From zero to a searchable, agent-ready index in ten minutes.
Six steps: install, configure a source, serve, search, and connect an agent. Everything runs on your machine — no SaaS, no API keys, no cloud round-trips.
- 1
Install StrataFS
Install the single StrataFS binary with your package manager — npm, pip, Homebrew, curl, Docker, or from source. The same binary ships the indexer, the REST API, and the MCP server.
- 2
Initialise a config
Run
stratafs config initto write a starter config.toml with a sensible default: your current directory indexed as a local, read-only source. - 3
Add a storage source
Point StrataFS at what you want to search. A local folder works out of the box; add an S3 bucket, GCS, Azure, SharePoint, Google Drive, or Jira project with a few lines of read-only credentials.
# ~/.stratafs/config.toml [[sources]] name = "docs" type = "local" path = "/Users/you/work/docs" [[sources]] name = "s3-archive" type = "s3" bucket = "acme-knowledge" region = "eu-west-1" # read-only IAM credentials from the environment - 4
Start the server
Run
stratafs serve. StrataFS indexes on first run — parse, chunk, and embed locally — then brings up the REST API on :8080 and the MCP server on :8081.$ stratafs serve ✓ indexed 12,481 files across 2 sources (41s) ✓ REST API http://localhost:8080 ✓ MCP server http://localhost:8081/mcp - 5
Run your first hybrid search
Query from the CLI or the REST API. One search fuses FTS5 BM25, vector cosine similarity, and metadata into a single ranked result set — no separate vector store to manage.
$ strata search "how do we rotate the signing keys" --top 3 1. ops/runbooks/key-rotation.md score 0.94 2. docs/security/secrets.mdx score 0.81 3. infra/terraform/kms.tf score 0.76 - 6
Connect an AI agent over MCP
Point Claude Desktop, Cursor, or any MCP client at http://localhost:8081/mcp. The agent discovers stratafs.search and answers from your actual files, offline.
{ "mcpServers": { "stratafs": { "url": "http://localhost:8081/mcp" } } }
What you get: a local, offline search engine that fuses keyword and semantic ranking in one query, plus a native MCP server that hands those results to any AI agent. No vector database to run, no data leaving your machine.
Where to go next
Ten minutes to a semantic filesystem.
Install, point it at your files, and let your agent search them — locally, over MCP.