{"id":45804159,"title":"Show HN: AgentML – SCXML for Deterministic AI Agents (MIT)","points":5,"user":"jeffreyajewett","time":1762202467,"time_ago":"15 hours ago","type":"link","content":"

Hey HN,

We’ve been experimenting with how to make AI agents more deterministic, observable, and production-safe, and that led us to build AgentML — an open-source language for defining agent behavior as state machines, not prompt chains.

My co-founder posted before but linked to the project website instead of the repo, so resharing here.

AgentML lets you describe your agent’s reasoning and actions as a finite-state model (think SCXML for agents). Each state, transition, and tool call is explicit and machine-verifiable.

That means you can:

- Reproduce any decision path deterministically

- Trace reasoning and tool calls for debugging or compliance

- Guarantee agents only take valid actions (e.g. “never send a payment before verification”)

- Run locally, in the cloud, or within MCP-based frameworks

Example:

```

<?xml version="1.0" encoding="UTF-8"?>

    <data id="papers" expr="[]"\n        schema='{"type":"array","description":"Fetched papers from Hugging Face"}' />\n\n    <data id="summary" expr="''"\n        schema='{"type":"string","description":"Summary of the papers"}' />\n

\n</datamodel>

    <onentry>\n\n        <log label="Researcher: "\n            expr="`Fetching papers from Hugging Face and summarizing with OpenAI\\n`" />\n\n        <openai:generate model="gpt-4o" location="summary" stream="false">\n\n            <openai:prompt>Summarize these recent AI/ML papers from Hugging Face:\n\n                {{fetch "https://huggingface.co/api/daily_papers"}}\n\n                Provide a concise summary of the key trends, breakthroughs, and developments in AI/ML research.\n            </openai:prompt>\n\n        </openai:generate>\n\n    </onentry>\n\n    <transition target="log_summary" />\n

\n</state>

    <onentry>\n\n        <log label="Researcher Summary: " expr="summary" />\n\n    </onentry>\n\n    <transition target="done" />\n

\n</state>

</agentml>

```

We’re using this in Agentflare to add observability, cost tracking, and compliance tracing for multi-agent systems — but AgentML itself is fully open-source (MIT licensed).

Repo: https://github.com/agentflare-ai/agentml\nDocs: https://docs.agentml.dev

We also launched SQLite-Graph, a Cypher-compatible graph extension for SQLite, which will serve as the base for AgentML’s native memory layer. It’s also MIT licensed: https://github.com/agentflare-ai/sqlite-graph

Would love feedback from anyone building with LLM orchestration frameworks, rule-based systems, or embedded MCP tool servers… especially around how to extend deterministic patterns to multi-agent coordination.

— Jeff @ Agentflare","url":"https://github.com/agentflare-ai/agentml","domain":"github.com","comments":[{"id":45804329,"level":0,"user":"gwillen85","time":1762203350,"time_ago":"15 hours ago","content":"

You can execute the above `OPENAI_API_KEY=YOUR_API_KEY amlx run researcher.aml`","comments":[]}],"comments_count":1}