- Add Code category in Key Features table (like Workflows)
- Add individual examples for each external agent:
- claude_code_example.py
- gemini_cli_example.py
- codex_cli_example.py
- cursor_cli_example.py
- external_agents_example.py (all integrations)
- code_editing_example.py
- Link to individual docs pages for each integration
- Organize code-related features under Code category
- Add autoagents_workflow_patterns.py with all 6 workflow patterns
- Update auto_agents_example.py with new features and CLI examples
- Add orchestrator-workers and evaluator-optimizer patterns to README CLI section
- Update pattern options table with all 6 patterns
- Add documentation links to examples
- Remove temporary debugging scripts (check_*.py, test_*.py)
- Improve exception handling in metrics display with specific error types
- Fix unused variable in cost estimation example
- Maintain zero performance overhead for existing features
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Add metrics parameter to Agent class with auto-display capability
- Fix missing run() method in PraisonAIAgents (add alias to start)
- Auto-display token usage when any agent has metrics=True
- Simplify all examples to use single parameter approach
- Maintain zero performance impact with O(1) operations
- Keep backward compatibility with existing methods
BREAKING: Massively simplified API - just Agent(metrics=True)!
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
Add 5 detailed examples demonstrating token tracking features:
- Basic token tracking for simple workflows
- Session metrics across multiple agents
- Cost estimation and budget management
- Telemetry integration for enterprise monitoring
- Advanced agent integration with real-time analysis
Each example includes documentation and best practices
for production usage. Examples are self-contained and
demonstrate zero-performance-impact implementation.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Fix ZeroDivisionError in 02_multi_agent_workflow_monitoring.py by adding zero-check for call counts
- Improve async handling in 04_async_agent_monitoring.py with better simulation and timing
- Fix UnboundLocalError in 05_error_handling_monitoring.py by correcting variable references
- Add zero-check protection in 07_hierarchical_agents_monitoring.py to prevent division errors
- Remove unused json import from 08_custom_tools_monitoring.py
- Remove unused timedelta import from 09_streaming_monitoring.py
- Fix UnboundLocalError in 10_comprehensive_dashboard.py by initializing avg_function_time variable
🤖 Generated with Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <[email protected]>
- Create examples/python/monitoring/ directory with 10 diverse examples
- Cover basic monitoring, multi-agent workflows, tools, async, errors, memory, hierarchical, custom tools, streaming, and comprehensive dashboard
- Each example demonstrates different aspects of the performance monitoring system
- Include detailed README with usage patterns and best practices
- Show practical integration of @monitor_function and track_api_call
- Demonstrate performance analytics, reporting, and optimization insights
Co-authored-by: Mervin Praison <[email protected]>
- Remove unused imports (inspect, timedelta, time, deque, etc.)
- Fix division by zero error in performance trend analysis
- Improve call chain building algorithm for better efficiency and accuracy
- Replace bare except clauses with specific Exception handling
- Use collections.deque with maxlen instead of manual truncation
- Remove unused variables and loop variables
- Fix f-strings without placeholders
- Replace magic numbers with named constants
- Improve error handling specificity
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
This implementation addresses all requirements from Issue #1035:
Features Added:
- PerformanceMonitor class for function performance evaluation with detailed statistics
- FunctionFlowAnalyzer for execution flow visualization and bottleneck identification
- API call tracking and performance comparison (fast vs slow calls)
- Real-time performance reporting and comprehensive analytics
- CLI interface for easy command-line access to performance data
- Full backward compatibility with existing telemetry system
Key Capabilities:
1. Function Performance Evaluation: Decorators and context managers for automatic timing
2. Function Flow Analysis: Visual execution flow with bottleneck detection
3. Execution Speed Analysis: Min/max/average timing with trend analysis
4. API Call Performance: Success rates, response times, reliability metrics
5. API Call Visibility: Complete tracking and history of all API interactions
Files Modified:
- Enhanced telemetry/__init__.py to expose new performance tools
- Added performance_monitor.py with core monitoring functionality
- Added performance_utils.py with advanced analysis and visualization
- Added performance_cli.py for command-line interface
