- Enhance message streaming with more precise content and tool call handling
- Add logic to only send and store meaningful assistant messages and tool calls
- Prevent sending empty or redundant messages during stream processing
- Improve tool call and content update tracking in agent stream method
- Optimize message inclusion in recursive thread generation
- Add support for initial non-tool response and subsequent tool-enabled processing
- Implement `PendingToolCall` to manage incremental tool call updates
- Update `Delta` and related types to support more flexible tool call streaming
- Modify agent stream processing to handle tool calls with improved state management
- Add robust handling for tool call deltas and function call arguments
- Implement a maximum recursion depth of 30 for agent thread processing
- Add recursion depth tracking to prevent potential infinite loops
- Provide user-friendly message when maximum recursion depth is reached
- Update debug logging to include current recursion depth
- Modify both synchronous and streaming thread processing methods
- Refactor agent stream processing to use a recursive approach for handling tool calls
- Enhance tool execution with more robust error handling and result tracking
- Improve stream chunk processing with detailed state management
- Add support for recursive thread generation based on tool call results
- Implement cloning for LiteLLMClient to support stream processing tasks
- Wrap agent tools in an Arc for safe concurrent access
- Modify tool addition methods to work with Arc-wrapped HashMap
- Ensure thread-safe tool registration and retrieval
- Update stream processing to use Arc-cloned tools reference
- Add comprehensive debug logging for agent thread processing
- Modify agent thread to use user ID for streaming subscription
- Update stream processing to include detailed tool call and content logging
- Improve error handling and visibility in stream processing
- Add user context to agent thread initialization
- Remove redundant tool execution handling in stream processing
- Add `ValueToolExecutor` to convert tool outputs to `serde_json::Value`
- Introduce `IntoValueTool` trait for easy value type conversion
- Update agent tool addition methods to use new value conversion mechanism
- Simplify tool registration by automatically converting tool outputs
- Remove previous manual boxing and type conversion logic
- Add generic Output type to ToolExecutor trait
- Update file tools to use strongly-typed output structs
- Modify agent and tool implementations to support generic output
- Improve error handling and result reporting in file-related tools
- Add more detailed status messages for file operations
- Added environment variable-based LLM client initialization in `Agent::new()`
- Introduced `add_tool()` and `add_tools()` methods for more flexible tool registration
- Implemented new `get_name()` method for `ToolExecutor` trait
- Added comprehensive test cases for Agent with and without tools
- Updated `AgentThread` with a convenient constructor method
- Temporarily commented out unused tool modules
- Added debug print in LiteLLM client for response logging
- Relocated `ToolExecutor` trait from `agent/types.rs` to `tools/mod.rs`
- Updated import paths in `agent/agent.rs` to reflect new trait location
- Simplified `types.rs` by removing unused imports and trait definition
- Prepared for more comprehensive tool management in the tools module
- Renamed `Thread` to `AgentThread` for clarity
- Modified `ToolExecutor` to return `serde_json::Value` instead of `String`
- Updated message processing to handle new message structures
- Improved content handling in streaming and tool call scenarios
- Simplified message content extraction and serialization
- Commented out imports and module exports for `LiteLLMClient` and `Agent`
- Removed active module implementations in `mod.rs` files
- Simplified type definitions and test cases
- Prepared for potential future reimplementation or restructuring
- Simplified message and tool structures in `types.rs`
- Updated `agent.rs` to use new type structures
- Enhanced error handling in agent tests with detailed error printing
- Updated `.cursorrules` with debugging recommendation
- Removed redundant fields and improved type flexibility
- Streamlined serialization and deserialization of AI client types
- Updated `types.rs` to support more complex message and response structures
- Added support for multi-content messages with `Content` struct
- Introduced optional fields and improved serialization handling
- Enhanced type flexibility for AI client responses
- Updated test cases to reflect new type structures
- Added `Default` implementation for `LiteLLMClient`
- Improved support for tool calls and streaming responses
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