Search Memory

Search Memory MCP

Free web search and long-term memory for your AI assistant. No API keys. Your data stays on your machine.

Works with Claude Desktop, Claude Code, Cursor, Windsurf, and any other MCP client.

Why Search Memory MCP?

• 🔎 Search the web for free. DuckDuckGo, Bing, Brave, and Google, with automatic fallback. No API keys, no subscriptions.
• 📄 Read any page as clean Markdown. Fast paths for GitHub, RSS, and static pages. PDF, DOCX and EPUB links are converted to text. A real browser only when a page needs it.
• 🧠 Remember across sessions. Your assistant can save notes and recall them later.
• 📚 Build a private knowledge base. Index pages and documents, then search them with keyword + semantic search and get answers with citations.
• 🔁 Research that remembers. One research call checks what you already indexed, searches the web, reads the top pages, answers with citations, and keeps the pages for next time. Every source shows when it was published (from page or PDF metadata) and when it was fetched or indexed, so you can tell how current an answer is.
• 🔒 Local first. Cache, memory, index, and models all run on your machine.
• 🛡️ Safer with untrusted pages. Hidden text and invisible characters are removed from fetched pages, and web content is marked as data so the assistant is told not to follow instructions inside it. See SECURITY.md.

Quick start

Add this to your MCP client config (for example claude_desktop_config.json or .cursor/mcp.json):

{

"mcpServers": { "search-memory": { "command": "npx", "args": ["-y", "search-memory-mcp@latest"] } } }

Restart your client and ask: *"Search the web for the latest Node.js release and remember the version number."*

Search and most pages work right away. Pages that need a real browser use Playwright Chromium (about 180 MB). It is downloaded during install, or on first use if your package manager skipped install scripts, so that first request can take a few minutes. To download it ahead of time, run npx playwright install chromium. Model-backed features download small local models on first use.

Requires Node.js 20.9 to 25 (Node 24 recommended). If your Node version is newer, or npm skipped install scripts, the server prints the exact fix when it starts. You can also run it in Docker with nothing installed on your machine except Docker.

Run in Docker (no local setup)

Docker brings its own Node 24, SQLite module and Chromium, so it works whatever Node version you have. Point your MCP client at the published image (linux/amd64 and linux/arm64):

{

"mcpServers": { "search-memory": { "command": "docker", "args": ["run", "-i", "--rm", "-v", "search-memory-data:/app/data", "-v", "search-memory-models:/root/.cache/huggingface", "ghcr.io/kefyusuf/search-memory-mcp:latest"] } } }

Docker downloads the image on first start. Run docker pull ghcr.io/kefyusuf/search-memory-mcp:latest to update, or use a version tag such as :1.0 to stay on one release. To build the image yourself instead, run docker build -t search-memory-mcp . in a clone and use search-memory-mcp as the image name.

The named volumes keep your cache, memory, knowledge base and downloaded models between runs. To let ingest_document read local files, mount the folder and allow it, for example "-v", "/home/me/notes:/notes:ro", "-e", "INGEST_ALLOWED_DIRS=/notes".

Features

Search & fetch

• Browser context pooling with a persistent Playwright browser instance.
• Web search through configurable providers with health tracking and ordered fallback. Supported scrapers: DuckDuckGo, Bing, Brave, Google, Yahoo, Marginalia. Optional SearXNG meta-search provider (self-hosted or trusted public instance) via SEARXNG_BASE_URL, optional Brave Search API via BRAVE_SEARCH_API_KEY, optional Tavily (news-topic search with publish dates) via TAVILY_API_KEY, and optional Exa (neural/semantic discovery) via EXA_API_KEY.
• Search responses report which providers were tried and why any failed, were empty, or were skipped (providerAttempts in structured output, a short "Provider notes" line in text output).
• Search-result cache lifetime follows the query type: news 15 minutes; shopping, local and general 1 hour; technical, research and navigational 24 hours.
• fetch_content and index_url extract text from PDF, DOCX and EPUB responses (detected by content type, or by extension for generic binary responses), up to 25 MB per document, without opening a browser.
• Federated search across providers with URL normalization, cross-provider deduplication, and Reciprocal Rank Fusion (RRF).
• Opt-in intent-aware search routing (strategy=auto) with heuristics, local classifier fallback, and versioned provider profiles.
• Domain filter (domain) and date-range filter (from_date / to_date).
• Query rewrite for local-index searches and opt-in web multi-query expansion (expand_query=true): abbreviation expansion, question normalization, and news year bias.
• Optional cross-encoder reranking (ENABLE_RERANKER).
• Deep-search answers with paragraph/sentence term scoring, stopword filtering, and per-source citations.
• Structured JSON output (format: "json") for machine-readable search results and answers. web_search, search_index and server_status also declare an MCP outputSchema and return structuredContent on every successful call.

