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rightmodeler
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About rightmodeler
rightmodeler is a command-line tool that replays existing agent traces through lower-cost candidate models and compares their outputs to previously accepted results. It automatically normalizes traces from platforms such as LangSmith, OpenAI SDK, Langfuse, and Braintrust into a consistent per-step schema, enabling consistent evaluation across providers. The tool evaluates each candidate model based on output agreement, cost savings, evidence quality, and abstention behavior. Once a suitable model is identified, rightmodeler packages the approved swap as a pull request with attached evidence, turning model migrations into a standard code review process. This approach streamlines model optimization while maintaining traceability and accountability in production environments. By automating the comparison and migration workflow, teams can safely reduce inference costs without sacrificing output reliability.
Key features
- CLI-based benchmarking of cheaper AI models against shipped traces
- Automatic detection and normalization of ten trace formats
- Evaluation metrics: output agreement, cost savings, evidence quality, abstentions
- Evidence-backed model swap packaging as pull requests
- Integration with LangSmith, OpenAI SDK, Langfuse, and Braintrust
- Standardized per-step schema for consistent comparisons
- Code review workflow for model migrations
- Open source availability
Use cases
- Optimizing AI agent costs by testing cheaper model alternatives against production traces
- Validating model swaps before deployment using historical trace comparisons
- Automating model migration workflows with evidence attached for review
Pros
- Automatically normalizes agent traces from multiple platforms into a consistent per-step schema for uniform evaluation
- Provides evidence-backed comparisons of candidate models against previously accepted outputs
- Generates pull requests with attached evidence for approved model migrations, enabling code review processes
- Supports a wide range of trace formats from platforms like LangSmith, OpenAI SDK, Langfuse, and Braintrust
- Enables cost reduction while maintaining output reliability through measured comparisons
Cons
- Requires existing agent traces for evaluation, limiting utility without prior deployment data
- Cascade risks may arise when replacing models in multi-step agent workflows
- Abstention behavior may lead to incomplete evaluations in some scenarios
- Dependent on provider key usage for replay functionality after initial free tier
Frequently asked questions about rightmodeler
What does rightmodeler do?
rightmodeler replays existing agent traces through lower-cost candidate models and compares their outputs to previously accepted results, enabling evidence-backed model optimization and migration.
Who is rightmodeler suitable for?
Teams using agentic workflows who want to reduce inference costs while maintaining output reliability through measured model comparisons and standardized code review processes.
How does rightmodeler handle different trace formats?
It automatically detects and normalizes traces from over 10 platforms into a consistent per-step schema for uniform evaluation across providers.
What kind of evidence does rightmodeler provide for model decisions?
The tool reports agreement rates, cost savings, evidence quality, sample sizes, and abstention behavior for each candidate model comparison.
Can rightmodeler be used for runtime gateways?
No, it functions as a reporting and migration tool rather than a runtime gateway, measuring candidates on existing traces.
How do I get started with rightmodeler?
Users can initialize the tool with 'npx rightmodeler init' and run audits on their existing traces to generate model swap pull requests with attached evidence.
