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.

[npm] [License: ISC]

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. Optional SearXNG meta-search provider (self-hosted or trusted public instance) via SEARXNG_BASE_URL, and optional Brave Search API via BRAVE_SEARCH_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 installnpm 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",        "CACHE_DB_PATH": "websearch_cache.db"      }    }  }}

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": "<your key>" to the same env object. Leave SEARCH_PROVIDERS unset so the default order (brave,duckduckgo,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.

Domain and date bounds are exact cache constraints, isolated from semantic query similarity. Provider candidates are cached and the requested domain/date filters are reapplied on every hit, including before deep page fetching. A cache entry with no eligible candidates triggers provider execution. Namespace filtering happens before the vector-store result limit so unrelated strategies cannot crowd out eligible entries.

SEARCH_PROVIDERS is an allowlist as well as the configured provider set. Auto routing never activates a provider omitted from SEARCH_PROVIDERS; the routing profile only changes ordering and how many configured providers are selected as primary candidates.

For aggregate auto profiles, secondary configured providers are contacted only if all selected primary providers return no usable result. A partial primary success is accepted instead of widening the request just to increase result count. This limits scraping load and reduces unnecessary blocking/CAPTCHA exposure.

Current routing profile: v2.

Profile v2 orders providers by measured reliability (npm run benchmark:providers, October 2026). Self-hosted SearXNG and the Brave Search API answered reliably, and DuckDuckGo was the only scraper that answered consistently. Bing and Google mostly blocked scraping clients, so they come last. These preferences are not permanent quality claims. They are versioned so later releases can tune them from deterministic and live evaluation evidence without scattering routing conditionals through the server.

Example: intent-aware search

{  "query": "PostgreSQL connection pooling best practices",  "strategy": "auto",  "max_results": 5}

Use domain for targeted searches such as react.dev or github.com. Intent detection always receives the original query; site:<domain> is appended only afterward for provider execution.

{  "query": "server components reference",  "domain": "react.dev",  "strategy": "auto",  "max_results": 5}

Use from_date / to_date (inclusive YYYY-MM-DD) to filter by detected publish dates in titles and snippets. Results without a detectable date are kept by default.

Use deep=true only when the client needs the server to fetch top pages and extract a likely answer from page text. Answers include per-source citations ([Source N]) mapped to the fetched URLs. The MCP client LLM remains responsible for final reasoning and summarization.

Search snippets with old detected dates include a short freshness warning so clients can treat stale sources carefully.

Example: federated search

{  "query": "postgres connection pooling strategies",  "strategy": "aggregate",  "max_results": 5}

Example: multi-query search

{  "query": "how to configure db backup",  "strategy": "auto",  "expand_query": true,  "max_results": 5}

expand_query defaults to false. When enabled, the server searches the original query plus up to two variants, then deduplicates URLs and combines query rankings with RRF before reranking. This can make up to three times as many provider requests. The original query determines intent and locale; every variant keeps the domain restriction and uses the same provider plan. Date filters apply to the combined results. Expanded searches use a separate cache namespace, and cache hits skip all provider requests.

Example: structured JSON output

{  "query": "postgres pooling",  "format": "json",  "max_results": 5}

Returns a stable payload with query, resultCount, results[] (title, url, snippet, source, optional scores), and meta.

Example: local knowledge base

{ "content": "PgBouncer multiplexes PostgreSQL connections...", "title": "PgBouncer Guide", "source": "https://example.com/pgbouncer" }

Then search it:

{ "query": "connection pooler", "max_results": 5 }

Or explore the entity graph:

{ "entity": "PgBouncer" }

fetch_content uses fast source-specific paths before opening a browser:

• GitHub repository, blob, tree, and raw URLs are read from raw.githubusercontent.com when possible.
• RSS or Atom feed URLs, plus common blog/news feed paths, are converted into a Markdown list of recent items.
• Regular HTML pages still use HTTP-first Readability parsing with Playwright fallback.

Configuration

File-backed SQLite stores use WAL journaling with synchronous=FULL; :memory: databases remain in memory. Use a writable local data directory: WAL requires shared memory on the same host and is unsuitable for a database shared over a network filesystem. While the server is running, the -wal and -shm files belong to the database; copying only the .db file does not provide a consistent live backup. See SQLite WAL documentation and the disposable backup/restore rehearsal. Production backup operations remain separate from these local regressions.

