JEjeffreyajewett·Nov 3, 2025Submission
Show HN: AgentML – SCXML for Deterministic AI Agents (MIT)
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="
Read the rest
UTF-8"?>
<agentml xmlns="github.com/agentflare-ai/agentml"
xmlns:openai="github.com/agentflare-ai/agentml-go/openai"
version="1.0"
datamodel="ecmascript"
name="researcher">
<datamodel>
<data id="papers" expr="[]"
schema='{"type":"array","description":"Fetched papers from Hugging Face"}' />
<data id="summary" expr="''"
schema='{"type":"string","description":"Summary of the papers"}' />
</datamodel>
<state id="start">
<onentry>
<log label="Researcher: "
expr="`Fetching papers from Hugging Face and summarizing with OpenAI\n`" />
<openai:generate model="gpt-4o" location="summary" stream="false">
<openai:prompt>Summarize these recent AI/ML papers from Hugging Face:
{{fetch "https://huggingface.co/api/daily_papers"}}
Provide a concise summary of the key trends, breakthroughs, and developments in AI/ML research.
</openai:prompt>
</openai:generate>
</onentry>
<transition target="log_summary" />
</state>
<state id="log_summary">
<onentry>
<log label="Researcher Summary: " expr="summary" />
</onentry>
<transition target="done" />
</state>
<final id="done" />
</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
Docs: 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