Overview
Byte is a self-hosted local AI agent that runs an open-source model locally and can program, search the internet, and use tools via MCP without relying on paid APIs. The project states that phases 0 through 6 work end-to-end locally, including an agent with tools, a WASM sandbox, hybrid RAG, MCP, an OpenAI-compatible endpoint, multiuser support with JWT, and an interactive CLI, with 480 tests. Its stack includes Python, FastAPI, LangGraph, Ollama, PostgreSQL with pgvector, MCP, AG-UI, Pyodide/WASM, and Docker.
The architecture routes web or CLI requests through FastAPI to a LangGraph agent with a Postgres checkpointer, then to tools for web search via Tavily, a Pyodide WASM sandbox, hybrid RAG via pgvector, and MCP servers such as n8n. It can run without a database in memory for testing, but production mode requires Postgres; web search and code execution are optional depending on API keys and sandbox configuration. The project also provides an OpenAI-compatible endpoint, an interactive CLI, JWT-based multiuser support, repository navigation tools, and optional observability integrations.
Pricing
Free
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Use Cases
- Teams use Byte to run a local AI agent that can program, search the web, and use MCP tools without paid APIs.
- Teams use Byte to execute code in a WASM sandbox via Pyodide.
- Teams use Byte to search the web through Tavily when configured.
- Teams use Byte to query documents with hybrid RAG using pgvector.
- Teams use Byte to connect MCP servers such as n8n.
- Teams use Byte to expose an OpenAI-compatible endpoint for OpenAI SDK clients.
- Teams use Byte to navigate a repository with list_files, read_file, and grep.
- Teams use Byte to manage multiuser access with JWT.
Features
Runs as a self-hosted agent
Uses a local LLM
Supports tool calling
Sandboxes execution with WASM
Combines hybrid RAG with pgvector
Connects through MCP
Includes prompt injection defenses
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