- Removed unnecessary force_shutdown_telemetry() call from basic-agents.py to enhance clarity.
- Updated llm parameter in gemini-sequential.py from "gemini-2.5-pro" to "gemini-2.5-flash" for improved performance.
- Simplified the for loop in realtime-streaming.py by removing the return_generator=True argument, maintaining functionality while improving readability.
These changes aim to enhance code efficiency and maintainability without altering existing functionality.
- Add interpreter shutdown detection using sys.is_finalizing() and thread tests
- Skip PostHog operations when interpreter is shutting down to prevent:
- cannot schedule new futures after interpreter shutdown errors
- can''t register atexit after shutdown errors
- Event loop and threading errors during shutdown
- Enhanced error handling with specific shutdown error message filtering
- Apply shutdown prevention to flush, thread cleanup, and shutdown operations
- Maintain full backward compatibility with existing API
- Add comprehensive debug logging for shutdown scenarios
Fixes issue #1011 where PostHog telemetry was causing shutdown errors
when force_shutdown_telemetry() was called during interpreter shutdown.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Add thread safety with shutdown locks to prevent concurrent shutdown calls
- Fix double thread pool shutdown issue that could cause undefined behavior
- Implement memory leak prevention with bounded metrics storage (max 1000 entries)
- Replace unsafe daemon threads with ThreadPoolExecutor for better control
- Increase flush timeout from 2s to 5s for improved reliability
- Add comprehensive error handling with proper logging levels
- Split shutdown logic into maintainable helper methods
- Maintain full backward compatibility with existing API
These changes address critical thread safety issues while preserving all
existing functionality and ensuring reliable telemetry shutdown behavior.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Added timeout-based flush operation in MinimalTelemetry.shutdown()
- Replaced blocking PostHog flush with threaded timeout approach
- Prevents indefinite blocking during program termination
- Maintains backward compatibility and data integrity
- Fixes issue where PostHog queue.join() would hang on exit
Resolves#1009
Co-authored-by: Mervin Praison <[email protected]>
- Simplified logging of parameters in debug mode for better clarity.
- Enhanced error handling during LLM calls and reflections to ensure robust rollback mechanisms.
- Improved structure for handling knowledge search and prompt normalization.
- Reduced code duplication by integrating common functionalities into streamlined processes.
- Replace 13 scattered _cleanup_telemetry() calls with single try...finally block
- Ensure cleanup happens on ALL exit paths regardless of success/failure
- Standardize test tolerances to 2 threads across all test files
- Improve test error messages to show specific thread details
- Maintain backward compatibility and all existing functionality
- Reduce code duplication by 95% in chat method
🤖 Generated with Claude Code
Co-authored-by: Mervin Praison <[email protected]>
- Modified Agent.start() method to automatically consume the generator by default
- Added return_generator=True parameter for backwards compatibility
- Made MCP import optional to prevent import errors
- Updated examples to demonstrate new behavior
- Fixes issue where basic-agents.py would stop without producing output
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
This commit addresses the critical issue where PraisonAI agents would not
terminate properly after completing their tasks, requiring manual interruption.
Root Cause: PostHog telemetry client running in async mode creates
background threads that persist after standard shutdown() calls.
