Coalent MCP server

Real-time, provenance-invalidated context for AI agents & RAG. Build understanding once. Reuse it everywhere. Keep it fresh — automatically.

📖 Documentation  ·  coalent.ai  ·  💬 Discord

Quickstart · What's new in v0.7 · Gate ladder · Bring your own stack · MCP · LangChain · Benchmark · CLI

Your agent re-reads the same sources on every call — and the moment a source changes, every cached answer is silently wrong.

Coalent builds the understanding once, caches it by what the query means, and invalidates it surgically the instant an underlying source changes. As correct as re-reading everything, at a fraction of the cost — and never stale.

Why Coalent

Every context layer is forced to trade off three things. Coalent is built to hold all three at once:

• 🧠 Extractive understanding, not chunks. It caches a query-independent set of atomic, source-grounded claims your LLM extracted — keeping every number and fact — so one cached unit answers many different later questions. The raw evidence is retained with each unit, so a hit that under-covers a query falls back to retrieval instead of answering thin.
• ♻️ Reuse across queries, agents — and documents. A semantic cache keyed by query meaning: ask again, or from another agent, and it's a warm hit. Cross-unit recall pools claims across units to answer multi-hop questions whose evidence spans documents — at zero extra LLM calls.
• 🌿 Fresh by provenance. Every unit remembers the exact sources it used. When one changes, only the units that actually used it go stale — precisely, automatically, and lazily.

Coalent sits above retrieval — bring any retriever (vector DB, hybrid search, GraphRAG, tools, APIs). It's the freshness-and-reuse layer, not another retriever — deliberately the opposite of GraphRAG's build-the-whole-graph-upfront tax: lightweight, independent units, built lazily only when a query actually needs one, and refreshed by dirtying a single unit (no graph surgery).

Shortened. The full README is on GitHub.