AI infrastructure for developer tools

Developer tools are an unusual market to build for, because your users will notice immediately when the AI is shallow. A coding assistant that forgets the project's conventions, a docs search that only matches keywords, an agent with no way to undo a bad run, these read as toys to engineers, who are precisely the people who can tell. The model is capable enough; what separates a real dev tool from a demo is the infrastructure that gives it project-scoped memory, search that understands code and docs, and a way to checkpoint and roll back agent state.

This page is for teams building AI-powered developer tools. Exabase gives you project-scoped context through Bases, memory of decisions and conventions through Memory, meaning-based search through Deep Search, and snapshot rollback through Base filesystem versioning. Scope per repo or per team, and each gets its own isolated, versioned context without you building the partitioning or the snapshot layer.

What you can build

Dev tools tend to be one of a few shapes, each on infrastructure that already exists.

A coding assistant with project memory that remembers architecture decisions, conventions, and past debugging sessions, and stops suggesting patterns the team already rejected. That's the coding assistants with project memory pattern: per-project Bases, persistent memory, searchable project docs.

A documentation search tool that finds by meaning across a codebase's docs, ADRs, and runbooks, so "where do we handle retries on failed webhooks" returns the relevant passage rather than a keyword match, Deep Search over project content.

An agent with safe rollback that can modify files and context inside a checkpointed environment and revert when a run goes wrong, the version control for agent context pattern.

An internal developer platform that gives your engineers AI tooling over your own repos and docs, the internal tools pattern pointed at engineering.

Developer-tool problems, solved

The problems dev-tool builders run into are specific, and each has an answer.

Project-scoped context that doesn't bleed. A coding assistant working in one repo shouldn't pull in another's conventions. Bases scope context per repo or per team, structurally isolated from one API call, so each project's memory and search contain only that project. It's the multi-tenant memory pattern applied to repos.

Memory of decisions, not just code. The valuable context is why the payments service was split out, which patterns were banned, what was tried and abandoned, and most of it lives in people's heads. Memory holds these decisions and keeps them current through contradiction resolution, so when the team migrates off a library the assistant stops recommending it. This is where teams outgrow their framework's built-in memory.

Search that finds by meaning. The relevant function rarely contains the words you'd search for. Deep Search matches semantically across docs, ADRs, and PR history, and holds quality as the corpus grows.

Undo for agents that modify state. An agent that can change files and context can also corrupt them. Base filesystem snapshots let you checkpoint state before a risky run and roll back, version control for the agent's context. Note this isolates and versions the agent's data, it isn't a code-execution sandbox; the distinction is covered in version control for agent context.

The infrastructure underneath

Four primitives carry most dev tools. Bases give per-repo or per-team isolation, and snapshot the context for rollback. Memory holds decisions, conventions, and debugging history. Deep Search finds code, docs, and PR context by meaning. Resources store the project's written material. One API key, rather than a vector database, storage, a memory layer, and a snapshot mechanism you build and maintain.

Context that deepens per project

A dev tool on this foundation gets more useful the longer it works a codebase. Every resolved bug, settled convention, and rejected approach adds to what the assistant knows, and because the memory self-organises, the picture sharpens rather than clogs as it fills. A project the tool has worked for six months is one where it genuinely carries the institutional knowledge, which also means that knowledge stops walking out when an engineer rotates off. Adding a repo or a team is one API call, so the per-project context scales without the isolation getting more fragile, and the snapshot history means engineers can let agents operate more freely, knowing a bad run is one revert away.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: coding assistants with project memory, version control for agent context, and internal tools powered by AI. The GitHub PR knowledge base example is a concrete build, and there's a free tier to build against.

FAQs

How is context kept scoped to one project?

Each repo or team gets its own Base, structurally isolated, so an assistant working in one project's Base sees only that project's memory and search. It's the multi-tenant SaaS pattern applied to repos.

Does it remember architecture decisions and conventions?

Yes. Memory holds decisions, conventions, and past debugging sessions, and keeps them current through contradiction resolution, so when a convention or library changes the assistant stops recommending the old one.

Can an agent roll back a bad run?

Yes. Bases support filesystem snapshots, so you checkpoint the agent's context before a risky operation and revert if it goes wrong. This versions the agent's data; it isn't a code-execution sandbox. See version control for agent context.

Does search understand code and docs, or just match keywords?

Deep Search matches by meaning, so a query about where something is handled surfaces the relevant code or doc even when it doesn't contain the search terms. It's hybrid, so exact identifiers still match precisely.

Why not just use my agent framework's built-in memory?

Built-in memory is usually a short conversation buffer that works until the project outgrows it. When and why that happens is covered in when to outgrow your framework's built-in memory.

Is this a finished dev tool or something I build on?

Something you build on. Exabase is the infrastructure, project-scoped context, memory, search, and snapshot rollback, and you build the coding assistant, docs search, or developer platform on top.