Codebase context
Codebase context for AI agents
Schemyx turns your codebase into an agent-readable lookup layer with stable keys, tiny responses, and safe fallback reads. Agents get the product, component, API, and style context they need without relearning the whole repo.
The problem
AI agents lose time when every task starts with a full-codebase tour.
Large prompts, repeated source scans, and stale project summaries create drift. One agent learns one convention, another misses it, and a third burns tokens rediscovering the same button, route, schema, or business rule.
Schemyx approach
Schemyx gives agents a shared context layer they can query by key.
The local scanner maps files, routes, components, services, data models, theme tokens, and review items into segmented recipes. MCP serves the exact recipe an agent needs, then points to safe fallback reads only when the recipe is not enough.
Why teams use it
Less drift, faster onboarding, and the same context across agents.
Outcome
Stable context across every agent
Teams can use Codex, Cursor, Claude, and other AI coding tools without each session inventing its own understanding of the product. The context layer gives every agent the same approved facts.
Outcome
Smaller context, less token waste
Instead of stuffing repo-wide instructions into every prompt, agents retrieve focused context packs. That keeps requests smaller and makes expensive full-source reads a fallback, not the default.
Outcome
Continuity for new teammates
The same lookup layer that agents use can power onboarding maps for developers: routes, APIs, database models, component contracts, and how they connect.
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.
style.components.ui.button.buttonVariants
Component styling
Button variants, sizes, defaults, hover states, and token-backed classes.
route.admin.beta-requests
Route context
Page purpose, rendered components, related services, and admin access rules.
api.POST.beta-access.requests
API behavior
Payload expectations, auth hints, side effects, email notifications, and errors.
model.User.betaAccess
Data model facts
Fields, relationships, ownership rules, and how generated code should touch them.
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 the repo
Schemyx reads routes, UI, styles, APIs, services, schemas, config, tests, and docs locally.
02
Create segmented recipes
The bundle stores graph nodes, aliases, terms, recipes, and review items instead of one giant context file.
03
Resolve the lookup
An agent searches for a concept like button styles, auth flow, or signup model and gets the best key.
04
Build with the exact rule
The agent fetches the recipe or context pack before writing, reducing drift and repeated source scans.
Who it is for
Built for teams and builders who want AI work to stay coherent.
Audience
Engineering teams
Keep product rules consistent across people, agents, branches, and models.
Audience
AI-native startups
Move faster without letting generated code fragment your UI, APIs, or data layer.
Audience
Freelancers and agencies
Save reusable project presets so the next client build starts with proven defaults.
FAQ
Questions teams ask before trusting agent context.
Does Schemyx replace reading source code?
No. Schemyx creates a fast first lookup layer. Source reads still happen when a recipe is not enough, but agents do not need to start every task by rereading the entire repo.
What kinds of codebase context can Schemyx store?
Schemyx can capture components, styles, routes, API handlers, services, models, theme tokens, config files, docs, tests, and review items.
How does this help teams?
Teams get continuity. New agents and new teammates can use the same codebase map instead of relying on memory, stale docs, or massive prompts.
Why is MCP important?
MCP gives AI coding tools a standard way to ask for local or hosted Schemyx context while they work inside a developer workflow.
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.