Make your agents smarter across your products, skills, standards.
Product Graph (VibeGraph) is the living graph for AI-native teams. It tracks the relationships that matter across enterprise products, features, skills, and standards so agents can collaborate with other agents, developers can work with better context, and product teams can understand impact across upstream and downstream systems.
Example
Agents build using skills and standards, then propose changes for PM to approve
How agents and humans work together:
- 1.Agents connect to the graph via MCP and read approved skills and standards.
- 2.Agents build product features using those shared skills and standards.
- 3.After building, agents propose structured changes back to the graph.
- 4.PM reviews the diff, approves what becomes canonical, and the graph updates.
Why Product Graph
Agents lack shared product knowledge
When agents build features, they need to know your product's structure, approved patterns, and business rules. Without a shared source of truth, every agent session starts from scratch—asking you to explain the same context over and over.
Repetitive context
You explain the same architecture, standards, and patterns to every agent in every chat session.
Inconsistent knowledge
Different agents (Claude, Copilot, Perplexity) build features using different interpretations of your product.
Stale documentation
Manual docs fall behind as products evolve. Teams waste time updating Confluence, Notion, and wikis.
What breaks as teams scale with agents
The operational cost of drift shows up before the product does.
Product knowledge goes stale fast.
Features ship, behavior changes, and docs fall behind.
Skills and standards get scattered.
Every repo, product, and team ends up with its own version.
Agent output moves fast, but governance does not.
Teams need speed without losing approvals, traceability, or control.
Benefits
Why this matters for every part of the team.
Benefits for agents
Agents become smarter because they can reuse enterprise skills and standards, cross-reference connected products, and coordinate with other agents using the same approved graph.
Benefits for developers
Developers stop rebuilding context repo by repo. Their agents can build and test with shared standards, product links, and system dependencies already in view.
Benefits for product teams
PMs, TPMs, and business users can understand capabilities across the enterprise and generate product guides or technical guides from current approved relationships.
Blast radius and impact analysis
Agents can trace upstream and downstream systems before a change ships, so cross-product dependencies and likely impact are visible earlier.
Governance with humans in the loop
Agents can move faster, but humans still control what becomes canonical through approvals, provenance, and reviewable changes.
Self-updating graph
As developers and agents work, the graph stays current automatically, reducing manual maintenance while preserving a shared enterprise knowledge layer.
Who it's for
One graph for every persona shaping the product
Product Graph is built for AI-native enterprises where product, engineering, platform, and business teams all need to work from the same shared context — whether they're shipping code, running agents, or reviewing roadmaps.
Product and TPM teams
Keep features, requirements, dependencies, and standards aligned as the roadmap changes.
Engineering teams
Reuse shared skills and standards instead of recreating them in every repo, service, or workflow.
Platform and AI teams
Distribute approved coding and non-coding agent skills through MCP at enterprise scale.
Business and leadership
Get visibility, consistency, and provenance without slowing teams down.
Coding agents and developers
Use the graph to reuse skills, follow standards, and understand upstream and downstream dependencies so agents can build and test before shipping changes.
Non-coding agents and business users
Use the same graph to generate product guides, technical guides, and dependency views without reverse-engineering repos.
Enterprise teams across products
Bring product, platform, and AI teams into one relationship layer for collaboration, impact analysis, and governance.
How it works
Built for the Agentic Era
The workflow stays familiar for teams, but the relationship layer underneath becomes structured, shareable, governable, and useful for autonomous build-and-test decisions.
Connect your agent
Add the Product Graph MCP server to your agent, pointed at your workspace. One token, one config block.
{
"mcpServers": {
"product-graph-my-app": {
"type": "http",
"url": "https://graphapi.vibelevel.ai/mcp?workspace=my-app"
}
}
}Works with Claude Code · Cursor · Windsurf · any MCP client.
Seed your graph
Open your product repo in the agent and ask it to scan the codebase. It proposes the initial graph — features, skills, and standards.
"Scan this project and create a product graph for workspace my-app."
Use it and keep it in sync
Agents read approved skills and standards over MCP as they build, then propose updates at the end of each session.
"Update the product graph in my-app with the changes from this session."
Humans stay in control
Proposals land in the Pending tab. Review the structured diff and one-click approve — only approved changes become canonical, with full provenance.
Examples
Ask the graph about work, not just context.
Because the graph links products, features, skills, standards, and dependent systems, teams can ask for impact analysis, product guides, technical guidance, and build-test plans directly from their agents.
"Show the upstream and downstream products, features, and standards affected by this change."
"What should the agent build and test across upstream and downstream systems before this change ships?"
"Create a technical guide for this product using the approved features, dependencies, and standards in the graph."
"Create a business-facing product guide that explains how this capability connects across products and teams."
Use cases
How teams use Product Graph in practice
Use cases from solo builders to enterprise teams, with the same human-review workflow and MCP-enabled agent collaboration.
Solo developer / personal project
Keep a living map of your own product that never goes stale.
- •Your coding agent scans your repo and seeds the graph.
- •At the end of each session it proposes updates and you approve.
- •Use it as context so the agent builds consistently with what exists.
Small team - shared private workspace
One graph the whole team co-maintains, kept private.
- •Create an org workspace and add teammates as members.
- •Everyone can propose changes and members approve them.
- •Nobody outside the team can see it.
Public product - readable by your users
Publish your product graph so others can build on it.
- •Anyone can browse it on the web with no login.
- •Any AI agent can read it via MCP to integrate with your product.
- •Only members can propose changes, so publishing never exposes editing.
Open source / community
Share publicly and accept contributions safely.
- •Publish so the community can consume your graph.
- •Add trusted contributors so they can propose changes.
- •Maintainers review and approve every proposal before it goes live.
Blast radius
See upstream and downstream impact before the change ships.
When the graph connects products, features, skills, and standards, agents and humans can trace what will be affected, where it flows next, and what needs to be tested before rollout.
Map dependencies
Before making a change, see what upstream systems feed into it and what downstream products depend on it.
Assess impact
Understand which products, features, skills, and standards are affected by the proposed change.
Plan testing
Let agents use the same dependency context to automatically plan build-and-test workflows.
MyApp depends on Upstream and impacts Downstream. Risk Skill analyzes the change. The app must follow the Test Policy standard — ensuring safe rollouts.
Governance
Humans stay in the loop while agents keep moving.
Agents and developers can propose updates, but PMs, product owners, and leads still approve what becomes canonical. That keeps the graph fast, reviewable, and governed.
Agent proposes
Your coding agent (or a dev) proposes a graph change via MCP — a new feature, updated skill, changed standard.
Lead reviews
The diff appears in the pending queue. Reviewer sees exactly what changed, what it connects to, and why.
Approver signs off
One click to approve. The change goes live with a full provenance trail — who proposed it, who approved it.
An agent proposed adding Dark Mode to MyApp, flagged pending. PM reviews the diff and approves it. The feature must follow the A11y standard — enforced automatically.
Stop maintaining documentation that agents outgrow in days.
Bring product relationships, agent skills, and standards into one living graph your whole team can trust.