root --LabApp.token='' --LabApp.password=''
networks:
- praison_network
# Development Testing Service
test:
build:
context: ..
dockerfile: docker/Dockerfile.dev
volumes:
- ../src:/app/src:rw
- ../tests:/app/tests:rw
- praison_data:/root/.praison
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
PRAISON_CONFIG_DIR: /root/.praison
PYTHONPATH: /app/src
PYTEST_CURRENT_TEST: "true"
command: python -m pytest tests/ -v
networks:
- praison_network
profiles:
- test
# Standalone database for development
database:
security_opt:
- "no-new-privileges:true"
image: postgres:15-alpine
environment:
POSTGRES_DB: praisonai_dev
POSTGRES_USER: praisonai
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-praisonai_dev_pass}
volumes:
- postgres_data:/var/lib/postgresql/data
read_only: true
tmpfs:
- /tmp
- /var/tmp
ports:
- "5432:5432"
networks:
- praison_network
profiles:
- database
# Redis for caching and session management
redis:
image: redis:7-alpine
ports:
- "6379:6379"
volumes:
- redis_data:/data
networks:
- praison_network
profiles:
- cache
# Development documentation server
docs:
build:
context: ..
dockerfile: docker/Dockerfile.dev
volumes:
- ../docs:/app/docs:rw
- ../README.md:/app/README.md:ro
ports:
- "8000:8000"
command: sh -c "cd docs && mkdocs serve --dev-addr 0.0.0.0:8000"
networks:
- praison_network
profiles:
- docs
volumes:
praison_data:
driver: local
postgres_data:
driver: local
redis_data:
driver: local
networks:
praison_network:
driver: bridge
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version: '3.8'
services:
# PraisonAI UI Service
ui:
build:
context: ..
dockerfile: docker/Dockerfile.ui
volumes:
- praison_data:/root/.praison
- .:/app
ports:
- "8082:8082"
environment:
FLASK_ENV: development
CHAINLIT_PORT: "8082"
CHAINLIT_HOST: "0.0.0.0"
OPENAI_API_KEY: ${OPENAI_API_KEY}
CHAINLIT_AUTH_SECRET: ${CHAINLIT_AUTH_SECRET}
PRAISON_CONFIG_DIR: /root/.praison
CHAINLIT_CONFIG_DIR: /root/.praison
CHAINLIT_DB_DIR: /root/.praison
command: praisonai ui --host 0.0.0.0
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8082"]
interval: 10s
timeout: 5s
retries: 3
networks:
- praison_network
# PraisonAI Chat Service
chat:
build:
context: ..
dockerfile: docker/Dockerfile.chat
volumes:
- praison_data:/root/.praison
- .:/app
ports:
- "8083:8083"
environment:
CHAINLIT_PORT: "8083"
CHAINLIT_HOST: "0.0.0.0"
OPENAI_API_KEY: ${OPENAI_API_KEY}
CHAINLIT_AUTH_SECRET: ${CHAINLIT_AUTH_SECRET}
PRAISON_CONFIG_DIR: /root/.praison
CHAINLIT_CONFIG_DIR: /root/.praison
CHAINLIT_DB_DIR: /root/.praison
command: praisonai chat --host 0.0.0.0 --port 8083
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8083"]
interval: 10s
timeout: 5s
retries: 3
networks:
- praison_network
# PraisonAI API Service
api:
build:
context: ..
dockerfile: docker/Dockerfile
volumes:
- praison_data:/root/.praison
ports:
- "8080:8080"
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
PRAISON_CONFIG_DIR: /root/.praison
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8080/health"]
interval: 10s
timeout: 5s
retries: 3
networks:
- praison_network
# PraisonAI Agents Service (standalone)
agents:
build:
context: ..