Solution:
- Enhanced telemetry shutdown mechanism with comprehensive thread cleanup
- Added force_shutdown_telemetry() function for robust cleanup
- Implemented timeout-based thread termination with proper error handling
- Updated agent._cleanup_telemetry() to use the new robust cleanup method
Impact:
- Agents now terminate cleanly without hanging
- Maintains full backward compatibility
- Comprehensive thread cleanup prevents resource leaks
- Timeout mechanism prevents infinite waiting
Co-authored-by: Mervin Praison <[email protected]>
- Added _cleanup_telemetry() calls to all return paths in chat() and achat() methods
- Ensures proper program termination without requiring Ctrl+C
- Maintains full backward compatibility with existing APIs
- Addresses syntax error concerns by implementing clean, error-free code
- Added test script to verify the fix works correctly
Co-authored-by: Mervin Praison <[email protected]>
- Fixed syntax error at line 1197 where code was outside try block
- Properly indented all main logic to be inside try block
- Ensures telemetry cleanup finally block executes correctly
- Maintains backward compatibility
- No breaking changes
Co-authored-by: Mervin Praison <[email protected]>
- Added telemetry cleanup to all agent entry points (chat, achat, execute, aexecute)
- Fixed streaming issue by adding cleanup to _start_stream method
- Removed duplicate cleanup from convenience methods
- Ensures proper program termination regardless of which method is used
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]>
- Fix memory record citation duplication (lines 861, 880, 933 in memory.py)
- Make vector dimensions configurable based on embedding model
- Add _get_embedding_dimensions method for proper dimension detection
- Support multiple embedding models (text-embedding-3-small, 3-large, ada-002)
- Improve citation format to use record IDs instead of duplicating text
🤖 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]>
- Fix chat_history_length initialization bug in both streaming methods
- Add safe array access checks for chunk.choices in OpenAI client
- Add JSON parsing error handling for malformed tool arguments
- Remove unused imports and variables
- Update test files to use mock LLM instead of real APIs
- Ensure robust error handling throughout streaming pipeline
These fixes address all critical issues identified by code review bots
while maintaining backward compatibility and streaming functionality.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Add thread locks to MCPToolRunner._tool_timings and MinimalTelemetry._metrics
- Fix initialization timeout bug where telemetry tracking was bypassed
- Move timing measurement after initialization check for accuracy
- Ensure telemetry is captured for all tool execution outcomes
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]>
- Add proper cleanup in Agent.start() and Agent.astart() methods using try-finally blocks
- Implement _cleanup_telemetry() method to ensure telemetry system shutdown
- Prevent hanging after agent execution by properly shutting down background threads
- Add test files to verify the termination fix works correctly
Fixes#987🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <[email protected]>
Fixes critical issues identified in PR review:
- Move early stopping logic from inside ''if tool_calls:'' block to ''else:'' block
- Fix logic placement in both sync and async execution paths
- Remove unreachable code in _generate_ollama_tool_summary()
- Ensure early stopping triggers correctly when Ollama returns empty responses after tool execution
This ensures the fix properly handles the Ollama edge case where it returns
empty responses after successful tool execution, preventing infinite loops
while allowing proper sequential tool calls.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
Ensures TelemetryCollector.stop() calls shutdown() instead of just flush()
for consistency with the main atexit handler and proper PostHog session cleanup.
Co-authored-by: Mervin Praison <[email protected]>
- Only trigger early stopping when no new tool calls are present in current response
- Prevents premature termination during sequential tool calls like get_stock_price -> multiply
- Maintains backward compatibility while fixing issue #964
- Applied to both sync and async execution paths
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Add early stopping logic for Ollama after first successful tool execution
- Enhance tool summary generation to create more natural, OpenAI-like responses
- Apply fix to both sync and async execution paths
- Prevent multiple tool calls when execution is complete
- Maintains full backward compatibility
Co-authored-by: Mervin Praison <[email protected]>
- Added _final_display_shown = False reset in achat() method to match chat() method
- Ensures proper display flag management for both sync and async agent interactions
- Maintains consistency across all agent conversation entry points
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Removed unused custom_display_fn function that was defined but never called
- Cleanup after streaming fix to avoid confusion
- No functional changes, streaming fix remains intact
Co-authored-by: Mervin Praison <[email protected]>
- Remove dependency on verbose=True for streaming display function
- Allow stream=True to work with verbose=False for real-time output
- Maintain backward compatibility with existing verbose=True usage
- Fixes issue where Gemini/Google Vertex AI streaming was buffered
Resolves#956
Co-authored-by: Mervin Praison <[email protected]>
Adds missing callback execution to tool-based responses and JSON/Pydantic
output paths in the achat() method to ensure consistent task metadata
propagation across all execution flows.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Modified display condition to show formatted output for both OpenAI and custom LLM providers (like Gemini)
- Added _final_display_shown flag to prevent duplicate displays within same execution
- Reset flag at start of each chat() call for new conversations
- Maintains backward compatibility and existing verbose control behavior
Fixes#958🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Add task_name, task_description, task_id parameters to achat method signature
- Update all achat call sites in agents.py to pass task parameters where available
- Provide fallback None values for API handler contexts without task context
- Fix Agent.aexecute to extract task info when available
- Resolves NameError: name ''task_name'' is not defined in parallel agent execution
Fixes persistent errors in agentic parallelization workflows where callbacks
expect task context parameters but they were not being passed correctly through
the async execution chain.
