A Python framework and runtime for building agents with instructions, tools, memory and knowledge, then serving them behind an API. It emphasises low overhead per agent instance.
Image from the business's own website.
Source: Official GitHub repository. Last checked 2026-09-18. Spot an error? Tell us.
Nearby and similar
A written set of principles for building reliable LLM software, including factors on owning your prompts, owning context construction and treating tools as structured output.
A library for building LLM applications that also auto-tunes them, optimizing prompt text and few-shot demonstrations against a training set. It aims to make prompt tuning as routine as model training.
An open-source, locally running SDK that provides one interface for calling many model providers, with batching, retries and caching handled in the library rather than a hosted service.
The community-maintained continuation of the original AutoGen codebase, providing conversational multi-agent patterns with configurable system messages and group chat managers.
A development framework for generative AI applications with a chained request syntax that builds structured prompts and enforces an expected output schema.
A Microsoft research project that runs prompt controllers as WebAssembly modules inside the inference loop, letting custom logic constrain and steer token generation in real time.