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Orchid - Orchestration interactive debugger - Record, inspect, & replay AI agents

1 starsPython

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

by brightmonkey·Jun 24, 2026·3 points·0 comments

AI Analysis

●●●BangerBig BrainSolve My Problem

Deterministic replay of agent runs without mocking—that's genuinely new.

Strengths
  • Zero-instrumentation proxy captures all traffic without code changes to existing agents
  • Replay feature enables deterministic testing without expensive API calls or mocking
  • MCP server integration lets coding agents debug your AI app directly from IDE
Weaknesses
  • Redaction only works on field names, not prompt contents—secrets in prompts still stored
  • Replay requires all network traffic through proxy, partial recording won't replay correctly
Target Audience

AI/ML developers, agent builders

Similar To

LangSmith · Arize · Helicone

Post Description

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.

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