Start with your repo.
Scan local code and package dependencies. Explicitly include other repositories and Markdown docs when the work crosses boundaries.
Local, deterministic extractionCodebase context for people & AI
Your AI can write code.
Give it the context it's missing.
Z1P maps code, dependencies and project docs into source-linked evidence your AI tools can retrieve. Pick up the work without explaining the codebase from scratch.
Runs locally. MIT licensed. No account required.
What depends on checkout.ts?
Files, relationships and the evidence behind them.
A head start on the hard part
The import is in one file. The reason is in a document. The dependency sits in another repo. Z1P gives you a way to follow those connections.
Scan local code and package dependencies. Explicitly include other repositories and Markdown docs when the work crosses boundaries.
Local, deterministic extractionPreserve where a record came from and how a relationship was extracted. Follow the path back to the source before trusting a conclusion.
Inspectable records and relationshipsUse the CLI or Model Context Protocol (MCP) to retrieve relevant records within explicit limits, then read the source needed for the task.
Works without a model subscriptionFollow the evidence
Choose a question to explore a small, illustrative checkout project. This is an interactive explanation, not a live scan or a hosted product preview.
A connection tells you where to investigate. It doesn't prove a change is safe or that a test covers the behaviour.
The local CLI and MCP adapter expose retrieval and graph navigation. Your client supplies the AI. Z1P Core doesn't require a particular model provider.
Core scans run locally without executing the repository or calling a model. Sharing requires an explicit destination. The core has no telemetry.
Formats, extraction and verification are open source. Signatures identify assertions; they don't make them true. Limits and uncertain relationships stay visible.
For the “we should try this” conversation
Pick a task your team knows is awkward. Use the free core to find the relevant files and connections. Check the result against the source, then decide whether it helps.
Get the team evaluation briefChoose a bug, unfamiliar module or dependency update.
Did you find the required sources? What was missed?
Compare investigation time and context used with your usual workflow.
Start free. Build the case for your team.
The local core is available today. We're building the hosted platform for teams that want the graph maintained for them.
Free / MIT licensed
For developers who want local control and an inspectable foundation.
Let's shape it/ pricing to be announced
Planned: shared context that keeps up with the work.
Opens a public GitHub issue. Please don't include private code or company information. No hosted service is available to buy yet.
From curious to hands-on
Build the current tools from the public source, then run a bounded scan. It outputs evidence records for inspection. Signed collections and MCP setup are covered in the guide.
The current scanner release isn't on npm yet. This source-build route includes it.
Read the setup guide# Build the current core from source
git clone https://github.com/forgesworn/context.git z1p-core
cd z1p-core
npm ci --ignore-scripts
npm run build
# Replace the path with your JS / TS project
node packages/context-tools/bin/encrypted-context.mjs scan-source /path/to/your-project
Before you start
No. Z1P Core provides codebase evidence and graph navigation through local tools. Your existing AI client can use its MCP adapter to retrieve context. It doesn't generate or change your code.
TypeScript and JavaScript use compiler-based extraction. Python, Rust, Go, Java, Kotlin, Swift, C, C++, C#, Ruby and PHP have more conservative lexical navigation. These levels are not equivalent: dynamic behaviour and ambiguous relationships may be omitted. See the scanner documentation for the exact limits.
Local core scans don't upload your code or contact a model. You control explicit uploads and sharing. If you connect an AI client, the evidence it retrieves may be sent to that client's provider under your configuration. Hosted platform data handling will be documented before launch.
Retrieving selected evidence can reduce the context you send compared with sending a whole repository. The saving depends on your task and workflow. Our reproducible benchmarks separate source navigation from the evidence needed to answer. We don't promise a percentage reduction in your bill.
Not yet. The hosted team platform is in development, and pricing hasn't been announced. You can evaluate the free core now and discuss what would make a team pilot worth paying for.
The published core is MIT licensed and works without our hosted service. The commercial plan is to charge for continuous operation and team workflows. Read the open-core boundary for the commitment and compatibility policy.
Spend less time reconstructing the picture.