Co-authored-by: Mervin Praison <[email protected]>
- After successful tool execution, give Ollama LLM explicit prompt for final answer
- Only generate tool summary if LLM fails to respond to explicit prompt
- Prevents premature tool summary generation after sequential execution
- Applied to both sync and async tool execution loops
- Preserves infinite loop prevention while enabling natural responses
Fixes#940
Co-authored-by: Mervin Praison <[email protected]>
- Replace hardcoded iteration threshold (3) with class constant OLLAMA_SUMMARY_ITERATION_THRESHOLD
- Add proper constant definition for better maintainability and readability
- Applied consistently to both sync and async methods
- Addresses reviewer feedback about magic numbers
- Ensures proper tool result accumulation across iterations
Co-authored-by: Mervin Praison <[email protected]>
- Add accumulated_tool_results list to store tool results across all iterations
- Prevents loss of tool execution history when generating summaries
- Applied to both sync (get_response) and async (get_response_async) methods
- Maintains backward compatibility with existing functionality
- Fixes issue where only current iteration''s tool results were available to summary generation
Co-authored-by: Mervin Praison <[email protected]>
- Remove redundant length check in _generate_ollama_tool_summary
- Simplify verbose conditional checks in both sync and async methods
- Add safety checks to prevent infinite loops after 5 iterations
- Maintain backward compatibility with other LLM providers
- Add comprehensive test suite to validate fixes
Co-authored-by: Mervin Praison <[email protected]>
- Replace manual symbol list with dynamic import using
- Use for automatic maintenance
- Remove manual tracking of imported symbols
- Reduce code from 49 to 17 lines
- Future-proof: automatically inherits new exports from praisonaiagents
Co-authored-by: Mervin Praison <[email protected]>
- Modified tool execution logic to allow at least 3 iterations before triggering summary generation
- This prevents Ollama from prematurely terminating when sequential tool calls are needed
- Applied fix to both conversation() and reasoning() methods in LLM class
- Resolves issue where Ollama would generate tool summaries too early, blocking proper tool chaining
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Mervin Praison <[email protected]>
- Fixed critical bug where __all__ list included symbols from praisonaiagents
even when import failed due to missing dependencies
- Implemented dynamic __all__ construction based on successfully imported symbols
- Maintained backward compatibility while fixing the cursor review issue
- Added comprehensive test files to verify fix and backward compatibility
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Changed register_callback to register_display_callback
- Ensures test runs correctly and validates the fix
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
This change allows users to import from the main PraisonAI package instead of
just from praisonaiagents, while maintaining full backward compatibility.
Changes:
- Modified src/praisonai/praisonai/__init__.py to re-export all classes from praisonaiagents
- Added comprehensive __all__ list for proper exports
- Maintained backward compatibility - all existing imports continue to work
New usage patterns now supported:
- from praisonai import Agent, Task, PraisonAIAgents
- from praisonai import Tools, Memory, Knowledge, etc.
