"Loop" matching MCP servers:

Enables Codex and DeepSeek Harness to maintain a local, traceable record of current task intent, separating requirements, evidence, unknowns, and disagreements while preserving corrections without erasing history.

Enables AI-moderated interviews that become cited, searchable transcripts, with a repository Q&A tool that returns exact transcript quotes.

Enables AI agents to index, search, and validate lessons learned across project repositories via a local SQLite index, providing learning-loop resources and verified-solution tracking without loading full files into conversations.

MCP server that provides tools to add to, search, and manage a wiki knowledge base, enabling AI chat tools to contribute and retrieve information from the wiki via natural language prompts.

The industry-standard persistent memory and state manager for long-running agentic workflows.

Provides persistent identity and memory for AI agents across MCP-compatible harnesses, enabling agents to retain their name, values, and episodic memories between sessions regardless of the client or model.

Local-first knowledge graph MCP server — hybrid BM25 + vector + graph retrieval over your personal vault with provenance-grade extraction. 8 tools for Claude Desktop including search, archaeology, and graph write.

Feynman's twelve-problems method as an MCP server — a persistent set of long-running problems plus an evoke loop that tests each new trick against all of them. Local, TypeScript, no LLM inside, no API key.

Bridges Hermes Agent to the Hermes Intelligence Platform API, enabling tools to read/write learning loop data (context, feedback, signals, memory, etc.) via stdio.

Cortex is an MCP server that helps build and maintain a project Memory Bank for AI assistants, enabling reproducible, high-quality AI-driven development with a plan-do-commit loop.

Enables programming agents to capture errors and conversation signals, reflect on root causes, consolidate reusable skills, and retrieve relevant context for future tasks, providing a self-learning memory loop.

Persistent brain, memory, loop controller, and reminder engine for AI coding agents.

Marrow gives your agent a governance loop that compounds. With @getmarrow/mcp, any MCP-compatible client can ask Marrow before risky work, inspect live loop state during work, collect required proof, and commit outcomes when the work is done. That means your agent stops operating without accountability and starts carrying forward real decision history. Your agent stops repeating the same mistake

Enables long-horizon autonomous task checkpointing and resumption through cryptographic event-sourced state persistence, idempotency key deduplication, timeout heartbeats, human-in-the-loop pause/resume tokens, and fail-safe recovery.

Provides AI agents with operational customer context, including typed revenue objects, persistent state, scoped tools, and human-in-the-loop handoffs through MCP, REST, and CLI.

Enables AI agents to resolve typed routing, loop-stall arbitration, and tool safety checks locally at sub-millisecond latency, avoiding unnecessary frontier LLM calls. It enforces reversible execution checkpoints, token budgets, and decayed episodic memory for safe, privacy-first autonomous operation.

Exposes Iceberg-backed ontology objects, links, and actions as typed MCP tools for LLM agents, enabling governed data access and operations without raw SQL.

MCP server for alive-analysis. Query, search, and retrieve structured analysis history written with the ALIVE loop (Ask→Look→Investigate→Voice→Evolve), plus dashboard JSON export.

MCP server for AI agents to read, write, and organize notes in a local-first, human-in-the-loop note-taking app.

Turns any source material into a guided learning experience with learning maps, notes, four-phase study loop, spaced repetition flashcards, and grounded Q\&A.