dockerfile: docker/Dockerfile.praisonaiagents
volumes:
- praison_data:/root/.praison
- ./examples:/app/examples
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
PRAISON_CONFIG_DIR: /root/.praison
networks:
- praison_network
volumes:
praison_data:
driver: local
networks:
praison_network:
driver: bridge
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#!/bin/bash
# PraisonAI Docker Quick Start Script
# This script helps users quickly get started with PraisonAI using Docker
set -e
# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color
# Function to print colored output
print_status() {
echo -e "${BLUE}[INFO]${NC} $1"
}
print_success() {
echo -e "${GREEN}[SUCCESS]${NC} $1"
}
print_warning() {
echo -e "${YELLOW}[WARNING]${NC} $1"
}
print_error() {
echo -e "${RED}[ERROR]${NC} $1"
}
# Check if Docker is installed
check_docker() {
if ! command -v docker &> /dev/null; then
print_error "Docker is not installed. Please install Docker first:"
echo " - Visit: https://docs.docker.com/get-docker/"
exit 1
fi
if ! command -v docker-compose &> /dev/null && ! docker compose version &> /dev/null; then
print_error "Docker Compose is not available. Please install Docker Compose:"
echo " - Visit: https://docs.docker.com/compose/install/"
exit 1
fi
print_success "Docker and Docker Compose are available"
}
# Check if .env file exists, create from template if not
setup_environment() {
if [ ! -f ".env" ]; then
print_status "Creating .env file from template..."
if [ -f ".env.template" ]; then
cp .env.template .env
else
cat > .env << EOF
# PraisonAI Docker Environment Configuration
OPENAI_API_KEY=your_openai_api_key_here
CHAINLIT_AUTH_SECRET=$(openssl rand -hex 32 2>/dev/null || echo "your_chainlit_secret_here")
FLASK_ENV=development
CHAINLIT_HOST=0.0.0.0
UI_PORT=8082
CHAT_PORT=8083
API_PORT=8080
EOF
fi
print_warning "Please edit .env file and add your API keys before continuing"
print_status "Required: OPENAI_API_KEY"
read -p "Press Enter to continue after updating .env file..."
else
print_success ".env file already exists"
fi
}
# Function to show menu
show_menu() {
echo ""
echo "=== PraisonAI Docker Quick Start ==="
echo ""
echo "1) Start UI Service (Web Interface) - Port 8082"
echo "2) Start Chat Service - Port 8083"
echo "3) Start API Service - Port 8080"
echo "4) Start All Services (Recommended)"
echo "5) Start Development Environment"
echo "6) View Service Status"
echo "7) View Logs"
echo "8) Stop All Services"
echo "9) Pull Latest Images"
echo "10) Clean Up (Remove containers and volumes)"
echo "11) Show Service URLs"
echo "0) Exit"
echo ""
}
# Start specific service
start_service() {
local service=$1
print_status "Starting $service service..."
docker-compose up -d $service
print_success "$service service started"
}
# Start all services
start_all_services() {
print_status "Starting all PraisonAI services..."
docker-compose up -d
print_success "All services started"
show_service_urls
}
# Start development environment
start_dev_environment() {
print_status "Starting development environment..."
if [ -f "docker-compose.dev.yml" ]; then
docker-compose -f docker-compose.yml -f docker-compose.dev.yml up -d ui-dev chat-dev api-dev
print_success "Development environment started"
echo ""
echo "Development services available:"
echo " - UI (with hot reload): http://localhost:${UI_PORT:-8082}"
echo " - Chat (with hot reload): http://localhost:${CHAT_PORT:-8083}"
echo " - API (with hot reload): http://localhost:${API_PORT:-8080}"
echo " - Jupyter Lab: http://localhost:8888"
echo ""
else
print_warning "Development configuration not found, starting regular services"
start_all_services
fi
}
# Show service status
show_status() {
print_status "Service Status:"
docker-compose ps
}
# Show logs
show_logs() {
echo ""
echo "Which service logs would you like to view?"
echo "1) All services"
echo "2) UI service"
echo "3) Chat service"
echo "4) API service"
echo "5) Agents service"
read -p "Enter choice (1-5): " log_choice
case $log_choice in
1) docker-compose logs -f ;;
2) docker-compose logs -f ui ;;
3) docker-compose logs -f chat ;;
4) docker-compose logs -f api ;;
5) docker-compose logs -f agents ;;
*) print_error "Invalid choice" ;;
esac
}
# Stop all services
stop_services() {
print_status "Stopping all services..."
docker-compose down
print_success "All services stopped"
}
# Pull latest images
pull_images() {
print_status "Pulling latest images..."
docker-compose pull
print_success "Images updated"
}
# Clean up
cleanup() {
print_warning "This will remove all containers, networks, and volumes"
read -p "Are you sure? (y/N): " confirm
if [[ $confirm == [yY] || $confirm == [yY][eE][sS] ]]; then
print_status "Cleaning up..."
docker-compose down -v --remove-orphans
docker system prune -f
print_success "Cleanup completed"
fi
}
# Show service URLs
show_service_urls() {
local ui_port=${UI_PORT:-8082}
local chat_port=${CHAT_PORT:-8083}
local api_port=${API_PORT:-8080}
echo ""
echo "=== Service URLs ==="
echo "UI Service: http://localhost:$ui_port"
echo "Chat Service: http://localhost:$chat_port"
echo "API Service: http://localhost:$api_port"
echo "API Health: http://localhost:$api_port/health"
echo ""
}
# Check system requirements
check_requirements() {
print_status "Checking system requirements..."
