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      "description": "Memory API for AI applications. Store and retrieve user memories, bookmarks, and knowledge with semantic search. One unified API for personal and enterprise memory.",
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      "name": "code-graph-rag",
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      "description": "Self-hosts a personal AI assistant as a single binary that combines 30+ model providers, RAG knowledge bases, MCP tools, and browser and shell agents, for individuals and teams.",
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      "why": "RAG Knowledge Base: Ingests PDFs, Word, and Markdown files into embedded stores and retrieves relevant passages before each answer."
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      "name": "LanceDB",
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      "description": "Stores and searches multimodal vectors in an embedded database built on the Lance columnar format, running in-process or against LanceDB Cloud.",
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        "memory-state"
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      "why": "Vector Search: Runs approximate nearest-neighbour search over embeddings stored in an embedded, zero-server database process."
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      "name": "PageIndex",
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      "url": "https://agentswitchboard.dev/agents/pageindex",
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      "description": "Indexes long documents as a reasoning-based hierarchical tree so agents retrieve by navigating structure instead of embedding similarity.",
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      "name": "AnythingLLM",
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      "description": "Runs an all-in-one, self-hosted RAG and agents app: chat with documents, build agents, and connect tools via MCP. Desktop and Docker with an API. By Mintplex Labs.",
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      "name": "Jina Reader",
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      "description": "Indexes and searches embeddings with 2- to 4-bit TurboQuant compression in a Rust library with Python bindings, for local RAG pipelines that need a small memory footprint.",
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      "name": "Markdownify MCP",
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      "why": "Audio Transcription: Transcribe audio files to Markdown text for inclusion in RAG pipelines or agent knowledge bases"
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      "name": "OpenKnowledge",
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      "url": "https://agentswitchboard.dev/agents/openknowledge",
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      "name": "PrivateGPT",
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      "name": "Haystack",
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      "description": "Manages Markdown knowledge bases as Git-backed vaults in a desktop app, and lets coding agents search, read and edit notes through a built-in MCP server or an in-app agent panel.",
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      "name": "You.com Search API",
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      "name": "Cherry Studio",
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      "description": "Runs a Windows, Mac, and Linux desktop client for OpenAI, Gemini, Anthropic, Ollama, and LM Studio models, with 300+ preset assistants and MCP server support.",
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      "why": "Team Knowledge Base: Builds and shares team-wide knowledge bases with role-based access control over models and features via an admin backend."
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    {
      "name": "Redis MCP",
      "slug": "redis-mcp",
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      "iconUrl": "https://agentswitchboard.dev/icons/redis-mcp.png?v=1e6cf548",
      "description": "Official Redis MCP server for agentic applications to read, write, and search Redis data structures including JSON, hash, set, sorted set, stream, and list types.",
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        "mcp"
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      "verified": true,
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      "why": "Vector Similarity Search: Execute vector similarity searches over Redis vector sets to support semantic retrieval and agent memory lookup workflows."
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    {
      "name": "Cognee",
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      "description": "Builds queryable memory for agents by turning documents and conversations into a knowledge graph plus vector store, self-hosted or via API and MCP.",
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      "name": "superagnt_",
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      "description": "Connects agents to social, web, SEO and lead-enrichment data across 10 platforms and 262 REST endpoints, reachable over one streamable-HTTP MCP server or a keyed REST API.",
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    {
      "name": "Weaviate",
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      "description": "Stores objects and vectors together, combining semantic search with structured filtering for scalable, cloud-native AI application backends.",
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      "verified": true,
      "official": false,
      "featured": false,
      "authType": "apiKey",
      "why": null
    },
    {
      "name": "Concho",
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      "description": "Models an entire enterprise codebase into a queryable knowledge layer, then serves sourced architectural and business-logic facts to agents over MCP.",
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        "research"
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        "mcp"
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      "official": false,
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      "why": "Sourced Fact Retrieval: Answers questions about the application with citations back to the specific code that supports each claim."
    },
    {
      "name": "Graphiti",
      "slug": "graphiti",
      "url": "https://agentswitchboard.dev/agents/graphiti",
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      "description": "Builds temporally aware knowledge graphs for agents in real time, tracking when each fact became true so memory reflects change instead of overwriting it.",
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      "why": "Hybrid Retrieval: Combines semantic, keyword and graph traversal search to retrieve context with low query latency."
