A
licenseA
qualityF
maintenanceProvides AI chat history compression tools through token-based trimming and AI-powered summarization strategies to manage conversation context within token limits.
2
41 npm
5
MIT
Continuous Context Session Compactor
Officialby alphaparkinc
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Continuous Context Session Compactorcompact this session to 32k tokens, preserving recent turns and system prompt"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Continuous Context Session Compactor Skill for Autonomous Agents
Enables long-horizon autonomous agents to run persistently without context window exhaustion. Automatically calculates token consumption and creates concise sliding summaries for elder turns.
Capabilities
Token Budget Guarding: Enforces strict configurable budget limits (e.g. 8k, 32k, 128k).
Sliding History Distillation: Preserves critical system instructions and recent conversation while compressing historical turns.
Zero Overhead: In-memory token estimation and distillation with zero external LLM dependencies.
Related MCP server: cycles-mcp-server
MCP Server Integration
python mcp_server.pyAvailable MCP Tools
compact_messages: Compact conversation array when exceeding token limits.
This server cannot be deployed
Maintenance
ActivityMaintained
ResponsivenessNo issues
Related MCP Connectors
Memory that reasons: continual learning for stateful agents. Better context, fewer tokens.
Multi-agent coordination outside your LLM context: dedupe parallel work, per-agent budgets, handoff
Long-term memory for AI agents: durable records, observable retrieval, governed context assembly.
Continuity for agents: a live memory you can read and cite right now, outliving the context window.
Related MCP Servers
- AlicenseAqualityAmaintenanceRuntime budget authority for autonomous agents - a set of tools to check, reserve, spend, and release budget before and after every costly, risky operation. The agent asks "can I afford this?" before acting, and reports what it actually used afterward.9157 npmApache 2.0
- AlicenseNot gradedqualityDmaintenanceVirtual Infinite Context for Agents and LLMs - maintains a continuous rolling context window, surfacing relevant memories while respecting token budgets.6MIT
- AlicenseNot gradedqualityBmaintenanceProvides deterministic context compaction and safety gating for AI agents, preserving original messages verbatim while dropping irrelevant ones and flagging destructive commands.2MIT