Stop feeding your AI chunks.
Coalent caches the understanding your LLM builds — not the raw chunks — reuses it across queries and agents, and refreshes only what a source change touched. Matches chunk-RAG's best accuracy at a fraction of the tokens — plus freshness and compounding reuse RAG can't give.
pip install coalent · sits on top of the stack you already run
Chunks in. Answers out.
High tokens. Low understanding.
Understanding in. Answers out.
Low tokens. High understanding.
Same answer, a fraction of the tokens — try it live ↓
Works with the tools you already run
Change a source. Watch it stay correct.
Understanding it can act on — and that never goes stale. Pick a question, then change a source.
Customers can be refunded within 30 days; partial refunds are pro-rated.
↑ live — only the unit that used the changed source rebuilds.
Understanding, reused, and always fresh.
Real understanding
Coalent synthesizes your sources into a structured, decision-ready briefing — summary, claims, facts — not a pile of fragments.
Reused everywhere
Built once, matched by meaning, reused across every query, session, and agent. Less retrieval, less LLM spend, lower latency.
Always fresh
When a source changes, only the understanding that used it is rebuilt — surgically, the moment it changes. Never stale.
Same answers. Always fresh. A fraction of the tokens.
On the structured-regime benchmark (synthetic templates; real embeddings, a deterministic number check, four answer models), Coalent matches naive dense RAG's accuracy at ~a third of the context tokens — routes to the right source ~every time (route@1 ≈ 1.00), answers multi-hop questions RAG can't, and never goes stale.
Real news (MultiHopRAG, pre-registered n=605): matches the best chunk-RAG arm at 0.79x its tokens · 95% null honesty
= naive accuracy (structured) · 0% stale · 0% fewer tokens (structured) · 0% of the tokens matches best chunk-RAG arm (real news) · see the benchmark →
Bring any retriever. Keep your model.
Coalent is the layer above retrieval — not a replacement. Plug in your vector DB, GraphRAG, tools, or APIs, and your model of choice. It's one call from any agent framework.
from coalent import SemanticCache, QdrantRetriever, LLMSynthesizer, OpenAIProvider
cache = SemanticCache(
QdrantRetriever(client=qdrant, collection="docs", embed=embed),
LLMSynthesizer(OpenAIProvider()),
)
ctx = cache.get("what is our refund policy?") # decision-ready, fresh
Store understanding,
not data.
Caches go stale because they store answers without knowing where they came from. Coalent keeps the lineage — so it always knows exactly what to forget.
The Coalent principle
Build it with us.
Questions, ideas, war stories — come hang out. We're building Coalent in the open, and Discord is the fastest way to reach us.
Give your AI living understanding.
Open source, Apache-2.0. Sits on top of what you already run.