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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. -->
@@ -1,296 +0,0 @@
name: "Claude Code Action"
description: "Run Claude Code in GitHub Actions workflows"
inputs:
anthropic_api_key:
description: "Anthropic API key"
required: false
claude_code_oauth_token:
description: "Claude Code OAuth Token for Claude Pro"
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
env:
INPUT_GITHUB_TOKEN: ${{ inputs.github_token }}
run: |
claude mcp add-json github '{
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"GITHUB_PERSONAL_ACCESS_TOKEN",
"ghcr.io/github/github-mcp-server:sha-ff3036d"
],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "'"${INPUT_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.
CRITICAL INSTRUCTION: If your task involves modifying codebase files to add a feature or fix a bug:
STEP 0 — SETUP GIT IDENTITY:
git config user.name "MervinPraison"
git config user.email "[email protected]"
STEP 1 — READ GUIDELINES:
MANDATORY: Before writing any code, you MUST carefully read "src/praisonai-agents/AGENTS.md" to understand this repository's extremely specific Architecture, Protocol-First design rules, and Agent-Centric Philosophy.
STEP 2 — ARCHITECTURE VALIDATION & ROUTING (MANDATORY before writing code):
Before implementing anything, answer these questions:
- CORE vs WRAPPER vs TOOLS ROUTING:
1. Core SDK (praisonaiagents/): Only core protocols, base classes, decorators. No heavy implementations.
2. Wrapper (praisonai/): CLI, heavy implementations, optional dependencies.
3. Tools (PraisonAI-Tools): Specific integrations, external tools (e.g. SurrealDB, Slack), and community extensions MUST go to the `MervinPraison/PraisonAI-Tools` repository. If the feature is a tool, clone `https://github.com/MervinPraison/PraisonAI-Tools`, implement it there, and create the PR using `gh pr create -R MervinPraison/PraisonAI-Tools`.
- Does it duplicate existing functionality? Check if Agent already supports this via existing params (reflection, planning, tools, hooks, memory).
- Does it inherit from Agent properly? New agent types MUST inherit Agent, not wrap it with composition.
- Does it add new dependencies? Only optional deps allowed, must be lazy-imported.
- Will agent.py grow larger? If the change adds >50 lines to agent.py, find a way to extract instead.
- Is there a name collision with existing exports in __init__.py?
If ANY of these conceptual checks fail (excluding routing), add a comment to the issue explaining why and close it. Do NOT create a PR.
STEP 3 — IMPLEMENT:
- Create a new git branch appropriately named (e.g. \`feature/issue-<number>\`)
- Follow protocol-driven design: protocols in core SDK, heavy implementations in wrapper
- Keep changes small and backward-compatible
STEP 4 — TEST:
- Run: cd src/praisonai-agents && PYTHONPATH=. python -m pytest tests/ -x -q --timeout=30
- Ensure no regressions
STEP 5 — CREATE PR:
- Commit with descriptive message, push the branch
- Use the GitHub CLI (\`gh pr create --title "..." --body "Fixes #<number>"\`) to open a Pull Request
CRITICAL: You MUST create the PR automatically using \`gh pr create\`. Do NOT just provide a link.
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
env:
ANTHROPIC_API_KEY: ${{ inputs.anthropic_api_key }}
CLAUDE_CODE_OAUTH_TOKEN: ${{ inputs.claude_code_oauth_token }}
GITHUB_TOKEN: ${{ inputs.github_token }}
INPUT_MAX_TURNS: ${{ inputs.max_turns }}
INPUT_ALLOWED_TOOLS: ${{ inputs.allowed_tools }}
INPUT_DISALLOWED_TOOLS: ${{ inputs.disallowed_tools }}
INPUT_TIMEOUT_MINUTES: ${{ inputs.timeout_minutes }}
run: |
echo "🚀 Running Claude Code with GitHub context..."
# Build command arguments
CMD_ARGS=("-p" "--verbose" "--output-format" "stream-json")
# Add max turns if specified
if [[ -n "$INPUT_MAX_TURNS" ]]; then
CMD_ARGS+=("--max-turns" "$INPUT_MAX_TURNS")
fi
# Add allowed tools (include GitHub tools by default)
TOOLS="mcp__github__get_issue,mcp__github__get_issue_comments,mcp__github__update_issue,mcp__github__search_issues,mcp__github__list_issues,mcp__github__create_comment,Read,Write,Edit,Bash"
if [[ -n "$INPUT_ALLOWED_TOOLS" ]]; then
TOOLS="$TOOLS,$INPUT_ALLOWED_TOOLS"
fi
CMD_ARGS+=("--allowedTools" "$TOOLS")
# Add disallowed tools
if [[ -n "$INPUT_DISALLOWED_TOOLS" ]]; then
CMD_ARGS+=("--disallowedTools" "$INPUT_DISALLOWED_TOOLS")
fi
echo "📝 Executing Claude Code with prompt from file..."
# Execute Claude Code with timeout, using stdin for the prompt
TIMEOUT_SECONDS=$(($INPUT_TIMEOUT_MINUTES * 60))
timeout $TIMEOUT_SECONDS claude "${CMD_ARGS[@]}" < "$PROMPT_FILE"
echo "✅ Claude Code execution completed"
@@ -1,92 +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"
claude_code_oauth_token:
description: "Claude Code OAuth token for Pro subscription access"
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:
persist-credentials: false
fetch-depth: 0
- name: Create prompt
id: generate_prompt
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
echo "PROMPT<<DELIMITER" >> $GITHUB_OUTPUT
cat /tmp/claude-prompts/claude-issue-triage-prompt.txt >> $GITHUB_OUTPUT
echo "DELIMITER" >> $GITHUB_OUTPUT
- name: Run Claude Code
uses: ./.github/actions/claude-code-action
with:
direct_prompt: ${{ steps.generate_prompt.outputs.PROMPT }}
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"
timeout_minutes: ${{ inputs.timeout_minutes }}
claude_code_oauth_token: ${{ inputs.claude_code_oauth_token }}
github_token: ${{ inputs.github_token }}
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name: 'Gemini CLI Action'
description: 'Uses the Gemini CLI to automate software development tasks.'
author: 'Gemini'
inputs:
prompt:
description: 'A specific prompt to guide Gemini.'
required: false
default: 'You are a helpful assistant.'
GEMINI_API_KEY:
description: 'Your Gemini API key.'
required: true
OTLP_GCP_WIF_PROVIDER:
description: 'The workload identity provider for GCP authentication.'
required: false
OTLP_GOOGLE_CLOUD_PROJECT:
description: 'The Google Cloud project for telemetry.'
required: false
settings_json:
description: |
A JSON string to configure the Gemini CLI. This will be written to .gemini/settings.json.
To enable observability with Google Cloud, include:
{"telemetry": {"enabled": true, "target": "gcp"}, "sandbox": false}
required: false
model:
description: |
The Gemini model to use. Options: gemini-2.5-pro, gemini-2.5-flash, gemini-2.5-flash-lite.
Use flash-lite for highest free-tier quota (1,000 RPD).
required: false
default: 'gemini-3.1-flash-lite-preview'
version:
description: |
The version of @google/gemini-cli to execute.
Can be a specific version from npm (e.g., '0.1.0', 'latest'), a branch name (e.g., 'main'), or a commit hash.
required: false
default: 'latest'
runs:
using: "composite"
steps:
- name: Configure Gemini CLI
if: inputs.settings_json != ''
run: |
mkdir -p .gemini
echo "$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
env:
INPUT_VERSION: ${{ inputs.version }}
run: |
if [[ "$INPUT_VERSION" == "latest" || "$INPUT_VERSION" =~ ^[0-9]+\.[0-9]+\.[0-9]+(-[a-zA-Z0-9\.-]+)?(\+[a-zA-Z0-9\.-]+)?$ ]]; then
echo "Installing Gemini CLI from npm: @google/gemini-cli@$INPUT_VERSION"
npm install -g @google/gemini-cli@$INPUT_VERSION
else
echo "Installing Gemini CLI from GitHub: github:google-gemini/gemini-cli#$INPUT_VERSION"
npm install -g github:google-gemini/gemini-cli#$INPUT_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 --model "$GEMINI_MODEL" --prompt "$PROMPT"
shell: bash
env:
GEMINI_API_KEY: ${{ inputs.GEMINI_API_KEY }}
GEMINI_MODEL: ${{ inputs.model }}
PROMPT: ${{ inputs.prompt }}
branding:
icon: 'terminal'
color: 'blue'
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framework: "praisonai"
process: workflow
topic: "Autonomous Issue Resolution"
planning: false
roles:
issue_analyst:
role: "GitHub Issue Analyst"
goal: "Read the GitHub issue, understand the required changes, and map the problem to the local codebase"
backstory: "You are an expert software analyst who reads bug reports and feature requests. You excel at taking a problem description and finding exactly which files in the codebase need to be modified. Your primary job is discovery and planning."
tools:
- "execute_command"
software_engineer:
role: "Autonomous Software Engineer"
goal: "Implement required code changes on a branch, run tests, commit, and push"
backstory: "You are a senior software engineer. You always branch first, implement changes using shell commands, verify with git diff, run tests, then commit and push. You never create a Pull Request unless the issue body explicitly requests one."
tools:
- "execute_command"
- "github_create_branch"
- "github_commit_and_push"
- "github_create_pull_request"
steps:
- name: Analyze Issue
agent: issue_analyst
action: >
Thoroughly analyze the issue and the repository. Follow these steps in order.
First, set up git identity: execute_command 'git config user.name PraisonAI' then
execute_command 'git config user.email [email protected]'. Do NOT pass cwd.
Second, read the issue: execute_command 'cat .github/triage-context.md'. Do NOT pass cwd.
Third, read architecture guidelines: execute_command 'cat src/praisonai-agents/AGENTS.md'.
Fourth, decide if this issue requires code changes:
- If YES: identify the exact files and lines that must change. Output a concrete file-level plan.
- If NO (question, discussion, duplicate, invalid): say so clearly. No code changes needed.
Output: routing decision (Core SDK / Wrapper / Tools / No-change), validation result, and exact files/lines to change.
expected_output: A decision on whether code changes are needed, and if so a concrete file-level plan.
- name: Implement Fix
agent: software_engineer
action: >
You are working inside the repository at /Users/praison/praisonai-package.
Do NOT ask for confirmation. Do NOT use placeholder paths. Act immediately.
Read the analyst's output first. If the analyst said NO code changes are needed, stop here and
output a brief explanation — do NOT create a branch or PR.
If code changes ARE needed, follow these steps:
Step 1 — Create branch: github_create_branch with branch_name=praisonai/issue-{{ISSUE_NUMBER}}.
Step 2 — Implement changes using execute_command (no cwd argument).
To insert a line: python3 -c "lines=open('FILE').readlines(); lines.insert(N,'LINE\n'); open('FILE','w').writelines(lines)"
To replace text: python3 -c "t=open('FILE').read(); open('FILE','w').write(t.replace('OLD','NEW'))"
After each edit verify: execute_command 'git diff'
Step 3 — Run tests: execute_command 'python3 -m pytest src/praisonai-agents/tests/ -x -q --timeout=30 -p no:warnings'
Step 4 — Commit and push: github_commit_and_push with a descriptive commit_message.
Step 5 — Check if the issue body EXPLICITLY contains the phrase "create pull request" or "create PR"
(case-insensitive). ONLY in that case create a PR using github_create_pull_request with
title='fix: resolve issue #{{ISSUE_NUMBER}}',
body='Fixes #{{ISSUE_NUMBER}}.\n\nAutonomously resolved by PraisonAI.',
head_branch=praisonai/issue-{{ISSUE_NUMBER}}, base_branch=main.
Otherwise do NOT create a PR — just confirm the branch was pushed successfully.
expected_output: Either "No code changes needed", or "Branch pushed, no PR requested", or "Branch pushed and PR created at URL".
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framework: "praisonai"
topic: "Pull Request Code Review Analysis"
roles:
security_reviewer:
role: "Security Code Reviewer"
goal: "Identify security vulnerabilities, authentication issues, input validation problems, and unsafe code practices in the PR changes"
backstory: "You are a cybersecurity expert specializing in code analysis. You have extensive experience in identifying common security flaws like injection attacks, authentication bypasses, exposed secrets, and unsafe data handling patterns."
tools:
- "execute_command"
performance_reviewer:
role: "Performance Code Reviewer"
goal: "Analyze code changes for performance implications, identify bottlenecks, inefficient algorithms, and resource usage issues"
backstory: "You are a performance optimization specialist with deep knowledge of algorithm efficiency, memory management, and system performance. You excel at spotting code that could cause performance degradation."
tools:
- "execute_command"
maintainability_reviewer:
role: "Code Quality & Maintainability Reviewer"
goal: "Evaluate code structure, readability, documentation, naming conventions, and adherence to best practices"
backstory: "You are a software engineering expert focused on code quality and maintainability. You ensure code follows established patterns, is well-documented, and will be easy for future developers to understand and modify."
tools:
- "execute_command"
lead_reviewer:
role: "Lead Technical Reviewer"
goal: "Synthesize all review feedback, make final recommendations, and post comprehensive review comments to the GitHub PR"
backstory: "You are a senior technical lead responsible for final review decisions. You coordinate input from security, performance, and maintainability reviewers to provide balanced, actionable feedback to the development team."
tools:
- "execute_command"
steps:
- name: security_analysis
agent: security_reviewer
action: |
Analyze the PR diff for security issues:
1. Extract PR details: `echo "$PR_DATA"`
2. Get changed files: `echo "$CHANGED_FILES"`
3. Review full diff: `gh pr diff ${PR_NUMBER}`
4. Look for:
- Hardcoded secrets, API keys, passwords
- SQL injection vulnerabilities
- XSS vulnerabilities
- Authentication/authorization bypasses
- Unsafe file operations
- Command injection risks
- Missing input validation
- Exposed sensitive data
5. Document findings with file paths and line numbers
expected_output: "Detailed security analysis report with specific vulnerabilities found, severity levels, and remediation suggestions"
- name: performance_analysis
agent: performance_reviewer
action: |
Analyze the PR diff for performance issues:
1. Review algorithm complexity changes
2. Check for inefficient database queries
3. Identify memory leaks or excessive allocations
4. Look for hot-path regressions
5. Check for blocking I/O operations
6. Analyze module-level imports that could slow startup
7. Review caching implementations
8. Check for recursive operations without termination
9. Document performance concerns with impact estimates
expected_output: "Performance analysis report highlighting bottlenecks, efficiency concerns, and optimization recommendations"
- name: maintainability_analysis
agent: maintainability_reviewer
action: |
Analyze the PR diff for code quality and maintainability:
1. Check naming conventions consistency
2. Evaluate code structure and organization
3. Review documentation completeness
4. Check for DRY violations
5. Analyze error handling patterns
6. Review test coverage for changes
7. Check adherence to repository coding standards
8. Evaluate API design consistency
9. Look for technical debt introduction
expected_output: "Code quality analysis with specific recommendations for improving maintainability and readability"
- name: final_review
agent: lead_reviewer
action: |
Synthesize all reviews and post final comprehensive feedback:
1. Collect input from security, performance, and maintainability reviews
2. Prioritize findings by severity (Critical, High, Medium, Low)
3. Create structured review comment using this format:
## 📋 Review Summary
[2-3 sentence overview and assessment]
## 🔍 General Feedback
[Overall patterns and observations]
## 🎯 Specific Feedback
### 🔴 Critical (if any)
[Security vulnerabilities, breaking changes, major bugs]
### 🟡 High (if any)
[Performance issues, design flaws, significant bugs]
### 🟢 Medium (if any)
[Code quality improvements, minor optimizations]
### 🔵 Low (if any)
[Documentation, naming suggestions, minor refactoring]
## ✅ Highlights (if any)
[Positive aspects worth mentioning]
4. Post comprehensive review: `echo "[REVIEW_CONTENT]" | gh pr comment ${PR_NUMBER} -F -`
5. If critical issues found, suggest not merging until resolved
expected_output: "Final review comment posted to GitHub PR with comprehensive analysis from all reviewers"
dependencies: [security_analysis, performance_analysis, maintainability_analysis]
-3
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@@ -1,3 +0,0 @@
# GitHub Issue #1350: Add debug log on successful branch checkout
The github_create_branch function in src/praisonai-agents/praisonaiagents/tools/github_tools.py should call logger.debug after a successful checkout.
-46
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@@ -1,46 +0,0 @@
name: Auto Issue Comment
on:
workflow_dispatch:
# issues:
# types: [opened]
jobs:
# Trigger Claude by adding the 'claude' label (only if not already labeled)
# Note: @claude in comments only works from human users (not bots)
# The claude.yml workflow listens for issues:labeled event with 'claude' label
trigger-claude:
# Skip if issue was already created with 'claude' label to avoid double-trigger
if: |
!contains(toJSON(github.event.issue.labels), 'claude')
runs-on: ubuntu-latest
permissions:
issues: write
steps:
- name: Add claude label to trigger analysis
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
// Check if 'claude' label already exists on this issue
const labels = context.payload.issue.labels.map(l => l.name);
if (labels.includes('claude')) {
console.log('Issue already has claude label, skipping');
return;
}
// Add label to trigger claude.yml via issues:labeled event
await github.rest.issues.addLabels({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
labels: ['claude']
});
// Also post instructions as a comment for context
await github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: '🤖 Auto-triaging with Claude. Analyzing issue and implementing a fix...\n\nClaude will:\n1. Classify this issue\n2. Search codebase for root cause\n3. Implement a minimal fix following AGENTS.md\n4. Create a PR with the changes'
});
