Driver AI
Driver · Coding
Driver AI compiles your repositories into structured codebase context that AI coding agents query over MCP, so tools like Claude Code and Cursor start every task with the same reliable map.
About Driver AI
What Is Driver AI
Driver AI is a platform that reads your entire codebase ahead of time and turns it into structured context for AI agents. You connect GitHub, GitLab, Bitbucket or Azure DevOps, and Driver analyzes every file, resolves symbols, traces call chains, and maps how services depend on each other. The result is a codebase map that agents can query instead of grepping through files on their own.
The problem it targets is context. An AI coding agent that starts from zero each session sees a small slice of a large repository, so it misses dependencies and guesses at structure. Teams patch the gap with markdown notes in the repo, retrieval pipelines, or long grounding prompts. Those approaches drift, conflict, or lose the relationships that make code understandable.
Driver takes a compiler style approach instead. The analysis runs ahead of time and produces the same output for the same commit, which makes agent behavior more predictable. The tradeoff is that it's an enterprise platform. There's no free tier and no public self serve checkout, so small teams and solo developers will find the pricing conversation heavier than the product's daily use.
Getting Started
- Request a pilot and connect the source control system that holds your main repositories.
- Let Driver ingest the codebases and build the initial atlas, which can take a while on very large monorepos.
- Set up SSO and map identity provider groups to Driver teams so each group sees the right context.
- Connect your agent of choice, such as Claude Code, Cursor, or an IDE extension, to the Driver MCP server.
- Ask the agent a structural question about the code and check that the answers match what your engineers know.
Product Information
A quick look at Driver AI's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Engineering teams running AI coding agents
- Platform and developer experience groups
- Engineering leaders inheriting a large codebase
Tasks
- Onboarding agents to an unfamiliar repository
- Planning a large refactor or migration
- Answering support questions about old code
Scenarios
- A monorepo with hundreds of services
- Post merger code consolidation
- Regulated environments that need audit trails
Key features
Compiler Style Codebase Analysis
Driver parses every file, builds syntax trees, resolves symbols, and follows call chains across services. Because the work happens ahead of time, the same commit produces the same context on every query. Agents stop sampling a random slice of the repository and start from a complete map.
Codebase Atlas for Navigation
The atlas divides a codebase into subsystems sized to the project, and each section explains what the code does, what has to change with it, and why. An agent opens only the sections a task touches, which keeps responses focused and cuts the token cost of exploring a large repository.
MCP Server for Any Agent
One MCP connection works with Claude Code, Cursor, and other MCP compatible clients. Engineers set it up once per developer and keep the same context across editors, terminals, and chat tools. Because Driver sits at the source control layer, the context isn't tied to one vendor's editor.
Commit Level Changelog
Driver keeps a changelog that traces how a system reached its current shape, month by month and year by year. That history answers the question agents usually can't: why the code looks this way. It's useful for migrations, incident reviews, and onboarding people onto legacy modules.
Enterprise Access and Deployment Options
SSO, SCIM provisioning, role based access, and audit logging come with the platform, and larger plans add single tenant or regulated deployments. Teams can run a shared SaaS instance or keep code context in an isolated network with custom data residency. MCP and REST access cover both interactive agents and headless jobs.
Pros and cons
Pros
- Deterministic context: the same commit yields the same analysis, which makes agent output easier to trust and compare across runs.
- Tool agnostic: context lives at the source control layer, so switching editors or agents doesn't throw away the work.
- Lower token burn: agents spend fewer tokens searching because they start with a map instead of exploring file by file.
- Scales past chat context limits: the atlas covers repositories far larger than any model window, including cross repository questions.
Cons
- No public pricing: you can't estimate cost from the website, and plans are sized by lines of code analyzed per year, so budgeting needs a sales conversation.
- No free or self serve tier: individual developers and small startups can't test it without going through a pilot.
- Setup depends on clean source control: if parts of your code live outside the connected repositories, the context will have gaps that agents can't detect.
- Another platform to secure: code context is sensitive, so security review, SSO configuration, and access mapping add work before the first useful query.
Frequently asked questions
It precomputes structured context for a codebase so AI agents can query a map of the code instead of exploring files session by session. The output covers structure, symbols, dependencies, and change history.
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