Backward compatibility maintained:
- from praisonaiagents import Agent, Task, PraisonAIAgents (still works)
Tests added:
- test_import_conversion.py: Comprehensive test suite
- test_issue_950.py: Specific test for the issue requirements
Resolves#950🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Added iteration count threshold (>=5) before generating Ollama tool summary
- Prevents premature termination after first tool call in sequential execution
- Maintains infinite loop prevention for truly stuck scenarios
- Applied fix to both sync and async methods in llm.py
- Preserves backward compatibility with other LLM providers
Fixes issue where Ollama would terminate after first tool call instead of
continuing with sequential execution (e.g., get_stock_price -> multiply)
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Add condition to check if final_response_text is already set before overwriting
- Fix applies to both sync (get_response) and async (get_response_async) methods
- Prevents tool summary from being lost when Ollama returns empty response after tool execution
- Maintains backward compatibility for non-Ollama providers
- Resolves issue where agent.start() returns None despite successful tool execution
Fixes the remaining issue from PR #943 where tool summary was generated but overwritten
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]>
- Extract duplicated Ollama tool summary logic into _generate_ollama_tool_summary() helper method
- Replace magic number 10 with OLLAMA_MIN_RESPONSE_LENGTH constant
- Fix test_tool_summary_generation() to call production code instead of reimplementing logic
- Add comprehensive test scenarios for different response types and provider types
- Maintain backward compatibility and fix functionality
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]>
- Fixed aggressive underscore collapsing that broke dunder names like __init__
- Now only collapses 5+ consecutive underscores instead of any consecutive underscores
- Preserves trailing underscores in double underscore patterns (__name__)
- Handles edge case where purely invalid characters become single underscore
- All 15 test cases now pass including magic method names
- Maintains backward compatibility for AutoGen v0.2, CrewAI, PraisonAI
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Add special handling for Ollama models in both sync and async tool execution loops
- When Ollama returns empty/minimal response after tool execution, generate summary from tool results
- Prevents infinite loops while maintaining functionality for other LLM providers
- Add comprehensive test suite to validate fix and ensure backward compatibility
- Fixes issue where Ollama models would repeat tool calls indefinitely
Co-authored-by: Mervin Praison <[email protected]>
AutoGen v0.4 requires agent names to be valid Python identifiers,
but role names like ''Narrative Designer'' contain spaces which are invalid.
- Added sanitize_agent_name_for_autogen_v4() function to convert any
string to a valid Python identifier
- Applied sanitization only in AutoGen v0.4 code path for backward compatibility
- AutoGen v0.2 and other frameworks remain unchanged
- Handles spaces, special characters, Python keywords, and edge cases
Resolves issue where ''Narrative Designer'' role causes error:
''The agent name must be a valid Python identifier''
Co-authored-by: Mervin Praison <[email protected]>
- Improved session memory persistence for agent chat history
- Optimised methods for saving and restoring agent chat histories
- Ensured compatibility with existing code while enhancing functionality
- Addressed issues related to chat history loss during session transitions
Co-authored-by: Mervin Praison <[email protected]>
- Fixed session memory persistence issue where state was not being restored correctly
- Disabled embeddings in default memory config to avoid OpenAI API key requirement
- Changed search queries from metadata-based to content-based for SQLite compatibility
- Fixed search query from "type:session_state" to "Session state:" to match stored content
- Fixed agent chat history search from "type:agent_chat_history" to "Agent chat history for"
- Session state persistence now works correctly across session save/restore cycles
- Agent chat history persistence now works correctly across session save/restore cycles
- Maintains backward compatibility with existing code
- Fixes#930🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Claude <[email protected]>
- Fix f-string issues on lines 275, 300 (remove unnecessary f prefixes)
- Fix chat history initialization to always set agent.chat_history even if empty
- Fix critical data loss bug in _save_agent_chat_histories method
- Now prioritizes live agent history but falls back to restored history
- Prevents chat history loss when agents aren''t re-instantiated after restore
Co-authored-by: Mervin Praison <[email protected]>
- Add increment_state method (lines 356-359) for numeric state management
- Fix empty chat history save condition to track agent existence properly
- Improve robustness by checking agent is not None before accessing chat_history
- Enhance memory persistence by saving even empty chat histories to track agent state
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Mervin Praison <[email protected]>
Co-Authored-By: Claude <[email protected]>
- Replace insecure eval() usage with ast-based safe expression evaluator in README and combined examples
- Remove unused os imports from all Python example files
- Update comments to show import os inline for API key setup
- Addresses security vulnerabilities identified in code review
Co-authored-by: Mervin Praison <[email protected]>
- Add Google Search tool example (gemini-google-search.py)
- Add URL Context tool example (gemini-url-context.py)
- Add Code Execution tool example (gemini-code-execution.py)
- Add combined tools example (gemini-internal-tools-combined.py)
- Add comprehensive documentation (README_GEMINI_INTERNAL_TOOLS.md)
These examples demonstrate how to use Gemini''s built-in internal tools
through PraisonAI''s existing tool system without any code changes.
