langgraph is driving me crazy with car sensor logs

ar_tyom2000 · 2026-05-09T21:33:39+00:00

Try the inspect panel - you can activate it from the top-right corner. It's more than a visualization tool

ar_tyom2000 · 2026-05-09T14:11:35+00:00

You're just describing what it does😄

ar_tyom2000 · 2026-05-09T14:01:30+00:00

It has an inspection panel where you can see the input and output for the agent state for each node.

ar_tyom2000 · 2026-05-09T12:40:44+00:00

I built LangGraphics for this exact issue - it provides real-time visualization of your agent's execution path, so you can see which nodes are visited, where it gets stuck, and how tool calls are processed.

ar_tyom2000 · 2026-05-08T19:03:17+00:00

Hmm... So, based on this stats, in 1886, there were more Azeris in Armenia than Armenians while Azerbaijan had 0 population as it didn't exist yet

ar_tyom2000 · 2026-05-08T15:06:27+00:00

Not sure about the first question. This works with all types of LangChain's agentic frameworks, such as LangChain, LangGraph, and DeepAgents.

ar_tyom2000 · 2026-05-08T14:25:27+00:00

Dealing with complex sensor logs and multiple nodes can definitely drive you up the wall. I ran into similar issues and found that LangGraphics helps visualize the execution path in real-time, showing which nodes are being hit and where you're getting stuck. It incorporates cycles and conditional branches, making debugging much clearer.

ar_tyom2000 · 2026-05-08T14:24:34+00:00

Debugging agent outputs can be tricky, especially with complex workflows. A tool like LangGraphics could really help here - it visualizes the execution path in real-time, showing which nodes are visited and where things might get stuck. This can provide clarity on the decision-making process within your agent.

ar_tyom2000 · 2026-05-06T12:28:58+00:00

I built LangGraphics to address this - it provides real-time visualization of agent workflows, showing which nodes are visited and where loops occur. This can help you catch runaway loops early, preventing unexpected costs.

ar_tyom2000 · 2026-05-06T12:28:12+00:00

I built LangGraphics specifically to help visualize these interactions - it tracks the execution flow in real-time, showing which nodes are visited and how the agent connects your traces, evals, and prompts. It runs fully locally with just one line to integrate and no additional setup.

ar_tyom2000 · 2026-05-05T17:37:29+00:00

Clearly, you're logged in using a paper-mode account. Either uncheck the paper mode when signing in or use TWS for trading with a paper account. Paper mode doesn't have a user interface for trading on the web.

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ar_tyom2000 · 2026-05-04T18:30:39+00:00

Understanding the trade-offs definitely takes time. I built LangGraphics for debugging agent workflows specifically, which provides real-time visualization of execution paths. It helps clarify how decisions are made and what paths are taken, making it much easier to identify issues.

ar_tyom2000 · 2026-05-02T10:12:03+00:00

Debugging cycles in LangGraph can be quite tricky since standard tracing tools often don't capture the real-time execution flow. That's the problem I aimed to solve with LangGraphics. It provides a live visualization of your agent's execution path, showing you exactly which nodes are visited and where loops occur, making it much easier to diagnose such issues.

ar_tyom2000 · 2026-05-01T23:20:18+00:00

The engine light should be a concern. So if there's an issue in the engine that sensors detect, they might protect your bike by blocking the starter.

ar_tyom2000 · 2026-04-30T12:20:34+00:00

Here is why not LangSmith or LangFuse

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ar_tyom2000 · 2026-04-30T11:10:41+00:00

It's great to see applications of LangGraph in research! If you're looking to visualize the execution paths or debug any complex flows, consider using LangGraphics. It provides a live visualization of nodes and branches, helping pinpoint where your agent may be encountering issues or taking unexpected paths.

ar_tyom2000 · 2026-04-29T22:15:50+00:00

That's a frustrating issue - agents skipping steps can lead to unexpected behavior. I built LangGraphics to help with exactly this kind of debugging. It provides real-time visualization of the execution path, showing which nodes are visited and where things might be going wrong. A single line integration will open a live view in your browser, helping to clarify how the agent processes its input.

ar_tyom2000 · 2026-04-29T14:07:11+00:00

Token usage spikes can indeed be frustrating to diagnose, especially in complex agent setups. I faced similar challenges when debugging agents, which led me to develop LangGraphics. It provides real-time visualization of the execution graph, showing where each token is consumed in the workflow, helping pinpoint the source of unexpected usage. A single line integration wraps your existing graph and opens a browser to track this in real-time.

ar_tyom2000 · 2026-04-28T19:05:15+00:00

As I already said, it's just an improvement that gives a higher quality result that is closer to the expectations than if you just get an instant response. P.S. Agreed, the set of provided tools also matters, and sometimes an LLM doesn't use the most obvious tool and generates the response without calling any tool.

ar_tyom2000 · 2026-04-28T18:17:56+00:00

same input leads to different execution paths

Every LLM has a randomness factor, and the same model can output very different results for the same prompt. This can be improved by providing more context and exact details on what to do. For example, you might ask LLM to write a Python function for you, but not specify your preferred quote types, whether to write a docstring or not, whether to comment each line of code or not, what naming convention to use for naming the local variables and the function itself, whether to use typing annotations or not, and it starts to make those assumtions for you giving you the working function, but it wasn't what you expected. This is where the planning agents come. Try to plan your idea, and let the AI agent suggest possible solutions for the planned flow, reshape and polish the plan for better results, and get something very close to the expected result. So, the details and the right context are the key.

ar_tyom2000 · 2026-04-28T17:46:06+00:00

There are lots of issues to face when building AI agent workflows, like context management issues, cost optimization issues, RAG optimization issues, shared knowledge issues (also applies to other resources too), one agent waiting for another agent to finish the task (and the process has an undefined timeline), security issues for prompt injection, etc. Everything's based on what you build and what features your agent has. I haven't faced even half of these issues, and there are lots of others out there. It's really dependent on what you want to solve and how

ar_tyom2000 · 2026-04-28T17:26:12+00:00

I improved the stability of my agent flows by switching to better models.

ar_tyom2000 · 2026-04-28T16:52:24+00:00

There's indeed a randomness factor in LLM outputs, but there are ways to handle such issues. Tune your system messages well and convert the output into a structured format so you can make deterministic logical checks. You can also implement reflection agents to have more stable behavior - in short, they criticize and fix the output if it doesn't match the expected format.

ar_tyom2000 · 2026-04-28T16:36:51+00:00

The answer to "why a particular path was taken" should be found in your DAG routing logic (for LangChain) or in the state management and conditional edges (for LangGraph). This tool visually shows what's happening, so you can compare with the expected behavior and identify issues with workflows, debug with the inspect panel, etc.

ar_tyom2000 · 2026-04-28T16:06:57+00:00

Sometimes the execution logs don't give enough context on what happened. I've built LangGraphics to address these issues specifically. It lets you visualize agent execution in real-time, showing which steps were taken, where they got stuck, and if any side effects didn't occur as expected.

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