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nomos
by dowhiledev
Flow-based agent framework with built-in audit trails and step-level provenance
Python
Updated Nov 9, 2025
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How It Works
Provide auditable, flow-based agents that record step-level decisions for review and reproducibility. Uses a step-guided execution model to capture inputs, intermediate outputs, and rationale at each stage so runs can be replayed and inspected. Includes a Python SDK to embed provenance into agent flows and produce machine-readable audit logs for downstream analysis.
Why It Matters
As agents take on higher-stakes tasks, being able to inspect exactly how a decision was reached is critical for trust and compliance. Nomos makes provenance and reproducibility first-class by capturing granular traces of agent behavior, which enables post-hoc evaluation and accountability. This matters for building an agent track record and for feeding trustworthy signals into continuous evaluation and reputation systems. By capturing granular traces of agent behavior provenance, enabling reproducibility and accountability.
Ideal For
Teams building or testing agents that require reproducible decision trails, auditability, and pre-production inspection. It benefits from a structured approach like Planning Pattern to guide development and evaluation.
How It's Used
- Audit agent decisions step-by-step with recorded inputs, outputs, and rationale
- Reproduce and debug failing agent flows by replaying captured execution traces
- Gate agent releases with auditable runs for compliance and pre-production testing
- Extract provenance signals to feed reputation or continuous evaluation systems
Topics
agentagent-testingagentic-aiflow-based-agentstep-guided-agent
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Keywords
multi-agent trustagent-testingagent track recordagent-evaluation