Co-authored-by: Mervin Praison <[email protected]>
- Add missing sys.path setup to test_self_reflection_with_tools_verification.py
- Remove unnecessary f-string prefixes from print statements
- Add missing newlines at end of files for PEP 8 compliance
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <[email protected]>
- Fixed duplicate callback issue in src/praisonai-agents/praisonaiagents/llm/llm.py where callbacks were executed multiple times
- Added callback_executed flag to track and prevent duplicate callback invocations
- Ensured callbacks are only executed once per interaction, either through display_interaction or direct execute_sync_callback calls
- Improved generation_time consistency by calculating once and reusing the value
- Added comprehensive test files and reproduction scripts to verify the fix
- Added ISSUE_878_REPRODUCTION_GUIDE.md documenting the issue and solution
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <[email protected]>
Co-Authored-By: Mervin Praison <[email protected]>
- Fix async functions to use execute_callback() instead of direct async_display_callbacks access
- Add missing execute_sync_callback() calls to sync display functions (instruction, tool_call, error, generating)
- Ensures both sync and async callbacks are triggered consistently for all display types
- Maintains backward compatibility through existing parameter filtering
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
Fixes the duplicate callback issue by:
- Adding execute_sync_callback calls to display_interaction and display_self_reflection
- Fixing test to patch the correct function location
- Ensuring backward compatibility by maintaining existing function signatures
- Adding comprehensive tests for callback system verification
This ensures that callbacks are called exactly once per LLM response while maintaining full backward compatibility.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Add callback inheritance in all 6 locations where loop tasks create subtasks in process.py
- Implement enhanced callback metadata support in task.py with run_context tracking
- Ensure backward compatibility with existing callback implementations
- Maintain proper callback chain for nested loop executions
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <[email protected]>
- Fix undefined variables in llm.py response() and aresponse() methods
by adding missing parameters to method signatures
- Fix backward compatibility by adding signature checking to callbacks
in display_interaction(), execute_callback(), and execute_sync_callback()
- Ensure consistent parameter propagation in agent.py get_response calls
- Legacy callbacks now work without TypeError, new callbacks get full context
Co-authored-by: Mervin Praison <[email protected]>
- Add _initialize_llm method to convert string identifiers to LLM instances
- Update __init__ to handle string LLM identifiers like ''gemini/gemini-2.5-flash-lite-preview-06-17''
- Maintain backward compatibility with existing LLM instances, dict configs, and None values
- Use same LLM initialization logic as Agent class for consistency
- Fixes issue #907 where LLMGuardrail failed with ''Unsupported LLM type'' error
Co-authored-by: Mervin Praison <[email protected]>
- Fixed bug in _build_completion_params method where output_json and output_pydantic
parameters were conditionally removed only when not None
- These custom parameters are now always removed before LiteLLM calls to prevent
"Unrecognized request arguments supplied" errors
- Maintains backward compatibility and preserves all existing functionality
- Addresses issue #905
Co-authored-by: Mervin Praison <[email protected]>
- Move test files from src/praisonai-agents/ to tests/ directory per repository guidelines
- Replace print statements and return values with proper assert statements
- Fix f-string issues without placeholders
- Add comprehensive exception handling for test failures
- Create improved test structure following repository patterns
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Use self.llm_model property instead of self.llm for more robust model name resolution
- Handles all LLM configuration types (strings, dicts, custom instances, base_url configs)
- Fixes potential AttributeError when self.llm is not set
- Maintains backward compatibility for all existing configurations
Co-authored-by: Mervin Praison <[email protected]>
Apply the same intelligent final answer detection logic that was added to the sync method (get_response) to the async method (get_response_async). This ensures consistent behavior between sync and async methods when detecting substantive final answers alongside tool calls.
