Codebase intelligence
Codebase intelligence for AI agents and new teammates
Schemyx maps the pieces agents need to understand: UI components, route structure, API handlers, services, models, theme tokens, and codebase review items. That map can serve agents today and onboarding views tomorrow.
The problem
The codebase graph is usually implicit.
New engineers and AI agents both struggle when knowledge is trapped in folder names, old PRs, and tribal memory. They need to know what connects to what before they make safe changes.
Schemyx approach
Schemyx makes the graph explicit.
The local bundle stores nodes, edges, groups, aliases, terms, recipes, and review items. Agents can fetch the smallest useful context, and teams can eventually visualize the same map for onboarding.
Why teams use it
Less drift, faster onboarding, and the same context across agents.
Outcome
See how product surfaces connect
Routes can point to rendered components. APIs can connect to services and models. Styles can connect to components and UI patterns.
Outcome
Use the same map for onboarding
A new hire can inspect the API layout, database relationships, and UI pattern map instead of asking a teammate to narrate the whole repo.
Outcome
Make uncertainty visible
When several conventions exist, Schemyx records review items so teams can decide what should become the approved path.
Lookup examples
The useful detail lives behind stable keys.
Schemyx is built for exact reads. Agents can ask for a concept, resolve a key, then fetch a tiny context response before they write.
graph/nodes.jsonl
Codebase nodes
Files, components, routes, APIs, services, models, styles, dependencies, and clusters.
graph/edges.jsonl
Codebase edges
Imports, renders, contains, depends_on, calls, styles, reads, and writes relationships.
index/aliases.json
Lookup aliases
Friendly names and tags agents can use before fetching an exact recipe.
review/items.json
Review items
Pattern collisions, duplicate keys, stale approvals, and missing canonical decisions.
Workflow
From codebase scan to agent-safe generation.
Schemyx keeps the heavy context work out of the prompt and turns repeated decisions into reusable lookup responses.
01
Scan code locally
Read only the files inside the workspace scope.
02
Create the graph
Normalize source facts into stable nodes and edges.
03
Resolve intent
Map an agent request to the most useful recipe or cluster.
04
Render maps later
Use the same graph to power visual onboarding, dependency views, and team review flows.
Who it is for
Built for teams and builders who want AI work to stay coherent.
Audience
Growing engineering teams
Help new teammates understand a product faster.
Audience
Teams running agents overnight
Give long-running work a smaller, more reliable context layer.
Audience
Platform-minded founders
Turn codebase knowledge into a reusable asset instead of a recurring explanation.
FAQ
Questions teams ask before trusting agent context.
Is this a visual code map today?
The local bundle already contains graph data. The long-term direction is to expose that graph visually for teams and onboarding.
Can it map backend code too?
Yes. Schemyx can extract API handlers, services, controllers, modules, models, SQL, Prisma, config, and environment hints.
How does this help AI agents?
Agents can retrieve the relevant part of the codebase map instead of searching blind or loading unrelated files.
Does the graph replace architecture docs?
No. It complements docs with current, source-derived structure that can be regenerated as the codebase changes.
Next step
Give every agent the same source of truth.
Request beta access or book a call to map Schemyx against your current stack, team standards, and agent workflow.