context-management MCP servers & tools

Local-first MCP server that snapshots your project's working state — files, git, plan, and decisions — into one structured context object and hands it to any MCP client in a single call. Save a session in one agent, continue it in another. Zero network calls by default; read-only over your project. Works with Claude Code, Cursor, and any MCP client. npx -y ctxfile

Penfield: persistent memory with hybrid search (BM25 + vector + graph), 24 relationship types for knowledge graphs, context checkpoints for cognitive handoff, artifact storage, and personality system. Works across Claude, Cursor, Windsurf, and any MCP client.

Graph-structured session memory for LLMs. Local OpenAI-compatible proxy that extracts tasks, decisions, and files into a typed knowledge graph, auto-checkpoints before context overflow, and resumes any session in 250 tokens. 6 MCP tools including whydecision (traces why a decision changed, with reasons and evidence). pip install tokenmizer

EOQ-based context economics for coding agents. Monitors LLM token cache with inventory theory, signals optimal session restart timing via live dashboard and terminal statusline. Zero context pollution — no metric ever enters the model's context.

LLM-driven context and memory management with wide-recall + precise-reranking RAG architecture. Features multi-dimensional retrieval (vector/timeline/knowledge graph), short/long-term memory, and complete MCP support (HTTP/WebSocket/SSE).

Efficient context management: code search, evidence packs, and memory for coding agents.

Persistent memory for AI agents — 98%+ retrieval recall, 99% token savings, 44 tools

Context GC for LLM agents: offload large tool outputs and recall them to save tokens.

Web search that doesn't wreck your AI's memory.

Agentic memory wrapper for autonomous AI loops with self-healing and context compaction.