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"Context Memory Systems and Technologies" matching MCP servers:

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    Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.
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    MIT
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    A high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.
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    MIT
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    Governed multi-agent memory for AI agents. Hybrid markdown + SQLite store with full-text search, vector retrieval, and LLM reranking. Three transports: MCP stdio, HTTP JSON-RPC, and MCP SSE. One Go binary
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    Apache 2.0
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    🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.
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    MIT
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    A memory, docs, and repo context layer for engineering agents, providing typed persistent memory, RAG document retrieval, and repository-aware tools to recall decisions, runbooks, and operational knowledge across sessions.
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    MIT
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    An MCP server that provides persistent memory for AI agents, enabling context retention across sessions and supporting vibe-loop workflows where agent promises and deliverables are tracked and validated.
    MIT
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    Local-first, file-based memory layer for AI agents — one shared Markdown vault across Claude, Codex, Gemini, Cursor and any MCP client. Provides read/write memory tools with an audit trail, per-agent trust levels, and Git sync; no cloud and no lock-in.
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    MIT
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    MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.
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    MIT
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    Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON
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    MIT
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    CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.
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    MIT
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    MCP server that enables AI assistants to drive systematic literature review workflows, including screening, downloading, converting, and reviewing papers through natural language conversation.
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    AGPL 3.0
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    Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.
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    MIT
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    Provides AI coding assistants with code search, impact analysis, task chaining, and persistent memory to turn AI demos into deliverable code.
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    MIT
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    Connect any AI client to the CaBrain memory system via MCP, exposing tools for memory recall, retain, and brain management.
    4
    MIT
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    MCP server for Lightpanda headless browser enabling ultra-fast HTML extraction, markdown conversion, and JavaScript execution with 16x lower memory footprint than Chrome.
    MIT
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    Manages Claude Code configuration and provides cross-session intelligent memory via the Luoshu system, enabling Claude to remember decisions and project context across conversations.
    MIT
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    Provides persistent memory for AI tools by building a local knowledge graph from conversations, enabling cross-session recall and context awareness without cloud dependencies.
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    MIT