condense.chat is a context compression layer for coding agents, RAG, and chat products.
condense.chat helps teams shrink prompts, retrieved documents, and tool output before those tokens hit an upstream model provider. It cuts context cost without asking you to rebuild the rest of the stack.
What condense.chat does
condense.chat sits between an application and upstream LLM APIs. It condenses large context blocks in-flight, forwards the request to the model provider, and streams the response back unchanged.
The product is meant for teams that already have a working stack but need to fit more useful context into the same window or lower the cost of oversized requests.
Who it is for
- Teams shipping coding agents with long prompts, tool traces, and file reads
- Teams running RAG pipelines with bulky retrieved context and citations
- Teams operating chat products with expensive prompt history and tool schemas
- LLM infrastructure engineers evaluating context compression at the proxy layer
Why the team built it
Modern agent stacks burn budget and context window on payloads that matter but are too large to forward forever. condense.chat exists to compress those payloads while keeping the integration simple.
The public site positions condense.chat as a private beta built by engineers who have shipped LLM infrastructure at Nord Security, kilo.health, nexos.ai, and basedcollective_.