Coalent gives your AI fresh, reusable understanding — not just raw search results. You ask a question; Coalent hands back a short, decision-ready answer built from your sources, reuses it for similar questions, and rewrites it the instant a source changes.
Think of a sharp research assistant: they write a briefing once, reuse it whenever the same kind of question comes up, and quietly update only the briefings whose sources changed. Coalent is that — as infrastructure that sits on top of the retrieval you already have.
Why teams use Coalent
- Always fresh. When a document, record, or API result changes, the understanding built on it updates surgically — so your agent never answers from stale knowledge.
- Reusable and fast. Build understanding once, reuse it across questions, sessions, and agents. Far less repeated retrieval and LLM work — lower cost, lower latency.
- Decision-ready context. Not a pile of chunks to wade through — the minimal, structured understanding the model actually needs to act, which improves answers and cuts tokens.
- Works with your stack. Keep your vector DB, GraphRAG, tools, or APIs. Coalent makes their output fresh and reusable — it doesn't replace them.
- Built for agents. A consistent, fresh memory layer that multiple agents and steps can share.
Where it's the best fit
Coalent shines wherever your knowledge changes and a stale answer is costly:
- support & internal knowledge assistants, ops / incident copilots
- pricing, inventory, policy, compliance — anything that must reflect now
- code, tickets, CRM, and other high-write knowledge
- multi-agent systems that need shared, current context
The pieces, in plain words
| Piece | What it is |
|---|---|
| Retriever | How Coalent fetches your information — point it at your vector DB, docs, or tools. |
| Synthesizer | Turns that information into a short, structured understanding (using your LLM). |
| Cognition unit | One cached understanding for one kind of question — plus the sources it came from. |
| Invalidation | When a source changes, Coalent forgets exactly the understanding that used it. |
See it in 30 seconds
from coalent import SemanticCache, InMemoryRetriever, StubSynthesizer
retriever = InMemoryRetriever()
retriever.add("confluence:98231", "Leave policy: 21 days of annual leave per year.")
cache = SemanticCache(retriever, StubSynthesizer())
ctx = cache.get("how much annual leave do we get?")
print(ctx.context) # decision-ready understanding for your LLM
# a source changed? Coalent rebuilds only what used it
cache.source_changed("confluence:98231", text="Leave policy: now 25 days.")
Two calls cover the whole idea: get() to read, and a change to keep it fresh.
Start here
- Installation — install the package.
- Quickstart — the full loop in a minute.
- How it works — the pieces, plainly, with one diagram.
Then build with the Examples — vector search, MCP/tools, Confluence/Jira, and agent frameworks.