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    {
      "name": "Keenable",
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      "description": "Serves a hundred-billion-document web index built for agents rather than human search pages, returning dense structured results over an API and MCP endpoint.",
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      "featured": false,
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      "why": "Content Retrieval: Fetches full document content directly from the index without a separate crawl step."
    },
    {
      "name": "Reddit MCP Buddy",
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      "description": "MCP server for Reddit — search subreddits, retrieve posts and comment threads, analyze user activity, and post content. No API key required for read access.",
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        "data-analytics"
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        "mcp"
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      "verified": true,
      "official": false,
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      "why": "Post Retrieval: Retrieve Reddit posts and full comment threads with nested replies, author info, and vote counts."
    },
    {
      "name": "Dify",
      "slug": "dify",
      "url": "https://agentswitchboard.dev/agents/dify",
      "iconUrl": "https://agentswitchboard.dev/icons/dify.png?v=2d10811c",
      "description": "Open-source LLM app and agent development platform. Build, test, and deploy RAG pipelines, chatbots, and multi-agent workflows via visual canvas and REST API.",
      "provider": "LF AI & Data Foundation",
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      "why": "RAG Pipeline: Ingest, chunk, and embed documents into a managed knowledge base with built-in retrieval"
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      "name": "GAIA by AMD",
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      "description": "AMD's open-source Python/C++ framework for AI agents running fully locally on AMD NPU/GPU. Includes MCP integration, RAG pipeline, speech-to-speech voice, and multi-agent support.",
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      ],
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      "why": "RAG Pipeline: Build retrieval-augmented generation pipelines over local documents with embedded vector search"
    },
    {
      "name": "MongoDB MCP",
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      "description": "Official MongoDB MCP server. AI agents query, analyze, and manage MongoDB databases with schema inspection, aggregation pipelines, and Atlas Vector Search support.",
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      "authType": "apiKey",
      "why": "Atlas Vector Search: Run semantic similarity searches using MongoDB Atlas Vector Search for RAG pipeline integration"
    },
    {
      "name": "Obsidian MCP",
      "slug": "obsidian-mcp",
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      "iconUrl": "https://agentswitchboard.dev/icons/obsidian-mcp.png?v=a4fe2e71",
      "description": "MCP server connecting AI agents to Obsidian vaults via the Local REST API plugin. Read, search, and write notes to ground agent workflows in a personal knowledge base.",
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        "research"
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      "official": false,
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      "authType": "apiKey",
      "why": "Note Write: Create and update Obsidian notes from agent outputs to build a persistent knowledge base"
    },
    {
      "name": "Supabase MCP",
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      "url": "https://agentswitchboard.dev/agents/supabase-mcp",
      "iconUrl": "https://agentswitchboard.dev/icons/supabase-mcp.png?v=54814e3d",
      "description": "Official Supabase MCP server. Lets AI agents manage Postgres databases, run queries, handle migrations, store pgvector embeddings, and control Supabase projects end-to-end.",
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      ],
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      "verified": true,
      "official": true,
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      "why": "pgvector Store: Store and query vector embeddings in Postgres with pgvector similarity search"
    },
    {
      "name": "Cloudflare Workers AI",
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      "description": "Runs open-source models on serverless GPUs across Cloudflare's network from Workers, Pages, or the REST API, for text generation, embeddings, speech, and image tasks.",
      "provider": "Cloudflare",
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        "language"
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      "verified": true,
      "official": true,
      "featured": false,
      "authType": "bearer",
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    },
    {
      "name": "Hindsight",
      "slug": "hindsight",
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      "description": "Gives agents a self-hosted long-term memory server that retains facts, recalls them with parallel semantic, keyword, graph, and time search, and reflects over them, via SDKs, REST, and MCP.",
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      "official": false,
      "featured": false,
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      "why": "Recall Memories: Searches a bank with semantic, BM25 keyword, graph, and temporal retrieval in parallel, then fuses and reranks results to a token…"
    },
    {
      "name": "LobeChat",
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      "description": "Deploys a self-hostable, multi-provider LLM chat framework with a plugin and MCP marketplace, artifacts, knowledge base, and branching. Extensible ChatGPT alternative by LobeHub.",
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      "official": false,