# Check available memory
if command -v free &> /dev/null; then
available_ram=$(free -m | awk 'NR==2{printf "%.1f", $7/1024}')
print_status "Available RAM: ${available_ram}GB"
if (( $(echo "$available_ram < 2.0" | bc -l 2>/dev/null || echo "1") )); then
print_warning "Low available memory. Recommended: 2GB+ available RAM"
fi
fi
# Check disk space
if command -v df &> /dev/null; then
available_space=$(df -BG . | awk 'NR==2{print $4}' | sed 's/G//')
print_status "Available disk space: ${available_space}GB"
if [ "$available_space" -lt 5 ]; then
print_warning "Low disk space. Recommended: 5GB+ available space"
fi
fi
}
# Main function
main() {
clear
echo "🤖 Welcome to PraisonAI Docker Setup!"
echo ""
# Check prerequisites
check_docker
check_requirements
setup_environment
# Main loop
while true; do
show_menu
read -p "Enter your choice (0-11): " choice
case $choice in
1)
start_service "ui"
echo "UI Service: http://localhost:${UI_PORT:-8082}"
;;
2)
start_service "chat"
echo "Chat Service: http://localhost:${CHAT_PORT:-8083}"
;;
3)
start_service "api"
echo "API Service: http://localhost:${API_PORT:-8080}"
;;
4)
start_all_services
;;
5)
start_dev_environment
;;
6)
show_status
;;
7)
show_logs
;;
8)
stop_services
;;
9)
pull_images
;;
10)
cleanup
;;
11)
show_service_urls
;;
0)
print_success "Thank you for using PraisonAI!"
exit 0
;;
*)
print_error "Invalid choice. Please try again."
;;
esac
echo ""
read -p "Press Enter to continue..."
done
}
# Run main function
main
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framework: praisonaiagents
services:
- ui
- chat
- realtime
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code:
ignore_files:
- "*/__pycache__/*"
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*.txt
*.csv
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# 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.
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# 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
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#!/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
)
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#!/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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"""
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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"""
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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#!/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"],
verbose=True
)
# 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,
verbose=True
)
# 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"],
verbose=True
)
# 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}")
@@ -1,27 +0,0 @@
"""
Embeddings Capability Example
Demonstrates text embedding generation using PraisonAI capabilities.
"""
from praisonai.capabilities import embed
# Single text embedding
print("=== Single Text Embedding ===")
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}")
# 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")
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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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"""
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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"""
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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"""
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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@@ -1,193 +0,0 @@
#!/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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# 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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#!/usr/bin/env python3
"""
Basic Context Compaction Example for PraisonAI Agents.
