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"scikit-learn" matching MCP servers:

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    MCP server for persistent, compounding memory that automatically captures corrections and insights across AI sessions, enabling agents to learn and improve over time.
    5
    364
    MIT
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    An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.
    20
    1,408
    MIT
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    Enables Claude to remember conversations and learn over time by storing and recalling messages, memory abstracts, and recent history using a local SQLite database.
    4
    25
    72
    MIT
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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.
    2
    MIT
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    A productivity coaching assistant designed for ADHD support that provides intelligent task management, goal tracking, and personalized recommendations. It utilizes a persistent memory system to learn user patterns and preferences, helping to reduce decision paralysis through actionable suggestions.
    28
    1
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    Persistence memory MCP server that enables AI coding agents to recall and learn from past sessions, storing scars, wins, patterns, and decisions for continuous improvement.
    30
    338
    9
    MIT
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    An MCP server that enables Claude to learn from user corrections by recording them as persistent rules in CLAUDE.md files. It manages project-specific or global guidelines to ensure the assistant avoids repeating the same mistakes in future sessions.
    2
    4
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    palaia gives your agents a persistent, searchable knowledge store. They save what they learn, find it by meaning, and share it across tools and sessions automatically.
    18
    MIT
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    Enables robots to store, retrieve, and consolidate episodic experiences including physical parameters, trajectories, and outcomes. It supports hybrid vector search with structured filtering and spatial sorting to help robotic agents learn from past successes and failures.
    28
    Apache 2.0
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    Engram MCP provides persistent, cross-session memory for AI agents by automatically encoding errors, decisions, and discoveries during development sessions. It enables local, intelligent recall and automated context management to help AI learn from experience and avoid recurring mistakes.
    20
    Business Source 1.1
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    mozg turns documentation into knowledge brains your agent queries over MCP instead of loading whole files into context. What makes it different: every brain sits an exam. Its goal becomes ~30 control questions, re-sat after every ingest — so 'trained 92%' is measured, not claimed, and the failing questions are listed publicly. Brains learn from use: searches that return nothing become exam questio
    8
    AGPL 3.0