Refactor app.py to import main from main_v2.py, enhancing modularity. Introduce main_v2.py with a new agent implementation, including OpenTelemetry integration and YAML-based prompt templates. Update requirements.txt to reflect the latest smolagents version. Add tasks.json for VSCode to streamline development workflows.

Integrate telemetry support in main.py by registering the SmolagentsInstrumentor for enhanced monitoring. Update the main function to simplify question enhancement instructions and improve clarity. Modify extract_final_answer utility to prioritize 'final_answer_text' in results. Update requirements.txt to include telemetry dependencies for smolagents.

Enhance agent capabilities by integrating YAML-based prompt templates for web, data analysis, and media agents in agents.py. Update main.py to initialize agents with these templates, improving task handling and response accuracy. Introduce utility functions for extracting final answers and managing prompts, streamlining the overall agent workflow.

Add new agent functionalities by creating agents for web browsing, data analysis, and media handling in agents.py. Introduce various tools in tools.py for web searching, webpage browsing, image analysis, PDF reading, CSV parsing, and date extraction, enhancing the overall capabilities of the agent system.

Add DuckDuckGoSearchTool and VisitWebpageTool to agent initialization in main.py. Update tools configuration to enhance agent functionality and enable visualization of tools used during execution.

Enable debug mode for LiteLLM in main.py to enhance logging capabilities. This change allows for improved traceability during execution.

Add sleep functionality in process_step to introduce a delay after processing each action step. Enhance logging to include step details and sleep notifications for better traceability during execution.

Implement question counter in BasicAgent and modify response logic in app.py. Update main function in main.py to streamline task execution and improve logging. Prepare for potential rate limiting with a placeholder for wait time.

Add .env.example for API configuration and update .gitignore to exclude .env files. Refactor app.py to implement BasicAgent class, replacing AgentRunner, and improve error handling and logging. Remove unused configuration and graph modules to streamline the codebase.

Add configuration, graph, runner, and tools modules to enhance agent functionality. Introduce a Configuration class for managing parameters, implement an AgentRunner for executing the agent graph, and create tools for general search and mathematical calculations. Update test_agent.py to reflect new import paths and improve overall code organization.

Refactor app.py and update import paths in test_agent.py to improve code organization. Introduce new files for agent configuration, graph definition, and tools, enhancing the overall structure and functionality of the agent system.

Enhance AgentRunner and graph functionality by adding answer extraction logic and improving logging throughout the processing flow. Update the handling of interrupts and state management to ensure clarity in debug output. Refactor the should_continue function in graph.py to better manage completion states and improve user interaction.

Enhance AgentRunner and graph functionality by introducing memory management and improved state handling. Update __call__ method to support both question input and resuming from interrupts, while adding new memory-related fields to track context, actions, and success/error counts. Refactor step callback logic for better user interaction and state management.

Refactor graph.py and test_agent.py by removing unused imports to streamline code and improve readability. This includes the removal of uuid, requests, and unnecessary components from langchain_core.

Refactor agent.py and graph.py to enhance agent functionality and logging. Introduce Configuration class for managing parameters, improve state handling in AgentRunner, and update agent graph to support step logging and user interaction. Add new tests for agent capabilities and update requirements for code formatting tools.

Add test_agent.py with unit tests for AgentRunner functionality, including tests for question handling and basic math calculations. Implement logging for better traceability during tests.

Fix logging in AgentNode's step callback in graph.py to display action directly instead of action step, improving clarity in debug output.

Refactor AgentNode in graph.py to utilize CodeAgent instead of ToolCallingAgent, update prompt template loading to use code_agent.yaml, and enhance step callback logging for improved debugging.

Enhance logging in AgentNode's __call__ method in graph.py to track state before and after processing, including detailed information about messages, question, and answer.

Add max_steps parameter to ToolCallingAgent in graph.py for improved agent control

Enhance graph.py to load prompt templates from local file and configure LiteLLM; add new prompt files for tool calling and code agent

Add initial implementation of AgentRunner and agent graph; include .gitignore and update requirements

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