This example demonstrates how to use context compaction with Agent:
1. Create an Agent with context= parameter for compaction
2. Use different compaction strategies via ManagerConfig
3. Monitor compaction results
Usage:
python basic_compaction.py
"""
from praisonaiagents import Agent
from praisonaiagents.context import ManagerConfig
def main():
print("=" * 60)
print("Agent-Centric Context Compaction Demo")
print("=" * 60)
# ==========================================================================
# Available Strategies
# ==========================================================================
print("\n--- Available Compaction Strategies ---")
strategies = [
("truncate", "Remove oldest messages first"),
("sliding_window", "Keep most recent messages within limit"),
("summarize", "Replace old messages with summary"),
("smart", "Intelligently select messages to keep"),
]
for name, desc in strategies:
print(f" {name}: {desc}")
# ==========================================================================
# Agent with Context Compaction via context= param
# ==========================================================================
print("\n--- Creating Agent with Context Compaction ---")
# Use context= parameter with ManagerConfig for compaction
agent = Agent(
name="LongChat",
instructions="You are a helpful assistant for extended conversations.",
context=ManagerConfig(
auto_compact=True,
compact_threshold=0.8,
strategy="sliding_window",
)
)
print(f"Agent: {agent.name}")
print(f"Context manager: {agent.context_manager is not None}")
if agent.context_manager:
print(f"Auto-compact: {agent.context_manager.config.auto_compact}")
print(f"Threshold: {agent.context_manager.config.compact_threshold}")
print(f"Strategy: {agent.context_manager.config.strategy}")
# ==========================================================================
# Different Strategy Examples via context= param
# ==========================================================================
print("\n--- Strategy Examples ---")
# Sliding window strategy
sliding_agent = Agent(
name="SlidingAgent",
instructions="You are helpful.",
context=ManagerConfig(
auto_compact=True,
strategy="sliding_window",
)
)
print(f" Sliding: strategy={sliding_agent.context_manager.config.strategy}")
# Summarize strategy
summarize_agent = Agent(
name="SummarizeAgent",
instructions="You are helpful.",
context=ManagerConfig(
auto_compact=True,
strategy="summarize",
)
)
print(f" Summarize: strategy={summarize_agent.context_manager.config.strategy}")
# Smart strategy
smart_agent = Agent(
name="SmartAgent",
instructions="You are helpful.",
context=ManagerConfig(
auto_compact=True,
strategy="smart",
)
)
print(f" Smart: strategy={smart_agent.context_manager.config.strategy}")
# ==========================================================================
# Context Manager Stats Demo
# ==========================================================================
print("\n--- Context Manager Stats Demo ---")
# Access context manager stats
if agent.context_manager:
stats = agent.context_manager.get_stats()
print(f" Model: {stats.get('model', 'N/A')}")
print(f" Model limit: {stats.get('model_limit', 'N/A')}")
print(f" Output reserve: {stats.get('output_reserve', 'N/A')}")
print(f" Usable budget: {stats.get('usable_budget', 'N/A')}")
else:
print(" Context manager not initialized")
print("\n" + "=" * 60)
print("Demo Complete!")
print("=" * 60)
if __name__ == "__main__":
main()
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"""
CLI Compare Example - Advanced Usage
This example demonstrates advanced comparison features including
custom mode configurations and result analysis.
"""
from praisonai.cli.features.compare import (
CompareHandler,
CompareResult,
ModeResult,
get_mode_config,
save_compare_result,
format_comparison_table,
)
# Create custom mode configurations
custom_modes = {
"fast": {"llm": "gpt-4o-mini"},
"accurate": {"llm": "gpt-4o", "planning": True},
"research": {"research": True, "web_search": True},
}
# Create handler with verbose output
handler = CompareHandler(verbose=True)
# Example: Manually create comparison results for analysis
# (useful for testing without API calls)
mock_result = CompareResult(
query="What are the latest AI trends?",
comparisons=[
ModeResult(
mode="basic",
output="AI trends include LLMs, multimodal models, and agents.",
execution_time_ms=1234.5,
model_used="gpt-4o-mini",
tools_used=[]
),
ModeResult(
mode="tools",
output="Based on recent search results, AI trends in 2024 include...",
execution_time_ms=2567.8,
model_used="gpt-4o-mini",
tools_used=["internet_search"]
),
ModeResult(
mode="research",
output="Comprehensive research shows that AI trends...",
execution_time_ms=5432.1,
model_used="gpt-4o-mini",
tools_used=["internet_search", "web_scraper"]
),
]
)
# Get summary statistics
summary = mock_result.get_summary()
print("Comparison Summary:")
print(f" Fastest mode: {summary['fastest']} ({summary['fastest_time_ms']:.1f}ms)")
print(f" Slowest mode: {summary['slowest']} ({summary['slowest_time_ms']:.1f}ms)")
# Format as table
print("\n" + format_comparison_table(mock_result))
# Convert to JSON for storage
print("\nJSON Output:")
print(mock_result.to_json())
# Save to file
# save_compare_result(mock_result, "advanced_comparison.json")
print("\n--- CLI Usage Examples ---")
print("""
# Basic comparison
praisonai "What is AI?" --compare "basic,tools"
# Compare with specific model
praisonai "Explain quantum computing" --compare "basic,planning,research" --model gpt-4o
# Save results to file
praisonai "Latest tech news" --compare "basic,tools,web_search" --compare-output results.json
# Verbose output
praisonai "Write a poem" --compare "basic,planning" --verbose
""")
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"""
CLI Compare Example - Basic Usage
This example demonstrates how to compare different CLI modes programmatically.
