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Compresr docs

Quick start

Send your first compression request in 30 seconds.

  1. Install the SDK

    Install the client for your language. The cURL path needs nothing - it ships with your OS.

    bash

  2. Get an API key

    Create a key in the dashboard, copy its cmp_... value, and export it as COMPRESR_API_KEY. See Authentication for the full key model and security guidance.

  3. Send a compression request

    Pass the long context you would otherwise send to your LLM, plus the query you want it to answer.

    • query: what the LLM needs to answer. Compression keeps the tokens relevant to it, so be specific — good: "What was the project's Q3 churn rate?", bad: "churn" (no intent, degrades to generic compression). More in Query-specific compression.
    • target_compression_ratio: how hard to compress (here 0.75 removes ~75% of tokens). Full semantics: Models & parameters.
    python

  4. Inspect the result

    This is the actual response from the live API for the call above (numbers will vary slightly run-to-run as duration_ms depends on load):

    text

    What the fields mean

    • compressed_context: the shortened text. Forward this to your LLM exactly as you would the original input.[N tokens dropped] markers show where spans were cut; pass disable_placeholders=true if you want a clean concatenation without them.
    • actual_compression_ratio: fraction of input tokens removed (here 0.6375 = ~64% removed). It is not an Nx factor.
    • target_compression_ratio: the value you asked for, echoed back.
    • duration_ms: server-side compression time. Network round-trip is on top of this.

From here, tune the parameters or wire Compresr into your stack:

  • SDK reference - Python, TypeScript, and raw REST: full method reference, async variants, streaming, batching.
  • Models - tune target_compression_ratio and other latte-only options.
  • Agent client - drop-in for anthropic.Anthropic() / openai.OpenAI() with automatic tool-output compression.
  • Web search - add Tavily or Brave to your agent loop in one line.
  • LangChain integration: first-party middleware for tool outputs, history, and outbound prompts, plus a BaseDocumentCompressor for RAG.
  • LangGraph integration: state-graph node, lossy checkpoint serializer, store wrapper, and multi-agent handoff tool.
  • LlamaIndex integration: query-engine postprocessor, tool wrapper, and Memory API block.
  • LiteLLM integration: drop the compresr guardrail into the proxy and compress tool messages across every provider transparently.