built an open-source reliability layer for AI agents , three tools, all live, zero infrastructure cost

Over the last few months I identified three problems that every developer building AI agents hits in production — and built a standalone open-source tool for each one.

Together they form the Thread Suite.

The Problem Space
When you deploy an AI agent to production, you face three specific failure modes:

Failure Mode 1 — Structural corruption
Your agent returns conversational text instead of JSON. Or missing fields. Or wrong types. Your database gets dirty data. Your pipeline crashes silently.

Failure Mode 2 — Behavior drift
Your agent starts behaving differently across runs. Hallucinating. Refusing. Formatting incorrectly. You find out when a user complains — not before.

Failure Mode 3 — Prompt degradation
You change a prompt and have no idea if performance improved or degraded. There's no version history. No metrics. No rollback.

The Three Tools

Iron-Thread
Middleware that sits between your AI model and your database. Validates output structure against a defined schema. Blocks failures. Auto-corrects using AI when a key is available.

pip install iron-thread

Live API: https://iron-thread-production.up.railway.app/docs
GitHub: https://github.com/eugene001dayne/iron-thread

TestThread
pytest for AI agents. Define expected behavior, run tests, get pass/fail results with AI-powered diagnosis.

pip install testthread

Live API: https://test-thread-production.up.railway.app/docs
GitHub: https://github.com/eugene001dayne/test-thread

PromptThread
Git for prompts — with performance data attached. Version control, A/B testing, regression alerts that fire automatically when pass rate drops or latency spikes, and golden set testing that runs your critical cases against every new version.

pip install promptthread

Live API: https://prompt-thread.onrender.com/docs
Dashboard: https://prompt-thread-dashboard.lovable.app
GitHub: https://github.com/eugene001dayne/prompt-thread

How They Connect

Iron-Thread  → Did the AI return the right structure?
TestThread   → Did the agent do the right thing?
PromptThread → Is my prompt the best version of itself?

Each tool works standalone. Together they form a complete reliability pipeline.

**The Build Stats**
- One person, 
- Celeron processor, 4GB RAM, Windows, VS Code
- Stack: FastAPI, Supabase, Railway/Render, Lovable
- Infrastructure cost: $0 and some help from claude
- Time: a few weeks of focused building

All three tools are MIT licensed, open source, and free to self-host.
What reliability problems are you hitting with your agents? Happy to answer any questions.