open source · MIT
The durable runtime for AI agents

Agents that survive crashes, wait for humans, and never blow the budget.

Every run: a persistent, auditable, replayable file, with OpenTelemetry traces of every step. Zero dependencies. No cluster, no cloud, no vendor.

Try the live demo
a run, live
0
runtime dependencies
~1 MB
vs 64–270 MB elsewhere¹
74 ms
cold start vs 200–505 ms¹
171
tests on every commit

¹ measured July 2026, methodology

Production asks harder questions than demos

Kill it. It doesn't care.

Every step persists before execution continues. nexus resume picks up exactly where a crashed run stopped, hours later, any process.

Dangerous tools wait for a human

Mark a tool requiresApproval, the run pauses durably. Policy rules auto-decide the easy cases. Everything journaled, with who decided.

Budgets enforced before spending

Token and USD caps checked before every model call. The $81k-in-a-week story (real, 2025-26) becomes a $50 halt and a Slack ping.

Replay any run for $0.00

Finished runs re-execute offline from their own record, or diff against a new model. Incidents become regression tests.

Traces to the stack you already run

One call ships OpenTelemetry spans (invoke_agent, chat, execute_tool) with token usage to Datadog, Grafana, Langfuse, any OTLP backend. Zero deps, no vendor SDK.

Every dollar, attributed

Tag a run with a customerId and it rides the record, journal, webhooks, and every span. "Whose runs doubled the bill?" becomes a filter, not a forensics project.

A run is a file, not a prayer

The Open Agent Run format (public domain) makes every run auditable, resumable, and replayable, by any tool, forever. That's the difference between a framework feature and a standard.

How it works → Enterprise playbooks & case studies → Star it on GitHub