"""
from praisonai.cli.features.compare import (
CompareHandler,
get_mode_config,
list_available_modes,
parse_modes,
)
# List all available comparison modes
print("Available modes:")
for mode in list_available_modes():
config = get_mode_config(mode)
print(f" - {mode}: {config}")
# Parse modes from comma-separated string
modes = parse_modes("basic,tools,planning")
print(f"\nParsed modes: {modes}")
# Create compare handler
handler = CompareHandler(verbose=True)
# Compare modes (this will run actual agent calls)
# Uncomment to run:
# result = handler.compare(
# query="What is artificial intelligence?",
# modes=["basic", "tools"],
# model="gpt-4o-mini"
# )
# handler.print_result(result)
# Save results to file
# result = handler.execute(
# query="Explain machine learning",
# modes_str="basic,planning",
# output_path="comparison_results.json"
# )
print("\nTo run comparison via CLI:")
print(' praisonai "What is AI?" --compare "basic,tools,planning"')
print(' praisonai "What is AI?" --compare "basic,research" --compare-output results.json')
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@@ -1,96 +0,0 @@
# Consolidated Params Examples
Examples demonstrating the **Agent-Centric API** with consolidated parameters.
## Precedence Ladder
```
Instance > Config > Array > Dict > String > Bool > Default
```
## Quick Reference
| Feature | Presets | Example |
|---------|---------|---------|
| `memory` | file, sqlite, redis, postgres, mem0, mongodb | `memory="sqlite"` |
| `output` | minimal, normal, verbose, debug, silent | `output="verbose"` |
| `execution` | fast, balanced, thorough, unlimited | `execution="balanced"` |
| `planning` | reasoning, read_only, auto | `planning="reasoning"` |
| `reflection` | minimal, standard, thorough | `reflection="standard"` |
| `guardrails` | strict, permissive, safety | `guardrails="strict"` |
| `web` | duckduckgo, tavily, google, bing, serper | `web="duckduckgo"` |
| `context` | sliding_window, summarize, truncate | `context="sliding_window"` |
| `autonomy` | suggest, auto_edit, full_auto | `autonomy="suggest"` |
| `caching` | enabled, disabled, prompt | `caching="prompt"` |
## Examples
### Basic Examples (Single Feature)
| File | Feature | Description |
|------|---------|-------------|
| `basic_agent.py` | Agent | Minimal agent with memory |
| `basic_memory.py` | memory | SQLite memory preset |
| `basic_output.py` | output | Verbose output preset |
| `basic_execution.py` | execution | Execution control |
| `basic_guardrails.py` | guardrails | Callable validator |
| `basic_reflection.py` | reflection | Self-reflection preset |
| `basic_web.py` | web | Web search preset |
| `basic_planning.py` | planning | Planning preset |
| `basic_autonomy.py` | autonomy | Autonomy preset |
| `basic_caching.py` | caching | Caching preset |
| `basic_knowledge.py` | knowledge | RAG with sources |
| `basic_context.py` | context | Context management |
| `basic_hooks.py` | hooks | Lifecycle callbacks |
### Multi-Agent Examples
| File | Description |
|------|-------------|
| `basic_agents.py` | Multi-agent with memory and planning |
### Workflow Examples
| File | Description |
|------|-------------|
| `basic_workflow.py` | Simple workflow with output preset |
| `basic_workflow_agentlike.py` | Workflow with agent-like params |
| `basic_step_override.py` | Step-level param overrides |
| `advanced_workflow_full_features.py` | All consolidated params |
### Advanced Examples
| File | Description |
|------|-------------|
| `advanced_output_execution.py` | Combined output + execution |
## Usage Forms
Each consolidated param supports multiple input forms:
```python
# Bool - enable with defaults
memory=True
# String preset
memory="sqlite"
# Dict config
memory={"backend": "sqlite", "user_id": "user123"}
# Config instance
memory=MemoryConfig(backend="sqlite", user_id="user123")
# Array with overrides (for some params)
guardrails=["strict", {"max_retries": 10}]
```
## Running Examples
```bash
# Set API key
export OPENAI_API_KEY=your_key_here
# Run any example
python basic_agent.py
```
@@ -1,17 +0,0 @@
"""
Advanced Output + Execution Example - Agent-Centric API
Demonstrates preset + override array syntax.
