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agentkit

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Basic Information

AgentKit is a LangChain-based starter kit developed by BCG X to help developers rapidly build full‚Äëstack, chat‚Äëbased Agent applications. It provides a configurable backend and frontend scaffold so teams can experiment with constrained agent architectures and produce production‚Äëgrade MVP demos. The repository bundles a React/Nextjs UI and a Python FastAPI backend integrated with LangChain and Langsmith configuration, and includes orchestration for long running tasks, caching and data storage. The project is intended as a developer toolkit to configure agents and tools, to define Action Plans and Meta Agents that route execution, and to expose streaming intermediary outputs to the UI. It includes documentation, a Chinook database demo and recommended deployment and development workflows using Docker Compose.

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1779
App URL
https://github.com/BCG-X-Official/agentkit
Github Repository
https://github.com/BCG-X-Official/agentkit/blob/main/README.md

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App Details

Features
AgentKit combines a modular tech stack and preconfigured tooling to accelerate agent app development. Frontend features include a React/Nextjs chat UI with streaming, rendering of tables, visualizations and code, and configurable UX components using Tailwind and DaisyUI. Backend components use Python 3.10, FastAPI, sqlmodel and Pydantic 2.x, LangChain and Langsmith settings, Celery with Redis for queues and background work, and Postgres with pgvector for vector storage. The kit provides Action Plans and a Meta Agent to constrain routes and improve reliability, integration examples for authentication via NextAuth, Docker Compose for local deployment, linting, tests and pre‚Äëcommit hooks, a tool library and optional features such as feedback collection and user settings.
Use Cases
The repository helps developer teams prototype, iterate and scale agent applications by providing an opinionated, end‚Äëto‚Äëend starter architecture and ready examples. Its routing model (Meta Agent plus Action Plans) reduces unexpected agent behavior by steering execution along predefined paths. Streaming intermediary outputs and toolstep visualizations improve user transparency and observability. Built‚Äëin components for task queues, caching, vector storage and authentication speed up common implementation work, while Docker Compose simplifies local setup. Documentation, demos and evaluation hooks with LangSmith support onboarding and testing. The README also highlights known LLM risks and recommends production‚Äëgrade security considerations when adapting the starter kit.

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