"scikit-learn" matching MCP servers:

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

An MCP server that makes your AI agent learn and evolve by capturing feedback as reusable, persistent skills that improve over time.

A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.

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.

Enables AI coding agents to evaluate code snippets and learn from execution outcomes through local bio-inspired associative memory, delivering instant zero-token bug and antipattern reflexes without LLM inference.

Enables AI coding agents to learn, from locally read lockfiles, what changes between the dependency version installed and a target version, which of the user's files that upgrade touches, and which vulnerabilities it resolves. It also performs OSV-matched vulnerability scans, ranks upgrade plans, and stores decision memory, running entirely on the user's machine with no account or API keys.

A package manager for AI agents that connects LLMs to a global registry of capabilities, allowing them to autonomously discover, install, and learn new skills from a centralized repository.

MCP server for persistent, compounding memory that automatically captures corrections and insights across AI sessions, enabling agents to learn and improve over time.

Enables Claude to remember conversations and learn over time by storing and recalling messages, memory abstracts, and recent history using a local SQLite database.

Enables AI agents to learn from their sessions by inspecting history, proposing rules or skills, and automatically persisting them to configuration files.

Enables low-cost agent models to control Windows applications through a compact, state-safe proxy over Open Computer Use, reducing model-visible context by up to 99.8% with support for record/replay and reusable UI component memory.

Enables LLM agents to interact with the Lean theorem prover through the Language Server Protocol, providing tools for analyzing Lean projects, accessing diagnostics, goal states, documentation, and searching for theorems using both local and external search services.

Enables AI clients to read DTU Learn classes, assignments, deadlines, exams, and recent calendar changes through a local MCP server, using a cached SQLite store for fast and resilient queries.

Enables persistent project memory for Hermes Agent by storing and retrieving wiki, code, and learning layers locally, with tools to recall, learn, retire, and ingest information beyond the context window.

An agent-native teaching OS that gives AI assistants a persistent classroom with courses, lessons, flashcards, and graded exercises via MCP tools.

Extracts and stores documentation content from Microsoft Learn and GitHub URLs into PocketBase with full-text search, metadata preservation, and automatic collection management for easy retrieval and organization.

Enables structured learning with a verified loop: define goals as observable claims, learn through teach-lab-test-gate per claim, and get independently graded by an adversarial examiner to ensure genuine progress.

Create videos from scripts and storyboards, manage projects, and export completed videos through the VideoGen API. Runs locally with npx and authenticates with your VideoGen API key. Learn more: https://videogen.io/videogen-mcp

MCP server for agentic interaction with the Lean theorem prover via LSP, providing tools for understanding, analyzing, and interacting with Lean projects.

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.