-525
View File
@@ -1,525 +0,0 @@
name: Auto PR Comment
# Review chain: CodeRabbit/Qodo/Gemini review first → Copilot → Claude (final)
# For human PRs: CodeRabbit/Qodo auto-review → this workflow triggers Copilot → then Claude
# For bot PRs: CodeRabbit/Qodo skip bots, so we trigger them explicitly first
#
# IMPORTANT: @copilot ignores bot comments. All @copilot mentions must use
# PAT_TOKEN so the comment posts as MervinPraison (a real user).
#
# Claude trigger runs IN-BAND (same workflow) to avoid GitHub Actions
# blocking bot-triggered workflows with action_required approval gates.
on:
issue_comment:
types: [created]
pull_request_review:
types: [submitted]
pull_request:
types: [opened]
env:
MAX_POLL_ATTEMPTS: 20
POLL_DELAY_MS: 30000
CLAUDE_TRIGGER_LOGINS: '["MervinPraison","github-actions[bot]"]'
jobs:
# ================================================================
# HUMAN PRs: Trigger @copilot AFTER CodeRabbit posts its summary
# ================================================================
copilot-after-coderabbit:
if: |
github.event_name == 'issue_comment' &&
github.event.issue.pull_request &&
github.event.comment.user.login == 'coderabbitai[bot]' &&
contains(github.event.comment.body, 'summarize by coderabbit')
runs-on: ubuntu-latest
permissions:
pull-requests: write
outputs:
pr_number: ${{ steps.trigger.outputs.pr_number }}
triggered: ${{ steps.trigger.outputs.triggered }}
steps:
- name: Post Copilot review request (as user via PAT)
id: trigger
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const comments = await github.rest.issues.listComments({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo
});
// Check for duplicates — posted by MervinPraison (PAT) or github-actions[bot] (legacy)
const alreadyPosted = comments.data.some(c =>
c.body.includes('@copilot') &&
(c.user.login === 'MervinPraison' || c.user.login === 'github-actions[bot]')
);
if (alreadyPosted) {
console.log('Copilot already triggered, skipping');
core.setOutput('triggered', 'false');
core.setOutput('pr_number', context.issue.number.toString());
return;
}
await github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: '@copilot Do a thorough review of this PR. Read ALL existing reviewer comments above from Qodo, Coderabbit, and Gemini first — incorporate their findings.\n\nReview areas:\n1. **Bloat check**: Are changes minimal and focused? Any unnecessary code or scope creep?\n2. **Security**: Any hardcoded secrets, unsafe eval/exec, missing input validation?\n3. **Performance**: Any module-level heavy imports? Hot-path regressions?\n4. **Tests**: Are tests included? Do they cover the changes adequately?\n5. **Backward compat**: Any public API changes without deprecation?\n6. **Code quality**: DRY violations, naming conventions, error handling?\n7. **Address reviewer feedback**: If Qodo, Coderabbit, or Gemini flagged valid issues, include them in your review\n8. Suggest specific improvements with code examples where possible'
});
core.setOutput('triggered', 'true');
core.setOutput('pr_number', context.issue.number.toString());
# Fallback: Trigger @copilot after Qodo review (if Coderabbit doesn't fire)
copilot-after-qodo:
if: |
github.event_name == 'pull_request_review' &&
github.event.review.user.login == 'qodo-code-review[bot]'
runs-on: ubuntu-latest
permissions:
pull-requests: write
outputs:
pr_number: ${{ steps.trigger.outputs.pr_number }}
triggered: ${{ steps.trigger.outputs.triggered }}
steps:
- name: Post Copilot review request (as user via PAT)
id: trigger
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const comments = await github.rest.issues.listComments({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo
});
const alreadyPosted = comments.data.some(c =>
c.body.includes('@copilot') &&
(c.user.login === 'MervinPraison' || c.user.login === 'github-actions[bot]')
);
if (alreadyPosted) {
console.log('Copilot already triggered, skipping');
core.setOutput('triggered', 'false');
core.setOutput('pr_number', context.issue.number.toString());
return;
}
await github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: '@copilot Do a thorough review of this PR. Read ALL existing reviewer comments above first.\n\nReview areas:\n1. **Bloat check**: Are changes minimal and focused?\n2. **Security**: Any hardcoded secrets, unsafe eval/exec, missing input validation?\n3. **Performance**: Any module-level heavy imports? Hot-path regressions?\n4. **Tests**: Are tests included? Do they cover the changes adequately?\n5. **Backward compat**: Any public API changes without deprecation?\n6. **Code quality**: DRY violations, naming conventions, error handling?\n7. Suggest specific improvements with code examples where possible'
});
core.setOutput('triggered', 'true');
core.setOutput('pr_number', context.issue.number.toString());
# ================================================================
# FALLBACK: Claude trigger for cases where Copilot chain fails/skips
# Ensures Claude always runs after sufficient review time
# ================================================================
claude-fallback-timeout:
if: |
github.event_name == 'pull_request' &&
github.event.action == 'opened'
runs-on: ubuntu-latest
timeout-minutes: 20
permissions:
issues: write
pull-requests: write
steps:
- name: Wait for review window (15 min)
run: sleep 900
- name: Check if Claude already triggered
id: check_claude
uses: actions/github-script@v7
env:
TRIGGER_LOGINS: ${{ env.CLAUDE_TRIGGER_LOGINS }}
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const triggerLogins = JSON.parse(process.env.TRIGGER_LOGINS);
const comments = await github.rest.issues.listComments({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo
});
const alreadyPosted = comments.data.some(c =>
c.body.includes('@claude') && triggerLogins.includes(c.user.login)
);
core.setOutput('already_triggered', alreadyPosted ? 'true' : 'false');
core.setOutput('comments_count', comments.data.length.toString());
// Collect all bot reviewer comments for context
const botReviews = comments.data.filter(c =>
['coderabbitai[bot]', 'qodo-code-review[bot]', 'gemini-code-assist[bot]',
'copilot-swe-agent', 'Copilot', 'greptile-apps[bot]'].some(bot =>
c.user.login.toLowerCase().includes(bot.toLowerCase())
)
);
core.setOutput('review_count', botReviews.length.toString());
- name: Aggregate reviews and trigger Claude
if: steps.check_claude.outputs.already_triggered != 'true'
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const comments = await github.rest.issues.listComments({issue_number: context.issue.number, owner: context.repo.owner, repo: context.repo.repo, per_page: 100});
const reviews = {coderabbit: [], qodo: [], gemini: [], copilot: [], greptile: [], others: []};
comments.data.forEach(c => {
const login = c.user.login.toLowerCase();
const body = c.body.substring(0, 500);
if (login.includes('coderabbit')) reviews.coderabbit.push(body);
else if (login.includes('qodo')) reviews.qodo.push(body);
else if (login.includes('gemini')) reviews.gemini.push(body);
else if (login.includes('copilot')) reviews.copilot.push(body);
else if (login.includes('greptile')) reviews.greptile.push(body);
else if (c.user.type === 'Bot') reviews.others.push(body);
});
const summaryParts = [];
if (reviews.coderabbit.length) summaryParts.push('CodeRabbit: ' + reviews.coderabbit.length + ' comments');
if (reviews.qodo.length) summaryParts.push('Qodo: ' + reviews.qodo.length + ' comments');
if (reviews.gemini.length) summaryParts.push('Gemini: ' + reviews.gemini.length + ' comments');
if (reviews.copilot.length) summaryParts.push('Copilot: ' + reviews.copilot.length + ' comments');
if (reviews.greptile.length) summaryParts.push('Greptile: ' + reviews.greptile.length + ' comments');
const reviewSummary = summaryParts.length ? summaryParts.join(' | ') : 'No bot reviews detected';
const claudeBody = '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. ' + reviewSummary + '.\n\n' +
'Review Context: ' + comments.data.length + ' total comments, ' +
(reviews.coderabbit.length + reviews.qodo.length + reviews.gemini.length + reviews.copilot.length + reviews.greptile.length) + ' bot reviews.\n\n' +
'Phase 1: Review per AGENTS.md principles (protocol-driven, backward compatible, no perf impact).\n' +
'Phase 2: FIX valid issues found by prior reviewers.\n' +
'Phase 3: Provide final verdict (approve or request changes).';
await github.rest.issues.createComment({issue_number: context.issue.number, owner: context.repo.owner, repo: context.repo.repo, body: claudeBody});
console.log('Claude fallback triggered on PR #' + context.issue.number + ' with ' + reviewSummary);
# ================================================================
# Claude FINAL review — polls for Copilot's response, then triggers
# This runs IN-BAND under the PAT context (MervinPraison), avoiding
# the action_required approval gate that blocks bot-triggered workflows.
# ================================================================
claude-after-copilot:
needs: [copilot-after-coderabbit]
if: |
always() &&
needs.copilot-after-coderabbit.result == 'success' &&
needs.copilot-after-coderabbit.outputs.triggered == 'true'
runs-on: ubuntu-latest
timeout-minutes: 15
permissions:
issues: write
pull-requests: write
steps:
- name: Wait for Copilot to respond (poll)
id: poll
uses: actions/github-script@v7
env:
PR_NUMBER: ${{ needs.copilot-after-coderabbit.outputs.pr_number }}
MAX_POLL_ATTEMPTS: ${{ env.MAX_POLL_ATTEMPTS }}
POLL_DELAY_MS: ${{ env.POLL_DELAY_MS }}
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const pr = parseInt(process.env.PR_NUMBER, 10);
const owner = context.repo.owner;
const repo = context.repo.repo;
// Poll for Copilot's response — check every 30s for up to 10 minutes
const maxAttempts = parseInt(process.env.MAX_POLL_ATTEMPTS, 10);
const delayMs = parseInt(process.env.POLL_DELAY_MS, 10);
for (let i = 0; i < maxAttempts; i++) {
console.log(`Poll attempt ${i + 1}/${maxAttempts}...`);
const comments = await github.rest.issues.listComments({
issue_number: pr,
owner,
repo,
per_page: 50,
sort: 'created',
direction: 'desc'
});
// Look for Copilot's response (copilot-swe-agent or Copilot)
const copilotResponse = comments.data.find(c =>
(c.user.login === 'copilot-swe-agent' ||
c.user.login === 'Copilot' ||
c.user.login.toLowerCase().includes('copilot')) &&
c.user.type === 'Bot'
);
if (copilotResponse) {
console.log(`Copilot responded at ${copilotResponse.created_at}`);
core.setOutput('copilot_responded', 'true');
return;
}
// Also check PR reviews for Copilot
const reviews = await github.rest.pulls.listReviews({
pull_number: pr,
owner,
repo
});
const copilotReview = reviews.data.find(r =>
r.user.login.toLowerCase().includes('copilot')
);
if (copilotReview) {
console.log(`Copilot review submitted at ${copilotReview.submitted_at}`);
core.setOutput('copilot_responded', 'true');
return;
}
if (i < maxAttempts - 1) {
console.log(`Copilot hasn't responded yet, waiting ${delayMs/1000}s...`);
await new Promise(resolve => setTimeout(resolve, delayMs));
}
}
console.log('Copilot did not respond within timeout, triggering Claude anyway');
core.setOutput('copilot_responded', 'timeout');
- name: Check if Claude already triggered
id: check_claude
uses: actions/github-script@v7
env:
PR_NUMBER: ${{ needs.copilot-after-coderabbit.outputs.pr_number }}
TRIGGER_LOGINS: ${{ env.CLAUDE_TRIGGER_LOGINS }}
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const pr = parseInt(process.env.PR_NUMBER, 10);
const triggerLogins = JSON.parse(process.env.TRIGGER_LOGINS);
const comments = await github.rest.issues.listComments({
issue_number: pr,
owner: context.repo.owner,
repo: context.repo.repo
});
const alreadyPosted = comments.data.some(c =>
c.body.includes('@claude') && triggerLogins.includes(c.user.login)
);
core.setOutput('already_triggered', alreadyPosted ? 'true' : 'false');
- name: Trigger Claude final review
if: steps.check_claude.outputs.already_triggered != 'true'
uses: actions/github-script@v7
env:
PR_NUMBER: ${{ needs.copilot-after-coderabbit.outputs.pr_number }}
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const pr = parseInt(process.env.PR_NUMBER, 10);
await github.rest.issues.createComment({
issue_number: pr,
owner: context.repo.owner,
repo: context.repo.repo,
body: '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. Read ALL comments above from Gemini, Qodo, CodeRabbit, and Copilot carefully before responding.\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
});
console.log(`Claude triggered on PR #${pr}`);
# ================================================================
# Claude after Qodo fallback path (same polling approach)
# ================================================================
claude-after-copilot-qodo:
needs: [copilot-after-qodo]
if: |
always() &&
needs.copilot-after-qodo.result == 'success' &&
needs.copilot-after-qodo.outputs.triggered == 'true'
runs-on: ubuntu-latest
timeout-minutes: 15
permissions:
issues: write
pull-requests: write
steps:
- name: Wait for Copilot to respond (poll)
id: poll
uses: actions/github-script@v7
env:
PR_NUMBER: ${{ needs.copilot-after-qodo.outputs.pr_number }}
MAX_POLL_ATTEMPTS: ${{ env.MAX_POLL_ATTEMPTS }}
POLL_DELAY_MS: ${{ env.POLL_DELAY_MS }}
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const pr = parseInt(process.env.PR_NUMBER, 10);
const owner = context.repo.owner;
const repo = context.repo.repo;
const maxAttempts = parseInt(process.env.MAX_POLL_ATTEMPTS, 10);
const delayMs = parseInt(process.env.POLL_DELAY_MS, 10);
for (let i = 0; i < maxAttempts; i++) {
console.log(`Poll attempt ${i + 1}/${maxAttempts}...`);
const comments = await github.rest.issues.listComments({
issue_number: pr, owner, repo,
per_page: 50, sort: 'created', direction: 'desc'
});
const copilotResponse = comments.data.find(c =>
(c.user.login === 'copilot-swe-agent' ||
c.user.login === 'Copilot' ||
c.user.login.toLowerCase().includes('copilot')) &&
c.user.type === 'Bot'
);
if (copilotResponse) {
console.log(`Copilot responded at ${copilotResponse.created_at}`);
core.setOutput('copilot_responded', 'true');
return;
}
const reviews = await github.rest.pulls.listReviews({
pull_number: pr, owner, repo
});
const copilotReview = reviews.data.find(r =>
r.user.login.toLowerCase().includes('copilot')
);
if (copilotReview) {
console.log(`Copilot review submitted at ${copilotReview.submitted_at}`);
core.setOutput('copilot_responded', 'true');
return;
}
if (i < maxAttempts - 1) {
await new Promise(resolve => setTimeout(resolve, delayMs));
}
}
console.log('Copilot did not respond within timeout, triggering Claude anyway');
core.setOutput('copilot_responded', 'timeout');
- name: Check if Claude already triggered
id: check_claude
uses: actions/github-script@v7
env:
PR_NUMBER: ${{ needs.copilot-after-qodo.outputs.pr_number }}
TRIGGER_LOGINS: ${{ env.CLAUDE_TRIGGER_LOGINS }}
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const pr = parseInt(process.env.PR_NUMBER, 10);
const triggerLogins = JSON.parse(process.env.TRIGGER_LOGINS);
const comments = await github.rest.issues.listComments({
issue_number: pr,
owner: context.repo.owner,
repo: context.repo.repo
});
const alreadyPosted = comments.data.some(c =>
c.body.includes('@claude') && triggerLogins.includes(c.user.login)
);
core.setOutput('already_triggered', alreadyPosted ? 'true' : 'false');
- name: Trigger Claude final review
if: steps.check_claude.outputs.already_triggered != 'true'
uses: actions/github-script@v7
env:
PR_NUMBER: ${{ needs.copilot-after-qodo.outputs.pr_number }}
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const pr = parseInt(process.env.PR_NUMBER, 10);
await github.rest.issues.createComment({
issue_number: pr,
owner: context.repo.owner,
repo: context.repo.repo,
body: '@claude You are the FINAL architecture reviewer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. Read ALL comments above from Gemini, Qodo, CodeRabbit, and Copilot carefully before responding.\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
});
console.log(`Claude triggered on PR #${pr}`);
# ================================================================
# Claude after Gemini — restores the trigger lost from the deleted
# chain-claude-after-copilot.yml workflow.
# Fires when Gemini posts a review comment on a PR or issue.
# ================================================================
claude-after-gemini:
if: |
github.event_name == 'issue_comment' &&
(
github.event.comment.user.login == 'gemini-code-assist[bot]' ||
contains(github.event.comment.body, 'Review completed by Gemini CLI') ||
contains(github.event.comment.body, 'The fix is ready for review. Please test')
)
runs-on: ubuntu-latest
permissions:
issues: write
pull-requests: write
steps:
- name: Check if Claude already triggered
id: check_claude
uses: actions/github-script@v7
env:
TRIGGER_LOGINS: ${{ env.CLAUDE_TRIGGER_LOGINS }}
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const triggerLogins = JSON.parse(process.env.TRIGGER_LOGINS);
const comments = await github.rest.issues.listComments({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo
});
const alreadyPosted = comments.data.some(c =>
c.body.includes('@claude') && triggerLogins.includes(c.user.login)
);
core.setOutput('already_triggered', alreadyPosted ? 'true' : 'false');
- name: Post Claude analysis request (Lead Engineer)
if: steps.check_claude.outputs.already_triggered != 'true'
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
await github.rest.issues.createComment({
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: '@claude You are the Lead Engineer. If the branch is under MervinPraison/PraisonAI (not a fork), you are able to make modifications to this branch and push directly. SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify praisonai-rust or praisonai-ts. Read ALL analysis and reviews above carefully (Gemini, CodeRabbit, Qodo, Copilot, etc).\n\n**Phase 1: Review per AGENTS.md**\n1. Protocol-driven: check heavy implementations vs core SDK\n2. Backward compatible: ensure zero feature regressions\n3. Performance: no hot-path regressions\n\n**Phase 2: FIX Valid Issues**\n4. For any VALID bugs or architectural flaws found by Gemini, CodeRabbit, Qodo, Copilot, or any other reviewer: implement the fix\n5. Push all code fixes directly to THIS branch (do NOT create a new PR)\n6. Comment a summary of exact files modified and what you skipped\n\n**Phase 3: Final Verdict**\n7. If all issues are resolved, approve the PR / close the Issue\n8. If blocking issues remain, request changes / leave clear action items'
});
# ================================================================
# BOT PRs: CodeRabbit/Qodo skip bot-authored PRs by default.
# Trigger them explicitly. Do NOT trigger Copilot here — it will
# be triggered by copilot-after-coderabbit/qodo when they finish.
# ================================================================
bot-pr-trigger-reviews:
if: |
github.event_name == 'pull_request' &&
github.event.action == 'opened' &&
(
github.event.pull_request.user.login == 'github-actions[bot]' ||
github.event.pull_request.user.login == 'praisonai-triage-agent[bot]' ||
github.event.pull_request.user.type == 'Bot'
)
runs-on: ubuntu-latest
permissions:
pull-requests: write
steps:
- name: Trigger CodeRabbit and Qodo reviews
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GH_TOKEN }}
script: |
const pr = context.payload.pull_request.number;
const owner = context.repo.owner;
const repo = context.repo.repo;
// 1. Trigger CodeRabbit review
await github.rest.issues.createComment({
issue_number: pr, owner, repo,
body: '@coderabbitai review'
});
console.log(`Triggered CodeRabbit for bot PR #${pr}`);
// 2. Trigger Qodo review
await github.rest.issues.createComment({
issue_number: pr, owner, repo,
body: '/review'
});
console.log(`Triggered Qodo for bot PR #${pr}`);
// 3. Trigger Gemini review (commented out)
// await github.rest.issues.createComment({
// issue_number: pr, owner, repo,
// body: '@gemini review this PR'
// });
// console.log(`Triggered Gemini for bot PR #${pr}`);
// NOTE: @copilot is NOT triggered here.
// It will be triggered by copilot-after-coderabbit or copilot-after-qodo
// once those bots finish, ensuring Copilot always reviews LAST.
console.log('Copilot will be triggered after CodeRabbit/Qodo complete.');
-153
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@@ -1,153 +0,0 @@
name: Performance Benchmarks
on:
workflow_dispatch:
inputs:
iterations:
description: 'Number of benchmark iterations'
required: false
default: '100'
fail_on_regression:
description: 'Fail if performance regresses'
required: false
default: 'true'
jobs:
benchmark:
runs-on: ubuntu-latest
timeout-minutes: 30
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
persist-credentials: false
- name: Set up Python 3.12
uses: actions/setup-python@v5
with:
python-version: '3.12'
cache: 'pip'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -e src/praisonai-agents[all]
pip install agno crewai langgraph
- name: Run Import Time Benchmark
id: import_time
env:
FAIL_ON_REGRESSION: ${{ inputs.fail_on_regression }}
run: |
cd src/praisonai-agents
python benchmarks/import_time.py 2>&1 | tee import_time_output.txt
# Extract import time
IMPORT_TIME=$(grep -oP 'Median:\s+\K[\d.]+' import_time_output.txt || echo "0")
echo "import_time=$IMPORT_TIME" >> $GITHUB_OUTPUT
# Check threshold (200ms)
if (( $(echo "$IMPORT_TIME > 200" | bc -l) )); then
echo "::warning::Import time ${IMPORT_TIME}ms exceeds 200ms threshold"
if [ "$FAIL_ON_REGRESSION" == "true" ]; then
echo "import_passed=false" >> $GITHUB_OUTPUT
fi
else
echo "import_passed=true" >> $GITHUB_OUTPUT
fi
- name: Run Memory Usage Benchmark
id: memory
env:
FAIL_ON_REGRESSION: ${{ inputs.fail_on_regression }}
run: |
cd src/praisonai-agents
python benchmarks/memory_usage.py 2>&1 | tee memory_output.txt
# Extract peak memory
PEAK_MEMORY=$(grep -oP 'Peak:\s+\K[\d.]+' memory_output.txt || echo "0")
echo "peak_memory=$PEAK_MEMORY" >> $GITHUB_OUTPUT
# Check threshold (30MB)
if (( $(echo "$PEAK_MEMORY > 30" | bc -l) )); then
echo "::warning::Memory usage ${PEAK_MEMORY}MB exceeds 30MB threshold"
if [ "$FAIL_ON_REGRESSION" == "true" ]; then
echo "memory_passed=false" >> $GITHUB_OUTPUT
fi
else
echo "memory_passed=true" >> $GITHUB_OUTPUT
fi
- name: Run Simple Benchmark
id: simple
run: |
cd src/praisonai-agents
python benchmarks/simple_benchmark.py 2>&1 | tee simple_output.txt
# Extract PraisonAI time
PRAISON_TIME=$(grep -oP 'PraisonAI\s+\K[\d.]+' simple_output.txt | head -1 || echo "0")
echo "praison_time=$PRAISON_TIME" >> $GITHUB_OUTPUT
- name: Run Tools Benchmark
id: tools
run: |
cd src/praisonai-agents
python benchmarks/tools_benchmark.py 2>&1 | tee tools_output.txt
# Extract PraisonAI time with tools
PRAISON_TOOLS_TIME=$(grep -oP 'PraisonAI\s+\K[\d.]+' tools_output.txt | head -1 || echo "0")
echo "praison_tools_time=$PRAISON_TOOLS_TIME" >> $GITHUB_OUTPUT
- name: Run Parameter Impact Benchmark
id: param_impact
env:
INPUT_ITERATIONS: ${{ inputs.iterations }}
run: |
cd src/praisonai-agents
python benchmarks/param_impact_benchmark.py --no-save --iterations "$INPUT_ITERATIONS" 2>&1 | tee param_impact_output.txt
- name: Check Lazy Imports
id: lazy_imports
run: |
cd src/praisonai-agents
python -c "
import sys
from praisonaiagents import Agent
agent = Agent(name='Test', output='silent')
# Check litellm is NOT loaded
if 'litellm' in sys.modules:
print('FAIL: litellm loaded during silent agent creation')
exit(1)
else:
print('PASS: litellm not loaded (lazy import working)')
"
- name: Generate Benchmark Summary
if: always()
run: |
echo "## Performance Benchmark Results" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "| Metric | Value | Threshold | Status |" >> $GITHUB_STEP_SUMMARY
echo "|--------|-------|-----------|--------|" >> $GITHUB_STEP_SUMMARY
echo "| Import Time | ${{ steps.import_time.outputs.import_time }}ms | <200ms | ${{ steps.import_time.outputs.import_passed == 'true' && '✅' || '❌' }} |" >> $GITHUB_STEP_SUMMARY
echo "| Peak Memory | ${{ steps.memory.outputs.peak_memory }}MB | <30MB | ${{ steps.memory.outputs.memory_passed == 'true' && '✅' || '❌' }} |" >> $GITHUB_STEP_SUMMARY
echo "| Agent Instantiation | ${{ steps.simple.outputs.praison_time }}μs | - | ℹ️ |" >> $GITHUB_STEP_SUMMARY
echo "| Agent with Tools | ${{ steps.tools.outputs.praison_tools_time }}μs | - | ℹ️ |" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### Lazy Import Check" >> $GITHUB_STEP_SUMMARY
echo "✅ litellm not loaded during silent agent creation" >> $GITHUB_STEP_SUMMARY
- name: Fail on Regression
if: inputs.fail_on_regression == 'true'
run: |
if [ "${{ steps.import_time.outputs.import_passed }}" == "false" ]; then
echo "::error::Import time regression detected"
exit 1
fi
if [ "${{ steps.memory.outputs.memory_passed }}" == "false" ]; then
echo "::error::Memory usage regression detected"
exit 1
fi
echo "All performance checks passed!"
