| --- |
| title: Operon LangGraph Visualizer |
| emoji: "\U0001F4CA" |
| colorFrom: blue |
| colorTo: green |
| sdk: gradio |
| sdk_version: "6.5.1" |
| app_file: app.py |
| pinned: false |
| license: mit |
| short_description: Visualize per-stage LangGraph compilation |
| --- |
| |
| # Operon LangGraph Visualizer |
|
|
| Compile an organism to a **per-stage LangGraph** and visualize the graph topology. Each organism stage becomes a LangGraph node with conditional edges that route based on continue/halt decisions. |
|
|
| ## What to Try |
|
|
| 1. Click **Visualize & Run** with defaults to see a 3-stage graph with execution results. |
| 2. Try the "4-stage incident" preset for a longer pipeline. |
| 3. Try the "5-stage deep" preset to see how deep-mode stages appear in the graph. |
| 4. Edit stages directly (name, role, mode -- one per line) to build custom topologies. |
| 5. Uncheck "Execute after compiling" to see the topology without running. |
|
|
| ## How It Works |
|
|
| `organism_to_langgraph()` creates one LangGraph node per `SkillStage`. Each node calls `organism.run_single_stage()`, so all structural guarantees (certificates, watcher interventions, halt-on-block) are handled by the organism. LangGraph provides the execution host, graph topology, observability, and checkpointing. |
|
|
| ## Graph Topology |
|
|
| - **START** -> stage_1 -> stage_2 -> ... -> stage_N -> **END** |
| - Each stage has a conditional edge: `continue` -> next stage, `halt`/`blocked` -> END |
| - Stage colors indicate mode: blue = fixed, amber = fuzzy, purple = deep |
| |
| ## Learn More |
| |
| [GitHub](https://github.com/coredipper/operon) | [PyPI](https://pypi.org/project/operon-ai/) | [Paper](https://github.com/coredipper/operon/tree/main/article) |
| |
