Modular & Extensible
Swap LLMs, vector stores, and data connectors with zero friction. Build custom pipelines for RAG, Web, SQL, or Knowledge Graphs.
ContextRouter
High-performance "shared brain" for LangGraph-powered AI agents. Build production-ready RAG systems with multi-source knowledge orchestration.
LangGraph Powered
Sophisticated state management and conditional routing using LangGraph. Designed for complex, multi-step agentic workflows.
Universal Model Support
Native support for OpenAI, Anthropic, Google Vertex AI, Groq, and local models via Ollama, vLLM, or HuggingFace.
Secure by Design
Built-in data provenance and security tracing via the Bisquit protocol. Full audit trail for every decision.
Why ContextRouter?
Traditional RAG implementations are tightly coupled to specific databases or LLMs. ContextRouter breaks these dependencies by separating Decision Logic from Technical Implementation.
This means you can:
- Switch providers instantly — Move from OpenAI to Vertex to local Ollama without code changes
- Scale independently — Upgrade your vector database without touching agent logic
- Test in isolation — Mock any component for reliable unit testing
- Future-proof your stack — New models and databases plug in seamlessly
Quick Links
Installation Get ContextRouter running in minutes with pip or uv.
Quick Start Build your first RAG agent with a simple example.
Models Configure LLMs from Vertex, OpenAI, Anthropic, or local providers.
RAG Pipeline Advanced retrieval with reranking, hybrid search, and citations.
Enterprise & Production
For teams needing advanced security, multi-tenancy, or enterprise integrations, we offer production deployment options at contextrouter.dev.