fix(agent_utils.py): remove debug print statement for max_input_tokens to clean up code
refactor(anthropic_token_limiter.py): update state_modifier to use anthropic_trim_messages for better token management and maintain message structure
feat(main.py): add DEFAULT_MODEL constant to centralize model configuration
feat(main.py): enhance logging and error handling for better debugging
feat(main.py): implement state_modifier for managing token limits in agent state
feat(anthropic_token_limiter.py): create utilities for handling token limits with Anthropic models
feat(output.py): add print_messages_compact function for debugging message output
test(anthropic_token_limiter.py): add unit tests for token limit utilities and state management
refactor(agent_utils.py): import run_research_agent and run_web_research_agent from their respective modules to streamline the code structure and enhance clarity
This commit introduces a new `Trajectory` model to the database, which tracks the sequence of actions taken by agents, including tool executions and their results. The addition of the `TrajectoryRepository` allows for storing and retrieving these trajectories, enabling better analysis of agent behavior and debugging of issues.
Additionally, the commit refactors existing code to utilize the new repository and model, improving the overall architecture and maintainability of the codebase. This change is essential for enhancing the capabilities of the agent system and providing a more robust framework for future development.
style(agent_utils.py): format imports and code for better readability
refactor(agent_utils.py): standardize model name and cost calculation logic for clarity and maintainability
chore(anthropic_callback_handler.py): create a new file for the AnthropicCallbackHandler implementation and related functions
style(agent_utils.py): format imports and code for better readability
refactor(agent_utils.py): standardize model name and cost calculation logic for clarity and maintainability
chore(anthropic_callback_handler.py): create a new file for the AnthropicCallbackHandler implementation and related functions
* chore: refactor code for improved readability and maintainability
- Standardize variable naming conventions for consistency.
- Improve logging messages for better clarity and debugging.
- Remove unnecessary imports and clean up code structure.
- Enhance error handling and logging in various modules.
- Update comments and docstrings for better understanding.
- Optimize imports and organize them logically.
- Ensure consistent formatting across files for better readability.
- Refactor functions to reduce complexity and improve performance.
- Add missing type hints and annotations for better code clarity.
- Improve test coverage and organization in test files.
style(tests): apply consistent formatting and spacing in test files for improved readability and maintainability
* chore(tests): remove redundant test for ensure_tables_created with no models to streamline test suite and reduce maintenance overhead
* fix(memory.py): update is_binary_file function to correctly identify binary files by returning True for non-text mime types
style(tests): format code for better readability and consistency in test files
test(tests): update assertions and test cases for better clarity and maintainability
feat(ciayn_agent.py): pass config to CiaynAgent for improved functionality
fix(ciayn_agent.py): handle tool execution errors more gracefully with msg_list
feat(fallback_handler.py): enhance handle_failure method to utilize msg_list for better context
feat(fallback_handler.py): implement init_msg_list to manage message history effectively
test(test_fallback_handler.py): add unit tests for init_msg_list to ensure correct behavior
Ariel Frischer
AI Christianson