Answers about condense.chat, its use cases, and how it integrates.

condense.chat is a context compression API and drop-in proxy. It compresses prompts, retrieved documents, and tool output before those tokens are sent to an upstream model provider.

It is positioned for teams shipping coding agents, agentic loops, RAG pipelines, and chat products where context cost and context window pressure are real product constraints.

Point your SDK at api.condense.chat/{provider} — /anthropic/v1/messages for the Anthropic SDK or Claude Code, /openai/v1/chat/completions for the OpenAI SDK. Two keys travel on each request: your condense key (ak_…) in the X-Condense-Auth-Token header, and your usual upstream provider key in its normal header (x-api-key for Anthropic, Authorization: Bearer for OpenAI). condense is a drop-in proxy, so there is no separate endpoint to call. The public docs show the base URL, headers, and example client setup.

The docs describe two provider-dialect proxy routes — POST /anthropic/v1/messages and POST /openai/v1/chat/completions — plus GET /healthz.

The public site presents condense.chat as a private beta. Use the Sign up button on the homepage — it takes you to sign-up, where you claim a key. Already have one? Log in.

The goal is to preserve the answer while sending fewer tokens. Compression targets redundant or low-value context — repeated boilerplate, verbose tool output, and retrieved passages that don't carry the signal — not the instructions that drive the response. You keep your own model, your own keys, and your own provider; condense only reshapes the context on the way in.

It depends on the workload, but the homepage reports typical savings around 55–70%. Coding agents with heavy tool output, RAG pipelines with citation-heavy payloads, and chat products carrying long histories see the largest reductions because they spend the most tokens on repeatable context.

Any workload where context is the cost — coding agents, agentic loops, RAG pipelines, and chat products are just common examples, not a fixed list. condense.chat works the same way for anything that sends large or repetitive context to a model.