-43
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@@ -1,43 +0,0 @@
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
with:
persist-credentials: false
- 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
-35
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@@ -1,35 +0,0 @@
name: Claude Issue Triage
description: "Automatically triage GitHub issues using Claude Code"
on:
workflow_dispatch:
# issues:
# types: [opened]
jobs:
triage-issue:
runs-on: ubuntu-latest
timeout-minutes: 10
permissions:
contents: read
issues: write
id-token: write
steps:
- name: Generate GitHub App Token
id: app-token
uses: actions/create-github-app-token@v1
with:
app-id: ${{ secrets.CLAUDE_APP_ID }}
private-key: ${{ secrets.CLAUDE_APP_PRIVATE_KEY }}
owner: ${{ github.repository_owner }}
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4
with:
token: ${{ steps.app-token.outputs.token }}
- name: Run Claude Issue Triage
uses: ./.github/actions/claude-issue-triage-action
with:
claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
github_token: ${{ steps.app-token.outputs.token }}
-21
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@@ -1,21 +0,0 @@
name: Claude OAuth
on:
workflow_dispatch:
inputs:
code:
description: 'Authorization code (leave empty for step 1)'
required: false
permissions:
actions: write # Required for cache management
contents: read # Required for basic repository access
jobs:
auth:
runs-on: ubuntu-latest
steps:
- uses: grll/claude-code-login@v1
with:
code: ${{ inputs.code }}
secrets_admin_pat: ${{ secrets.SECRETS_ADMIN_PAT }}
-291
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@@ -1,291 +0,0 @@
name: Claude Assistant
on:
issue_comment:
types: [created]
pull_request_review_comment:
types: [created]
issues:
types: [assigned, labeled, opened]
pull_request_review:
types: [submitted]
jobs:
# ================================================================
# Job 1: Lightweight triage for ALL new issues (including external users)
# Posts welcome comment + adds triage labels. No Claude code execution.
# ================================================================
issue-triage:
if: |
github.event_name == 'issues' &&
github.event.action == 'opened' &&
!contains(toJSON(github.event.issue.labels), 'claude')
runs-on: ubuntu-latest
permissions:
issues: write
contents: read
steps:
- name: Generate GitHub App Token
id: app-token
uses: actions/create-github-app-token@v1
with:
app-id: ${{ secrets.CLAUDE_APP_ID }}
private-key: ${{ secrets.CLAUDE_APP_PRIVATE_KEY }}
owner: ${{ github.repository_owner }}
- name: Triage and acknowledge issue
uses: actions/github-script@v7
with:
github-token: ${{ steps.app-token.outputs.token }}
script: |
const issue = context.payload.issue;
const title = (issue.title || '').toLowerCase();
const body = (issue.body || '').toLowerCase();
const content = title + ' ' + body;
const isOwner = ['OWNER', 'MEMBER', 'COLLABORATOR'].includes(
issue.author_association
);
// --- Label triage ---
const labels = [];
// Kind labels
if (content.includes('feature request') || content.includes('[feature')) {
labels.push('enhancement');
} else if (content.includes('bug') || content.includes('error') || content.includes('crash') || content.includes('traceback')) {
labels.push('bug');
} else if (content.includes('question') || content.includes('how do i') || content.includes('how to')) {
labels.push('question');
}
// Area labels
if (content.includes('security') || content.includes('cve') || content.includes('vulnerability')) {
labels.push('security');
}
if (content.includes('performance') || content.includes('slow') || content.includes('memory leak')) {
labels.push('performance');
}
if (content.includes('documentation') || content.includes('docs')) {
labels.push('documentation');
}
if (content.includes('typescript') || content.includes('javascript') || content.includes('npm')) {
labels.push('javascript');
}
// Apply labels
if (labels.length > 0) {
await github.rest.issues.addLabels({
issue_number: issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
labels
});
}
// --- Acknowledgment comment (external users only) ---
if (!isOwner) {
await github.rest.issues.createComment({
issue_number: issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: [
`👋 Thanks for opening this issue, @${issue.user.login}!`,
'',
'A maintainer will review this shortly. In the meantime:',
'- Make sure you\'ve included steps to reproduce (for bugs)',
'- Check [existing issues](https://github.com/MervinPraison/PraisonAI/issues) for duplicates',
'- Review the [documentation](https://docs.praison.ai) for related guides',
'',
'_A maintainer can trigger deeper analysis by commenting `@claude` on this issue._'
].join('\n')
});
}
// --- For owner: add 'claude' label to trigger the code-fix job ---
if (isOwner) {
await github.rest.issues.addLabels({
issue_number: issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
labels: ['claude']
});
}
# ================================================================
# Job 2: Full Claude code-fix (owner/collaborator only)
# Triggers on: labeled 'claude', @claude comments, assignments
# ================================================================
claude-response:
# Allow: human users, github-actions[bot] (auto-comments/labels)
# Block: dependabot, cursor, renovate, other bots
# Skip 'opened' events — triage job handles those above
if: |
github.event.action != 'opened' &&
(github.event.action != 'labeled' || github.event.label.name == 'claude') &&
(github.event_name != 'issue_comment' || contains(github.event.comment.body, '@claude')) &&
(
!contains(github.actor, '[bot]') ||
github.actor == 'github-actions[bot]' ||
github.actor == 'praisonai-triage-agent[bot]'
) &&
github.actor != 'dependabot[bot]' &&
github.actor != 'cursor[bot]' &&
github.actor != 'renovate[bot]'
runs-on: ubuntu-latest
permissions:
contents: write
pull-requests: write
issues: write
actions: read
id-token: write
steps:
- name: Generate GitHub App Token
id: app-token
uses: actions/create-github-app-token@v1
with:
app-id: ${{ secrets.CLAUDE_APP_ID }}
private-key: ${{ secrets.CLAUDE_APP_PRIVATE_KEY }}
owner: ${{ github.repository_owner }}
- name: Check for Fork PR
id: check_fork
uses: actions/github-script@v7
with:
script: |
const isPR = context.eventName === 'pull_request' || (context.payload.issue && context.payload.issue.pull_request);
if (isPR) {
const prNumber = context.payload.pull_request ? context.payload.pull_request.number : context.payload.issue.number;
const pr = await github.rest.pulls.get({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: prNumber
});
core.setOutput('pr_branch', pr.data.head.ref);
if (pr.data.head.repo && pr.data.head.repo.full_name !== context.repo.full_name) {
core.setOutput('is_fork', 'true');
core.setOutput('clone_url', pr.data.head.repo.clone_url);
core.setOutput('branch', pr.data.head.ref);
return;
}
}
core.setOutput('is_fork', 'false');
- name: Checkout repository
uses: actions/checkout@v4
with:
persist-credentials: false
fetch-depth: 0
token: ${{ steps.app-token.outputs.token }}
- name: Fetch PR branch and Setup Remote
if: github.event.issue.pull_request
run: |
# Fetch PR head from base repo and put it into local branch
git fetch origin pull/${{ github.event.issue.number }}/head:${{ steps.check_fork.outputs.pr_branch }}
# If it's a fork, make origin point to local to trick claude-code-action's `git fetch origin <branch>`
if [ "${{ steps.check_fork.outputs.is_fork }}" == "true" ]; then
git remote set-url origin file://$(pwd)
fi
- uses: anthropics/claude-code-action@beta
env:
GH_TOKEN: ${{ steps.app-token.outputs.token }}
with:
allowed_bots: 'praisonai-triage-agent[bot]'
claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
github_token: ${{ steps.app-token.outputs.token }}
trigger_phrase: "@claude"
label_trigger: "claude"
direct_prompt: |
${{ steps.check_fork.outputs.is_fork == 'true' && 'CRITICAL: THIS IS A PULL REQUEST FROM A FORK. YOU DO NOT HAVE PUSH PERMISSIONS TO THIS REPOSITORY. ONLY READ FILES, VALIDATE CODE, AND PROVIDE YOUR COMMENTS/FEEDBACK DIRECTLY IN THIS PR VIA COMMENTS. DO NOT ATTEMPT TO PUSH OR CREATE PULL REQUESTS.\n\n' || 'NOTE: The branch is under MervinPraison/PraisonAI (not a fork). You are able to make modifications and push directly to this branch.\n\n' }}
You are working on the PraisonAI SDK. Follow AGENTS.md strictly.
STEP 0 — SETUP GIT IDENTITY & AUTH (GLOBAL — required for all repos):
git config --global user.name "MervinPraison"
git config --global user.email "[email protected]"
gh auth setup-git
STEP 1 — READ GUIDELINES:
Read AGENTS.md to understand the architecture rules.
SCOPE: Focus ONLY on Python packages (praisonaiagents, praisonai). Do NOT modify or touch praisonai-rust or praisonai-ts packages.
STEP 2 — ARCHITECTURE VALIDATION & ROUTING (MANDATORY before writing code):
Before implementing anything, answer these questions:
- CORE vs WRAPPER vs TOOLS vs DOCS vs PLUGINS ROUTING:
1. Core SDK (praisonaiagents/): Only core protocols, base classes, decorators. No heavy implementations.
2. Wrapper (praisonai/): CLI, heavy implementations, optional dependencies.
3. Tools (PraisonAI-Tools): Capabilities Agents actively call during execution (e.g. SurrealDB tool, Slack tool).
4. Documentation (PraisonAIDocs): Documentation pages, guides, API references.
5. Plugins (PraisonAI-Plugins): Framework lifecycle extensions replacing systemic behavior (e.g. tracing, logging, metrics, hooks, guardrails).
For items routed to an EXTERNAL repository (Tools, Docs, Plugins), follow STEP 3-ALT below.
- Does it duplicate existing functionality? Check if Agent already supports this via existing params (reflection, planning, tools, hooks, memory).
- Does it inherit from Agent properly? New agent types MUST inherit Agent, not wrap it with composition.
- Does it add new dependencies? Only optional deps allowed, must be lazy-imported.
- Will agent.py grow larger? If the change adds >50 lines to agent.py, find a way to extract instead.
- Is there a name collision with existing exports in __init__.py?
If ANY of these conceptual checks fail (excluding routing), add a comment to the issue explaining why and close it. Do NOT create a PR.
STEP 3 — IMPLEMENT (for changes in THIS repo — PraisonAI):
- Create a fix branch: git checkout -b claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
- Implement a minimal, focused fix
- Follow protocol-driven design: protocols in core SDK, heavy implementations in wrapper
- Keep changes small and backward-compatible
- Commit: git add -A && git commit -m "fix: <description> (fixes #$ISSUE_NUMBER)"
- Push: git push origin claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
CRITICAL: After pushing, you MUST immediately run: gh pr create --title "fix: <title>" --body "Fixes #$ISSUE_NUMBER" --head claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
Do NOT stop after pushing. Do NOT say "manually create a PR". Always create the PR automatically.
STEP 3-ALT — IMPLEMENT IN EXTERNAL REPO (for PraisonAI-Tools, PraisonAIDocs, PraisonAI-Plugins):
When work must happen in a different repository, follow these steps EXACTLY. Do NOT attempt to use `cd`, as directory state is not preserved. Use `git -C` for all commands.
a) Clone the repository:
gh repo clone MervinPraison/<REPO_NAME> /tmp/<REPO_NAME>
b) Copy GitHub authentication from the main repository so push works seamlessly:
git -C /tmp/<REPO_NAME> config http."https://github.com/".extraheader "$(git config --get http."https://github.com/".extraheader)"
c) Create a feature branch (NEVER commit to main):
git -C /tmp/<REPO_NAME> checkout -b claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
d) Make your changes using absolute paths (e.g. edit /tmp/<REPO_NAME>/docs.json)
e) Commit:
git -C /tmp/<REPO_NAME> add -A
git -C /tmp/<REPO_NAME> commit -m "feat: <description> (fixes #$ISSUE_NUMBER)"
f) Push the branch:
git -C /tmp/<REPO_NAME> push origin claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d)
g) Create PR in the EXTERNAL repo:
gh pr create -R MervinPraison/<REPO_NAME> --head claude/issue-$ISSUE_NUMBER-$(date +%Y%m%d) --title "feat: <title>" --body "Fixes MervinPraison/PraisonAI#$ISSUE_NUMBER
<body>"
STEP 4 — TEST:
- For SDK changes: cd src/praisonai-agents && PYTHONPATH=. python -m pytest tests/ -x -q --timeout=30
- For docs changes: verify files exist and are valid markdown/MDX
- Ensure no regressions
STEP 5 — CREATE PR:
- Commit with descriptive message, push, and create PR using `gh pr create`
- For external repos, use `gh pr create -R MervinPraison/<REPO_NAME>`
CRITICAL: You MUST create the PR automatically using `gh pr create`. Do NOT just provide a link or say "manual push required".
NOTE: If you worked on an external repo, the wrapper action will report "No commits" for the main repo at the end. This is expected, do not worry about it.
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
-58
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@@ -1,58 +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
with:
persist-credentials: false
with:
persist-credentials: false
- 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
@@ -1,82 +0,0 @@
name: Gemini Automated Issue Triage
# on:
# issue_comment:
# types: [created]
on:
workflow_dispatch:
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:
persist-credentials: false
with:
persist-credentials: false
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.
-204
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@@ -1,204 +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' ||
github.event.comment.author_association == 'CONTRIBUTOR'))
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:
persist-credentials: false
with:
persist-credentials: false
with:
token: ${{ steps.generate_token.outputs.token }}
- name: Set up Git
run: |
git config --local user.email "[email protected]"
git config --local user.name "MervinPraison"
- 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: google-github-actions/run-gemini-cli@v0
env:
GH_TOKEN: ${{ steps.generate_token.outputs.token }}
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: ${{ secrets.GEMINI_API_KEY }}
gemini_model: 'gemini-2.5-flash'
prompt: |
You are a helpful GitHub issue assistant. Your task is to analyze and help with GitHub issues.
**Issue Details:**
- Issue Number: #${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 ${ISSUE_NUMBER} --json title,body,labels,assignees`
- Analyze issue content thoroughly
- Search codebase for related files using `glob`, `search_file_content`, `list_directory`
**STEP 2: If Code Changes Are Needed**
MANDATORY FIRST STEP: Before writing any code, you MUST use `read_file` to carefully read "src/praisonai-agents/AGENTS.md". You must honor this repository's extremely specific Architecture, Protocol-First design rules, and Agent-Centric Philosophy!
Create a new branch with 'gemini/' prefix:
- Use: `git checkout -b gemini/issue-${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: |
{
"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"
]
}
-254
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@@ -1,254 +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:
persist-credentials: false
with:
persist-credentials: false
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**
MANDATORY FIRST STEP: Before writing any code, you MUST use `read_file` to carefully read "src/praisonai-agents/AGENTS.md". You must honor this repository's extremely specific Architecture, Protocol-First design rules, and Agent-Centric Philosophy!
**IMPORTANT:** DO NOT create a new branch! Work on the EXISTING PR branch.
- Use: `git fetch origin` to get latest changes
- Get PR branch name: `gh pr view ${PR_NUMBER} --json headRefName`
- Switch to PR branch: `git checkout BRANCH_NAME` (use actual branch name from PR)
- Use: `git pull origin BRANCH_NAME` to get latest PR changes
**STEP 3: Make Code Modifications**
- Use `read_file` to examine existing code
- Use `write_file` or `replace` to make improvements
- Use `search_file_content` to find related code
- Make focused, targeted improvements to the PR
**STEP 4: Test Changes (if applicable)**
- Run relevant tests if test files exist
- Use `run_shell_command` to execute test commands
**STEP 5: Commit and Push Changes to EXISTING PR Branch**
- Use: `git add .` (or specific files)
- Use: `git commit -m "Gemini: [PR #${PR_NUMBER}] Brief description of improvements"`
- Use: `git push origin BRANCH_NAME` (push to EXISTING PR branch)
**STEP 6: Review and Comment on PR**
Write comprehensive review and post comment with your analysis and any improvements made.
Use: `gh pr comment ${PR_NUMBER} -b "MESSAGE"`
Review Guidelines:
- Focus on code quality, security, performance, and maintainability
- Check for common issues: potential bugs, security vulnerabilities, performance bottlenecks
- Verify error handling and edge cases
- Look for code style and best practices
- Comment on architecture and design decisions if significant
- Be constructive and specific in feedback
- Highlight both issues and positive aspects
- Suggest improvements with examples when possible
Review Areas:
- **Security**: Authentication, authorization, input validation, data sanitization
- **Performance**: Algorithms, database queries, caching, resource usage
- **Reliability**: Error handling, logging, testing coverage, edge cases
- **Maintainability**: Code structure, documentation, naming conventions
- **Functionality**: Logic correctness, requirements fulfillment
Output Format:
Structure your review using this exact format with markdown:
## 📋 Review Summary
Provide a brief 2-3 sentence overview of the PR and overall assessment.
## 🔍 General Feedback
- List general observations about code quality
- Mention overall patterns or architectural decisions
- Highlight positive aspects of the implementation
- Note any recurring themes across files
## 🎯 Specific Feedback
Only include sections below that have actual issues. If there are no issues in a priority category, omit that entire section.
### 🔴 Critical
(Only include this section if there are critical issues)
Issues that must be addressed before merging (security vulnerabilities, breaking changes, major bugs):
- **File: `filename:line`** - Description of critical issue with specific recommendation
### 🟡 High
(Only include this section if there are high priority issues)
Important issues that should be addressed (performance problems, design flaws, significant bugs):
- **File: `filename:line`** - Description of high priority issue with suggested fix
### 🟢 Medium
(Only include this section if there are medium priority issues)
Improvements that would enhance code quality (style issues, minor optimizations, better practices):
- **File: `filename:line`** - Description of medium priority improvement
### 🔵 Low
(Only include this section if there are suggestions)
Nice-to-have improvements and suggestions (documentation, naming, minor refactoring):
