Interactive visualization of the AI capability graph.
Every typed Effector declares an interface: what it accepts, what it produces, what it composes with. When you collect thousands of typed Effectors, a structure emerges — a capability graph where nodes are capabilities and edges are composition relationships.
effector-graph makes this graph visible, navigable, and interactive.
┌─────────────┐
│ CodeDiff │
└──────┬──────┘
│ input
┌────────────┼────────────┐
▼ ▼ ▼
┌──────────┐ ┌───────────┐ ┌────────┐
│code-review│ │security- │ │ lint │
│ @1.2.0 │ │scan @2.0 │ │ @3.1.0 │
└────┬─────┘ └─────┬─────┘ └───┬────┘
│ │ │
ReviewReport SecurityReport LintReport
│ │ │
└──────────────┼─────────────┘
▼
┌────────────────┐
│aggregate-report│
│ @1.0.0 │
└───────┬───────┘
│
AggregateReport
│
▼
┌───────────────┐
│ slack-notify │
│ @0.5.0 │
└───────────────┘
This isn't a static diagram. It's a live, queryable visualization of what your agents can do and how their capabilities connect.
The capability graph is the emergent structure that arises when AI agent tools get typed interfaces. It's important for three reasons:
1. Discovery. Developers building agent workflows need to find capabilities that fit their pipeline. A visual graph lets you trace paths: "I have a CodeDiff — what can I do with it? Where does it lead?" You explore by following typed edges, not by searching keywords.
2. Understanding. Multi-agent systems are complex. A team deploying a 12-step agent workflow needs to see the full composition — types flowing between steps, parallel branches, conditional paths, fallback routes. Without visualization, composition errors hide until runtime.
3. Trust. The graph reveals the supply chain. Which Effectors are signed? Which have audit reports? Where are the unsigned community dependencies? Visual trust indicators make security posture legible at a glance.
Research supports this. The GAP framework (NeurIPS 2025, arXiv:2510.25320) showed that explicit dependency graphs outperform sequential agent planning. The MCP-Zero paper (arXiv:2506.01056) found that agents don't explore available tools — they need mechanisms for active discovery. effector-graph provides the human-facing interface for both.
npx effector-graph serve
✓ Loaded 847 typed Effectors from registry
✓ Graph: 847 nodes, 3,241 composition edges
✓ Serving at http://localhost:4200Opens a browser-based visualization with:
- Force-directed graph layout — capabilities cluster by type affinity
- Type-based filtering — show only Effectors matching a type signature
- Path tracing — highlight all valid composition paths between two Effectors
- Trust overlay — color nodes by signing status (signed/unsigned/audited)
- Cost heatmap — visualize cost distribution across a pipeline
- Search — find Effectors by type, name, or description
Render a specific pipeline from effector-compose:
npx effector-graph render ./pipeline.effector.yml --format svg
npx effector-graph render ./pipeline.effector.yml --format png
npx effector-graph render ./pipeline.effector.yml --format html # interactiveDrop the graph into any web page:
<script src="https://unpkg.com/effector-graph/widget.js"></script>
<effector-graph
registry="https://registry.effectorhq.dev"
filter="input:CodeDiff"
height="600px"
/>For terminal-first workflows:
# Show all Effectors that accept CodeDiff
npx effector-graph query --input CodeDiff
# Show composition paths from code-review to slack-notify
npx effector-graph path code-review@1.2.0 slack-notify@0.5.0
# Show the dependency tree of a pipeline
npx effector-graph tree ./pipeline.effector.yml
# Export the full graph as JSON (for custom visualization)
npx effector-graph export --format json > graph.jsonThe default mode. Shows the full capability graph with type-colored nodes:
- 🟢 Signed & audited — verified by effector-audit
- 🟡 Signed, not audited — identity verified, content not scanned
- 🔴 Unsigned — no provenance information
- ⬜ Local — workspace-level Effectors, not published
Edges represent composition compatibility (structural subtype matching). Edge thickness indicates how many real pipelines use that composition.
Focused view for a single pipeline. Shows:
- Step sequence with type annotations on each edge
- Parallel branches rendered as swim lanes
- Conditional branches rendered as decision diamonds
- Cost per step and cumulative cost
- Permission aggregation across the pipeline
Compare two versions of a pipeline or two competing pipeline designs:
npx effector-graph diff pipeline-v1.yml pipeline-v2.ymlHighlights: added steps, removed steps, changed types, cost difference, permission changes.
Overview of a capability registry:
npx effector-graph dashboard --registry https://registry.effectorhq.devShows: type distribution, most-composed Effectors, trust coverage, cost distribution, growth trends.
effector-graph
├── core/ # Graph data model and algorithms
│ ├── graph.js # Capability graph construction from typed Effectors
│ ├── layout.js # Force-directed and hierarchical layout algorithms
│ ├── query.js # Type-based graph queries
│ └── path.js # Composition path finding (typed BFS/DFS)
├── renderers/ # Output formats
│ ├── svg.js # Static SVG rendering
│ ├── html.js # Interactive HTML (D3.js-based)
│ ├── terminal.js # ASCII art for CLI
│ └── json.js # Machine-readable graph export
├── web/ # Browser-based UI
│ ├── app.jsx # React application
│ ├── components/ # Explorer, Pipeline, Diff, Dashboard views
│ └── widget.js # Embeddable web component
├── cli/ # Command-line interface
│ └── index.js # CLI commands (serve, render, query, path, tree, export)
└── integrations/ # IDE and tool integrations
├── vscode.js # VS Code extension API
└── compose.js # Integration with effector-compose
- v0.1 — CLI graph queries, static SVG rendering, pipeline visualization
- v0.2 — Interactive web UI with force-directed layout
- v0.3 — Trust overlay, cost heatmap, permission aggregation
- v0.4 — Embeddable widget, registry dashboard
- v0.5 — VS Code extension, IDE integration
- v1.0 — Production-ready visualization platform
Visualization needs design sensibility. We especially need:
- UX design — The graph should be beautiful and intuitive, not just technically correct
- Performance — Rendering 10,000+ nodes smoothly requires WebGL or clever D3 optimization
- Accessibility — Screen reader support, color-blind safe palettes, keyboard navigation
- Real-world testing — Use the graph with your actual agent workflows and tell us what's missing
Part of the effectorHQ studio. We build hands for AI.