Hey Product Hunt 👋

I built PromptMatrix because I run a 22-agent multi-agent swarm — and every time I needed to update an agent's briefing, tool schema, or persona, I had to hunt for hardcoded strings across config files, commit code, and restart the entire system.

Multiply that by 22 agents, multiple times a week.

The deeper problem: the people who actually understand what the AI should say — product managers, domain experts, ops — have zero path to change it. They file tickets. Engineers handle it between sprint tasks. A two-word tone adjustment takes 5 days and a full deployment cycle.

That's a governance failure. Not an engineering one.

So I built the fix: PromptMatrix — the CI/CD pipeline and runtime control plane for AI prompts.

Here is the workflow:

→ Non-Engineers Edit Visually: Propose prompt adjustments in plain text without touching Git or terminal configs.

→ Automated Eval Gates: Built-in rule-based & LLM-as-a-judge tests score clarity, safety, and variables before anything merges.

→ 10-Second Peer Approval: Engineers review line-by-line diffs and approve with one click.

→ Sub-5ms Runtime Hot-Patching: Approved instructions go live on the very next API call via edge caches — zero application redeployments. Ever.

→ Audit-Ready & Reversible: Immutable audit trail with 1-click instant rollbacks to any historical version.

The runtime integration is a single line:

# Replace hardcoded strings once — works with LangGraph, CrewAI, OpenClaw, or raw SDKs

prompt = pm.serve("assistant.persona")

# Any teammate can propose a change → automated evals pass → approved → live in <5ms