- **File: `filename:line`** - Description of suggestion or enhancement
**Note**: If no specific issues are found in any category, simply state "No specific issues identified in this review."
## ✅ Highlights
(Only include this section if there are positive aspects to highlight)
- Mention specific good practices or implementations
- Acknowledge well-written code sections
- Note improvements from previous versions
---
*Review completed by Gemini CLI*
@@ -1,72 +0,0 @@
name: PraisonAI Issue Triage
on:
issue_comment:
types: [created]
workflow_dispatch:
inputs:
issue_number:
description: 'Issue Number'
required: true
type: string
jobs:
triage:
runs-on: ubuntu-latest
if: >
github.event_name == 'workflow_dispatch' ||
(github.event_name == 'issue_comment' &&
!github.event.issue.pull_request &&
contains(github.event.comment.body, '@praisonai') &&
github.actor != 'github-actions[bot]' &&
github.event.comment.user.type != 'Bot' &&
(github.event.comment.author_association == 'OWNER' ||
github.event.comment.author_association == 'MEMBER' ||
github.event.comment.author_association == 'COLLABORATOR'))
steps:
- name: Checkout Repository
uses: actions/checkout@v4
with:
persist-credentials: false
with:
persist-credentials: false
with:
fetch-depth: 0
- name: Generate GitHub App Token
id: generate_token
uses: tibdex/github-app-token@v2
with:
app_id: ${{ secrets.PRAISONAI_APP_ID }}
private_key: ${{ secrets.PRAISONAI_APP_PRIVATE_KEY }}
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Set up Git
run: |
git config --local user.email "[email protected]"
git config --local user.name "PraisonAI Bot"
- name: Install PraisonAI
run: |
pip install uv
cd src/praisonai-agents && uv pip install --system -e ".[all]"
cd ../praisonai && uv pip install --system -e ".[all]"
- name: Run PraisonAI Issue Triage
env:
GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
GH_TOKEN: ${{ steps.generate_token.outputs.token }}
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
PRAISONAI_AUTO_APPROVE: "true"
run: |
if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
export ISSUE_NUMBER="${{ inputs.issue_number }}"
else
export ISSUE_NUMBER="${{ github.event.issue.number }}"
fi
praisonai github triage --issue $ISSUE_NUMBER --agent-file .github/praisonai-issue-triage.yaml
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@@ -1,72 +0,0 @@
name: PraisonAI PR Reviewer
on:
pull_request:
types: [opened, synchronize, reopened, ready_for_review]
issue_comment:
types: [created]
workflow_dispatch:
inputs:
pr_number:
description: 'Pull Request Number'
required: true
type: string
jobs:
review:
runs-on: ubuntu-latest
if: >
(github.event_name == 'pull_request' && github.event.pull_request.draft == false) ||
github.event_name == 'workflow_dispatch' ||
(github.event_name == 'issue_comment' &&
github.event.issue.pull_request &&
contains(github.event.comment.body, '@praisonai') &&
github.actor != 'github-actions[bot]' &&
github.event.comment.user.type != 'Bot' &&
(github.event.comment.author_association == 'OWNER' ||
github.event.comment.author_association == 'MEMBER' ||
github.event.comment.author_association == 'COLLABORATOR'))
steps:
- name: Determine checkout ref
id: dest
run: |
if [ "${{ github.event_name }}" = "issue_comment" ]; then
echo "ref=refs/pull/${{ github.event.issue.number }}/head" >> "$GITHUB_OUTPUT"
elif [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
echo "ref=refs/pull/${{ inputs.pr_number }}/head" >> "$GITHUB_OUTPUT"
else
echo "ref=${{ github.event.pull_request.head.sha }}" >> "$GITHUB_OUTPUT"
fi
- name: Checkout Repository
uses: actions/checkout@v4
with:
persist-credentials: false
with:
persist-credentials: false
with:
ref: ${{ steps.dest.outputs.ref }}
fetch-depth: 0
- name: Generate GitHub App Token
id: generate_token
uses: tibdex/github-app-token@v2
with:
app_id: ${{ secrets.PRAISONAI_APP_ID }}
private_key: ${{ secrets.PRAISONAI_APP_PRIVATE_KEY }}
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install PraisonAI
run: pip install "praisonai[all]"
- name: Run PraisonAI PR Review
env:
GITHUB_TOKEN: ${{ steps.generate_token.outputs.token }}
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
run: |
praisonai workflow run .github/praisonai-reviewer.yaml
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@@ -1,42 +0,0 @@
name: Python Package (Multi-Version)
on:
schedule:
- cron: '0 4 * * 1' # Weekly Monday 4am UTC
workflow_dispatch:
jobs:
build:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.10", "3.11", "3.12"]
steps:
- uses: actions/checkout@v4
with:
persist-credentials: false
- 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' }}" >> $GITHUB_ENV
echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
- name: Install dependencies
run: |
cd src/praisonai
python -m pip install --upgrade pip
python -m pip install ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
python -m pip install duckduckgo_search pytest pytest-asyncio pytest-cov
python -m pip install "praisonaiagents[knowledge]"
- name: Run tests
run: |
cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --timeout=60 || echo "Some tests failed"
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@@ -1,42 +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
with:
persist-credentials: false
- 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/
-550
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@@ -1,550 +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:
persist-credentials: false
with:
persist-credentials: false
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
env:
INPUT_TAG: ${{ github.event.inputs.tag }}
run: |
COMMIT_HASH=$(git rev-parse HEAD)
echo "commit_hash=$COMMIT_HASH" >> $GITHUB_OUTPUT
echo "Found commit: $COMMIT_HASH for tag $INPUT_TAG"
# Now checkout the fork at main branch
- name: Checkout fork
uses: actions/checkout@v4
with:
persist-credentials: false
with:
persist-credentials: false
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 "MervinPraison"
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
- name: Hard reset to tag commit
env:
COMMIT_HASH: ${{ steps.get_commit.outputs.commit_hash }}
run: |
git reset --hard $COMMIT_HASH
- name: Setup Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
cache: 'pip'
- name: Install Poetry
run: |
curl -sSL https://install.python-poetry.org | python3 -
echo "$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:
persist-credentials: false
with:
persist-credentials: false
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
env:
INPUT_TAG: ${{ github.event.inputs.tag }}
run: |
# Add upstream remote
git remote add upstream https://github.com/MervinPraison/PraisonAI.git
# Delete local tag if it exists
git tag -d "$INPUT_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/$INPUT_TAG$"; then
echo "::error::Tag $INPUT_TAG not found in upstream repository"
exit 1
fi
# Get the commit hash from the upstream tag
UPSTREAM_COMMIT=$(git rev-list -n 1 refs/tags/"$INPUT_TAG")
if [ -z "$UPSTREAM_COMMIT" ]; then
echo "::error::Could not find commit for tag $INPUT_TAG"
exit 1
fi
echo "upstream_commit=$UPSTREAM_COMMIT" >> $GITHUB_OUTPUT
echo "Found commit: $UPSTREAM_COMMIT for tag $INPUT_TAG"
# Create tag at the same commit
- name: Create tag at commit
env:
GIT_ASKPASS: /bin/echo
PAT_TOKEN: ${{ secrets.PAT_TOKEN }}
INPUT_TAG: ${{ github.event.inputs.tag }}
run: |
git config --global user.name "MervinPraison"
git config --global user.email "[email protected]"
# Remove tag if it exists locally
git tag -d "$INPUT_TAG" || true
# Remove tag if it exists remotely
git push origin :refs/tags/"$INPUT_TAG" || true
- name: Fetch tag from upstream
env:
UPSTREAM_COMMIT: ${{ steps.get_upstream_commit.outputs.upstream_commit }}
run: |
git fetch upstream $UPSTREAM_COMMIT --force
- name: Reset to upstream commit
env:
UPSTREAM_COMMIT: ${{ steps.get_upstream_commit.outputs.upstream_commit }}
run: |
git reset --hard $UPSTREAM_COMMIT
# Create and push tag
git tag -a "$INPUT_TAG" -m "Release $INPUT_TAG (synced from upstream MervinPraison/PraisonAI@$INPUT_TAG)"
git push origin "$INPUT_TAG"
# 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
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@@ -1,205 +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
with:
persist-credentials: false
with:
persist-credentials: false
- 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
env:
INPUT_TEST_TYPE: ${{ github.event.inputs.test_type || 'all' }}
run: |
# Determine test type from input or default to 'all'
TEST_TYPE="$INPUT_TEST_TYPE"
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()
env:
INPUT_TEST_TYPE: ${{ github.event.inputs.test_type || 'all' }}
EVENT_NAME: ${{ github.event_name }}
run: |
echo "# 📋 Comprehensive Test Report" > comprehensive_report.md
echo "" >> comprehensive_report.md
echo "**Python Version:** ${{ matrix.python-version }}" >> comprehensive_report.md
echo "**Test Type:** $INPUT_TEST_TYPE" >> comprehensive_report.md
echo "**Trigger:** $EVENT_NAME" >> comprehensive_report.md
echo "**Status:** 🟢 All Tests Passed" >> 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
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@@ -1,74 +0,0 @@
name: Core Tests
on:
push:
branches: [ main, develop ]
paths:
- 'src/**'
- 'pyproject.toml'
- '.github/workflows/test-core.yml'
pull_request:
branches: [ main, develop ]
paths:
- 'src/**'
- 'pyproject.toml'
- '.github/workflows/test-core.yml'
workflow_dispatch:
jobs:
test-core:
runs-on: ubuntu-latest
timeout-minutes: 20
steps:
- uses: actions/checkout@v4
with:
persist-credentials: false
- name: Set up Python 3.11
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" >> $GITHUB_PATH
- name: Install dependencies
run: |
cd src/praisonai
uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
uv pip install --system duckduckgo_search
uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
uv pip install --system "praisonaiagents[knowledge]"
- name: Set environment variables
run: |
echo "OPENAI_API_KEY=${{ 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
- name: Run Unit Tests
run: |
cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings \
--cov=praisonai --cov-report=xml --cov-branch \
--timeout=60 || echo "Some unit tests failed"
continue-on-error: true
- name: Run Integration Tests
run: |
cd src/praisonai && python -m pytest tests/integration/ -v --tb=short --disable-warnings \
--timeout=60 \
--ignore=tests/integration/test_tracker_complex_tasks.py \
--ignore=tests/integration/test_serve_integration.py \
|| echo "Some integration tests failed"
- name: Upload coverage to Codecov
if: always()
uses: codecov/codecov-action@v5
with:
token: ${{ secrets.CODECOV_TOKEN || '' }}
files: src/praisonai/coverage.xml
flags: core-tests
fail_ci_if_error: false
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@@ -1,178 +0,0 @@
name: Extended Tests
on:
schedule:
- cron: '0 2 * * *'
workflow_dispatch:
jobs:
test-examples:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
persist-credentials: false
with:
persist-credentials: false
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: 3.11
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$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 ${{ github.workspace }}/src/praisonai-agents/ui.py
echo "✅ Example script testing completed"
continue-on-error: true
performance-test:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
persist-credentials: false
with:
persist-credentials: false
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: 3.11
- name: Install UV
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$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
-158
View File
@@ -1,158 +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
with:
persist-credentials: false
with:
persist-credentials: false
- 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
-560
View File
@@ -1,560 +0,0 @@
name: Optimized Test Suite
on:
push:
branches: [ main, develop ]
paths:
- 'src/**'
- 'pyproject.toml'
- '.github/workflows/test-optimized.yml'
pull_request:
branches: [ main, develop ]
paths:
- 'src/**'
- 'pyproject.toml'
- '.github/workflows/test-optimized.yml'
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
with:
persist-credentials: false
- 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
with:
persist-credentials: false
- 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: ${{ secrets.CODECOV_TOKEN || '' }}
files: src/praisonai/coverage.xml
flags: main-tests
fail_ci_if_error: false
# ============================================
# OPENAI LIVE TESTS - Real OpenAI API calls
# Only runs if OPENAI_API_KEY secret is set
# Safe for forks (skips gracefully if no key)
# ============================================
openai-live:
needs: smoke
runs-on: ubuntu-latest
timeout-minutes: 8
# This job always runs but steps are conditional on secret availability
steps:
- name: Check if OpenAI key is available
id: check-key
run: |
if [ -n "${{ 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
with:
persist-credentials: false
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
with:
persist-credentials: false
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
env:
GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
run: |
if [ -n "$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
with:
persist-credentials: false
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
env:
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
run: |
if [ -n "$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
with:
persist-credentials: false
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
env:
XAI_API_KEY: ${{ secrets.XAI_API_KEY }}
run: |
if [ -n "$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
with:
persist-credentials: false
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
env:
COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
run: |
if [ -n "$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
with:
persist-credentials: false
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
-180
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@@ -1,180 +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
env:
INPUT_FRAMEWORK: ${{ github.event.inputs.framework }}
INPUT_CONFIRM_COSTS: ${{ github.event.inputs.confirm_costs }}
run: |
echo "⚠️ WARNING: This workflow will make real API calls!"
echo "💰 This may incur charges on your API accounts"
echo "🎯 Framework: $INPUT_FRAMEWORK"
echo "✅ Cost confirmation: $INPUT_CONFIRM_COSTS"
- name: Checkout code
uses: actions/checkout@v4
with:
persist-credentials: false
with:
persist-credentials: false
- 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()
env:
INPUT_FRAMEWORK: ${{ github.event.inputs.framework }}
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:** $INPUT_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:** $GITHUB_ACTOR" >> real_test_report.md
echo "" >> real_test_report.md
echo "## 🧪 Test Results" >> real_test_report.md
echo "" >> real_test_report.md
if [ "$INPUT_FRAMEWORK" == "autogen" ] || [ "$INPUT_FRAMEWORK" == "all" ]; then
echo "### AutoGen Real Tests:" >> real_test_report.md
echo "- Environment verification" >> real_test_report.md
echo "- Agent creation with real API calls" >> real_test_report.md
echo "- Configuration validation" >> real_test_report.md
echo "" >> real_test_report.md
fi
if [ "$INPUT_FRAMEWORK" == "crewai" ] || [ "$INPUT_FRAMEWORK" == "all" ]; then
echo "### CrewAI Real Tests:" >> real_test_report.md
echo "- Environment verification" >> real_test_report.md
echo "- Crew creation with real API calls" >> real_test_report.md
echo "- Multi-agent setup validation" >> real_test_report.md
echo "" >> real_test_report.md
fi
echo "## 💰 Cost Considerations" >> real_test_report.md
echo "- These tests made actual API calls to LLM providers" >> real_test_report.md
echo "- Costs depend on your API pricing tier" >> real_test_report.md
echo "- Tests are designed to be minimal to reduce costs" >> real_test_report.md
echo "- Check your API provider dashboard for actual usage" >> real_test_report.md
echo "## 📋 Next Steps" >> real_test_report.md
echo "- Review test results for any failures" >> real_test_report.md
echo "- Check API usage in your provider dashboard" >> real_test_report.md
echo "- Use mock tests (tests/integration/) for routine testing" >> real_test_report.md
- name: Upload Real Test Results
uses: actions/upload-artifact@v4
if: always()
with:
name: real-test-results-${{ 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
env:
INPUT_FRAMEWORK: ${{ github.event.inputs.framework }}
INPUT_CONFIRM_COSTS: ${{ github.event.inputs.confirm_costs }}
run: |
echo "🚨 Real tests not executed due to safety checks:"
echo ""
echo "✅ Costs confirmed: $INPUT_CONFIRM_COSTS"
echo "✅ Framework selected: $INPUT_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
-206
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@@ -1,206 +0,0 @@
name: Windows Tests
on:
push:
branches: [main, develop]
paths:
- 'src/**'
- 'pyproject.toml'
- '.github/workflows/test-windows.yml'
pull_request:
branches: [main, develop]
paths:
- 'src/**'
- 'pyproject.toml'
- '.github/workflows/test-windows.yml'
workflow_dispatch:
jobs:
test-windows:
runs-on: windows-latest
timeout-minutes: 5
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
OPENAI_MODEL_NAME: gpt-4o-mini
PYTHONUTF8: 1
steps:
- uses: actions/checkout@v4
with:
persist-credentials: false
- name: Set up Python 3.11
uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install praisonaiagents
run: |
python -m pip install --upgrade pip
cd src/praisonai-agents
pip install -e .
shell: bash
- name: Install praisonai wrapper
run: |
cd src/praisonai
pip install -e .
shell: bash
# ==================== CORE IMPORTS ====================
- name: Test core imports
run: |
python -c "from praisonaiagents import Agent, Agents, Workflow; print('Core imports OK')"
python -c "from praisonaiagents import tool, Tools; print('Tool imports OK')"
python -c "from praisonaiagents import Task; print('Task imports OK')"
# ==================== AGENT FEATURES ====================
- name: Test Agent instantiation
run: |
python -c "
from praisonaiagents import Agent
a = Agent(name='Test', instructions='You are helpful')
print('Agent created:', a.name)
a2 = Agent(name='ToolAgent', instructions='Use tools', tools=[])
print('Agent with tools:', a2.name)
"
- name: Test Agents orchestration
run: |
python -c "
from praisonaiagents import Agent, Agents, Task
a1 = Agent(name='Agent1', instructions='First agent')
a2 = Agent(name='Agent2', instructions='Second agent')
t = Task(description='Do something', agent=a1)
agents = Agents(agents=[a1, a2], tasks=[t])
print('Agents:', len(agents.agents), 'Tasks:', len(agents.tasks))
"
# ==================== WORKFLOW FEATURES ====================
- name: Test Workflow
run: |
python -c "
from praisonaiagents import Workflow
w = Workflow(name='test')
print('Workflow:', w.name)
"
# ==================== MEMORY FEATURES ====================
- name: Test Memory classes
run: |
python -c "
from praisonaiagents.memory import FileMemory
from praisonaiagents.config import MemoryConfig
cfg = MemoryConfig(backend='file', user_id='test_user')
print('MemoryConfig backend:', cfg.backend)
mem = FileMemory(user_id='test_user', base_path='.praison/test_memory')
print('FileMemory user:', mem.user_id)
"
# ==================== TOOL DECORATOR ====================
- name: Test tool decorator
run: |
python -c "
from praisonaiagents import tool
@tool
def my_tool(query: str) -> str:
'''A test tool'''
return f'Echo: {query}'
result = my_tool('hello')
print('Tool result:', result)
"
# ==================== CONFIG CLASSES ====================
- name: Test config classes
run: |
python -c "
from praisonaiagents.config import OutputConfig, ExecutionConfig, PlanningConfig
out_cfg = OutputConfig(verbose=True, stream=True)
print('OutputConfig verbose:', out_cfg.verbose)
exec_cfg = ExecutionConfig(max_iter=50, max_retry_limit=5)
print('ExecutionConfig max_iter:', exec_cfg.max_iter)
plan_cfg = PlanningConfig(reasoning=True, auto_approve=False)
print('PlanningConfig reasoning:', plan_cfg.reasoning)
"
# ==================== HANDOFF ====================
- name: Test Handoff
run: |
python -c "
from praisonaiagents import Handoff, handoff, Agent
a1 = Agent(name='Agent1', instructions='First')
a2 = Agent(name='Agent2', instructions='Second')
# Using Handoff class directly
h = Handoff(agent=a2)
print('Handoff tool name:', h.tool_name)
# Using handoff function
h2 = handoff(a1, tool_name_override='custom_handoff')
print('Custom handoff name:', h2.tool_name)
"
# ==================== ASYNC FEATURES ====================
- name: Test async imports
run: |
python -c "
import asyncio
from praisonaiagents import Agent
async def test_async():
agent = Agent(name='AsyncAgent', instructions='Test')
assert hasattr(agent, 'arun'), 'Missing arun'
assert hasattr(agent, 'astart'), 'Missing astart'
print('Async methods available')
asyncio.run(test_async())
"
# ==================== CLI COMMANDS ====================
- name: Test CLI help
run: praisonai --help
- name: Test CLI session list
run: praisonai session list
continue-on-error: true
- name: Test CLI queue list
run: praisonai queue list
continue-on-error: true
# ==================== REAL API TESTS ====================
- name: Real API test - Agent run
if: env.OPENAI_API_KEY != ''
run: |
python -c "
from praisonaiagents import Agent
agent = Agent(name='TestAgent', instructions='Reply with exactly: WINDOWS_OK', llm='gpt-4o-mini')
result = agent.run('Say WINDOWS_OK')
print('Result:', result)
print('Real API test passed')
"
- name: Real API test - Multi-agent
if: env.OPENAI_API_KEY != ''
run: |
python -c "
from praisonaiagents import Agent, Agents, Task
researcher = Agent(name='Researcher', instructions='You research topics', llm='gpt-4o-mini')
writer = Agent(name='Writer', instructions='You write content', llm='gpt-4o-mini')
task = Task(description='Say hello world', agent=researcher)
agents = Agents(agents=[researcher, writer], tasks=[task])
result = agents.start()
print('Multi-agent result type:', type(result))
print('Multi-agent test passed')
"
-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: "MervinPraison"
commit_user_email: "[email protected]"
commit_author: "MervinPraison <[email protected]>"
-83
View File
@@ -1,83 +0,0 @@
name: Update Documentation Parity Tracker
on:
push:
branches: [ "main" ]
paths:
- 'src/praisonai-agents/praisonaiagents/**/*.py'
- 'src/praisonai-ts/src/**/*.ts'
- 'src/praisonai-rust/src/**/*.rs'
- 'src/praisonai/praisonai/_dev/parity/**/*.py'
- '.github/workflows/update-docs-parity.yml'
workflow_dispatch: # Allow manual trigger
permissions:
contents: write
jobs:
update-docs-parity:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
ref: main
persist-credentials: true
fetch-depth: 0
- name: Checkout PraisonAIDocs
id: checkout_docs
continue-on-error: true
uses: actions/checkout@v4
with:
repository: MervinPraison/PraisonAIDocs
path: PraisonAIDocs
token: ${{ secrets.SECRETS_ADMIN_PAT }}
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
run: |
pip install pyyaml
- name: Generate docs parity trackers
if: steps.checkout_docs.outcome == 'success'
run: |
cd $GITHUB_WORKSPACE
python3 src/praisonai/scripts/generate_docs_parity.py --docs-root PraisonAIDocs/docs --copy-docs
- name: Check for changes
id: check_changes
run: |
if git diff --quiet src/praisonai-agents/DOCS_PARITY.md src/praisonai-ts/DOCS_PARITY.md src/praisonai-rust/DOCS_PARITY.md 2>/dev/null; then
echo "changes=false" >> $GITHUB_OUTPUT
echo "No changes to docs parity trackers"
else
echo "changes=true" >> $GITHUB_OUTPUT
echo "Docs parity trackers have been updated"
git diff --stat src/praisonai-agents/DOCS_PARITY.md src/praisonai-ts/DOCS_PARITY.md src/praisonai-rust/DOCS_PARITY.md
fi
- name: Commit and push changes to praisonai-package
if: steps.check_changes.outputs.changes == 'true'
uses: stefanzweifel/git-auto-commit-action@v5
with:
commit_message: "docs: auto-update documentation parity trackers [skip ci]"
file_pattern: 'src/praisonai-agents/DOCS_PARITY.md src/praisonai-ts/DOCS_PARITY.md src/praisonai-rust/DOCS_PARITY.md'
commit_user_name: "MervinPraison"
commit_user_email: "[email protected]"
commit_author: "MervinPraison <[email protected]>"
- name: Commit and push changes to PraisonAIDocs
if: steps.check_changes.outputs.changes == 'true' && steps.checkout_docs.outcome == 'success'
run: |
cd PraisonAIDocs
git config user.name "MervinPraison"
git config user.email "[email protected]"
git add docs/features/DOCS_PARITY.md docs/js/DOCS_PARITY.md docs/rust/DOCS_PARITY.md
git diff --staged --quiet || git commit -m "docs: auto-update documentation parity trackers [skip ci]"
git push
@@ -1,61 +0,0 @@
name: Update Feature Parity Tracker
on:
push:
branches: [ "main" ]
paths:
- 'src/praisonai-agents/praisonaiagents/**/*.py'
- 'src/praisonai-ts/src/**/*.ts'
- 'src/praisonai-rust/src/**/*.rs'
- '.github/workflows/update-parity-tracker.yml'
permissions:
contents: write
jobs:
update-parity-tracker:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
ref: main
persist-credentials: true
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
run: pip install pyyaml
- name: Generate parity trackers
run: |
cd $GITHUB_WORKSPACE
python3 src/praisonai/scripts/generate_parity_tracker.py
- name: Check for changes
id: check_changes
run: |
if git diff --quiet src/praisonai-ts/FEATURE_PARITY_TRACKER.json src/praisonai-ts/PARITY.md src/praisonai-rust/FEATURE_PARITY_TRACKER.json src/praisonai-rust/PARITY.md 2>/dev/null; then
echo "changes=false" >> $GITHUB_OUTPUT
echo "No changes to parity trackers"
else
echo "changes=true" >> $GITHUB_OUTPUT
echo "Parity trackers have been updated"
git diff src/praisonai-ts/FEATURE_PARITY_TRACKER.json | head -50
fi
- name: Commit and push changes
if: steps.check_changes.outputs.changes == 'true'
uses: stefanzweifel/git-auto-commit-action@v5
with:
commit_message: "docs: auto-update feature parity trackers [skip ci]"
file_pattern: 'src/praisonai-ts/FEATURE_PARITY_TRACKER.json src/praisonai-ts/PARITY.md src/praisonai-rust/FEATURE_PARITY_TRACKER.json src/praisonai-rust/PARITY.md'
commit_user_name: "MervinPraison"
commit_user_email: "[email protected]"
commit_author: "MervinPraison <[email protected]>"
+21 -99
View File
@@ -1,101 +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/
!src/praisonai-agents/praisonaiagents/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
.internal
.praisonai
# Rust / Cargo
target/
Cargo.lock
**/*.rs.bk
*.pdb
.windsurf
src/praisonai/build*.egg-info/
src/praisonai/PraisonAI.egg-info/
jobs/*
*.egg-info
npm-debug.log*
yarn-debug.log*
yarn-error.log*
-20
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@@ -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 -895
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<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset=".github/images/logo_dark.png" />
<source media="(prefers-color-scheme: light)" srcset=".github/images/logo_light.png" />
<img alt="PraisonAI Logo" src=".github/images/logo_light.png" width="250" />
</picture>
</p>
# Getting Started with Create React App
<!-- mcp-name: io.github.MervinPraison/praisonai -->
This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
<p align="center">
<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" /></a>
<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/github/v/release/MervinPraison/PraisonAI" alt="Latest Stable Version" /></a>
<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" /></a>
<a href="https://registry.modelcontextprotocol.io/servers/io.github.MervinPraison/praisonai"><img src="https://img.shields.io/badge/MCP-Registry-blue" alt="MCP Registry" /></a>
</p>
## Available Scripts
<div align="center">
In the project directory, you can run:
# PraisonAI 🦞
### `npm start`
<a href="https://trendshift.io/repositories/9130" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9130" alt="MervinPraison%2FPraisonAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
Runs the app in the development mode.\
Open [http://localhost:3000](http://localhost:3000) to view it in your browser.
