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wtf is flowlens-agentops?

try1004/flowlens-agentops — explained in plain English

Analysis updated 2026-05-18

28PythonAudience · developerComplexity · 4/5LicenseSetup · moderate

TL;DR

A read only diagnostics dashboard that explains why multi-agent AI runs failed, built on top of the DeerFlow agent runtime.

Mindmap

mindmap
  root((FlowLens AgentOps))
    What it does
      Records runtime events
      Builds execution timeline
      Computes run metrics
      Attributes failure cause
    Tech stack
      Python
      FastAPI
      LangGraph
      Next.js
    Use cases
      Debug multi-agent failures
      Try no-key replay scenarios
      Monitor connected DeerFlow runs
    Audience
      Developers
      Ops teams

Code map

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Why would anyone build with this?

REASON 1

Investigate why a specific multi-agent run failed using the root cause and evidence view.

REASON 2

Explore the dashboard risk free using the seven no-key replay scenarios like tool errors or timeouts.

REASON 3

Monitor a live DeerFlow deployment's runs, metrics, and errors through connected mode.

REASON 4

Export redacted diagnostic data for a run to share or archive.

What's in the stack?

PythonFastAPILangGraphNext.jsDocker

How it stacks up

try1004/flowlens-agentopsademola-lou/threejsgnmalicankiraz1/codexqb
Stars282828
LanguagePythonPythonPython
Setup difficultymoderatemoderateeasy
Complexity4/53/53/5
Audiencedeveloperdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you spin it up?

Difficulty · moderate Time to first run · 30min

Full connected mode needs a working DeerFlow setup with authentication and a configured model provider.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

Wtf does this do

FlowLens AgentOps is a diagnostics and monitoring layer for systems that run multiple AI agents together. It is built on top of a project called DeerFlow, which supplies the underlying agent runtime, tools, memory, and sandbox, while FlowLens adds the ability to watch those runs, understand what happened during them, and figure out why something failed. The core problem it addresses is that when a multi-agent run fails, a saved checkpoint can restore the system to a working state, but it does not tell you why the failure happened, and a final error message loses all the decisions and steps that led up to it. FlowLens records a lightweight, ongoing log of runtime events and turns that log into a normalized timeline of everything that happened across models, tools, subagents, and other parts of the system. It also produces run metrics such as duration, token usage, and error counts, and it applies rule based logic to point at a likely cause of failure, complete with supporting evidence, other contributing factors, a confidence level, and a suggested next step. There is also an optional AI generated explanation layer, though the rule based result itself cannot be changed by it. The product includes a shared dashboard with several views: a run explorer for searching past runs, an execution timeline you can filter by phase, health metrics, and a root cause view. It also ships seven ready made replay scenarios, such as a tool error or a subagent timeout, that let you explore the dashboard with no API key, no account, and no backend required. A separate connected mode reads real data from a live DeerFlow system instead. Getting started with the no key replay mode involves cloning the repository and running it either through a Node package manager or through Docker, after which a demo dashboard opens in your browser. Running the fully connected version requires a working DeerFlow setup, including authentication and at least one configured AI model provider. The diagnostics path is explicitly read only: it does not change checkpoints, prompts, or the underlying run records it inspects. The README states its test suite passes across backend and frontend checks and includes a secret scan. FlowLens is released under the MIT License, the same license used by the DeerFlow project it builds on.

Yoink these prompts

Prompt 1
Walk me through running FlowLens AgentOps in no-key replay mode with pnpm.
Prompt 2
Explain how FlowLens attributes a failure cause to a multi-agent run.
Prompt 3
What is the difference between FlowLens's replay mode and connected mode?
Prompt 4
Show me how to set up FlowLens AgentOps against my own DeerFlow deployment.
Prompt 5
Summarize what data FlowLens's diagnostics export API returns for a run.

Frequently asked questions

wtf is flowlens-agentops?

A read only diagnostics dashboard that explains why multi-agent AI runs failed, built on top of the DeerFlow agent runtime.

What language is flowlens-agentops written in?

Mainly Python. The stack also includes Python, FastAPI, LangGraph.

What license does flowlens-agentops use?

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

How hard is flowlens-agentops to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is flowlens-agentops for?

Mainly developer.

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