"Persistent" matching MCP servers:

The open-source, self-organizing memory for all your AI tools. Persistent memory over MCP (remember, recall, observe): background agents extract entities, resolve conflicts, and keep cited syntheses current in an append-only, encrypted, single-tenant vault.

A persistent Python REPL MCP server for AI agents with stateful execution, real timeouts, crash isolation, and an MCP bridge to call other tools in the project.

Persistent memory management for AI assistants like Claude, enabling creation, search, and retrieval of memories with tagging and triggers.

An MCP server that gives AI models a persistent notebook backed by a folder of Markdown files, with tools to discover, read, write, edit, and delete notes.

AI memory that works like yours. Neural pathway architecture gives your AI persistent recall, contextual awareness, and cross-session continuity the way human memory actually works. 14 MCP tools. Works with Claude Code, Cursor, Windsurf, and any MCP client. $50/mo unlimited.

Persistent local semantic memory for MCP tools, enabling storing and searching notes by meaning using local embeddings and SQLite.

Gives AI agents persistent, local-first memory using SQLite and on-device embeddings, enabling semantic search and recall across sessions with no cloud calls.

Universal work attestation for autonomous agents. Register any AI agent or machine with persistent cryptographic identity, attest completed work with tamper-evident on-chain records, and query trust scores. The reputation layer for the agent economy. 3 MCP tools over SSE. Settled on Solana.

Enables interactive Python execution with a persistent IPython kernel through MCP, retaining namespace state and providing structured output logs for agent and tool integrations.

An MCP server that enables AI assistants to perform web searches on Perplexity.ai using browser automation instead of an official API. It supports persistent authenticated sessions and returns search results along with cited sources directly to the client.

An MCP server that provides LLMs with full read/write access to an Open Knowledge Format (OKF) knowledge bundle, enabling structured, persistent long-term memory.

Enables AI agents to search and retrieve consolidated Swedish statutes (SFS) from the Riksdagen open data API, with verifiable citations and persistent identifiers.

Educational MCP server with 17 tools for mathematical calculations, matrix operations, statistical analysis, and data visualization. Includes persistent workspace, cloud hosting, and safety features like restricted eval.

Persistent project context for Google Gemini. 12 MCP tools for .faf Project DNA — auto-detect your stack, validate, score, and sync across CLAUDE.md, GEMINI.md, and AGENTS.md. Python/FastMCP. IANA-registered format (application/vnd.faf+yaml). 183 tests. One file, every AI platform.

Enables LLMs like Claude to bidirectionally chat with diffusion-based LLMs (e.g., DiffusionGemma, LLaDA) via a persistent llama-diffusion-cli process, with support for restarting sessions and full configuration.

Give your AI agent its own cloud computer: create and drive hosted plori agents over MCP (invoke agents, read replies, human-in-the-loop, scheduled runs). This repo is the integration front door and ships the npx plori-mcp stdio bridge to the hosted endpoint.

MCP server for local, offline-capable library documentation. It resolves library IDs and retrieves up-to-date docs via hybrid search from a persistent SQLite cache.

Enables searching and retrieval of UK legislation (Acts, Statutory Instruments) from legislation.gov.uk with persistent identifiers and verifiable citations.

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.

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.