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Basic Information

microchain is a lightweight Python library for building function-calling LLM agents with minimal overhead. It provides abstractions to connect language model generators to a small execution engine so developers can define callable functions as plain Python objects and expose them to an LLM. The README shows how to install via pip and instantiate generators for OpenAI chat and text APIs, use Hugging Face or Vicuna-style chat templates, and wrap generators into an LLM object. The library emphasizes explicit function definitions with type annotations, automated help text generation, and a simple Agent/Engine orchestration model where registered functions, prompt templates, and bootstrapped calls drive the agent behavior. The project includes examples and demonstrates step-by-step reasoning and deterministic function execution.

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App Details

Features
The project centers on function-based LLM interaction. Key features include: Python Function classes with description and example_args properties and type-annotated __call__ signatures, automatic help generation for inclusion in prompts, an Engine that registers functions and exposes engine.help to the agent prompt, and an Agent that runs iterations, executes function calls and records results. It supports multiple generator backends such as OpenAITextGenerator and OpenAIChatGenerator, and template helpers like HFChatTemplate and VicunaTemplate to shape model input. Bootstrapping allows predefined function calls whose outputs are prepended to chat history. Examples and a small API surface focus on clarity and low bloat, favoring explicit function invocation and simple orchestration over heavy frameworks.
Use Cases
microchain helps developers prototype and run LLM agents that need reliable, structured interactions with application logic. By turning domain capabilities into first-class Python functions with typed arguments and example invocations, the repo makes it easy to instruct models to call code rather than generate free text, which improves correctness and traceability. The Engine/Agent pattern simplifies registering capabilities, including a built-in help generator for prompt composition and bootstrap support to seed reasoning or example calls. Support for OpenAI and Hugging Face chat/text backends and straightforward templates lowers integration friction. Overall it is useful for building calculators, deterministic function-driven assistants, and small agent workflows where control, transparency, and minimal dependencies matter.

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