Overview
fastapi-langgraph-chatbot is a local chatbot built with FastAPI and LangGraph. It is designed for local execution without external Docker, PostgreSQL, or Redis. The project supports multiple LLM providers, including Groq, Ollama, OpenAI, Google Gemini, and Anthropic. It uses SQLModel with SQLite by default and supports PostgreSQL through DATABASE_URL. Chat history and session management are persisted to a local chatbot.db file.
The application includes retrieval-augmented generation. Users can upload .txt, .pdf, and .md documents via API or UI. An asynchronous background pipeline handles chunking, deduplication, and vector storage using local Qdrant. Context-aware retrieval is available for specific sessions or users. It also provides rolling memory and summarization with LangGraph to keep state context within LLM token limits.
Built-in LangGraph agent tools include a math evaluator, a real-time date/time tool, and web search via DuckDuckGo. Responses can be streamed in real time using Server-Sent Events. The web UI includes dark/light mode, code syntax highlighting with copy buttons, a chat history session manager synced with the database, Markdown rendering, and export of chat history as Markdown.
Pricing
Free
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Use Cases
- Teams use fastapi-langgraph-chatbot to run a local chatbot without external Docker, PostgreSQL, or Redis.
- Teams use fastapi-langgraph-chatbot to select from multiple LLM providers such as Groq, Ollama, OpenAI, Gemini, and Anthropic.
- Teams use fastapi-langgraph-chatbot to persist chat history and manage sessions in SQLite or PostgreSQL.
- Teams use fastapi-langgraph-chatbot to upload documents and perform retrieval-augmented generation.
- Teams use fastapi-langgraph-chatbot to keep conversations within LLM token limits through rolling memory and summarization.
- Teams use fastapi-langgraph-chatbot to stream responses token-by-token in a web interface.
- Teams use fastapi-langgraph-chatbot to export chat history as Markdown.
- Teams use fastapi-langgraph-chatbot to perform web search through a built-in agent tool.
Features
AI assistant
Natural language interface
Workflow automation
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