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Postman for AI - design, evaluate, and debug LLM interactions with full transparency.

17 starsTypeScript

Reticle – Postman for AI Agents

by alchaplinsky·Mar 17, 2026·2 points·3 comments

AI Analysis

●●SolidSolve My ProblemCozyShip It

Local-first Postman for AI agents when LangSmith requires cloud accounts.

Strengths
  • Step-by-step agent execution view reveals decision-making at each step
  • SQLite storage keeps all prompts, API keys, and run history on your machine
  • Compare outputs across OpenAI, Anthropic, and Google models side by side
Weaknesses
  • Beta status with 11 open issues indicates early development stage
  • Agent debugging tools exist in LangSmith and Braintrust already
Target Audience

Developers building and debugging LLM-powered applications

Similar To

LangSmith · Braintrust · Promptfoo

Post Description

I’ve been building a lot with LLMs lately and kept thinking: why doesn’t this tool exist?

The workflow usually ends up being: write some code, run it, tweak a prompt, add logs just to understand what actually happened. It works in some cases, breaks in others, and it’s hard to see why. You also want to know that changing a prompt or model didn’t quietly break everything.

Reticle puts the whole loop in one place.

You define a scenario (prompt + variables + tools), run it against different models, and see exactly what happened - prompts, responses, tool calls, results. You can then run evals against a dataset to see whether a change to the prompt or model breaks anything.

There’s also a step-by-step view for agent runs so you can see why it made a decision. Everything runs locally. Prompts, API keys, and run history stay on your machine (SQLite).

Stack: Tauri + React + SQLite + Axum + Deno.

Still early and definitely rough around the edges. Is this roughly how people are debugging LLM workflows today, or do you do it differently?

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