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
by Alpha-Park
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 conversation to fit an 8k token budget while keeping recent turns"
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: PromptThrift MCP
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
Compress coding-agent context ~64%. Hosted Neural Keep MCP for Cursor/Claude/Codex.
Memory that reasons: continual learning for stateful agents. Better context, fewer tokens.
Reduces AI Agent token usage by 40% via three-stage SOP workflow.
Rules-based context reduction, planning checks, research prompts and fixed experiences for agents.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceEnables 70-90% LLM API cost reduction by compressing conversation history via local Gemma 4 models or heuristics, featuring token counting, model routing, and pinned facts for preserving critical context.1MIT
- AlicenseNot gradedqualityCmaintenanceDeterministic context compression for MCP agents, reducing token usage via 11 tools for prompts, history, shell output, file deltas, and code navigation without ML or GPU.9MIT
- AlicenseNot gradedqualityBmaintenanceProvides deterministic context compaction and safety gating for AI agents, preserving original messages verbatim while dropping irrelevant ones and flagging destructive commands.2MIT