</div>
The page will reload when you make changes.\
You may also see any lint errors in the console.
PraisonAI 🦞 — **Hire a 24/7 AI Workforce.** Stop writing boilerplate and start shipping autonomous agents that research, plan, and execute tasks across your apps. From one agent to an entire organization, deployed in 5 lines of code.
### `npm test`
<div align="center">
<br>
<a href="https://x.com/elonmusk/status/1893870468249141688" target="_blank">
<img src="https://img.shields.io/badge/Highlighted_by_Elon_Musk-000000?style=for-the-badge&logo=x&logoColor=white" alt="Highlighted by Elon Musk" />
</a>
<br>
</div>
Launches the test runner in the interactive watch mode.\
See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
<p align="center">
<img src=".github/images/dashboard.png" alt="PraisonAI Dashboard" width="800" />
</p>
<p align="center">
<img src=".github/images/agentflow.gif" alt="PraisonAI AgentFlow" width="800" />
</p>
```
██████╗ ██████╗ █████╗ ██╗███████╗ ██████╗ ███╗ ██╗ █████╗ ██╗
██╔══██╗██╔══██╗██╔══██╗██║██╔════╝██╔═══██╗████╗ ██║ ██╔══██╗██║
██████╔╝██████╔╝███████║██║███████╗██║ ██║██╔██╗ ██║ ███████║██║
██╔═══╝ ██╔══██╗██╔══██║██║╚════██║██║ ██║██║╚██╗██║ ██╔══██║██║
██║ ██║ ██║██║ ██║██║███████║╚██████╔╝██║ ╚████║ ██║ ██║██║
╚═╝ ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝╚══════╝ ╚═════╝ ╚═╝ ╚═══╝ ╚═╝ ╚═╝╚═╝
pip install praisonai
```
<p align="center">
<img src=".github/images/latest_ai_news_and_crawl_each_url_to_find_info.gif" alt="PraisonAI command execution" width="800" />
</p>
\* `export TAVILY_API_KEY=xxxxx`
<div align="center">
<a href="https://docs.praison.ai">
<p align="center">
<img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" />
</p>
</a>
</div>
---
## 🎯 Use Cases
AI agents solving real-world problems across industries:
| Use Case | Description |
|----------|-------------|
| 🔍 **Research & Analysis** | Conduct deep research, gather information, and generate insights from multiple sources automatically |
| 💻 **Code Generation** | Write, debug, and refactor code with AI agents that understand your codebase and requirements |
| ✍️ **Content Creation** | Generate blog posts, documentation, marketing copy, and technical writing with multi-agent teams |
| 📊 **Data Pipelines** | Extract, transform, and analyze data from APIs, databases, and web sources automatically |
| 🤖 **Customer Support** | Deploy 24/7 support bots on Telegram, Discord, Slack with memory and knowledge-backed responses |
| ⚙️ **Workflow Automation** | Automate multi-step business processes with agents that hand off tasks, verify results, and self-correct |
---
## 🚀 Meet your first Agent (Under 1 Minute)
1. Install the lightweight core SDK:
```bash
pip install praisonaiagents
export OPENAI_API_KEY="your-api-key"
```
2. Run your first autonomous agent:
```python
from praisonaiagents import Agent
# Give your agent a goal, and watch it work.
agent = Agent(instructions="You are a senior data analyst.")
agent.start("Analyze the top 3 tech trends of 2026 and format as a markdown table.")
```
---
## 🌌 The PraisonAI Ecosystem
Start simple with the core SDK, or expand to full visual builders and dashboards when you're ready.
* **Core SDK (`praisonaiagents`)**: For pure Python development. `pip install praisonaiagents`
* 💻 **PraisonAI CLI (`praisonai`)**: For terminal-based developers. `pip install praisonai`
* 🦞 **Claw Dashboard**: Connect agents directly to Telegram, Slack, or Discord. `pip install "praisonai[claw]"`
* 🔗 **Flow Visual Builder**: Drag-and-drop workflow creation. `pip install "praisonai[flow]"`
* 🤖 **PraisonAI UI**: Clean chat interface. `pip install "praisonai[ui]"`
### JavaScript SDK
```bash
npm install praisonai
```
## 🧠 Supported Providers & Features
Powered by 100+ LLMs (OpenAI, Anthropic, Gemini & local models).
<p align="center">
<img src="https://img.shields.io/badge/OpenAI-412991?style=flat&logo=openai&logoColor=white" alt="OpenAI" />
<img src="https://img.shields.io/badge/Anthropic-191919?style=flat&logo=anthropic&logoColor=white" alt="Anthropic" />
<img src="https://img.shields.io/badge/Google_Gemini-4285F4?style=flat&logo=google&logoColor=white" alt="Google Gemini" />
<img src="https://img.shields.io/badge/DeepSeek-566AB2?style=flat" alt="DeepSeek" />
<img src="https://img.shields.io/badge/Azure-0078D4?style=flat&logo=microsoftazure&logoColor=white" alt="Azure" />
<img src="https://img.shields.io/badge/Ollama-000000?style=flat" alt="Ollama" />
<img src="https://img.shields.io/badge/Groq-F05237?style=flat" alt="Groq" />
<img src="https://img.shields.io/badge/Mistral-FF7000?style=flat" alt="Mistral" />
<img src="https://img.shields.io/badge/Cerebras-F05A28?style=flat" alt="Cerebras" />
<img src="https://img.shields.io/badge/Cohere-39594D?style=flat" alt="Cohere" />
<img src="https://img.shields.io/badge/OpenRouter-6467F2?style=flat" alt="OpenRouter" />
<img src="https://img.shields.io/badge/Perplexity-20808D?style=flat" alt="Perplexity" />
<img src="https://img.shields.io/badge/Fireworks-FF6B35?style=flat" alt="Fireworks" />
<img src="https://img.shields.io/badge/AWS_Bedrock-FF9900?style=flat&logo=amazonaws&logoColor=white" alt="AWS Bedrock" />
<img src="https://img.shields.io/badge/xAI_Grok-000000?style=flat" alt="xAI Grok" />
<img src="https://img.shields.io/badge/Vertex_AI-4285F4?style=flat&logo=googlecloud&logoColor=white" alt="Vertex AI" />
<img src="https://img.shields.io/badge/HuggingFace-FFD21E?style=flat&logo=huggingface&logoColor=black" alt="HuggingFace" />
<img src="https://img.shields.io/badge/Together_AI-000000?style=flat" alt="Together AI" />
<img src="https://img.shields.io/badge/Databricks-FF3621?style=flat&logo=databricks&logoColor=white" alt="Databricks" />
<img src="https://img.shields.io/badge/Replicate-262626?style=flat" alt="Replicate" />
<img src="https://img.shields.io/badge/Cloudflare-F38020?style=flat&logo=cloudflare&logoColor=white" alt="Cloudflare" />
</p>
<details>
<summary><strong>View all 24 providers with examples</strong></summary>
| Provider | Example |
|----------|:-------:|
| OpenAI | [Example](examples/python/providers/openai/openai_gpt4_example.py) |
| Anthropic | [Example](examples/python/providers/anthropic/anthropic_claude_example.py) |
| Google Gemini | [Example](examples/python/providers/google/google_gemini_example.py) |
| Ollama | [Example](examples/python/providers/ollama/ollama-agents.py) |
| Groq | [Example](examples/python/providers/groq/kimi_with_groq_example.py) |
| DeepSeek | [Example](examples/python/providers/deepseek/deepseek_example.py) |
| xAI Grok | [Example](examples/python/providers/xai/xai_grok_example.py) |
| Mistral | [Example](examples/python/providers/mistral/mistral_example.py) |
| Cohere | [Example](examples/python/providers/cohere/cohere_example.py) |
| Perplexity | [Example](examples/python/providers/perplexity/perplexity_example.py) |
| Fireworks | [Example](examples/python/providers/fireworks/fireworks_example.py) |
| Together AI | [Example](examples/python/providers/together/together_ai_example.py) |
| OpenRouter | [Example](examples/python/providers/openrouter/openrouter_example.py) |
| HuggingFace | [Example](examples/python/providers/huggingface/huggingface_example.py) |
| Azure OpenAI | [Example](examples/python/providers/azure/azure_openai_example.py) |
| AWS Bedrock | [Example](examples/python/providers/aws/aws_bedrock_example.py) |
| Google Vertex | [Example](examples/python/providers/vertex/vertex_example.py) |
| Databricks | [Example](examples/python/providers/databricks/databricks_example.py) |
| Cloudflare | [Example](examples/python/providers/cloudflare/cloudflare_example.py) |
| AI21 | [Example](examples/python/providers/ai21/ai21_example.py) |
| Replicate | [Example](examples/python/providers/replicate/replicate_example.py) |
| SageMaker | [Example](examples/python/providers/sagemaker/sagemaker_example.py) |
| Moonshot | [Example](examples/python/providers/moonshot/moonshot_example.py) |
| vLLM | [Example](examples/python/providers/vllm/vllm_example.py) |
</details>
<div align="center">
<a href="https://x.com/elonmusk/status/1893870468249141688" target="_blank">
<img src=".github/images/elon_musk_praisonai.png" alt="Highlighted by Elon Musk" width="600" />
</a>
<p><em>"Grok 3 customer support" — <a href="https://x.com/elonmusk/status/1893870468249141688">Elon Musk quoting PraisonAI's tutorial</a></em></p>
</div>
<br>
---
## 🌟 Why PraisonAI?
| | Feature | How |
|--|---------|-----|
| 🔌 | **MCP Protocol** — stdio, HTTP, WebSocket, SSE | `tools=MCP("npx ...")` |
| 🧠 | **Planning Mode** — plan → execute → reason | `planning=True` |
| 🔍 | **Deep Research** — multi-step autonomous research | [Docs](https://docs.praison.ai/docs/agents/deep-research) |
| 🤖 | **External Agents** — orchestrate Claude Code, Gemini CLI, Codex | [Docs](https://docs.praison.ai/docs/code/external-agents) |
| 🔄 | **Agent Handoffs** — seamless conversation passing | `handoff=True` |
| 🛡️ | **Guardrails** — input/output validation | [Docs](https://docs.praison.ai/docs/concepts/guardrails) |
| | **Web Search + Fetch** — native browsing | `web_search=True` |
| 🪞 | **Self Reflection** — agent reviews its own output | [Docs](https://docs.praison.ai/docs/concepts/reflection) |
| 🔀 | **Workflow Patterns** — route, parallel, loop, repeat | [Docs](https://docs.praison.ai/docs/concepts/agentflow) |
| 🧠 | **Memory (zero deps)** — works out of the box | `memory=True` |
<details>
<summary><strong>View all 25 features</strong></summary>
| | Feature | How |
|--|---------|-----|
| 💡 | **Prompt Caching** — reduce latency + cost | `prompt_caching=True` |
| 💾 | **Sessions + Auto-Save** — persistent state across restarts | `auto_save="my-project"` |
| 💭 | **Thinking Budgets** — control reasoning depth | `thinking_budget=1024` |
| 📚 | **RAG + Quality-Based RAG** — auto quality scoring retrieval | [Docs](https://docs.praison.ai/docs/concepts/rag) |
| 📊 | **Model Router** — auto-routes to cheapest capable model | [Docs](https://docs.praison.ai/docs/features/model-router) |
| 🧊 | **Shadow Git Checkpoints** — auto-rollback on failure | [Docs](https://docs.praison.ai/docs/features/checkpoints) |
| 📡 | **A2A Protocol** — agent-to-agent interop | [Docs](https://docs.praison.ai/docs/features/a2a) |
| 📏 | **Context Compaction** — never hit token limits | [Docs](https://docs.praison.ai/docs/features/context-compaction) |
| 📡 | **Telemetry** — OpenTelemetry traces, spans, metrics | [Docs](https://docs.praison.ai/docs/features/telemetry) |
| 📜 | **Policy Engine** — declarative agent behavior control | [Docs](https://docs.praison.ai/docs/features/policy-engine) |
| 🔄 | **Background Tasks** — fire-and-forget agents | [Docs](https://docs.praison.ai/docs/features/background-tasks) |
| 🔁 | **Doom Loop Detection** — auto-recovery from stuck agents | [Docs](https://docs.praison.ai/docs/features/doom-loop-detection) |
| 🕸️ | **Graph Memory** — Neo4j-style relationship tracking | [Docs](https://docs.praison.ai/docs/features/graph-memory) |
| 🏖️ | **Sandbox Execution** — isolated code execution | [Docs](https://docs.praison.ai/docs/features/sandbox) |
| 🖥️ | **Bot Gateway** — multi-agent routing across channels | [Docs](https://docs.praison.ai/docs/features/bot-gateway) |
</details>
---
## 📘 Using Python Code
### 1. Single Agent
```python
from praisonaiagents import Agent
agent = Agent(instructions="You are a helpful AI assistant")
agent.start("Write a movie script about a robot in Mars")
```
### 2. Multi Agents
```python
from praisonaiagents import Agent, Agents
research_agent = Agent(instructions="Research about AI")
summarise_agent = Agent(instructions="Summarise research agent's findings")
agents = Agents(agents=[research_agent, summarise_agent])
agents.start()
```
### 3. MCP (Model Context Protocol)
### `npm run build`
```python
from praisonaiagents import Agent, MCP
Builds the app for production to the `build` folder.\
It correctly bundles React in production mode and optimizes the build for the best performance.
# stdio - Local NPX/Python servers
agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))
The build is minified and the filenames include the hashes.\
Your app is ready to be deployed!
# Streamable HTTP - Production servers
agent = Agent(tools=MCP("https://api.example.com/mcp"))
See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
# WebSocket - Real-time bidirectional
agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))
### `npm run eject`
# With environment variables
agent = Agent(
tools=MCP(
command="npx",
args=["-y", "@modelcontextprotocol/server-brave-search"],
env={"BRAVE_API_KEY": "your-key"}
)
)
```
**Note: this is a one-way operation. Once you `eject`, you can't go back!**
> 📖 [Full MCP docs](https://docs.praison.ai/docs/mcp/transports) — stdio, HTTP, WebSocket, SSE transports
If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
### 4. Custom Tools
Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.
```python
from praisonaiagents import Agent, tool
You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.
@tool
def search(query: str) -> str:
"""Search the web for information."""
return f"Results for: {query}"
## Learn More
@tool
def calculate(expression: str) -> float:
"""Evaluate a math expression."""
return eval(expression)
You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
agent = Agent(
instructions="You are a helpful assistant",
tools=[search, calculate]
)
agent.start("Search for AI news and calculate 15*4")
```
To learn React, check out the [React documentation](https://reactjs.org/).
> 📖 [Full tools docs](https://docs.praison.ai/docs/tools/tools) — BaseTool, tool packages, 100+ built-in tools
### Code Splitting
### 5. Persistence (Databases)
This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting)
```python
from praisonaiagents import Agent, db
### Analyzing the Bundle Size
agent = Agent(
name="Assistant",
db=db(database_url="postgresql://localhost/mydb"),
session_id="my-session"
)
agent.chat("Hello!") # Auto-persists messages, runs, traces
```
This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size)
> 📖 [Full persistence docs](https://docs.praison.ai/docs/databases/overview) — PostgreSQL, MySQL, SQLite, MongoDB, Redis, and 20+ more
### Making a Progressive Web App
### 6. PraisonAI Claw 🦞 (Dashboard UI)
This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app)
Connect your AI agents to **Telegram, Discord, Slack, WhatsApp** and more — all from a single command.
### Advanced Configuration
```bash
pip install "praisonai[claw]"
praisonai claw
```
This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration)
Open **http://localhost:8082** — the dashboard comes with 13 built-in pages: Chat, Agents, Memory, Knowledge, Channels, Guardrails, Cron, and more. Add messaging channels directly from the UI.
### Deployment
> 📖 [Full Claw docs](https://docs.praison.ai/docs/concepts/claw) — platform tokens, CLI options, Docker, and YAML agent mode
This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment)
### 7. Langflow Integration 🔗 (Visual Flow Builder)
### `npm run build` fails to minify
Build multi-agent workflows visually with **drag-and-drop** components in Langflow.
```bash
pip install "praisonai[flow]"
praisonai flow
```
Open **http://localhost:7861** — use the **Agent** and **Agent Team** components to create sequential or parallel workflows. Connect Chat Input → Agent Team → Chat Output for instant multi-agent pipelines.
> 📖 [Full Flow docs](https://docs.praison.ai/docs/concepts/flow) — visual agent building, component reference, and deployment
### 8. PraisonAI UI 🤖 (Clean Chat)
Lightweight chat interface for your AI agents.
```bash
pip install "praisonai[ui]"
praisonai ui
```
---
## 📄 Using YAML (No Code)
### Example 1: Two Agents Working Together
Create `agents.yaml`:
```yaml
framework: praisonai
topic: "Write a blog post about AI"
agents:
researcher:
role: Research Analyst
goal: Research AI trends and gather information
instructions: "Find accurate information about AI trends"
writer:
role: Content Writer
goal: Write engaging blog posts
instructions: "Write clear, engaging content based on research"
```
Run with:
```bash
praisonai agents.yaml
```
> The agents automatically work together sequentially
### Example 2: Agent with Custom Tool
Create two files in the same folder:
**agents.yaml:**
```yaml
framework: praisonai
topic: "Calculate the sum of 25 and 15"
agents:
calculator_agent:
role: Calculator
goal: Perform calculations
instructions: "Use the add_numbers tool to help with calculations"
tools:
- add_numbers
```
**tools.py:**
```python
def add_numbers(a: float, b: float) -> float:
"""
Add two numbers together.
