LangGraphics is a live visualization tool for LangGraph agents. It's especially useful when working with large networks: graphs with many nodes, branching conditions, and cycles are hard to reason about from the logs alone.
Seeing the execution path visually makes it immediately obvious which branches were taken, where loops occurred, and where the agent got stuck or failed. It also helps when onboarding to an unfamiliar graph - a single run tells you more about the workflow than reading the graph definition ever could.
One line is all it takes - wrap the compiled graph of your agent workflow with LangGraphics' watch function before
invoking it, and the visualization opens in your browser automatically, tracking the agent in real time.
from langgraph.graph import StateGraph, MessagesState
from langgraphics import watch
workflow = StateGraph(MessagesState)
workflow.add_node(...)
workflow.add_edge(...)
graph = watch(workflow.compile())
await graph.ainvoke({"messages": [...]})Works with any LangGraph agent, no matter how simple or complex the graph is. Add it during a debugging session, or keep it in while you're actively building - it has no effect on how the agent behaves or what it returns.
| Feature | LangGraphics | LangFuse | LangSmith |
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| Fully local |  |
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| Standalone | |
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| Easy to learn | |
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| One-line setup | |
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| Data stays local | |
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| No API key required | |
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| Live execution graph | |
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| No refactoring required | |
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| Self-hosted | |
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| No vendor lock-in | |
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| Unlimited free usage | |
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| Graph visualization | |
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| Cost & latency tracking | |
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| Prompt evaluation | |
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Any contribution is welcome. Feel free to open an issue or a discussion if you have any questions not covered here. If you have any ideas or suggestions, please open a pull request.
Copyright (C) 2026 Artyom Vancyan. MIT