Orchid – Local-first record and replay for AI agent debugging

Hacker News

Orchid (Orchestration interactive debugger) is a zero-instrumentation proxy that captures every API & LLM call in your agent pipeline, then lets you inspect and replay the entire run locally, step by step. No instrumentation, no vendor lock-in, no cloud dependency. It also provides a visual inspector and MCP server, so you can inspect the session yourself or use your favorite agentic coding IDE to debug your agent runs. I built it because I was tired of debugging agent failures by grepping through logs, and the available AI observability tools all seemed to require intrusive instrumentation and/or sending my prompts and responses to a cloud service. I wanted something that would let me debug agent runs locally, without having to worry about vendor lock-in or data privacy. Orchid is that tool. The call inspection features work extremely well, at least for my use cases, but the replay feature is perhaps more interesting. It makes LLM pipeline testing deterministic without mocking or re-running expensive API calls. Free, self-hosted, runs on your machine or infrastructure: https://github.com/mario-guerra/orchid-trace Would love feedback from anyone building multi-step agentic systems or struggling with non-deterministic LLM test failures.

Visit product View on Hacker NewsJun 24, 2026

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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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

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