Docker

A GitHub Actions workflow (.github/workflows/docker.yml) builds the image, runs the MCP smoke test against it over stdio, and publishes ghcr.io/kefyusuf/search-memory-mcp for linux/amd64 and linux/arm64: :latest and :sha-<commit> from main, semver tags from v* tags. To run the smoke test against any image locally: SMOKE_DOCKER_IMAGE=<image> npm run smoke:mcp.

npm run docker:buildnpm run docker:up

Optional SearXNG

Measured provider reliability varies (see Troubleshooting), so the compose file includes an optional self-hosted SearXNG service as a second reliable source. It starts only with the searxng profile and is reachable only inside the compose network:

SEARXNG_SECRET=$(openssl rand -hex 32) \SEARCH_PROVIDERS=searxng,duckduckgo \SEARXNG_BASE_URL=http://searxng:8080 \docker compose --profile searxng up

docker/searxng/settings.yml enables the JSON API that this server reads and turns off SearXNG's public rate limiter. The Docker image workflow starts this service in CI and checks that it answers a JSON search.

Docker Compose stores the SQLite cache in a named volume mounted at /app/data and stores Hugging Face models in a separate named volume. The container sets CACHE_DB_PATH=/app/data/websearch_cache.db.

Both Docker stages use Node 24, matching .nvmrc and CI. The runtime explicitly initializes the native SQLite binding after installing dependencies with lifecycle scripts disabled. To check the compiled stdio interface with a disposable database, run from the repository root:

docker compose -p search-memory-mcp-check run --rm -T \  -v "${PWD}/scripts:/app/scripts:ro" \  -e CACHE_DB_PATH=/tmp/search-memory-mcp-smoke.db \  search-memory-mcp node scripts/smoke-mcp.mjsdocker compose -p search-memory-mcp-check down --volumes --remove-orphans

The smoke check verifies initialization, tool discovery, status, memory save/recall/deletion, argument validation and loopback blocking. It does not qualify live search providers or model inference. Use a distinct Compose project name for checks so the development data volumes remain separate.

If a trusted TLS inspection proxy intercepts browser downloads, supply its public CA certificate as the optional BuildKit build_ca secret:

docker build --secret id=build_ca,src=/path/to/trusted-ca.pem -t search-memory-mcp:latest .

The certificate is used only during dependency installation and is not copied into the image. Runtime HTTPS trust is a separate configuration concern; keep TLS verification enabled.

Development

See the production roadmap and sector comparison for release scope, production gaps, and measurable launch gates.

npm run buildnpm run typechecknpm testnpm run smoke:mcpnpm run eval:retrievalnpm audit --audit-level=moderatenpm pack --dry-run --json

npm run e2e runs a user scenario against the compiled server through the MCP SDK client: memory, local PDF/DOCX ingestion (including refusing hidden and outside files), knowledge search, entity graph, then web search, page and PDF fetch, and research. Web steps are reported as SKIP when web search is unavailable, so the local steps can run anywhere; run it on a normal network to cover the whole system. It exits with 1 when a step fails.

npm run smoke:mcp starts the compiled server over stdio, verifies the web_search strategy values (fallback, aggregate, auto), checks the knowledge/memory tools, confirms routing diagnostics from server_status, and confirms that fetch_content blocks localhost. It does not perform a live provider search, keeping CI independent of search-engine HTML/network availability.

npm run eval:retrieval runs an offline FTS-only retrieval baseline (recall@k, precision@k, MRR) over the knowledge index using fixtures in evals/retrieval/cases.jsonl. Embeddings are disabled for both ingestion and search, so the evaluation does not load or download models. It does not measure semantic/hybrid retrieval quality.

npm run benchmark:providers (after building) sends real queries from evals/providers/queries.jsonl (16 EN/TR queries across intents) to each provider in SEARCH_PROVIDERS, one provider at a time, and prints success, empty, error and p50/p95 latency per provider. Use --limit N for fewer queries, --delay MS between requests (default 1500), --json PATH to keep the raw attempts, and --dump DIR to save the raw response of every empty or failed attempt (useful to tell a block or consent page from changed result markup). It contacts real search engines, so it is not part of CI; results depend on your network and the engines' blocking at that moment.

