"Documentation for Vercel AI SDK" matching MCP servers:

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables semantic memory storage and retrieval with natural language queries, deployed on Vercel with PostgreSQL pgvector and OpenAI embeddings.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Your coding agent remembers how it solved past tasks and reuses it next time: trigger, steps, outcome, lessons. Hooks recall the right skill before a task; skills strengthen with confirmed use and unused ones decay. Local SQLite, no API key, nothing leaves your machine. 9 MCP tools, works with Claude Code, Codex and Cursor. Agent-written memory stays untrusted until you approve it.

Local-first external brain for Claude Code, Codex, and any MCP client. Stores decisions, entities, and session artifacts in one SQLite file and exposes MCP tools for recall, page, promote, review, graph-query, and source-status.

Official Telys MCP server: private, on-device AI memory and retrieval powered by native Mojo kernels. 19 tools: memory CRUD, semantic + BM25 search, single-key filters, compaction/IVF/tuning and self-refreshing repo auto-indexing. Stdio. Introspection needs no credentials; execution requires a one-time free telys login. Source: packages/telys-sdk/telys/mcp.py. Registry: io.github.thyn-ai/telys.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

Free Industry 4.0 / IIoT tools, no API key or account: lint MQTT Sparkplug B topics, check a Unified Namespace (UNS) for naming and hierarchy problems, and run an industrial AI-readiness diagnostic for manufacturing digital transformation and smart-factory projects. Works in Claude Code, Codex and any MCP client.

Enables git-versioned project state for AI sessions, capturing decisions, constraints, and rejected paths with confidence and provenance. It provides a deterministic resume context so new sessions continue without losing direction or re-proposing rejected work.

Enables users to access, search, and get recommendations from AWS documentation through natural language queries. Supports both global AWS documentation and AWS China documentation with tools to fetch pages, search content, and discover related resources.

Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.

MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

Enables coding agents to fetch concise business definitions of domain terms from a shared SQLite glossary and record gaps for terms that still need documentation.

Enables AI assistants to access Fathom meeting recordings, summaries, transcripts, teams, and webhooks through the Model Context Protocol.

AI reads your codebase like a book - one page at a time. Pharaoh gives it the index and a map of how every chapter connects.

Provides a local-first, provenance-aware memory layer that enables MCP-capable AIs to store, recall, validate, and reason over facts with contradiction detection, trust weighting, deduplication, and encryption, supporting offline private operation without GPUs or API keys.

Provides persistent, searchable memory across AI coding agent and chat history (Claude Code, Codex, Gemini CLI, ChatGPT, and more) via retrieval-augmented generation, enabling semantic and hybrid search to retain context across sessions.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Provides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.

Design contract layer for AI agents. Scans Figma, code, Storybook, and token files, reconciles conflicts, and serves a single machine-readable source of truth so every agent gets the same authoritative design rules before it builds. Local-first.

Provides a hybrid memory architecture with a thin SQLite index and Markdown cold storage, enabling AI agents to write, query, link, and rebuild long-term memories via MCP tools, model-agnostic and zero third-party dependencies.

An MCP server that provides persistent memory for AI assistants by automatically recording and injecting project-specific rules, decisions, and configurations through session hooks. It utilizes Gemini to analyze conversations, manage token-efficient context, and perform autonomous background tasks like documentation updates.

Graph-based MCP server for persistent AI memory, session checkpointing, context compression, and cross-session context management for LLM applications.

Local, zero-dependency memory MCP for AI agents: agents stage, you promote, every entry has a receipt.

Local-first bitemporal memory for AI agents with deterministic supersession, point-in-time recall, erasure proofs, and tamper-evident audit history.

A persistent, project-scoped memory layer for AI agents, supporting hybrid retrieval (vector, keyword, and tag matching) and sharing across different MCP clients like Claude Code, Qoder, or Cursor.

Repository-native protocol and MCP server for coordinating work items, documentation, changelogs, and project memory between humans and AI agents, using Markdown files in a Git repository as the canonical data source.

Enables AI assistants to search, retrieve, and browse Markdown documentation in a local folder, then answer questions with cited sources.

Provides a compressed knowledge graph of the NVIDIA AI developer stack for deterministic traversal, enabling agents to answer questions about dependencies and prerequisites with minimal tokens.