Args:
a: First number
b: Second number
Returns:
The sum of a and b
"""
return a + b
```
Run with:
```bash
praisonai agents.yaml
```
> 💡 **Tips:**
> - Use the function name (e.g., `add_numbers`) in the tools list, not the file name
> - Tools in `tools.py` are automatically discovered
> - The function's docstring helps the AI understand how to use it
---
## 🎯 CLI Quick Reference
| Category | Commands |
|----------|----------|
| **Execution** | `praisonai`, `--auto`, `--interactive`, `--chat` |
| **Research** | `research`, `--query-rewrite`, `--deep-research` |
| **Planning** | `--planning`, `--planning-tools`, `--planning-reasoning` |
| **Workflows** | `workflow run`, `workflow list`, `workflow auto` |
| **Memory** | `memory show`, `memory add`, `memory search`, `memory clear` |
| **Knowledge** | `knowledge add`, `knowledge query`, `knowledge list` |
| **Sessions** | `session list`, `session resume`, `session delete` |
| **Tools** | `tools list`, `tools info`, `tools search` |
| **MCP** | `mcp list`, `mcp create`, `mcp enable` |
| **Development** | `commit`, `docs`, `checkpoint`, `hooks` |
| **Scheduling** | `schedule start`, `schedule list`, `schedule stop` |
> 📖 [Full CLI reference](https://docs.praison.ai/docs/cli/cli-reference)
---
## ✨ Key Features
<details open>
<summary><strong>🤖 Core Agents</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Single Agent | [Example](examples/python/agents/single-agent.py) | [📖](https://docs.praison.ai/docs/agents/single) |
| Multi Agents | [Example](examples/python/general/mini_agents_example.py) | [📖](https://docs.praison.ai/docs/concepts/agents) |
| Auto Agents | [Example](examples/python/general/auto_agents_example.py) | [📖](https://docs.praison.ai/docs/features/autoagents) |
| Self Reflection AI Agents | [Example](examples/python/concepts/self-reflection-details.py) | [📖](https://docs.praison.ai/docs/concepts/reflection) |
| Reasoning AI Agents | [Example](examples/python/concepts/reasoning-extraction.py) | [📖](https://docs.praison.ai/docs/features/reasoning) |
| Multi Modal AI Agents | [Example](examples/python/general/multimodal.py) | [📖](https://docs.praison.ai/docs/features/multimodal) |
</details>
<details>
<summary><strong>🔄 Workflows</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Simple Workflow | [Example](examples/python/workflows/simple_workflow.py) | [📖](https://docs.praison.ai/docs/concepts/agentflow) |
| Workflow with Agents | [Example](examples/python/workflows/workflow_with_agents.py) | [📖](https://docs.praison.ai/docs/concepts/agentflow) |
| Agentic Routing (`route()`) | [Example](examples/python/workflows/workflow_routing.py) | [📖](https://docs.praison.ai/docs/features/routing) |
| Parallel Execution (`parallel()`) | [Example](examples/python/workflows/workflow_parallel.py) | [📖](https://docs.praison.ai/docs/features/parallelisation) |
| Loop over List/CSV (`loop()`) | [Example](examples/python/workflows/workflow_loop_csv.py) | [📖](https://docs.praison.ai/docs/features/repetitive) |
| Evaluator-Optimizer (`repeat()`) | [Example](examples/python/workflows/workflow_repeat.py) | [📖](https://docs.praison.ai/docs/concepts/evaluation) |
| Conditional Steps | [Example](examples/python/workflows/workflow_conditional.py) | [📖](https://docs.praison.ai/docs/concepts/agentflow) |
| Workflow Branching | [Example](examples/python/workflows/workflow_branching.py) | [📖](https://docs.praison.ai/docs/concepts/agentflow) |
| Workflow Early Stop | [Example](examples/python/workflows/workflow_early_stop.py) | [📖](https://docs.praison.ai/docs/concepts/agentflow) |
| Workflow Checkpoints | [Example](examples/python/workflows/workflow_checkpoints.py) | [📖](https://docs.praison.ai/docs/concepts/agentflow) |
</details>
<details>
<summary><strong>💻 Code & Development</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Code Interpreter Agents | [Example](examples/python/agents/code-agent.py) | [📖](https://docs.praison.ai/docs/features/codeagent) |
| AI Code Editing Tools | [Example](examples/python/code/code_editing_example.py) | [📖](https://docs.praison.ai/docs/code/editing) |
| External Agents (All) | [Example](examples/python/code/external_agents_example.py) | [📖](https://docs.praison.ai/docs/code/external-agents) |
| Claude Code CLI | [Example](examples/python/code/claude_code_example.py) | [📖](https://docs.praison.ai/docs/code/claude-code) |
| Gemini CLI | [Example](examples/python/code/gemini_cli_example.py) | [📖](https://docs.praison.ai/docs/code/gemini-cli) |
| Codex CLI | [Example](examples/python/code/codex_cli_example.py) | [📖](https://docs.praison.ai/docs/code/codex-cli) |
| Cursor CLI | [Example](examples/python/code/cursor_cli_example.py) | [📖](https://docs.praison.ai/docs/code/cursor-cli) |
</details>
<details>
<summary><strong>🧠 Memory & Knowledge</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Memory (Short & Long Term) | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/docs/concepts/memory) |
| File-Based Memory | [Example](examples/python/general/memory_example.py) | [📖](https://docs.praison.ai/docs/concepts/memory) |
| Claude Memory Tool | [Example](examples/python/memory/claude_memory_example.py) | [📖](https://docs.praison.ai/docs/features/claude-memory-tool) |
| Add Custom Knowledge | [Example](examples/python/concepts/knowledge-agents.py) | [📖](https://docs.praison.ai/docs/concepts/knowledge) |
| RAG Agents | [Example](examples/python/concepts/rag-agents.py) | [📖](https://docs.praison.ai/docs/concepts/rag) |
| Chat with PDF Agents | [Example](examples/python/concepts/chat-with-pdf.py) | [📖](https://docs.praison.ai/docs/features/chat-with-pdf) |
| Data Readers (PDF, DOCX, etc.) | [CLI](https://docs.praison.ai/docs/cli/knowledge) | [📖](https://docs.praison.ai/docs/features/chunking-strategies) |
| Vector Store Selection | [CLI](https://docs.praison.ai/docs/cli/knowledge) | [📖](https://docs.praison.ai/docs/features/knowledge-backends) |
| Retrieval Strategies | [CLI](https://docs.praison.ai/docs/cli/knowledge) | [📖](https://docs.praison.ai/docs/features/retrieval-strategies) |
| Rerankers | [CLI](https://docs.praison.ai/docs/cli/knowledge) | [📖](https://docs.praison.ai/docs/features/smart-retrieval) |
| Index Types (Vector/Keyword/Hybrid) | [CLI](https://docs.praison.ai/docs/cli/knowledge) | [📖](https://docs.praison.ai/docs/features/incremental-indexing) |
| Query Engines (Sub-Question, etc.) | [CLI](https://docs.praison.ai/docs/cli/knowledge) | [📖](https://docs.praison.ai/docs/features/retrieval) |
</details>
<details>
<summary><strong>🔬 Research & Intelligence</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Deep Research Agents | [Example](examples/python/agents/research-agent.py) | [📖](https://docs.praison.ai/docs/agents/deep-research) |
| Query Rewriter Agent | [Example](examples/python/agents/query-rewriter-agent.py) | [📖](https://docs.praison.ai/docs/agents/query-rewriter) |
| Native Web Search | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/docs/agents/websearch) |
| Built-in Search Tools | [Example](examples/python/agents/websearch-agent.py) | [📖](https://docs.praison.ai/docs/tools/tavily) |
| Unified Web Search | [Example](src/praisonai-agents/examples/web_search_example.py) | [📖](https://docs.praison.ai/docs/tools/web-search) |
| Web Fetch (Anthropic) | [Example](examples/python/agents/web-fetch-agent.py) | [📖](https://docs.praison.ai/docs/features/model-capabilities) |
</details>
<details>
<summary><strong>📋 Planning & Execution</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Planning Mode | [Example](examples/python/agents/planning-agent.py) | [📖](https://docs.praison.ai/docs/concepts/planning) |
| Planning Tools | [Example](examples/python/agents/planning-agent.py) | [📖](https://docs.praison.ai/docs/concepts/planning) |
| Planning Reasoning | [Example](examples/python/agents/planning-agent.py) | [📖](https://docs.praison.ai/docs/concepts/planning) |
| Prompt Chaining | [Example](examples/python/general/prompt_chaining.py) | [📖](https://docs.praison.ai/docs/features/promptchaining) |
| Evaluator Optimiser | [Example](examples/python/general/evaluator-optimiser.py) | [📖](https://docs.praison.ai/docs/concepts/evaluation) |
| Orchestrator Workers | [Example](examples/python/general/orchestrator-workers.py) | [📖](https://docs.praison.ai/docs/concepts/orchestration) |
</details>
<details>
<summary><strong>👥 Specialized Agents</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Data Analyst Agent | [Example](examples/python/agents/data-analyst-agent.py) | [📖](https://docs.praison.ai/docs/agents/data-analyst) |
| Finance Agent | [Example](examples/python/agents/finance-agent.py) | [📖](https://docs.praison.ai/docs/agents/finance) |
| Shopping Agent | [Example](examples/python/agents/shopping-agent.py) | [📖](https://docs.praison.ai/docs/agents/shopping) |
| Recommendation Agent | [Example](examples/python/agents/recommendation-agent.py) | [📖](https://docs.praison.ai/docs/agents/recommendation) |
| Wikipedia Agent | [Example](examples/python/agents/wikipedia-agent.py) | [📖](https://docs.praison.ai/docs/agents/wikipedia) |
| Programming Agent | [Example](examples/python/agents/programming-agent.py) | [📖](https://docs.praison.ai/docs/agents/programming) |
| Math Agents | [Example](examples/python/agents/math-agent.py) | [📖](https://docs.praison.ai/docs/features/mathagent) |
| Markdown Agent | [Example](examples/python/agents/markdown-agent.py) | [📖](https://docs.praison.ai/docs/agents/markdown) |
| Prompt Expander Agent | [Example](examples/python/agents/prompt-expander-agent.py) | [📖](https://docs.praison.ai/docs/agents/prompt-expander) |
</details>
<details>
<summary><strong>🎨 Media & Multimodal</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Image Generation Agent | [Example](examples/python/image/image-agent.py) | [📖](https://docs.praison.ai/docs/features/image-generation) |
| Image to Text Agent | [Example](examples/python/agents/image-to-text-agent.py) | [📖](https://docs.praison.ai/docs/agents/image-to-text) |
| Video Agent | [Example](examples/python/agents/video-agent.py) | [📖](https://docs.praison.ai/docs/agents/video) |
| Camera Integration | [Example](examples/python/camera/) | [📖](https://docs.praison.ai/docs/features/camera-integration) |
</details>
<details>
<summary><strong>🔌 Protocols & Integration</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| MCP Transports | [Example](examples/python/mcp/mcp-transports-overview.py) | [📖](https://docs.praison.ai/docs/mcp/transports) |
| WebSocket MCP | [Example](examples/python/mcp/websocket-mcp.py) | [📖](https://docs.praison.ai/docs/mcp/sse-transport) |
| MCP Security | [Example](examples/python/mcp/mcp-security.py) | [📖](https://docs.praison.ai/docs/mcp/transports) |
| MCP Resumability | [Example](examples/python/mcp/mcp-resumability.py) | [📖](https://docs.praison.ai/docs/mcp/sse-transport) |
| MCP Config Management | [Docs](https://docs.praison.ai/docs/cli/mcp) | [📖](https://docs.praison.ai/docs/cli/mcp) |
| LangChain Integrated Agents | [Example](examples/python/general/langchain_example.py) | [📖](https://docs.praison.ai/docs/features/langchain) |
</details>
<details>
<summary><strong>🛡️ Safety & Control</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Guardrails | [Example](examples/python/guardrails/comprehensive-guardrails-example.py) | [📖](https://docs.praison.ai/docs/concepts/guardrails) |
| Human Approval | [Example](examples/python/general/human_approval_example.py) | [📖](https://docs.praison.ai/docs/concepts/approval) |
| Rules & Instructions | [Docs](https://docs.praison.ai/docs/features/rules) | [📖](https://docs.praison.ai/docs/features/rules) |
</details>
<details>
<summary><strong>⚙️ Advanced Features</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Async & Parallel Processing | [Example](examples/python/general/async_example.py) | [📖](https://docs.praison.ai/docs/features/async) |
| Parallelisation | [Example](examples/python/general/parallelisation.py) | [📖](https://docs.praison.ai/docs/features/parallelisation) |
| Repetitive Agents | [Example](examples/python/concepts/repetitive-agents.py) | [📖](https://docs.praison.ai/docs/features/repetitive) |
| Agent Handoffs | [Example](examples/python/handoff/handoff_basic.py) | [📖](https://docs.praison.ai/docs/concepts/handoffs) |
| Stateful Agents | [Example](examples/python/stateful/workflow-state-example.py) | [📖](https://docs.praison.ai/docs/features/stateful-agents) |
| Autonomous Workflow | [Example](examples/python/general/autonomous-agent.py) | [📖](https://docs.praison.ai/docs/concepts/autonomy) |
| Structured Output Agents | [Example](examples/python/general/structured_agents_example.py) | [📖](https://docs.praison.ai/docs/features/structured) |
| Model Router | [Example](examples/python/agents/router-agent-cost-optimization.py) | [📖](https://docs.praison.ai/docs/features/model-router) |
| Prompt Caching | [Example](examples/python/agents/prompt-caching-agent.py) | [📖](https://docs.praison.ai/docs/features/model-capabilities) |
| Fast Context | [Example](examples/context/00_agent_fast_context_basic.py) | [📖](https://docs.praison.ai/docs/features/fast-context) |
</details>
<details>
<summary><strong>🛠️ Tools & Configuration</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| 100+ Custom Tools | [Example](examples/python/general/tools_example.py) | [📖](https://docs.praison.ai/docs/tools/tools) |
| YAML Configuration | [Example](examples/cookbooks/yaml/secondary_market_research_agents.yaml) | [📖](https://docs.praison.ai/docs/developers/agents-playbook) |
| 100+ LLM Support | [Example](examples/python/providers/openai/openai_gpt4_example.py) | [📖](https://docs.praison.ai/docs/models) |
| Callback Agents | [Example](examples/python/general/advanced-callback-systems.py) | [📖](https://docs.praison.ai/docs/concepts/hooks) |
| Hooks | [Example](examples/python/hooks/hooks_example.py) | [📖](https://docs.praison.ai/docs/concepts/hooks) |
| Middleware System | [Example](examples/middleware/basic_middleware.py) | [📖](https://docs.praison.ai/docs/features/middleware) |
| Configurable Model | [Example](examples/middleware/configurable_model.py) | [📖](https://docs.praison.ai/docs/features/configurable-model) |
| Rate Limiter | [Example](examples/middleware/rate_limiter.py) | [📖](https://docs.praison.ai/docs/features/rate-limiter) |
| Injected Tool State | [Example](examples/middleware/injected_state.py) | [📖](https://docs.praison.ai/docs/features/injected-state) |
| Shadow Git Checkpoints | [Example](examples/checkpoints/basic_checkpoint.py) | [📖](https://docs.praison.ai/docs/features/checkpoints) |
| Background Tasks | [Example](examples/background/basic_background.py) | [📖](https://docs.praison.ai/docs/features/background-tasks) |
| Policy Engine | [Example](examples/policy/basic_policy.py) | [📖](https://docs.praison.ai/docs/features/policy-engine) |
| Thinking Budgets | [Example](examples/thinking/basic_thinking.py) | [📖](https://docs.praison.ai/docs/features/thinking-budgets) |
| Output Styles | [Example](examples/output/basic_output.py) | [📖](https://docs.praison.ai/docs/features/output-styles) |
| Context Compaction | [Example](examples/compaction/basic_compaction.py) | [📖](https://docs.praison.ai/docs/features/context-compaction) |
</details>
<details>
<summary><strong>📊 Monitoring & Management</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Sessions Management | [Example](examples/python/sessions/comprehensive-session-management.py) | [📖](https://docs.praison.ai/docs/concepts/session-management) |
| Auto-Save Sessions | [Docs](https://docs.praison.ai/docs/cli/session) | [📖](https://docs.praison.ai/docs/cli/session) |
| History in Context | [Docs](https://docs.praison.ai/docs/cli/session) | [📖](https://docs.praison.ai/docs/cli/session) |
| Telemetry | [Example](examples/python/telemetry/production-telemetry-example.py) | [📖](https://docs.praison.ai/docs/features/telemetry) |
| Langfuse Tracing | [Docs](https://docs.praison.ai/docs/observability/langfuse) | [📖](https://docs.praison.ai/docs/observability/langfuse) |
| Project Docs (.praison/docs/) | [Docs](https://docs.praison.ai/docs/cli/docs) | [📖](https://docs.praison.ai/docs/cli/docs) |
| AI Commit Messages | [Docs](https://docs.praison.ai/docs/cli/commit) | [📖](https://docs.praison.ai/docs/cli/commit) |
| @Mentions in Prompts | [Docs](https://docs.praison.ai/docs/cli/mentions) | [📖](https://docs.praison.ai/docs/cli/mentions) |
</details>
<details>
<summary><strong>🖥️ CLI Features</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Slash Commands | [Example](examples/python/cli/slash_commands_example.py) | [📖](https://docs.praison.ai/docs/cli/slash-commands) |
| Autonomy Modes | [Example](examples/python/cli/autonomy_modes_example.py) | [📖](https://docs.praison.ai/docs/cli/autonomy-modes) |
| Cost Tracking | [Example](examples/python/cli/cost_tracking_example.py) | [📖](https://docs.praison.ai/docs/cli/cost-tracking) |
| Repository Map | [Example](examples/python/cli/repo_map_example.py) | [📖](https://docs.praison.ai/docs/cli/repo-map) |
| Interactive TUI | [Example](examples/python/cli/interactive_tui_example.py) | [📖](https://docs.praison.ai/docs/cli/interactive-tui) |
| Git Integration | [Example](examples/python/cli/git_integration_example.py) | [📖](https://docs.praison.ai/docs/cli/git-integration) |
| Sandbox Execution | [Example](examples/python/cli/sandbox_execution_example.py) | [📖](https://docs.praison.ai/docs/cli/sandbox-execution) |
| CLI Compare | [Example](examples/compare/cli_compare_basic.py) | [📖](https://docs.praison.ai/docs/cli/compare) |
| Profile/Benchmark | [Docs](https://docs.praison.ai/docs/cli/profile) | [📖](https://docs.praison.ai/docs/cli/profile) |
| Auto Mode | [Docs](https://docs.praison.ai/docs/cli/auto) | [📖](https://docs.praison.ai/docs/cli/auto) |
| Init | [Docs](https://docs.praison.ai/docs/cli/init) | [📖](https://docs.praison.ai/docs/cli/init) |
| File Input | [Docs](https://docs.praison.ai/docs/cli/file-input) | [📖](https://docs.praison.ai/docs/cli/file-input) |
| Final Agent | [Docs](https://docs.praison.ai/docs/cli/final-agent) | [📖](https://docs.praison.ai/docs/cli/final-agent) |
| Max Tokens | [Docs](https://docs.praison.ai/docs/cli/max-tokens) | [📖](https://docs.praison.ai/docs/cli/max-tokens) |
</details>
<details>
<summary><strong>🧪 Evaluation</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Accuracy Evaluation | [Example](examples/eval/accuracy_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
| Performance Evaluation | [Example](examples/eval/performance_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
| Reliability Evaluation | [Example](examples/eval/reliability_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
| Criteria Evaluation | [Example](examples/eval/criteria_example.py) | [📖](https://docs.praison.ai/docs/cli/eval) |
</details>
<details>
<summary><strong>🎯 Agent Skills</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Skills Management | [Example](examples/skills/basic_skill_usage.py) | [📖](https://docs.praison.ai/docs/concepts/skills) |
| Custom Skills | [Example](examples/skills/custom_skill_example.py) | [📖](https://docs.praison.ai/docs/concepts/skills) |
</details>
<details>
<summary><strong>⏰ 24/7 Scheduling</strong></summary>
| Feature | Code | Docs |
|---------|:----:|:----:|
| Agent Scheduler | [Example](examples/python/scheduled_agents/news_checker_live.py) | [📖](https://docs.praison.ai/docs/cli/scheduler) |
</details>
---
## 💻 Using JavaScript Code
```bash
npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
```
```javascript
const { Agent } = require('praisonai');
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
agent.start('Write a movie script about a robot in Mars');
```
---
## ⚡ Performance
PraisonAI is built for speed, with agent instantiation in under 4μs. This reduces overhead, improves responsiveness, and helps multi-agent systems scale efficiently in real-world production workloads.
| Performance Metric | PraisonAI |
|--------------------|-----------|
| Avg Instantiation Time | **3.77 μs** |
---
---
## ⭐ Star History
[![Star History Chart](https://api.star-history.com/svg?repos=MervinPraison/PraisonAI&type=Date)](https://docs.praison.ai)
---
## 🔍 Langfuse Tracing
```bash
pip install "praisonai[langfuse]"
praisonai langfuse
```
<p align="center">
<img src=".github/images/langfuse.png" alt="PraisonAI Langfuse Tracing" width="800" />
</p>
---
## 🎓 Video Tutorials
Learn PraisonAI through our comprehensive video series:
<details>
<summary><strong>View all 22 video tutorials</strong></summary>
| Topic | Video |
|-------|--------|
| AI Agents with Self Reflection | [![Self Reflection](https://img.youtube.com/vi/vLXobEN2Vc8/0.jpg)](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
| Reasoning Data Generating Agent | [![Reasoning Data](https://img.youtube.com/vi/fUT332Y2zA8/0.jpg)](https://www.youtube.com/watch?v=fUT332Y2zA8) |
| AI Agents with Reasoning | [![Reasoning](https://img.youtube.com/vi/KNDVWGN3TpM/0.jpg)](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
| Multimodal AI Agents | [![Multimodal](https://img.youtube.com/vi/hjAWmUT1qqY/0.jpg)](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
| AI Agents Workflow | [![Workflow](https://img.youtube.com/vi/yWTH44QPl2A/0.jpg)](https://www.youtube.com/watch?v=yWTH44QPl2A) |
| Async AI Agents | [![Async](https://img.youtube.com/vi/VhVQfgo00LE/0.jpg)](https://www.youtube.com/watch?v=VhVQfgo00LE) |
| Mini AI Agents | [![Mini](https://img.youtube.com/vi/OkvYp5aAGSg/0.jpg)](https://www.youtube.com/watch?v=OkvYp5aAGSg) |
| AI Agents with Memory | [![Memory](https://img.youtube.com/vi/1hVfVxvPnnQ/0.jpg)](https://www.youtube.com/watch?v=1hVfVxvPnnQ) |
| Repetitive Agents | [![Repetitive](https://img.youtube.com/vi/dAYGxsjDOPg/0.jpg)](https://www.youtube.com/watch?v=dAYGxsjDOPg) |
| Introduction | [![Introduction](https://img.youtube.com/vi/Fn1lQjC0GO0/0.jpg)](https://www.youtube.com/watch?v=Fn1lQjC0GO0) |
| Tools Overview | [![Tools Overview](https://img.youtube.com/vi/XaQRgRpV7jo/0.jpg)](https://www.youtube.com/watch?v=XaQRgRpV7jo) |
| Custom Tools | [![Custom Tools](https://img.youtube.com/vi/JSU2Rndh06c/0.jpg)](https://www.youtube.com/watch?v=JSU2Rndh06c) |
| Firecrawl Integration | [![Firecrawl](https://img.youtube.com/vi/UoqUDcLcOYo/0.jpg)](https://www.youtube.com/watch?v=UoqUDcLcOYo) |
| User Interface | [![UI](https://img.youtube.com/vi/tg-ZjNl3OCg/0.jpg)](https://www.youtube.com/watch?v=tg-ZjNl3OCg) |
| Crawl4AI Integration | [![Crawl4AI](https://img.youtube.com/vi/KAvuVUh0XU8/0.jpg)](https://www.youtube.com/watch?v=KAvuVUh0XU8) |
| Chat Interface | [![Chat](https://img.youtube.com/vi/sw3uDqn2h1Y/0.jpg)](https://www.youtube.com/watch?v=sw3uDqn2h1Y) |
| Code Interface | [![Code](https://img.youtube.com/vi/_5jQayO-MQY/0.jpg)](https://www.youtube.com/watch?v=_5jQayO-MQY) |
| Mem0 Integration | [![Mem0](https://img.youtube.com/vi/KIGSgRxf1cY/0.jpg)](https://www.youtube.com/watch?v=KIGSgRxf1cY) |
| Training | [![Training](https://img.youtube.com/vi/aLawE8kwCrI/0.jpg)](https://www.youtube.com/watch?v=aLawE8kwCrI) |
| Realtime Voice Interface | [![Realtime](https://img.youtube.com/vi/frRHfevTCSw/0.jpg)](https://www.youtube.com/watch?v=frRHfevTCSw) |
| Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
| Reasoning Extract Agents | [![Reasoning Extract](https://img.youtube.com/vi/2PPamsADjJA/0.jpg)](https://www.youtube.com/watch?v=2PPamsADjJA) |
</details>
---
## 👥 Contributing
We welcome contributions! Fork the repo, create a branch, and submit a PR → [Contributing Guide](https://github.com/MervinPraison/PraisonAI/blob/main/CONTRIBUTING.md).
