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Initial deploy: LangGraph Visualizer
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metadata
title: Operon LangGraph Visualizer
emoji: 📊
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 | PyPI | Paper