gitdb.co is a code-native database for AI agents and the engineers who ship with them. AST-aware retrieval in single-digit milliseconds. Code-aware memory that survives across sessions. A2A swarm agents that hand off in ~15 tokens because code never leaves the database.
Here's what an agentic data layer looks like — agent swarms query in parallel, engineers stream what they need, and your data stays put in one secure home. This is GitDB at work.
Import from your existing Git host in a single pass, or start fresh with a new repo. After import, GitDB is the canonical home for your source — and the last place it lives.
Your AI agents connect with per-seat API keys and pull only the lines they need. Engineers open repos as `gitdb://…` workspaces in the VS Code extension, or review changes in the web reviewer — code is streamed file-by-file, never written to a laptop disk.
Every read, write, and merge is recorded with a clear identity. Scoped access for every human and every agent. Bulk-access bursts trigger alerts in real time — and the whole picture is one query away when you need it.
Point Claude Code, Cursor, or Windsurf at a single MCP endpoint and you get the whole surface — AST search, semantic search, read, write, commit, merge, memory. AST-aware reads save ~95% of tokens vs full file reads, on every call.
Fixes, gotchas, and architectural decisions are anchored to the exact AST node they describe — and flagged stale the moment the code changes. Recall is exact-cosine over 1536-dim vectors: 100% recall, not approximate.
Multi-agent workflows out of the box, with per-role persistent memory so each agent gets smarter over time. Handoffs cost ~15 tokens because code never leaves the database — your swarm doesn't re-read the repo on every turn.
The next decade of software will be written by teams of agents working in parallel, at machine speed. GitDB is the data layer built for that future — agent-native, multi-agent ready, and fast enough to keep up.
Forget cloning. GitDB gives your agents first-class tools to find a function, patch a file, or open a PR — all in single-digit milliseconds. They reason on the code that matters and skip the 9,000 tokens of file boilerplate.
Build a swarm of specialist agents that hand work off to each other through GitDB. Each handoff is a tiny pointer — file paths and line ranges, not raw code — so a 4,000-token handover collapses to about 15. The team gets more done, and the LLM bill barely moves.
Stop stitching together a code host, a vector database, an audit service, and a memory store. GitDB consolidates all of it on one engine — so your agents share context instead of paying to rebuild it on every call.
Agents don't need to read the whole file to patch one function — and they don't need to re-discover what they already learned. GitDB feeds your model exactly the code it asked for, then remembers what it figured out. Smaller inputs, smarter agents, a smaller invoice.
Agents read exact line ranges, not whole files. Semantic search and structured queries serve up only the code your model asked for.
Long-term agent memory means you stop paying to re-discover the same solution every time. Skills, decisions, and patterns stay learned.
Stack the two — smaller inputs and recallable memory — and a task that used to burn 20K tokens now costs about 2K. Same answer, ten times less spend.
Find code by what it does, not what it's called. Sub-millisecond vector search over every function in every commit.
gitdb_search_code("rate limit auth handler")
→ ranked matches across 12 repos in 0.3msFind a function by name, list every caller, or jump across modules — structured queries over a live AST index.
gitdb_find_function("process_payment")
gitdb_find_callers("auth_middleware")`self.handler.process()` gets resolved to the right definition across 20 modules — no more ambiguous grep hits.
There is no `git clone`, no ZIP export, no bulk repo download. Engineers open repos as `gitdb://…` workspaces in VS Code; agents pull line ranges through a tool API. Code lives in GitDB and nowhere else.
gitdb_read_lines("src/auth.py", 42, 60)
gitdb_write_lines("src/auth.py", 42, new_code)
gitdb_commit("feat: add rate limiting")Stray API keys, banned dependencies, and policy violations get caught before they ever land. Your standards run inside the database, so every contributor and every agent ships clean code by default.
Spec-writer, coder, reviewer, tester — a swarm of specialist agents shipping features in parallel. Each one has its own identity. Handoffs are pointer-sized (paths + line ranges), so the team stays cheap to run and easy to trace.
Pricing
Every tier ships with the same retrieval engine. Each step up unlocks one big capability — memory, swarm, governance.
GitDB + MCP for solo developers. No AI agent.
Add one AI agent on top of unlimited repos.
Run a 5-agent A2A swarm with per-role memory.
Maximum throughput — 10 agents in parallel.
* SOC2, HIPAA, FedRAMP, and ITAR certifications are actively in progress. Contact us for current attestation status.
Bring your agents to a database built for them. AST-aware retrieval, code-aware memory, and a full A2A swarm — all from $30 per dev per month.