"""
from praisonaiagents import Agent
# Advanced: Preset with overrides using array syntax
agent = Agent(
instructions="You are a helpful assistant.",
output=["verbose", {"stream": True, "metrics": True}],
execution=["thorough", {"max_iter": 30}],
)
response = agent.start("Explain quantum computing in simple terms.")
print(response)
@@ -1,55 +0,0 @@
"""Advanced Workflow with all consolidated params configured."""
from praisonaiagents import Agent
from praisonaiagents.workflows import Workflow, WorkflowStep
from praisonaiagents.workflows.workflow_configs import (
WorkflowOutputConfig, WorkflowPlanningConfig, WorkflowMemoryConfig,
)
# Full-featured workflow with all consolidated params
workflow = Workflow(
name="FullFeaturedWorkflow",
description="Demonstrates all agent-like consolidated params",
steps=[
WorkflowStep(
name="researcher",
action="Research: {{input}}",
agent=Agent(instructions="You are a researcher."),
knowledge=["research_docs/"], # Step-specific knowledge
web="duckduckgo", # Use DuckDuckGo for this step
),
WorkflowStep(
name="analyzer",
action="Analyze the research",
agent=Agent(instructions="You are an analyst."),
reflection="thorough", # Thorough reflection for analysis
guardrails="strict", # Strict validation
),
WorkflowStep(
name="writer",
action="Write the final report",
agent=Agent(instructions="You are a writer."),
autonomy=True, # Enable autonomy for writing
),
],
# Workflow-level consolidated params
output=WorkflowOutputConfig(verbose=True, stream=True),
planning=WorkflowPlanningConfig(enabled=True, reasoning=True),
memory=WorkflowMemoryConfig(backend="file"),
# Agent-like params
autonomy=True,
knowledge=["shared_docs/"],
guardrails=True,
web=True,
reflection=True,
)
if __name__ == "__main__":
print(f"Workflow: {workflow.name}")
print(f"Output verbose: {workflow._verbose}")
print(f"Planning enabled: {workflow._planning_enabled}")
print(f"Memory config: {workflow._memory_config}")
print(f"Autonomy: {workflow.autonomy}")
print(f"Knowledge: {workflow.knowledge}")
print(f"Guardrails: {workflow.guardrails}")
print(f"Web: {workflow.web}")
print(f"Reflection: {workflow.reflection}")
@@ -1,12 +0,0 @@
"""Basic Agent example with consolidated params."""
from praisonaiagents import Agent
# Minimal agent with memory
agent = Agent(
instructions="You are a helpful assistant.",
memory=True,
)
if __name__ == "__main__":
response = agent.chat("Hello!")
print(response)
@@ -1,17 +0,0 @@
"""Basic Agents (multi-agent) example with consolidated params."""
from praisonaiagents import Agent, Agents
# Create agents
writer = Agent(instructions="You write content.")
editor = Agent(instructions="You edit content.")
# Multi-agent with consolidated params
agents = Agents(
agents=[writer, editor],
memory=True,
planning=True,
)
if __name__ == "__main__":
result = agents.start("Write a haiku about AI")
print(result)
@@ -1,18 +0,0 @@
"""
Basic Autonomy Example - Agent-Centric API
Demonstrates autonomy with consolidated params.
Presets: suggest, auto_edit, full_auto
"""
from praisonaiagents import Agent
# Basic: Enable autonomy with preset
agent = Agent(
instructions="You are an autonomous assistant.",
autonomy="suggest", # Presets: suggest, auto_edit, full_auto
)
if __name__ == "__main__":
response = agent.start("Help me organize my project files.")
print(response)
@@ -1,23 +0,0 @@
"""
Basic Caching Example - Agent-Centric API
Demonstrates caching with consolidated params.
Presets: enabled, disabled, prompt
"""
from praisonaiagents import Agent
# Basic: Enable caching with preset
agent = Agent(
instructions="You are a helpful assistant with caching enabled.",
caching="prompt", # Presets: enabled, disabled, prompt
)
if __name__ == "__main__":
# First call - will cache
response = agent.start("What is the capital of France?")
print(response)
# Second call - may use cache
response = agent.start("What is the capital of France?")
print(response)
@@ -1,18 +0,0 @@
"""
Basic Context Example - Agent-Centric API
Demonstrates context management with consolidated params.
