Buyer guide
An AI codebase context tool for teams that need continuity
Schemyx helps engineering teams turn repeated codebase knowledge into reusable lookup data. Agents can find the right rule faster, teammates onboard with clearer maps, and generated work stays closer to the system you already built.
The problem
Most AI coding workflows still rely on expensive repetition.
Teams paste architecture notes, tell agents where files live, explain design conventions, then do it again next session. That repetition costs time, tokens, and trust.
Schemyx approach
Schemyx makes repeated context reusable.
The scanner builds local codebase bundles and MCP serves focused recipes. The output is structured enough for agents, but useful enough for teams to inspect, review, and eventually visualize.
Why teams use it
Less drift, faster onboarding, and the same context across agents.
Outcome
Faster starts for every task
Agents can resolve what matters before writing: the right component contract, endpoint behavior, data model, theme token, or product guardrail.
Outcome
Lower cleanup cost
When generated code follows approved patterns up front, engineers spend less time fixing visual drift, route mistakes, API mismatch, and inconsistent naming.
Outcome
A product memory layer
Schemyx is not just a prompt helper. It is product memory that can stay current as the codebase changes and the team grows.
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.
recipes/component.jsonl
Component recipes
Names, props, class expressions, imports, usage, and related files.
recipes/api.jsonl
API recipes
HTTP methods, route paths, auth hints, environment usage, and model references.
recipes/style.jsonl
Style recipes
Theme tokens, Tailwind classes, cva variants, CSS variables, and UI pattern evidence.
review/items.json
Review queue
Duplicate or uncertain conventions that need a team decision before agents treat them as canonical.
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
Install local MCP
Connect the coding tools your team already uses.
02
Generate a codebase bundle
Scan the repo locally and write a segmented Schemyx context directory.
03
Search before generation
Agents resolve a concept like auth flow, pricing card, or beta request API.
04
Use exact recipes
Generated code starts from the same known facts instead of a fresh guess.
Who it is for
Built for teams and builders who want AI work to stay coherent.
Audience
Engineering managers
Reduce drift as more people and agents touch the same product.
Audience
Founders
Ship faster without letting AI-generated features fragment the product.
Audience
Agencies
Carry reusable stack and component presets from one client build to the next.
FAQ
Questions teams ask before trusting agent context.
Is Schemyx a documentation generator?
Schemyx can support documentation and onboarding, but the primary output is agent-readable lookup data for code generation workflows.
Will this save token cost?
It can reduce repeated context in prompts and lower unnecessary source reads. Exact savings depend on the team, model, task volume, and how often agents currently reread the same code.
Does Schemyx require hosted source code?
No. The scanner runs locally and can write local bundles. Hosted configs can be used when teams want shared account-backed workflows.
Who should try it first?
Teams already using AI coding tools heavily, especially when generated code drifts across UI, API, data, or product standards.
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.