ContextOS Documentation
Context orchestration for LLM applications. Retrieve candidates, rank them, and build a context payload within a strict token budget.
Get Started
Install ContextOS and build your first context.
Core Concepts
Understand retrieval, reranking, and context planning.
API Reference
Learn the public ContextOS API.
Architecture
Understand how the pipeline works.
Overview
What is ContextOS?
ContextOS is an open-source context orchestration layer for LLM applications. It is designed for systems that need to combine dense retrieval, lexical retrieval, reranking, and hard token budget constraints into a single, predictable context payload.
What problem does it solve?
In a typical RAG system, retrieval is treated as identical to context selection. An application queries a vector database, gets the top-K documents, and shoves them into a prompt.
ContextOS solves the problem that retrieval ≠ context selection.
Retrieval's job is simply to find candidates. But the LLM context window is a constrained resource. You need a way to deduplicate, re-score, and strategically select from those candidates to fit inside your token budget while preserving critical system instructions and recent conversation turns.
What ContextOS does
What ContextOS does NOT do
- It is not an LLM wrapper. You bring your own LLM client (OpenAI, Anthropic, vLLM, etc.).
- It is not responsible for generation. It only prepares the context.
- It is not a vector database. It uses PostgreSQL and pgvector under the hood, but acts as a higher-level orchestrator.
- It is not a chatbot framework like LangChain or LlamaIndex.
- It is not a hosted SaaS. It is an open-source Python library you deploy in your own stack.