Presets: sliding_window, summarize, truncate
"""
from praisonaiagents import Agent
# Basic: Enable context management with preset
agent = Agent(
instructions="You are a helpful assistant with context management.",
context="sliding_window", # Presets: sliding_window, summarize, truncate
)
if __name__ == "__main__":
response = agent.start("Tell me a long story about a dragon.")
print(response)
@@ -1,16 +0,0 @@
"""
Basic Execution Example - Agent-Centric API
Demonstrates execution preset usage.
"""
from praisonaiagents import Agent
# Basic: Use fast execution preset
agent = Agent(
instructions="You are a helpful assistant.",
execution="fast", # Presets: fast, balanced, thorough, unlimited
)
response = agent.start("What is the capital of France?")
print(response)
@@ -1,26 +0,0 @@
"""
Basic Guardrails Example - Agent-Centric API
Demonstrates guardrails with callable validator.
"""
from praisonaiagents import Agent
# Define a simple guardrail validator
def validate_response(output):
"""Validate that response doesn't contain forbidden words."""
forbidden = ["error", "fail", "cannot"]
text = str(output).lower()
for word in forbidden:
if word in text:
return (False, f"Response contains forbidden word: {word}")
return (True, output)
# Basic: Use callable guardrail
agent = Agent(
instructions="You are a helpful assistant.",
guardrails=validate_response,
)
response = agent.start("What is 2 + 2?")
print(response)
@@ -1,28 +0,0 @@
"""
Basic Hooks Example - Agent-Centric API
Demonstrates lifecycle hooks with consolidated params.
Valid keys: on_step, on_tool_call, middleware
"""
from praisonaiagents import Agent
# Define hook callbacks
def on_step_callback(step_info):
print(f"Step: {step_info}")
def on_tool_call_callback(tool_info):
print(f"Tool called: {tool_info}")
# Basic: Enable hooks with dict
agent = Agent(
instructions="You are a helpful assistant.",
hooks={
"on_step": on_step_callback,
"on_tool_call": on_tool_call_callback,
},
)
if __name__ == "__main__":
response = agent.start("What is 2 + 2?")
print(response)
@@ -1,18 +0,0 @@
"""
Basic Knowledge (RAG) Example - Agent-Centric API
Demonstrates knowledge with consolidated params.
Supports: bool, list of sources, dict config, KnowledgeConfig
"""
from praisonaiagents import Agent
# Basic: Enable knowledge with list of sources
agent = Agent(
instructions="You are a helpful assistant with document knowledge.",
knowledge=["docs/"], # List of file/folder paths
)
if __name__ == "__main__":
response = agent.start("What does the documentation say about installation?")
print(response)
@@ -1,21 +0,0 @@
"""
Basic Memory Example - Agent-Centric API
Demonstrates the simplest memory usage with consolidated params.
"""
from praisonaiagents import Agent
# Basic: Enable memory with preset
agent = Agent(
instructions="You are a helpful assistant with memory.",
memory="sqlite", # Preset: sqlite, redis, postgres, file
)
# Run a simple task
response = agent.start("Remember that my favorite color is blue.")
print(response)
# Follow-up to test memory
response = agent.start("What is my favorite color?")
print(response)
@@ -1,16 +0,0 @@
"""
Basic Output Example - Agent-Centric API
Demonstrates output preset usage.
"""
from praisonaiagents import Agent
# Basic: Use verbose output preset
agent = Agent(
instructions="You are a helpful assistant.",
output="verbose", # Presets: minimal, normal, verbose, debug, silent
)
response = agent.start("What is 2 + 2?")
print(response)
@@ -1,18 +0,0 @@
"""
Basic Planning Example - Agent-Centric API
Demonstrates planning with consolidated params.
Presets: reasoning, read_only, auto
"""
from praisonaiagents import Agent
# Basic: Enable planning with preset
agent = Agent(
instructions="You are a strategic planner.",
planning="reasoning", # Presets: reasoning, read_only, auto
)
if __name__ == "__main__":
response = agent.start("Plan a 3-day trip to Tokyo.")
print(response)
@@ -1,18 +0,0 @@
"""
Basic Reflection Example - Agent-Centric API
Demonstrates reflection (self-reflect) with consolidated params.
Presets: minimal, standard, thorough
"""
from praisonaiagents import Agent
# Basic: Enable reflection with preset
agent = Agent(
instructions="You are a helpful assistant that reflects on your answers.",
reflection="standard", # Presets: minimal, standard, thorough
)
if __name__ == "__main__":
response = agent.start("Explain quantum computing in simple terms.")
print(response)
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