See Your LangGraph Agent Execute in Real Time

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

NOTE ON TIMING: LangGraphics isn't a weekend project. Its first commit is from January 2026. This year's rules ask for new projects built within the challenge window, so I'm not presenting this as a prize entry. I'm sharing it because the challenge asks the question this project was built to answer: who did you build it for?

What I Built

I built it for a group of people building an agent system on LangGraph. Their graph grew the way agent systems do:

• conditional routing
• reflection loops that retry
• subgraphs nested inside subgraphs

Eventually, debugging became the hardest part of the work. When a run went wrong, they had to reconstruct the execution path from a flat log stream. Which branch fired? How many times did the loop run? Which node actually failed, and what state did it get?

LangSmith and LangFuse are capable products, but they got in the way here. Both required accounts and API keys, data had to leave the machine (or you had to deploy a self-hosted stack first), and integrating them took real effort. They also show a tree of spans, not the graph you wrote.

The team needed something much smaller:

• one line
• no account
• no API key
• fully local
• no change to the agent's behavior

That's LangGraphics.

from langgraphics import watch
graph = watch ( workflow . compile ()) # ← the only line you add
await graph . ainvoke ({ " messages " : [...]})

A browser tab opens showing your graph, and it updates live as the agent runs.

Demo

proactive-agent / langgraphics - Visualize live LangGraph execution and see how your agent thinks as it runs.

LangGraphics is a live visualization tool for LangGraph-based agent orchestration frameworks such as LangGraph and DeepAgents. It's especially useful when working with large networks where graphs with many nodes, branching conditions, and cycles are hard to reason about from the logs alone.

Why It Helps

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.

How to Use

One line is all it takes - wrap the compiled graph of your agent workflow with LangGraphics' watch function before invoking it, the visualization opens in your browser automatically, tracks the agent in real time, and makes tracing easy using the inspect panel.

from langgraph . graph import StateGraph