Local knowledge base (RAG)

• Document chunking (paragraph-first with overlap).
• Hybrid retrieval: FTS5 keyword search + sqlite-vec semantic search fused with RRF. Degrades to FTS-only when embeddings are unavailable.
• ingest_document, index_url, search_index, and list_index tools for building a citable local corpus.
• Entity graph over indexed documents with find_related (related docs + co-occurring entities).

Memory & observability

• Session memory (remember / recall / forget) with topic, tags, and session scoping.
• Per-search execution traces (stages, timings, cache). Recent searches surface in server_status; TRACE_SEARCHES=true logs full traces.
• Retrieval eval harness (npm run eval:retrieval) with recall@k, precision@k, and MRR.

Safety & infrastructure

• HTTP-first page fetching with GitHub Raw and RSS fast paths plus Playwright fallback.
• SSRF protection for fetch_content and index_url by blocking localhost and private network targets.
• Token-bucket rate limiting for search and fetch tools.
• Semantic cache backed by SQLite and sqlite-vec, namespaced per execution strategy/plan.

Requirements

• Node.js 20.9.0 or newer.
• Node.js 24 is the development baseline (.nvmrc); CI uses the same version. After switching Node versions, reinstall dependencies so native modules such as better-sqlite3 match the active runtime.
• npm.
• Network access during installation for npm packages, Playwright Chromium, and first-run model downloads.

Installation

npm install

npm run build

The postinstall script downloads Playwright Chromium. On first use of model-backed features, Transformers.js downloads the required model files to the local Hugging Face cache. The first request that loads a model can be slow; later requests reuse the local cache. Keep ENABLE_CROSSLINGUAL=false and ENABLE_RERANKER=false for the lightest first run. Obvious strategy=auto intents are resolved by heuristics without loading the intent classifier; ambiguous auto queries may trigger a first-run classifier download.

MCP Client Configuration

Add the built server to your MCP client config:

The package, command and MCP server identity are search-memory-mcp. Use your actual checkout path in the configuration below. Existing clients that launch node with an absolute build/index.js path can keep that path even if the checkout directory still has its previous name. Restart the MCP connection after rebuilding to load the updated server identity. Keep existing CACHE_DB_PATH values to retain stored data.

{

"mcpServers": { "websearch": { "command": "node", "args": ["path/to/search-memory-mcp/build/index.js"], "env": { "RATE_LIMIT_SEARCH_PER_MIN": "10", "RATE_LIMIT_FETCH_PER_MIN": "20", "ENABLE_CROSSLINGUAL": "false" } } } }

If the package is installed globally or through a package runner, use the binary entrypoint:

{

"mcpServers": { "websearch": { "command": "search-memory-mcp", "args": [], "env": { "ENABLE_CROSSLINGUAL": "false" } } } }

To use the Brave Search API, add "BRAVE_SEARCH_API_KEY": " " to the same env object. Leave SEARCH_PROVIDERS unset so the default order (brave,duckduckgo,marginalia,bing with a key) applies; an explicit SEARCH_PROVIDERS replaces it. On Windows, Claude Desktop reads %APPDATA%\Claude\claude_desktop_config.json; restart the app fully after editing it.

For package-runner based clients, use the npx configuration from Quick start.

Tools

Search strategies

Semantic query cache keys are namespaced by execution strategy (and by plan fingerprint for auto), so a cached fallback result is never reused for aggregate or a different auto plan. Deep-search page content continues to use the normal content cache