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    Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.
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    Apache 2.0
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    Persistent memory for AI agents with knowledge graph, Hebbian learning, 4-signal RRF fusion search, GraphRAG enrichment, FSRS spaced repetition, and anti-hallucination grounding. 12 tools, PostgreSQL.
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    Apache 2.0
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    Cognitive prosthetic for AI agents. Indexes conversation history from ChatGPT, Claude Code, Cursor, and Gemini CLI into searchable embeddings. 25 MCP tools including tunnel_state (resume where you left off), switching_cost (quantify context-switch penalty), thinking_trajectory (track idea evolution), and alignment_check (decisions vs principles). LanceDB + Parquet, 12ms recall, local-first.
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    MIT
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    Enables AI agents to track, search, and retrieve their progress across projects with persistent memory using SQLite storage and LLM-powered summarization. Supports logging completed work, searching previous entries, and retrieving context for multi-step or multi-agent workflows.
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    MIT
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    A narrative graph engine that enables LLMs to generate, track, and mutate complex fictional worlds while maintaining consistency between factions, characters, and locations. It acts as a specialized RAG framework for storytelling, allowing models to manage thousands of entities without exceeding context limits.
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    MIT
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    MCPJungle is a self-hosted MCP Registry and Gateway suitable for both local and enterprise deployments. It provides a single URL for your MCP clients to access servers and a central platform for you to track your MCPs and client-server interactions.
    Last updated
    1,178
    Mozilla Public 2.0
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    Connects AI assistants to the Maestro research framework to orchestrate multi-agent research missions, including planning, research, and writing phases. It enables users to launch research tasks, track real-time progress, and retrieve comprehensive structured reports and notes.
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    Provides stateful prompt optimization using research-backed techniques like APE and OPRO, learning from historical performance data via a vector database. It enables users to automatically refine prompts, retrieve high-performing examples, and track performance analytics through iterative feedback.
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    A TypeScript MCP server for managing persistent knowledge graphs with entities, directional relations, and time-based observations. It enables users to create, search, and track structured information with built-in concurrency control and JSON file storage.
    Last updated