The fix prevents redundant tool execution loops in async contexts by checking if the LLM provides substantive content (>10 characters) after tool execution, breaking the loop when appropriate.
Co-authored-by: Mervin Praison <[email protected]>
- Fixed issue where self-reflection failed when tools were present
- Added tools=formatted_tools parameter to reflection regeneration calls
- Ensures tools remain available throughout the reflection process
- Maintains backward compatibility with existing code
- Resolves issue #901🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Fixed issue where self-reflection was not working when tools were present
- Added tools=formatted_tools parameter to completion calls during reflection regeneration
- Self-reflection now maintains tool access throughout the reflection process
- Backward compatibility maintained, no existing features removed
- Resolves issue #901🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Mervin Praison <[email protected]>
- Add agent_name, agent_role, agent_tools, task_name, task_description, task_id to callbacks
- Update display_interaction() and adisplay_interaction() to accept new parameters
- Enhance LLM.get_response() to accept and pass task context
- Modify Agent.chat() to accept task parameters
- Update task execution in agents.py to pass task information
- Maintain backward compatibility with existing callbacks
- Add test scripts to verify functionality
Co-authored-by: Mervin Praison <[email protected]>
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <[email protected]>
body: '@claude review this issue and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes.'
})
body: '@claude review this issue and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. **Please create a PR using gh tool with your changes**.'
const reviewText = '@claude review if the changes made are as per the requirements';
const reviewText = '@claude review if the changes made in the above branch are as per the requirements and make changes to the above branch if issues still exist.';
"# ZeroScript: AI-Powered Browser Test Automation using PraisonAI Agent & OpenAI"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "O4PKfpalLsmn"
},
"source": [
"# Description"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CEG1pfumLysm"
},
"source": [
"AI-powered browser testing using PraisonAI Agent and OpenAI. Executes YAML-defined test cases, captures screenshots, logs interactions, and generates JSON reports. Ideal for intelligent, automated UI testing.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lbgxOG4gL1Wf"
},
"source": [
"[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/ZeroScript_AI_TestExecutor.ipynb)\n"
"AI-powered browser testing using PraisonAI Agent and OpenAI. Executes YAML-defined test cases, captures screenshots, logs interactions, and generates JSON reports. Ideal for intelligent, automated UI testing.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lbgxOG4gL1Wf"
},
"source": [
"[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/ZeroScript_AI_TestExecutor.ipynb)\n"
# Routing logic - this could be enhanced with more sophisticated rules
instructions="""You are a cost-optimization router. For simple tasks like basic questions,
definitions, or simple calculations, use the cheapest model. For moderate complexity tasks
like writing, analysis, or research, use the balanced model. For complex tasks requiring
deep reasoning, creativity, or specialized knowledge, use the premium model.""",
tools=[duckduckgo],
verbose=True
)
# Example tasks of varying complexity levels
# Simple task - should route to cheap model
print("="*60)
print("TESTING SIMPLE TASK (should use cheap model)")
print("="*60)
simple_result=router.start("What is the capital of France?")
print(f"Simple task result: {simple_result}")
# Moderate complexity task - should route to balanced model
print("\n"+"="*60)
print("TESTING MODERATE TASK (should use balanced model)")
print("="*60)
moderate_result=router.start("Write a brief analysis of renewable energy trends in 2024")
print(f"Moderate task result: {moderate_result}")
# Complex task - should route to premium model
print("\n"+"="*60)
print("TESTING COMPLEX TASK (should use premium model)")
print("="*60)
complex_result=router.start("Conduct a comprehensive strategic analysis of the impact of quantum computing on cybersecurity, including potential vulnerabilities and mitigation strategies")
print(f"Complex task result: {complex_result}")
# Research task with tool usage - should route appropriately
print("\n"+"="*60)
print("TESTING RESEARCH TASK WITH TOOLS")
print("="*60)
research_result=router.start("Research and analyze the latest developments in artificial intelligence regulation globally")
backstory="You are a research analyst who backs all claims with evidence and citations.",
instructions="Support all statements with references to research, studies, or credible data sources."
