# Frequently Asked Questions Source: https://stratadb.org/docs/faq ## What it is ### What is StrataDB? An embedded, multi-model database. It runs inside your process against a local directory — one binary, one database directory, no server. Five data capabilities share one storage substrate: key-value, JSON documents, an event log, vectors, and a graph. On top of that substrate you get git-style branches and time travel. See [Your first database](/docs/getting-started/first-database) for a hands-on tour. ### Is it a server? No. StrataDB is embedded and in-process, like SQLite or DuckDB. You do not start a daemon, open a port, or manage a connection pool — you point the CLI or the [Python SDK](/docs/python) at a directory and it opens the database in-process. There is no network mode in this line. ### Is it a replacement for Postgres or Redis? No — it complements them. Keep Postgres for relational application data and Redis for a shared cache. Reach for StrataDB when you want branch-isolated, versioned state with a KV, JSON, event, vector, and graph model in one place — agent memory, experiment isolation, and replayable history are the sweet spot. ### Why not just use SQLite? SQLite is a great relational store, but it has no branch-scoped isolation, no built-in vector or graph capability, and no per-commit time travel. StrataDB gives you those on one substrate instead of asking you to build them yourself. ## Storage and durability ### Durable mode versus cache mode? Point StrataDB at a path and it runs **durable**: writes go through a write-ahead log and survive the process exiting. Run it with `--cache` and it runs **in-memory** — nothing is written to disk and the data is gone when the process ends. Durable mode is the default for any named path; cache mode is for tests, scratch work, and ephemeral MCP servers. ### Where does my data live? In the directory you name. A durable database directory holds a write-ahead log, a manifest, and lock files — treat the directory as one unit; do not edit files inside it by hand. Global CLI configuration lives separately under your home directory, so uninstalling the binary never touches your databases. ### How does branching relate to git? The mental model is the same: fork a branch, get an isolated line of data, and writes on the fork are invisible to its parent. A fork is cheap because it shares the parent's data until you change something (copy-on-write), and every write is a commit you can read back with `--as-of`. Where it differs from git: this release has no merge command. You fork, isolate, and time-travel; you do not merge two divergent branches back together. The workflow is fork-and-replay, not fork-and-merge. See [Branches](/docs/concepts/branches) and [Commits](/docs/concepts/commits). ## What changed in this line The V1 line is a clean break, so a few things from earlier previews are gone or deferred. Straight answers: ### What happened to the state cell? Removed. There is no separate state-cell capability. Use the KV store for mutable keyed state, or a JSON document for structured state. ### What happened to sessions and transactions? Removed as a public surface. There is no `begin` / `commit` / `rollback`. Writes auto-commit, and multi-item batches commit itemwise or under one shared commit — you do not manage a transaction by hand. ### What happened to branch bundles? Replaced by hub clones. Instead of exporting a bundle file, you clone a dataset from a hub into a new local database with `strata clone`, and `strata remote` shows where a database was cloned from. See [Cloning datasets](/docs/guides/cloning-datasets). ### What happened to search? Vector similarity search is here — create a collection and run `vector query`. The broader standalone search surface and its optimizer are deferred beyond this line; the substrate for them is in place. See [Vectors](/docs/data/vectors). ### Can it run local models? Not with the released binary. The shipped build executes inference through cloud providers (Anthropic, OpenAI, Google — bring an API key); local GGUF execution is compiled in only when the binary is built with the local feature. The model catalog commands work either way. See [Inference](/docs/inference). ## Language SDKs and MCP ### Is there a Python or Node SDK? The **Python SDK** (`stratadb`) links the engine in your process and speaks the same command surface, value shapes, and error codes as the CLI — see the [Python SDK](/docs/python) section. Its V1 wheels are rolling out to PyPI. A Node SDK is later work. Beyond those, the `strata` CLI and its MCP server mean any language that can run a subprocess or speak MCP can drive Strata today. ### How do I use it from an AI agent? The Model Context Protocol server is built into the binary — run `strata mcp serve`; there is no separate package to install. See [For AI agents](/docs/agents) and [Agents and MCP](/docs/agents). ## Still stuck? Check [Troubleshooting](/docs/troubleshooting) for concrete failure modes, or the [error reference](/docs/reference/error-reference) to look up a specific code.