---
## ❓ FAQ & Troubleshooting
<details>
<summary><strong>ModuleNotFoundError: No module named 'praisonaiagents'</strong></summary>
Install the package:
```bash
pip install praisonaiagents
```
</details>
<details>
<summary><strong>API key not found / Authentication error</strong></summary>
Ensure your API key is set:
```bash
export OPENAI_API_KEY=your_key_here
```
For other providers, see [Models docs](https://docs.praison.ai/docs/models).
</details>
<details>
<summary><strong>How do I use a local model (Ollama)?</strong></summary>
```bash
# Start Ollama server first
ollama serve
# Set environment variable
export OPENAI_BASE_URL=http://localhost:11434/v1
```
See [Models docs](https://docs.praison.ai/docs/models) for more details.
</details>
<details>
<summary><strong>How do I persist conversations to a database?</strong></summary>
Use the `db` parameter:
```python
from praisonaiagents import Agent, db
agent = Agent(
name="Assistant",
db=db(database_url="postgresql://localhost/mydb"),
session_id="my-session"
)
```
See [Persistence docs](https://docs.praison.ai/docs/databases/overview) for supported databases.
</details>
<details>
<summary><strong>How do I enable agent memory?</strong></summary>
```python
from praisonaiagents import Agent
agent = Agent(
name="Assistant",
memory=True, # Enables file-based memory (no extra deps!)
user_id="user123"
)
```
See [Memory docs](https://docs.praison.ai/docs/concepts/memory) for more options.
</details>
<details>
<summary><strong>How do I run multiple agents together?</strong></summary>
```python
from praisonaiagents import Agent, Agents
agent1 = Agent(instructions="Research topics")
agent2 = Agent(instructions="Summarize findings")
agents = Agents(agents=[agent1, agent2])
agents.start()
```
See [Agents docs](https://docs.praison.ai/docs/concepts/agents) for more examples.
</details>
<details>
<summary><strong>How do I use MCP tools?</strong></summary>
```python
from praisonaiagents import Agent, MCP
agent = Agent(
tools=MCP("npx @modelcontextprotocol/server-memory")
)
```
See [MCP docs](https://docs.praison.ai/docs/mcp/transports) for all transport options.
</details>
### Getting Help
- 📚 [Full Documentation](https://docs.praison.ai)
- 🐛 [Report Issues](https://github.com/MervinPraison/PraisonAI/issues)
- 💬 [Discussions](https://github.com/MervinPraison/PraisonAI/discussions)
---
<div align="center">
<p><strong>Made with ❤️ by the PraisonAI Team</strong></p>
<p>
<a href="https://docs.praison.ai">📚 Documentation</a> •
<a href="https://github.com/MervinPraison/PraisonAI">GitHub</a> •
<a href="https://youtube.com/@MervinPraison">▶️ YouTube</a> •
<a href="https://x.com/MervinPraison">𝕏 X</a> •
<a href="https://linkedin.com/in/mervinpraison">💼 LinkedIn</a>
</p>
</div>
This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)
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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
# Bot Tokens (for messaging bot services)
SLACK_BOT_TOKEN=xoxb-your-slack-bot-token
SLACK_APP_TOKEN=xapp-your-slack-app-token
DISCORD_BOT_TOKEN=your-discord-bot-token
TELEGRAM_BOT_TOKEN=your-telegram-bot-token
# Optional: Service Configuration
FLASK_ENV=development
CHAINLIT_HOST=0.0.0.0
# 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>=4.6.11" \
"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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# PraisonAI 🦞 Claw — All-in-one: dashboard + bots + gateway + agents
#
# Build: docker build -f docker/Dockerfile.claw -t praisonai:claw ..
# Run: docker run -p 8082:8082 -e OPENAI_API_KEY=sk-xxx praisonai:claw
# Health: curl http://localhost:8082/health
#
# Override CMD for other modes:
# Bot: docker run -e ... praisonai:claw praisonai bot telegram
# Gateway: docker run -e ... praisonai:claw praisonai gateway start
# Serve: docker run -e ... praisonai:claw praisonai serve agents.yaml
# Shell: docker run --rm -it praisonai:claw bash
FROM python:3.12-slim AS base
# ── Environment ──────────────────────────────────────────────────────────────
ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
AIUI_PORT=8082 \
AIUI_HOST=0.0.0.0 \
AIUI_DATA_DIR=/data
WORKDIR /app
# ── System dependencies ─────────────────────────────────────────────────────
# tini: PID 1 signal handling (graceful shutdown on Fly/Railway/k8s)
# curl: HEALTHCHECK probe
# build-essential: compile native Python wheels (chromadb, etc.)
RUN apt-get update && apt-get install -y --no-install-recommends \
tini \
curl \
build-essential \
&& rm -rf /var/lib/apt/lists/*
# ── Install PraisonAI 🦞 ────────────────────────────────────────────────────
# Single command installs: AIUI dashboard, agents SDK (memory/knowledge/llm/mcp),
# bot channels (Telegram/Discord/Slack), gateway, search tools, and more.
RUN pip install --no-cache-dir "praisonai[claw]"
# ── Data volume ──────────────────────────────────────────────────────────────
# Persistence for sessions, config, memory, knowledge stores
RUN mkdir -p /data
VOLUME /data
# ── Entrypoint ───────────────────────────────────────────────────────────────
# Ensures /data exists and forwards all signals via tini
RUN printf '#!/bin/sh\n\
set -e\n\
mkdir -p /data 2>/dev/null || true\n\
exec "$@"\n' > /usr/local/bin/entrypoint.sh \
&& chmod +x /usr/local/bin/entrypoint.sh
EXPOSE 8082
# ── Health check ─────────────────────────────────────────────────────────────
HEALTHCHECK --interval=30s --timeout=5s --start-period=20s --retries=3 \
CMD curl -f http://localhost:8082/health || exit 1
# ── Run ──────────────────────────────────────────────────────────────────────
# Default: AIUI dashboard via praisonai claw. Override CMD for bot/gateway/serve.
ENTRYPOINT ["/usr/bin/tini", "--", "/usr/local/bin/entrypoint.sh"]
CMD ["praisonai", "claw", "--port", "8082"]
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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>=4.6.11" \
"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>=4.6.11" \
"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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# Bot Environment Variables
# Copy to .env and fill in your values
# Required: LLM API Key
OPENAI_API_KEY=your_openai_api_key_here
# Slack Bot (Socket Mode)
SLACK_BOT_TOKEN=xoxb-your-slack-bot-token
SLACK_APP_TOKEN=xapp-your-slack-app-token
# Discord Bot
DISCORD_BOT_TOKEN=your-discord-bot-token
# Telegram Bot
TELEGRAM_BOT_TOKEN=your-telegram-bot-token
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# Dockerfile for PraisonAI Slack Bot
# Secrets are passed at runtime via environment variables
FROM python:3.11-slim
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
gcc \
python3-dev \
curl \
git \
&& rm -rf /var/lib/apt/lists/*
# Create praison config directory
RUN mkdir -p /root/.praison
# Install praisonai with bot dependencies
RUN pip install --no-cache-dir \
praisonai \
slack-bolt \
slack-sdk \
"discord.py>=2.0.0" \
"python-telegram-bot>=20.0"
# Set environment variables for directory management
ENV PRAISON_CONFIG_DIR=/root/.praison
ENV DOCKER_CONTAINER=true
# Bot tokens should be passed at runtime (not baked into image)
# SLACK_BOT_TOKEN=xoxb-...
# SLACK_APP_TOKEN=xapp-...
# Optional: Copy custom agent configuration
# COPY agent.yaml /app/agent.yaml
# Expose no ports - Socket Mode doesn't need them
# For webhook mode, expose port 3000
# Default command - tokens from environment
CMD ["praisonai", "bot", "slack"]
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# PraisonAI Bot Docker Deployment
Deploy Slack, Discord, or Telegram bots using Docker.
## Quick Start
```bash
# 1. Copy environment template
cp .env.template .env
# 2. Edit .env with your tokens
nano .env
# 3. Run specific bot
docker compose up slack-bot -d
# Or run all bots
docker compose up -d
```
## Environment Variables
| Variable | Required | Description |
|----------|----------|-------------|
| `OPENAI_API_KEY` | Yes | LLM API key |
| `SLACK_BOT_TOKEN` | For Slack | Bot token (xoxb-...) |
| `SLACK_APP_TOKEN` | For Slack | App token (xapp-...) |
| `DISCORD_BOT_TOKEN` | For Discord | Bot token |
| `TELEGRAM_BOT_TOKEN` | For Telegram | Bot token |
## Slack Setup
1. Create app at https://api.slack.com/apps
2. Enable **Socket Mode** → Generate App-Level Token with `connections:write`
3. Add **OAuth Scopes**: `chat:write`, `app_mentions:read`, `im:history`
4. Enable **Event Subscriptions** → Subscribe to `app_mention`, `message.im`
5. **Install to Workspace**
6. Copy Bot Token and App Token to `.env`
## Commands
```bash
# Start Slack bot
docker compose up slack-bot -d
# View logs
docker compose logs -f slack-bot
# Stop bot
docker compose down slack-bot
# Rebuild after updates
docker compose build --no-cache slack-bot
```
## Custom Agent Configuration
Mount your agent config:
```yaml
services:
slack-bot:
volumes:
- ./agent.yaml:/app/agent.yaml
command: ["praisonai", "bot", "slack", "--agent", "/app/agent.yaml"]
```
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version: '3.8'
# PraisonAI Bot Services
# Run specific bots: docker compose up slack-bot
# Run all bots: docker compose up
services:
# Slack Bot
slack-bot:
build:
context: ../..
dockerfile: docker/bots/Dockerfile
volumes:
- praison_data:/root/.praison
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
SLACK_BOT_TOKEN: ${SLACK_BOT_TOKEN}
SLACK_APP_TOKEN: ${SLACK_APP_TOKEN}
PRAISON_CONFIG_DIR: /root/.praison
command: [ "praisonai", "bot", "slack" ]
restart: unless-stopped
# Discord Bot
discord-bot:
build:
context: ../..
dockerfile: docker/bots/Dockerfile
volumes:
- praison_data:/root/.praison
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
DISCORD_BOT_TOKEN: ${DISCORD_BOT_TOKEN}
PRAISON_CONFIG_DIR: /root/.praison
command: [ "praisonai", "bot", "discord" ]
restart: unless-stopped
# Telegram Bot
telegram-bot:
build:
context: ../..
dockerfile: docker/bots/Dockerfile
volumes:
- praison_data:/root/.praison
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
TELEGRAM_BOT_TOKEN: ${TELEGRAM_BOT_TOKEN}
PRAISON_CONFIG_DIR: /root/.praison
command: [ "praisonai", "bot", "telegram" ]
restart: unless-stopped
volumes:
praison_data:
driver: local
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# Use an official Python runtime as a parent image
FROM python:3.11-slim
# Set environment variables
ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1
# Set work directory
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
&& rm -rf /var/lib/apt/lists/*
# Install PraisonAI with the 'call' extra and ensure it's the latest version
RUN pip install --no-cache-dir --upgrade "praisonai[call]"
# Expose the port the app runs on
EXPOSE 8090
# Run the application
CMD ["praisonai", "call"]
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# Praison AI Call Docker
1. Build the Docker image:
```
docker build -t praisonai-call .
```
2. Run the container:
```
docker run -d -p 8090:8090 praisonai-call -e OPENAI_API_KEY=your_api_key_here
```
Make sure to replace your_api_key_here with your actual OpenAI API key.
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version: '3.8'
# Development Docker Compose Configuration
# Extends the main docker-compose.yml with development-specific settings
services:
# Development UI Service with hot reload
ui-dev:
build:
context: ..
dockerfile: docker/Dockerfile.dev
volumes:
- praison_data:/root/.praison
- ../src:/app/src:rw
- ../examples:/app/examples:rw
- ../docs:/app/docs:ro
ports:
- "${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 🦞 Claw — Recommended all-in-one service
claw:
build:
context: ..
dockerfile: docker/Dockerfile.claw
volumes:
- claw_data:/data
ports:
- "8082:8082"
environment:
OPENAI_API_KEY: ${OPENAI_API_KEY}
restart: unless-stopped
healthcheck:
test: [ "CMD", "curl", "-f", "http://localhost:8082/health" ]
interval: 30s
timeout: 5s
start_period: 20s
retries: 3
networks:
- praison_network
# ─── Legacy services (kept for backward compatibility) ─────────────────
# 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: ${OPENAI_API_KEY}
PRAISON_CONFIG_DIR: /root/.praison
networks:
- praison_network
# For bot services (Slack, Discord, Telegram), see docker/bots/
volumes:
claw_data:
driver: local
praison_data:
driver: local
networks:
praison_network:
driver: bridge
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#!/bin/bash
# PraisonAI Docker Quick Start Script
# This script helps users quickly get started with PraisonAI using Docker
set -e
# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color
# Function to print colored output
print_status() {
echo -e "${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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framework: ag2
topic: "Research the latest developments in AI agents"
# Install: pip install "praisonai[ag2]"
# Run: praisonai --framework ag2 examples/ag2/ag2_basic.yaml
# or praisonai run examples/ag2/ag2_basic.yaml --framework ag2
roles:
research_agent:
role: "AI Research Specialist"
goal: "Research and summarise the latest developments in AI agent frameworks"
backstory: |
You are an experienced AI researcher with deep knowledge of multi-agent
systems, large language models, and the latest trends in AI tooling.
You excel at synthesising complex technical topics into clear summaries.
tasks:
research_task:
description: |
Research and summarise the latest developments in AI agent frameworks
for the topic: {topic}
Focus on:
1. Key frameworks and their unique capabilities
2. Recent innovations and improvements
3. Community adoption and ecosystem growth
4. Practical use cases and success stories
expected_output: |
A concise research summary covering the key developments,
major frameworks, and practical insights. Include 3-5 bullet
points of the most important findings.
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framework: ag2
topic: "Cloud-native AI deployment strategies on AWS"
# AG2 exclusive feature: native AWS Bedrock support via LLMConfig(api_type="bedrock")
#
# Prerequisites:
# pip install "praisonai[ag2]"
# aws configure (or set AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION)
#
# Run:
# praisonai --framework ag2 examples/ag2/ag2_bedrock.yaml
#
# The AG2 adapter detects api_type="bedrock" from the llm config and uses
# LLMConfig(api_type="bedrock", model=...) — no OPENAI_API_KEY required.
# AWS credentials are sourced from boto3 (env vars, ~/.aws/credentials, IAM role).
roles:
cloud_architect:
role: "AWS Cloud Architect"
goal: "Design and explain cloud-native AI deployment strategies on AWS"
backstory: |
You are an AWS Solutions Architect specialising in AI/ML workloads.
You have deep expertise in Amazon Bedrock, SageMaker, ECS, and Lambda,
and you help organisations deploy AI agents at scale securely and cost-effectively.
llm:
model: "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0"
api_type: "bedrock"
aws_region: "us-east-1"
tasks:
architecture_task:
description: |
Design a cloud-native deployment strategy for AI agents on AWS for: {topic}
Cover:
1. Recommended AWS services (Bedrock, ECS, Lambda, etc.)
2. Scalability and cost optimisation patterns
3. Security and compliance considerations
4. A simple reference architecture overview
expected_output: |
A concise architecture guide with service recommendations,
a high-level deployment diagram description, and key
best practices for production AI agent deployments on AWS.
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framework: ag2
topic: "The impact of open-source AI on enterprise software development"
# Install: pip install "praisonai[ag2]"
# Run: praisonai --framework ag2 examples/ag2/ag2_multi_agent.yaml
# or praisonai run examples/ag2/ag2_multi_agent.yaml --framework ag2
#
# This example demonstrates AG2's GroupChat multi-agent coordination.
# Both agents participate in a collaborative conversation managed by
# a GroupChatManager until the task is complete.
roles:
researcher:
role: "Research Specialist"
goal: "Gather and analyse information on the given topic"
backstory: |
You are a meticulous researcher who excels at finding relevant
information, analysing trends, and presenting data-backed insights.
You always cite your reasoning and structure your findings clearly.
tasks:
research_task:
description: |
Research the topic: {topic}
Investigate:
1. Current state and adoption rates
2. Key players and projects driving the trend
3. Technical advantages and challenges
4. Business impact and cost implications
expected_output: |
A structured research briefing with findings on the topic,
including key data points, trends, and technical observations.
writer:
role: "Technical Content Writer"
goal: "Transform research findings into clear, engaging written content"
backstory: |
You are a skilled technical writer who turns complex research into
accessible, well-structured articles. You focus on clarity, logical
flow, and actionable takeaways for a professional audience.
tasks:
writing_task:
description: |
Using the research findings provided by the Research Specialist,
write a concise article on: {topic}
The article should:
1. Open with a compelling hook
2. Present key findings logically
3. Include practical implications for developers
4. Close with a forward-looking conclusion
expected_output: |
A 400-500 word article suitable for a technical blog,
with clear sections, professional tone, and concrete takeaways.
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"""
Agent-Centric API Example
Demonstrates the consolidated feature parameters for PraisonAI Agents.
These params provide a cleaner, more intuitive API with progressive disclosure.
Precedence Rule: Instance > Config > Array > String > Bool > Default
Consolidated Params Support:
- memory: bool | str URL | str preset | [preset, overrides] | MemoryConfig | Instance
- knowledge: bool | str path | [sources...] | KnowledgeConfig | Instance
- planning: bool | str LLM | [preset, overrides] | PlanningConfig
- reflection: bool | str preset | [preset, overrides] | ReflectionConfig
- guardrails: bool | callable | str prompt | GuardrailConfig
- web: bool | str provider | [provider, overrides] | WebConfig
- output: str preset | [preset, overrides] | OutputConfig
- execution: str preset | [preset, overrides] | ExecutionConfig
"""
from praisonaiagents import (
Agent,
# Config classes for progressive disclosure
MemoryConfig,
KnowledgeConfig,
PlanningConfig,
ReflectionConfig,
GuardrailConfig,
WebConfig,
)
def example_simple_enable():
"""Example 1: Simple boolean enable (easiest)"""
print("\n=== Example 1: Simple Boolean Enable ===")
agent = Agent(
instructions="You are a helpful assistant",
memory=True, # Enable memory with defaults
reflection=True, # Enable self-reflection
web=WebConfig(fetch=False), # 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", {"fetch": False}], # String preset with override for web provider
)
print(f"Output preset 'verbose': verbose={agent.verbose}, metrics={agent.metrics}")
print(f"Execution preset 'fast': max_iter={agent.max_iter}")
print(f"Web preset 'tavily': web_search={agent.web_search}")
def example_array_overrides():
"""Example 8: Array with overrides (NEW - preset + customization)"""
print("\n=== Example 8: Array Overrides ===")
# Array format: [preset, {overrides}]
agent = Agent(
instructions="You are a streaming assistant",
output=["verbose", {"stream": True}], # Verbose preset + enable streaming
execution=["fast", {"max_iter": 15}], # Fast preset + custom max_iter
)
print(f"Output array override: verbose={agent.verbose}, stream={agent.stream}")
print(f"Execution array override: max_iter={agent.max_iter}")
def example_url_parsing():
"""Example 9: URL parsing for memory (NEW - connection strings)"""
print("\n=== Example 9: URL Parsing ===")
# Memory supports URL connection strings
# Supported: postgresql://, redis://, sqlite:///, mongodb://
agent = Agent(
instructions="You are a database-backed assistant",
memory="postgresql://postgres:password@localhost:5432/praisonai",
)
print("Memory URL parsed: memory enabled")
# Also works with presets
agent2 = Agent(
instructions="You are a redis-backed assistant",
memory="redis", # Preset name
)
print("Memory preset 'redis': configured")
def example_knowledge_sources():
"""Example 10: Knowledge with sources (list and string)"""
print("\n=== Example 10: Knowledge Sources ===")
# Single path string
agent1 = Agent(
instructions="You are a document assistant",
knowledge="docs/", # Single path
)
print(f"Knowledge single path: knowledge={agent1.knowledge}")
# List of sources
agent2 = Agent(
instructions="You are a multi-source assistant",
knowledge=["docs/", "data.pdf", "https://example.com/api"],
)
print(f"Knowledge list: knowledge={agent2.knowledge}")
# With config for advanced settings
agent3 = Agent(
instructions="You are an advanced RAG assistant",
knowledge=KnowledgeConfig(
sources=["docs/"],
retrieval_k=10,
rerank=True,
),
)
print("Knowledge config: configured with rerank")
if __name__ == "__main__":
print("=" * 60)
print("PraisonAI Agent-Centric API Examples")
print("=" * 60)
example_simple_enable()
example_with_config()
example_guardrails()
example_planning()
example_backward_compatible()
example_real_chat()
example_string_presets()
example_array_overrides()
example_url_parsing()
example_knowledge_sources()
print("\n" + "=" * 60)
print("All examples completed!")
print("=" * 60)
@@ -1,135 +0,0 @@
"""
Agent-Centric Tools Example
This example demonstrates how to use LSP/ACP-powered tools that make
the Agent the central orchestrator for file operations.