)
# Create tasks with specific guardrails
# Task with email format guardrail
email_task=Task(
name="email_communication",
description="Write a business proposal for a new software project, including contact information",
expected_output="Professional business proposal with contact email addresses",
agent=email_agent,
guardrail=email_format_guardrail,
max_retries=3
)
# Task with word count guardrail
content_task=Task(
name="detailed_analysis",
description="Write an analysis of cloud computing benefits for small businesses",
expected_output="Comprehensive analysis of cloud computing benefits (minimum 50 words)",
agent=content_agent,
guardrail=word_count_guardrail,
max_retries=3
)
# Task with professional tone guardrail
business_task=Task(
name="professional_advice",
description="Provide advice on handling difficult client relationships",
expected_output="Professional advice maintaining appropriate business tone",
agent=business_agent,
guardrail=professional_tone_guardrail,
max_retries=3
)
# Task with factual accuracy guardrail
research_task=Task(
name="research_report",
description="Analyze the impact of artificial intelligence on job markets",
expected_output="Research-backed analysis with citations and evidence",
agent=research_agent,
guardrail=factual_accuracy_guardrail,
max_retries=3
)
# LLM-based guardrail example
llm_guardrail_agent=Agent(
name="LLMGuardrailAgent",
role="Content Quality Validator",
goal="Validate content quality using LLM-based assessment",
backstory="You are a quality assurance specialist who uses AI to validate content quality.",
instructions="Create marketing content that is engaging, accurate, and compliant with advertising standards."
)
# Task with LLM-based guardrail
llm_guardrail_task=Task(
name="marketing_content",
description="Create a marketing email for a new fitness app targeting busy professionals",
expected_output="Engaging marketing email that is accurate and compliant",
agent=llm_guardrail_agent,
# LLM-based guardrail using natural language validation
guardrail="Validate that this marketing content is: 1) Factually accurate with no false claims, 2) Compliant with advertising standards (no misleading statements), 3) Engaging and professional in tone, 4) Includes a clear call-to-action. If any criteria are not met, explain what needs to be improved.",
This directory contains examples demonstrating how to use Google Gemini's built-in internal tools through PraisonAI. These tools are natively supported by Gemini models and provide powerful capabilities without requiring external tool implementations.
## Available Internal Tools
### 1. Google Search (`googleSearch`)
- **Purpose**: Real-time web search with automatic result grounding
- **Use Cases**: Current events, latest information, fact-checking
- **Example**: `gemini-google-search.py`
### 2. URL Context (`urlContext`)
- **Purpose**: Fetch and analyze content from specific URLs
- **Use Cases**: Document analysis, web content summarization, research
- **Example**: `gemini-url-context.py`
### 3. Code Execution (`codeExecution`)
- **Purpose**: Execute Python code snippets within the conversation
- **Use Cases**: Calculations, data analysis, code validation
- **Example**: `gemini-code-execution.py`
## Prerequisites
1.**Gemini API Key**: Set your Google AI Studio API key
```bash
export GEMINI_API_KEY="your-api-key-here"
```
2. **Compatible Model**: Use Gemini models that support internal tools:
- `gemini/gemini-2.0-flash` (recommended)
- `gemini/gemini-2.0-flash-thinking-exp`
- Other Gemini 2.0+ models
3. **PraisonAI Installation**: Ensure you have the latest version
instructions="You are a system architecture AI agent. "
"Help users design scalable, efficient, and robust system architectures. "
"Provide guidance on microservices, cloud infrastructure, database design, API design, and system integration patterns.",
llm="openrouter/moonshotai/kimi-k2"
)
response=agent.start("Hello! I'm your system architecture assistant. "
"How can I help you design and optimize your system architecture today?")
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