ContextCompress is a middleware layer that intelligently compresses AI agent conversation context in real-time — reducing token costs 20–30% while guaranteeing preservation of critical information.
Integrate the SDK in minutes. Watch token costs drop while critical information stays intact.
Add the ContextCompress SDK to your AI agent. It wraps your existing API calls and monitors context window usage in real-time.
When context reaches your threshold, the compression engine activates — summarizing low-density content, pruning stale context, and preserving your sacred facts.
Real-time token savings, compression ratios, and information retention rates — all visible so you can trust what your agent is remembering.
Paste a sample conversation below. Tag a sacred fact with ✦ your fact here syntax. Hit compress.
Tag any fact as sacred and the compression engine will never drop it without explicit override. Audit trail included.
Summarize — condenses while preserving facts. Prune — removes stale context. Structure — converts to token-efficient format.
Real-time token savings, compression ratios, retention rates, and sacred fact status — updated with every compression event.
Manage shared context across multiple agents. De-duplicate overlapping context. All agents see compressed but consistent state.
Trigger compression at any context window percentage (default: 70%). Customize per agent, per workflow.
Python SDK with 3-line integration. Works with OpenAI, Anthropic, Ollama, and any LangChain/LlamaIndex workflow.
Save more on tokens than you pay for the product. Most customers break even in the first week.
Overage pricing: $15/additional 1M tokens (Starter) · $10/additional 1M tokens (Growth)
Works with any LLM. Drop in the SDK, tag your sacred facts, and you're done.
# Install the SDK pip install context-compress-sdk # Integrate with your agent (3 lines) from context_compress import ContextCompress ctx = ContextCompress(api_key="your-api-key", org_id="your-org") # Tag sacred facts — these are NEVER compressed ctx.tag_sacred("budget", "$50,000") ctx.tag_sacred("deadline", "March 15, 2026") # Wrap your LLM call — compression happens transparently response = ctx.chat_completion( messages=messages, model="claude-3-5-sonnet", compression_threshold=0.7 # compress at 70% of context window ) # Your messages are now compressed. Response is identical. Tokens are saved. # View savings in real-time: app.contextcompress.ai/dashboard
Join the teams running longer AI conversations without losing a single critical fact.
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