- Added comprehensive demo and examples
Integration:
- Seamless integration with existing PraisonAI telemetry infrastructure
- Zero breaking changes to existing functionality
- Optional import structure for graceful degradation
- Thread-safe operations with memory management
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Fixed core ContextAgent functionality with proper imports and async support
- Added comprehensive error handling and robust implementation
- Cleaned up imports and fixed async method patterns
- Moved test file from root to proper tests/ directory location
- Made implementation fully functional with all required methods
- Enhanced examples with working context engineering workflows
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <[email protected]>
- Add ContextAgent class with comprehensive Context Engineering capabilities
- Implements automated context generation for AI coding assistants
- Features: codebase analysis, pattern extraction, validation loops, PRP generation
- 10x better than prompt engineering, 100x better than vibe coding
- Includes factory function create_context_agent() for easy instantiation
- Add comprehensive examples in agents/ and concepts/ directories
- Full backward compatibility maintained with existing PraisonAI functionality
- Complete test suite with 7/7 tests passing
Co-authored-by: Mervin Praison <[email protected]>
- Fix ID generation consistency between MongoDB and SQLite storage
- Fix inconsistent error handling in memory operations
- Fix security issue with hardcoded API key placeholders in examples
- Fix unused imports in mongodb_tools.py
- Fix pymongo version to >= 4.6.3 to address security vulnerabilities
- Improve datetime import organization
- Ensure consistent storage order for better reliability
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
Add comprehensive MongoDB integration for PraisonAI agents including:
**Memory Provider**:
- MongoDB as persistent memory storage option
- Vector search capabilities with Atlas Vector Search
- Support for both key-value and vector operations
- Quality scoring and filtering
- User-specific memory isolation
- Graceful fallback to existing SQLite/ChromaDB
**Knowledge Store**:
- MongoDB as vector store for knowledge base
- Document processing with embeddings
- Vector similarity search for knowledge retrieval
- Chunking and metadata management
- Compatible with existing Mem0/ChromaDB workflow
**Tools Integration**:
- Complete MongoDB tools suite (CRUD operations)
- Vector search and text search capabilities
- Collection management and indexing
- Production-ready connection handling
- Error handling and logging
**Examples**:
- Memory provider example with persistent storage
- Knowledge store example with vector search
- Tools integration example with database operations
- Comprehensive business scenario simulation
- Complete documentation and best practices
**Technical Details**:
- Optional dependency: pip install ''praisonaiagents[mongodb]''
- Backward compatibility maintained
- Supports both local MongoDB and Atlas
- Configurable connection pooling and timeouts
- Comprehensive error handling
Resolves#992🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Mervin Praison <[email protected]>
- Enhanced telemetry.track_tool_usage() to accept execution_time parameter
- Added timing instrumentation to MCPToolRunner.call_tool() method
- Tool usage timing is now captured and stored in telemetry metrics
- Maintains backward compatibility with no breaking changes
- Addresses issue #988 where tool usage time was always 0
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Move callback systems to general/
- Move memory examples to memory/
- Move state management to stateful/
- Move hierarchical agents to agents/
- Move guardrails to guardrails/
- Move self-reflection to concepts/
Co-authored-by: Mervin Praison <[email protected]>
Simplified all 7 advanced examples to follow flat format requirements:
- Removed 20+ custom classes across all examples
- Reduced total lines from 2,829 to 514 (82% reduction)
- Limited to 1-2 agents max per example
- Focused on core functionality demonstration
- Maintained advanced feature examples while making them accessible
Changes per file:
- advanced-callback-systems.py: 404→59 lines, removed 3 classes
- advanced-graph-memory-integration.py: 226→75 lines, reduced to 1 agent
- advanced-memory-search-optimization.py: 568→76 lines, removed 4 classes
- advanced-state-management-patterns.py: 437→80 lines, removed 3 classes
- autoagents-hierarchical-generation.py: 158→73 lines, removed custom class
- production-guardrails-patterns.py: 480→65 lines, removed 5 classes
- self-reflection-optimization-patterns.py: 556→46 lines, removed 4 classes
All examples now follow the established flat format pattern with simple,
direct agent/task creation and minimal code complexity.
🤖 Generated with Claude Code
Co-Authored-By: Claude <[email protected]>
Mervin Praison