function-calling

Enables LLM models to invoke external tools and APIs by defining function schemas (name, description, parameters) that the model understands natively. The system translates natural language model outputs into structured function calls by parsing the model's function_call response format, matching it against registered schemas, and executing the corresponding handler. Supports OpenAI's function calling API format with extensible provider adapters for other LLM backends.

Provides a component-based architecture (ToolComponent) where developers can register custom tools by defining Python classes with decorated methods that map to function schemas. The system automatically generates JSON Schema from method signatures, binds handler functions to schema definitions, and manages the lifecycle of tool instances. Supports dependency injection for tool initialization and context passing between tool calls.

Intercepts the LLM's function_call response format, parses the function name and parameters from the model output, validates parameters against the registered schema, and routes the call to the appropriate handler. Implements error handling for invalid function names, missing parameters, or type mismatches, with fallback mechanisms to re-prompt the model or return structured error responses. Manages the execution context and passes results back to the model for multi-turn reasoning.

Implements a loop where the agent invokes a function, receives the result, and passes it back to the LLM as context for the next reasoning step. The system maintains conversation history including function calls and results, allowing the model to refine its approach based on tool outcomes. Supports conditional branching where the model decides whether to call another tool, return a final answer, or request clarification based on intermediate results.

Abstracts the differences between OpenAI's function_call format, Anthropic's tool_use format, and other LLM providers behind a unified interface. The system translates between provider-specific schemas and a canonical internal representation, allowing agent code to remain provider-agnostic. Supports dynamic provider switching at runtime and fallback to alternative providers if the primary provider fails.