After building, node scripts/eval-local-quality.mjs prints a broader synthetic FTS/entity characterization as JSON. See fixture labels and limitations. Keep generated reports in ignored .cache/; this diagnostic does not change runtime behavior or enforce a release threshold.

Deterministic TR/EN routing fixtures live in evals/search-routing/queries.jsonl and are exercised by the normal Vitest suite. They validate intent coverage, conservative heuristic behavior, ambiguity defer cases, and provider-allowlist enforcement without loading the real classifier or contacting providers.

Troubleshooting

• The server downloads Chromium automatically the first time a page needs a browser. If that download fails or stalls (for example offline or behind a proxy), it stops after CHROMIUM_INSTALL_TIMEOUT_MS; run npx playwright install chromium manually.
• If the server says it could not load its SQLite module (better-sqlite3), follow the steps it prints: use Node 24 (the version in .nvmrc); if npm 11 skipped install scripts, run npm install-scripts approve better-sqlite3; then npm rebuild better-sqlite3. Or run it in Docker.
• If the first model-backed request is slow, allow the Transformers.js model download to complete and retry.
• If search returns no results, change SEARCH_PROVIDERS order/set or try a direct fetch_content URL.
• Provider reliability varies by network. In a live run in October 2026 (npm run benchmark:providers), DuckDuckGo answered every query; Bing sometimes returned a "no results" page for ordinary queries, which looks like a soft block; Brave answered HTTP 429; and Google returned a JavaScript-required page to every request, which is now reported as an error. Keep duckduckgo first, and add a self-hosted SearXNG (SEARXNG_BASE_URL) if you need a second reliable source. Measure your own network with npm run benchmark:providers.
• If aggregate mode is too slow or triggers provider blocking, use the default fallback strategy.
• If auto chooses too broad a search plan for your use case, use explicit fallback or aggregate; explicit strategies bypass the auto planner.
• If Docker cannot find Chromium, rebuild the image with npm run docker:build.
• If cache files appear in the project root, set CACHE_DB_PATH to a dedicated data directory.
• If knowledge search only hits keywords, embeddings may still be indexing; search_index falls back to FTS-only until vectors are ready.

Known limitations

• Default search depends mostly on DuckDuckGo. Search engines block scraping clients. In our measurements, Brave answers with HTTP 429 and Google serves a JavaScript-only page; both now show up as provider errors. When Bing blocks a client, it returns a normal-looking "no results" page that cannot be told apart from a real empty result. For dependable results, set a BRAVE_SEARCH_API_KEY or add a self-hosted SearXNG (SEARXNG_BASE_URL) as a second source; see Troubleshooting.
• Hidden-content filtering is best effort. Elements hidden by the hidden attribute, aria-hidden, <template> or inline styles are removed. Content hidden by external stylesheets or scripts is not. Web content is wrapped in <untrusted_web_content> markers, but the client model must still treat it as data.
• Scanned PDFs are not read. PDF text is extracted without OCR, so image-only pages produce no text.
• The first run downloads models and a browser. The embedding model and Playwright Chromium are fetched on first use. Offline machines need them pre-installed, or the Docker image.
• Single local user. The server runs over stdio for one local client. There is no authentication and no shared HTTP endpoint yet.
• Web steps need an open network. npm run e2e reports web steps as SKIP when search engines are unreachable.

npm Packaging

The npm package includes only build/, README.md, LICENSE, and SECURITY.md. npm pack runs npm run build through prepack so the package contains compiled JavaScript instead of local planning files, tests, caches, or source-only artifacts.

Releases are published by .github/workflows/release.yml when a v* tag is pushed. The workflow publishes the npm package with provenance and registers the server in the MCP Registry. docker.yml publishes the Docker image for the same tag. npm publishing uses trusted publishing (GitHub OIDC), so no npm token is stored. Before tagging, set the version in package.json and server.json (both entries).

Security

See SECURITY.md for reporting instructions and current dependency audit notes.

License

ISC