"scikit-learn" matching MCP servers:

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

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.

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

Enables AI-powered journaling in Obsidian with dynamic reflection prompts generated from recent entries and automatic backlinks between related diary entries. Supports adaptive templates that learn from writing patterns and smart content similarity linking.

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.

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.

Intercepts Claude conversations via MITM proxy and stores them in Mem0, enabling Claude to search and recall past conversations, learn user preferences, and maintain context across sessions.

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.

Provides AI agents with tools to search and publish to Aiskimo, a public record of agent actions and failures. It enables agents to register, post caveats, and learn from others' experiences directly from their toolset.

Captures and encodes developer rejections of AI output into reusable constraints, enabling agents to learn from past mistakes and apply taste consistently across projects.

Helps AI agents decompose goals into costed execution plans, checkpoint progress with deviation tracking, and learn from past project data.

Enables AI agents to design RF filters and SMPS-EMC from spec using three MCP servers that drive LTspice, Qucs-S, and scikit-rf, with closed-form synthesis, real-component optimization, and CISPR-aware compliance checking.

An MCP server that enables AI agents to automatically discover, install, and learn to use new tools without manual configuration.

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.

Provides 300+ MCP tools for orchestrating AI agents — including swarm coordination, self-learning memory, and task management — enabling MCP clients like Claude Code and Cursor to spawn, coordinate, and learn from specialist agents across sessions.

Implements Agentic Context Engineering to create self-improving AI coding assistants that learn from execution feedback and build persistent knowledge playbooks. Reduces token usage by 86.9% while improving code accuracy by 10.6% through incremental context updates.

Enables AI agents to share persistent, conflict-safe memory by providing tools to recall, learn, reinforce, and retire lessons, using CockroachDB for storage and AWS Bedrock for embeddings.

Enables AI agents to learn from their work by recording tasks, extracting patterns, detecting mistakes, and proactively surfacing insights, all using the agent's own model through a cooperative intelligence pattern.