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    },
    {
      "name": "MinerU",
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      "description": "Converts PDFs, Office documents, and scanned pages into LLM-ready Markdown or JSON, preserving tables, formulas, and reading order for agent pipelines.",
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      "official": false,
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    },
    {
      "name": "Zvec",
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      "description": "Embeds an in-process vector database into applications for dense, sparse, full-text, and hybrid search with durable local storage, via Python, Node.js, Go, Rust, and Dart SDKs.",
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    },
    {
      "name": "Crawl4AI",
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      "description": "Crawls and scrapes web pages into clean Markdown for RAG pipelines, with CSS, XPath, and LLM-driven structured extraction, deep crawling, and full browser session control.",
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      "name": "CowAgent",
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      "description": "Runs a self-hosted personal agent that plans multi-step tasks, calls built-in and MCP tools, installs skills, and answers on Web, Telegram, Slack, WeChat, and Feishu.",
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    },
    {
      "name": "QMD",
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      "iconUrl": "https://agentswitchboard.dev/icons/qmd.png?v=bb5319c1",
      "description": "Indexes local markdown notes, meeting transcripts, and docs for on-device BM25, vector, and LLM-reranked hybrid search, exposed to agents through a CLI, an MCP server, and a Node SDK.",
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      "why": "Document Retrieval: Fetches a document by path or docid with fuzzy suggestions, or batches retrieval by glob pattern, list, or docids with multi-get."
    },
    {
      "name": "TencentDB Agent Memory",
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      "description": "Give a team of agents shared long-term memory — turns conversations, docs, and code into governed Chat Memory, Skill, LLM-Wiki, and Code-Graph assets reusable across frameworks.",
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      "description": "Captures Claude Code tool usage as searchable observations and replays summarized context into new sessions, giving coding agents memory that survives restarts.",
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        "code-devtools"
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    },
    {
      "name": "Elasticsearch MCP Server",
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      "description": "Official Elastic MCP server letting agents query Elasticsearch indices, run search and aggregations, and inspect mappings in natural language.",
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    {
      "name": "Exa AI",
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      "description": "Neural search engine API that understands meaning — find similar pages, search by content type, and get clean extracted text.",
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    },
    {
      "name": "Funes",
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      "description": "Give coding agents durable memory of past sessions — a local-first layer combining vector and BM25 search over prior conversations, usable from Claude Code, Codex, and other harnesses.",
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    {
      "name": "Linkup",
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      "url": "https://agentswitchboard.dev/agents/linkup",
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      "description": "Production-grade web search API for AI — sourced, cited answers with full-text snippets from trusted sources. REST API, Python/Node SDKs, and MCP server for agent workflows.",
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      "verified": true,
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    },
    {
      "name": "LLM Wiki",
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      "url": "https://agentswitchboard.dev/agents/llm-wiki",
      "iconUrl": "https://agentswitchboard.dev/icons/llm-wiki.png?v=84c41a45",
      "description": "Builds and maintains an interlinked wiki from your documents in a desktop app, and exposes hybrid search, graph and file reads to agents through a local HTTP API and MCP server.",
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        "browser-extension"
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      "verified": true,
      "official": false,
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      "authType": "apiKey",
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    },
    {
      "name": "Mem0",
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      "description": "Persistent memory layer for AI agents and LLMs. Stores, searches, and retrieves user and session memories across frameworks. SDKs for Python and JS, managed or self-hosted.",
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    {
      "name": "obsidian-wiki",
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      "description": "Compiles what you and your coding agent learn into a linked Obsidian markdown wiki through agent skills, a CLI, and an MCP memory server, for developers who want persistent agent memory.",
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    },
    {
      "name": "OzBrain",
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      "description": "Shared knowledge layer that lets multiple agents read and write to a common memory, so context learned by one agent is available to the others.",
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  ],
  "relatedTasks": [
    "give-an-agent-memory",
    "search-the-web",
    "read-pdfs-and-documents",
    "work-in-notion-and-docs"
  ],
  "stacks": []
}