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Basic Information

Superagent is an open source AI assistant framework and API designed to let developers add powerful AI assistants to their applications. It provides infrastructure to build assistants that use large language models, retrieval-augmented generation, and generative AI. The project targets common AI application types such as document question answering, chatbots, co-pilots, content generation, data aggregation, and workflow automation agents. It exposes a REST API and language SDKs so teams can integrate assistants into services and products. The repository emphasizes extensibility and production readiness by supporting memory, streaming responses, vectorization, third-party vector stores, and concurrency. The project is community driven, backed by Y Combinator, and includes documentation, tutorials, and example SDKs for common developer environments.

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Features
The README lists core capabilities including memory management for agents, streaming output support, and SDKs in Python and TypeScript with a community Swift SDK. Superagent exposes a REST API and supports API connectivity patterns, vectorization workflows, and integration with third-party vector stores such as Weaviate and Pinecone. It supports both proprietary and open-source LLMs and includes concurrency support for handling multiple API requests. The project provides documentation, tutorials, demo media, and community channels for contributors. The combination of RAG-friendly tooling, vector store compatibility, SDKs, streaming, and memory features is presented as the main technical feature set.
Use Cases
Superagent helps developers accelerate building production AI assistants by providing reusable infrastructure and APIs for common assistant capabilities. It reduces integration overhead for combining LLMs with vector stores and retrieval systems, enabling quick setups for document Q&A, chatbots, and copilot-style assistants. Streaming and memory features improve interactive user experiences, while SDKs and a REST API make it easier to integrate into existing stacks. Support for multiple LLM types and third-party vector stores gives teams flexibility to choose models and storage that meet their needs. Community documentation, tutorials, and contribution guidelines aim to lower the barrier to adoption and customization for teams building AI-driven features.

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