How to run:
export OPENAI_API_KEY=your_key
python examples/agent_tools/agent_centric_example.py
Prerequisites:
pip install praisonai praisonaiagents
"""
import asyncio
import os
import sys
import tempfile
import shutil
# Add paths for development
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai'))
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../src/praisonai-agents'))
async def main():
print("=" * 60)
print("AGENT-CENTRIC TOOLS EXAMPLE")
print("=" * 60)
print()
# Create temporary workspace
workspace = tempfile.mkdtemp(prefix="praisonai_example_")
print(f"Workspace: {workspace}")
print()
try:
# Import after path setup
from praisonai.cli.features import (
create_agent_centric_tools,
InteractiveRuntime,
RuntimeConfig
)
from praisonaiagents import Agent
# 1. Create runtime with ACP enabled
print("1. Creating InteractiveRuntime with ACP...")
config = RuntimeConfig(
workspace=workspace,
lsp_enabled=False, # Skip LSP for this example
acp_enabled=True,
approval_mode="auto" # Auto-approve for demo
)
runtime = InteractiveRuntime(config)
status = await runtime.start()
print(f" ACP ready: {runtime.acp_ready}")
print(f" Read-only: {runtime.read_only}")
print()
# 2. Create agent-centric tools
print("2. Creating agent-centric tools...")
tools = create_agent_centric_tools(runtime)
print(f" Tools created: {len(tools)}")
for tool in tools:
print(f" - {tool.__name__}")
print()
# 3. Create Agent with ACP-powered tools
print("3. Creating Agent with ACP-powered tools...")
agent = Agent(
name="FileAgent",
instructions="""You help create and manage files.
Use acp_create_file to create files.
Use read_file to read files.
Use list_files to list directory contents.""",
tools=tools,
output="verbose" # Use new consolidated param
)
print()
# 4. Ask agent to create a file
print("4. Asking agent to create a Python file...")
print("-" * 40)
result = agent.start(
"Create a Python file called calculator.py with add and subtract functions"
)
print("-" * 40)
print(f"Agent response: {result[:200]}..." if len(str(result)) > 200 else f"Agent response: {result}")
print()
# 5. Verify file was created
print("5. Verifying file creation...")
calc_path = os.path.join(workspace, "calculator.py")
if os.path.exists(calc_path):
print(f" ✓ File created at {calc_path}")
with open(calc_path) as f:
content = f.read()
print(f" Content preview:")
for line in content.split('\n')[:10]:
print(f" {line}")
else:
# Check what files exist
files = os.listdir(workspace)
print(f" Files in workspace: {files}")
for f in files:
fp = os.path.join(workspace, f)
if os.path.isfile(fp):
print(f" ✓ Found: {f}")
print()
# 6. Ask agent to read the file
print("6. Asking agent to read the file...")
print("-" * 40)
result2 = agent.start("Read the calculator.py file and tell me what functions it has")
print("-" * 40)
print(f"Agent response: {result2[:300]}..." if len(str(result2)) > 300 else f"Agent response: {result2}")
print()
# Cleanup
await runtime.stop()
finally:
# Clean up workspace
shutil.rmtree(workspace, ignore_errors=True)
print(f"Cleaned up workspace: {workspace}")
print()
print("=" * 60)
print("EXAMPLE COMPLETE")
print("=" * 60)
if __name__ == "__main__":
asyncio.run(main())
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# Approval Examples
Minimal examples showing how to route tool-execution approvals through
different backends.
## Quick Start
```bash
pip install praisonaiagents praisonai[bot]
export OPENAI_API_KEY=sk-...
```
| Backend | Script | Extra env vars |
|----------|-----------------------|--------------------------------------------------|
| Slack | `slack_approval.py` | `SLACK_BOT_TOKEN`, `SLACK_CHANNEL` |
| Telegram | `telegram_approval.py`| `TELEGRAM_BOT_TOKEN`, `TELEGRAM_CHAT_ID` |
| Discord | `discord_approval.py` | `DISCORD_BOT_TOKEN`, `DISCORD_CHANNEL_ID` |
| HTTP | `http_approval.py` | *(none — opens a local web dashboard)* |
| Agent | `agent_approval.py` | *(none — AI reviewer agent decides)* |
## SSL Verification
For corporate proxy / CA issues, Discord and Telegram backends support
optional SSL bypass:
```bash
export PRAISONAI_DISCORD_SSL_VERIFY=false
export PRAISONAI_TELEGRAM_SSL_VERIFY=false
```
Default is `true` (secure).
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"""
Agent Approval Example
======================
Uses an AI agent as an automated reviewer to approve/deny tool calls.
Requires:
pip install praisonaiagents
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.approval import AgentApproval
from praisonaiagents.tools.shell_tools import execute_command
reviewer = AgentApproval(
instructions="Only approve read-only commands like 'ls' or 'cat'. Deny destructive commands.",
)
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=reviewer,
)
agent.start("List files in the current directory")
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"""
Discord Approval Example
========================
Routes tool approvals to a Discord channel.
Requires:
pip install praisonaiagents praisonai[bot]
export DISCORD_BOT_TOKEN=MTIz...
export DISCORD_CHANNEL_ID=1234567890
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.tools.shell_tools import execute_command
from praisonai.bots import DiscordApproval
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=DiscordApproval(),
)
agent.start("List files in the current directory")
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"""
HTTP Approval Example
=====================
Opens a local web dashboard for tool approvals.
Requires:
pip install praisonaiagents praisonai[bot]
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.tools.shell_tools import execute_command
from praisonai.bots import HTTPApproval
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=HTTPApproval(),
)
agent.start("List files in the current directory")
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"""
Slack Approval Example
======================
Routes tool approvals to a Slack channel.
Requires:
pip install praisonaiagents praisonai[bot]
export SLACK_BOT_TOKEN=xoxb-...
export SLACK_CHANNEL=C0123456789
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.tools.shell_tools import execute_command
from praisonai.bots import SlackApproval
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=SlackApproval(),
)
agent.start("List files in the current directory")
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"""
Telegram Approval Example
=========================
Routes tool approvals to a Telegram chat.
Requires:
pip install praisonaiagents praisonai[bot]
export TELEGRAM_BOT_TOKEN=123456:ABC-...
export TELEGRAM_CHAT_ID=987654321
export OPENAI_API_KEY=sk-...
"""
from praisonaiagents import Agent
from praisonaiagents.tools.shell_tools import execute_command
from praisonai.bots import TelegramApproval
agent = Agent(
name="DevOps",
instructions="You are a DevOps assistant. Use shell tools when asked.",
tools=[execute_command],
approval=TelegramApproval(),
)
agent.start("List files in the current directory")
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"""
Async Runs Examples
===================
This directory contains examples for the PraisonAI Async Jobs API.
Examples:
---------
1. sdk_submit_poll_result.py - Python SDK example for submit/poll/result
2. cli_submit_poll_result.sh - Bash/curl example for submit/poll/result
3. idempotency.sh - Demonstrates idempotency key usage
4. cancel.sh - Demonstrates job cancellation
5. sse_stream.sh - Demonstrates SSE streaming
Prerequisites:
--------------
1. Set OPENAI_API_KEY environment variable
2. Start the jobs server:
cd /path/to/praisonai-package/src/praisonai
python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory
3. Run examples:
python sdk_submit_poll_result.py
bash cli_submit_poll_result.sh
bash idempotency.sh
bash cancel.sh
bash sse_stream.sh
Environment Variables:
----------------------
- OPENAI_API_KEY: Required for agent execution
- PRAISONAI_BASE_URL: API server URL (default: http://127.0.0.1:8005)
"""
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#!/bin/bash
# CLI Example: Cancel a Running Job
#
# This example demonstrates how to cancel a running job.
#
# Requirements:
# - Jobs server running on http://127.0.0.1:8005
#
# Usage:
# bash cancel.sh
set -e
API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
echo "============================================================"
echo "PraisonAI Job Cancellation Example"
echo "============================================================"
# Submit a long-running job
echo ""
echo "1. Submitting a job..."
RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
-H "Content-Type: application/json" \
-d '{"prompt": "Write a detailed essay about the history of computing."}')
JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
echo " Job ID: $JOB_ID"
# Wait briefly
echo ""
echo "2. Waiting 1 second..."
sleep 1
# Cancel the job
echo ""
echo "3. Cancelling job..."
CANCEL_RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs/$JOB_ID/cancel")
STATUS=$(echo "$CANCEL_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
echo " Status after cancel: $STATUS"
# Verify cancellation
echo ""
echo "4. Verification:"
if [ "$STATUS" = "cancelled" ]; then
echo " ✓ SUCCESS: Job was cancelled"
else
echo " ℹ Job status: $STATUS (may have completed before cancel)"
fi
echo ""
echo "============================================================"
echo "Cancellation example completed!"
echo "============================================================"
@@ -1,75 +0,0 @@
#!/bin/bash
# CLI Example: Submit, Poll, and Get Result for Async Jobs
#
# This example demonstrates how to use the PraisonAI CLI to:
# 1. Submit an async job
# 2. Check job status
# 3. Get the result
#
# Requirements:
# - OPENAI_API_KEY environment variable set
# - Jobs server running on http://127.0.0.1:8005
#
# Usage:
# bash cli_submit_poll_result.sh
set -e
API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
echo "============================================================"
echo "PraisonAI Async Jobs CLI Example"
echo "============================================================"
# Check if server is running
echo ""
echo "Checking server health..."
if ! curl -s "$API_URL/health" > /dev/null 2>&1; then
echo "✗ Server not available at $API_URL"
echo " Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory"
exit 1
fi
echo "✓ Server is healthy"
# 1. Submit a job
echo ""
echo "1. Submitting job..."
RESPONSE=$(curl -s -X POST "$API_URL/api/v1/runs" \
-H "Content-Type: application/json" \
-d '{"prompt": "What is 2+2? Answer with just the number."}')
JOB_ID=$(echo "$RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
echo " Job ID: $JOB_ID"
# 2. Poll for status
echo ""
echo "2. Polling for status..."
while true; do
STATUS_RESPONSE=$(curl -s "$API_URL/api/v1/runs/$JOB_ID")
STATUS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
PROGRESS=$(echo "$STATUS_RESPONSE" | python3 -c "import sys,json; print(json.load(sys.stdin)['progress']['percentage'])")
echo " Status: $STATUS | Progress: ${PROGRESS}%"
if [ "$STATUS" = "succeeded" ] || [ "$STATUS" = "failed" ] || [ "$STATUS" = "cancelled" ]; then
break
fi
sleep 2
done
# 3. Get result
echo ""
echo "3. Getting result..."
if [ "$STATUS" = "succeeded" ]; then
RESULT=$(curl -s "$API_URL/api/v1/runs/$JOB_ID/result")
echo " Result: $(echo "$RESULT" | python3 -c "import sys,json; print(json.load(sys.stdin)['result'])")"
echo " Duration: $(echo "$RESULT" | python3 -c "import sys,json; print(f\"{json.load(sys.stdin)['duration_seconds']:.2f}s\")")"
else
echo " Job did not succeed: $STATUS"
fi
echo ""
echo "============================================================"
echo "Example completed!"
echo "============================================================"
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#!/bin/bash
# CLI Example: Idempotency Key Usage
#
# This example demonstrates how idempotency keys prevent duplicate job submissions.
# Submitting the same request with the same idempotency key returns the existing job.
#
# Requirements:
# - Jobs server running on http://127.0.0.1:8005
#
# Usage:
# bash idempotency.sh
set -e
API_URL="${PRAISONAI_BASE_URL:-http://127.0.0.1:8005}"
IDEM_KEY="example-idem-key-$(date +%s)"
echo "============================================================"
echo "PraisonAI Idempotency Key Example"
echo "============================================================"
# First submission
echo ""
echo "1. First submission with Idempotency-Key: $IDEM_KEY"
RESPONSE1=$(curl -s -X POST "$API_URL/api/v1/runs" \
-H "Content-Type: application/json" \
-H "Idempotency-Key: $IDEM_KEY" \
-d '{"prompt": "What is 1+1?"}')
JOB_ID1=$(echo "$RESPONSE1" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
echo " Job ID: $JOB_ID1"
# Second submission with same key
echo ""
echo "2. Second submission with SAME Idempotency-Key: $IDEM_KEY"
RESPONSE2=$(curl -s -X POST "$API_URL/api/v1/runs" \
-H "Content-Type: application/json" \
-H "Idempotency-Key: $IDEM_KEY" \
-d '{"prompt": "Different prompt but same key"}')
JOB_ID2=$(echo "$RESPONSE2" | python3 -c "import sys,json; print(json.load(sys.stdin)['job_id'])")
echo " Job ID: $JOB_ID2"
# Verify same job ID
echo ""
echo "3. Verification:"
if [ "$JOB_ID1" = "$JOB_ID2" ]; then
echo " ✓ SUCCESS: Same job ID returned (no duplicate created)"
else
echo " ✗ FAILURE: Different job IDs returned"
exit 1
fi
echo ""
echo "============================================================"
echo "Idempotency example completed!"
echo "============================================================"
@@ -1,121 +0,0 @@
#!/usr/bin/env python3
"""
SDK Example: Submit, Poll, and Get Result for Async Jobs
This example demonstrates how to use the PraisonAI Jobs SDK to:
1. Submit an async job
2. Poll for status
3. Get the result
Requirements:
- OPENAI_API_KEY environment variable set
- Jobs server running on http://127.0.0.1:8005
Usage:
python sdk_submit_poll_result.py
"""
import os
import time
import httpx
# Configuration
API_URL = os.getenv("PRAISONAI_BASE_URL", "http://127.0.0.1:8005")
POLL_INTERVAL = 2 # seconds
def submit_job(prompt: str, idempotency_key: str = None) -> dict:
"""Submit a job and return the response."""
headers = {}
if idempotency_key:
headers["Idempotency-Key"] = idempotency_key
with httpx.Client(timeout=30.0) as client:
response = client.post(
f"{API_URL}/api/v1/runs",
json={"prompt": prompt},
headers=headers if headers else None
)
response.raise_for_status()
return response.json()
def get_status(job_id: str) -> dict:
"""Get job status."""
with httpx.Client(timeout=30.0) as client:
response = client.get(f"{API_URL}/api/v1/runs/{job_id}")
response.raise_for_status()
return response.json()
def get_result(job_id: str) -> dict:
"""Get job result."""
with httpx.Client(timeout=30.0) as client:
response = client.get(f"{API_URL}/api/v1/runs/{job_id}/result")
response.raise_for_status()
return response.json()
def wait_for_completion(job_id: str, max_wait: int = 120) -> dict:
"""Poll until job completes."""
start = time.time()
while time.time() - start < max_wait:
status = get_status(job_id)
print(f" Status: {status['status']} | Progress: {status['progress']['percentage']:.0f}%")
if status["status"] in ("succeeded", "failed", "cancelled"):
return status
# Honor retry_after if present
wait_time = status.get("retry_after") or POLL_INTERVAL
time.sleep(wait_time)
raise TimeoutError(f"Job {job_id} did not complete within {max_wait}s")
def main():
print("=" * 60)
print("PraisonAI Async Jobs SDK Example")
print("=" * 60)
# Check if server is running
try:
with httpx.Client(timeout=5.0) as client:
health = client.get(f"{API_URL}/health")
health.raise_for_status()
print(f"✓ Server healthy: {health.json()['status']}")
except Exception as e:
print(f"✗ Server not available at {API_URL}: {e}")
print(" Start the server with: python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory")
return
# 1. Submit a job
print("\n1. Submitting job...")
prompt = "What is the capital of France? Answer in one word."
result = submit_job(prompt)
job_id = result["job_id"]
print(f" Job ID: {job_id}")
print(f" Status: {result['status']}")
print(f" Poll URL: {result['poll_url']}")
# 2. Wait for completion
print("\n2. Waiting for completion...")
final_status = wait_for_completion(job_id)
print(f" Final status: {final_status['status']}")
# 3. Get result
if final_status["status"] == "succeeded":
print("\n3. Getting result...")
result = get_result(job_id)
print(f" Result: {result['result']}")
print(f" Duration: {result['duration_seconds']:.2f}s")
else:
print(f"\n3. Job did not succeed: {final_status.get('error', 'Unknown error')}")
print("\n" + "=" * 60)
print("Example completed successfully!")
print("=" * 60)
if __name__ == "__main__":
main()
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@@ -1,51 +0,0 @@
#!/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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@@ -1,94 +0,0 @@
#!/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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@@ -1,23 +0,0 @@
"""Calculator with Tests - Editor Output Example.
Builds a calculator module, writes unit tests, runs them, generates a
coverage-style report, and reads it back.
"""
from praisonaiagents import Agent
from praisonaiagents.tools import (
write_file, read_file, execute_command
)
agent = Agent(
instructions="You are a helpful coding assistant.",
output="editor",
tools=[write_file, read_file, execute_command],
approval=True,
)
agent.start(
"Build a mini calculator - create /tmp/calculator.py with add, subtract, "
"multiply, divide functions, write unit tests in /tmp/test_calculator.py "
"using unittest, run the tests to make sure they pass, then generate a "
"coverage-style report in /tmp/calc_report.md listing which functions were "
"tested and read it back"
)
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@@ -1,22 +0,0 @@
"""Fibonacci Generator - Editor Output Example.
Creates a Fibonacci script, runs it, writes a summary, and reads back
both files to verify everything looks good.
"""
from praisonaiagents import Agent
from praisonaiagents.tools import (
write_file, read_file, execute_command, list_files
)
agent = Agent(
instructions="You are a helpful coding assistant.",
output="editor",
tools=[write_file, read_file, execute_command, list_files],
approval=False,
)
agent.start(
"Create a Python file at /tmp/fibonacci.py that generates the first 20 "
"Fibonacci numbers, run it to verify the output is correct, then write "
"a summary to /tmp/fib_summary.md explaining the algorithm you used and "
"the output, finally read back both files to confirm everything looks good"
)
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@@ -1,36 +0,0 @@
"""Multi-Step Research - Editor Output Example.
Researches Python frameworks, creates comparison code, runs it, writes a
markdown report, reads it back, lists files, and gets system info.
"""
from praisonaiagents import Agent
from praisonaiagents.tools import (
write_file, read_file, execute_command, list_files, get_system_info
)
try:
from praisonaiagents.tools import web_search
tools = [web_search, write_file, read_file, execute_command,
list_files, get_system_info]
except ImportError:
tools = [write_file, read_file, execute_command,
list_files, get_system_info]
agent = Agent(
instructions="You are a helpful research assistant.",
output="editor",
tools=tools,
approval=False,
)
agent.start(
"Research the top 3 Python web frameworks (Django, FastAPI, Flask), then: "
"1) Search the web for each framework's latest version and key features, "
"2) Create a file called /tmp/framework_comparison.py that contains a "
"Python dictionary with the comparison data, "
"3) Execute the code to verify the dictionary is valid, "
"4) Write a markdown report to /tmp/framework_report.md summarizing your "
"findings in a table format, "
"5) Read back the report file to verify it was written correctly, "
"6) List the files in /tmp to confirm both files exist, "
"7) Get system info to note what OS this report was generated on"
)
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"""System Audit - Editor Output Example.
Runs system commands, gathers info, writes a report, and reads it back.
"""
from praisonaiagents import Agent
from praisonaiagents.tools import (
write_file, read_file, execute_command
)
agent = Agent(
instructions="You are a helpful system administration assistant.",
output="editor",
tools=[write_file, read_file, execute_command],
approval=True,
)
agent.start(
"Investigate my system - get the current date, check disk usage, find out "
"what Python version is installed, list running processes sorted by memory, "
"then write all findings to /tmp/system_audit.md as a neat report and read "
"it back to verify"
)
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