LazyAgent – All in one observerbility TUI app for coding agents

Hacker News

Hi HN, I made tui observerbility tool for ai agents. Once subagents start spawning other subagents, basic questions get hard to answer: what is running right now, what tool did it just call, did the child agent actually do what the parent asked. I wanted a way to verify that each agent is doing the work that fits its role, and to spot when a run goes off track. Lazyagent is a terminal TUI that collects events from Claude Code, Codex, and OpenCode and shows them in one place. Also it can show your token usage information about the sessions. Features: Filter events by type: tool calls, user prompts, session lifecycle, system events, or code changes only. See which agent or subagent is responsible for each action. The agent tree shows parent-child relationships, so you can trace exactly what a spawned subagent did vs what the parent delegated. View code diffs at a glance. Editing events render syntax-highlighted diffs inline, with addition/deletion stats. Search across all events. You know a file was touched but not which agent did it -- type / and find it. Check token usage per session. A single overlay shows cost, model calls, cache hit rate, per-model breakdowns, and which tools ran the most. Watch a run in real time, or go back through a completed session to audit what happened. Please let me know if there's any feature you want.

Visit product View on Hacker NewsApr 23, 2026

Share card

Actual performance

6points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

18%18% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Fantail SLMs for Coding Agents61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Fantail SLMs for Coding Agents

Hacker News1

Cloud Coding Agents35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cloud Coding Agents

Hacker News2

Druids – coordinate and deploy coding agents across machines40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Druids – coordinate and deploy coding agents across machines

Hacker News9

Grass 2.097%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The always-on computer for your coding agents

Product Hunt+111iOS

Coordination Layer for Coding Agents39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Coordination Layer for Coding Agents

Hacker News9

Graphify C# – Compiler-accurate Find Usages for coding agents45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Graphify C# – Compiler-accurate Find Usages for coding agents

Hacker News46

Keep up with your coding agents – Git Watchttower36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Keep up with your coding agents – Git Watchttower

Hacker News2

Crew4496%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Turn coding agents into specialist teams

Product Hunt+110

Coding Agents swarming your codebase50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Coding Agents swarming your codebase

Hacker News9

GitWarren95%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Review code with your coding agents before committing

Product Hunt+91Open Source

Browse all products Analyze your launch