What is rightmodeler?
rightmodeler helps engineering teams determine whether expensive AI models in existing agent workflows can be replaced with cheaper alternatives without sacrificing the quality they have already accepted in production.
Instead of evaluating models only against generic benchmarks or manually testing a few prompts, rightmodeler works from the team's own historical agent traces. It ingests recorded agent activity, maps the workflow into individual steps, replays those steps through cheaper candidate models, and evaluates how closely each candidate performs compared with the output that the team previously accepted.
The resulting audit reports model cost, candidate savings, quality agreement, evidence, sample size, and abstentions. This gives teams a step-by-step view of where they may be overpaying for model capability and where a cheaper model is not sufficiently reliable.
rightmodeler supports trace formats from Claude Code, Codex, LangSmith/LangGraph, OpenAI SDK, Langfuse, Braintrust, Phoenix/OpenInference, OpenTelemetry GenAI, and LiteLLM StandardLoggingPayload.
Candidate model replays can run through OpenRouter, LiteLLM, or Vercel AI Gateway using the user's own provider credentials. rightmodeler itself does not sit inside the production request path and does not act as a runtime model gateway.
The tool also accounts for cascade risk in multi-step agent workflows. A cheaper model may look acceptable when one step is tested independently but produce downstream problems when its output becomes input for later steps. rightmodeler’s end-to-end replay capability is designed to surface this type of risk.
The current rightmodeler skill can generate an evidence-backed swap plan that is reviewed and approved by the engineering team. The planned agent product is intended to go further by automatically opening pull requests when new model opportunities are identified.
Instead of evaluating models only against generic benchmarks or manually testing a few prompts, rightmodeler works from the team's own historical agent traces. It ingests recorded agent activity, maps the workflow into individual steps, replays those steps through cheaper candidate models, and evaluates how closely each candidate performs compared with the output that the team previously accepted.
The resulting audit reports model cost, candidate savings, quality agreement, evidence, sample size, and abstentions. This gives teams a step-by-step view of where they may be overpaying for model capability and where a cheaper model is not sufficiently reliable.
rightmodeler supports trace formats from Claude Code, Codex, LangSmith/LangGraph, OpenAI SDK, Langfuse, Braintrust, Phoenix/OpenInference, OpenTelemetry GenAI, and LiteLLM StandardLoggingPayload.
Candidate model replays can run through OpenRouter, LiteLLM, or Vercel AI Gateway using the user's own provider credentials. rightmodeler itself does not sit inside the production request path and does not act as a runtime model gateway.
The tool also accounts for cascade risk in multi-step agent workflows. A cheaper model may look acceptable when one step is tested independently but produce downstream problems when its output becomes input for later steps. rightmodeler’s end-to-end replay capability is designed to surface this type of risk.
The current rightmodeler skill can generate an evidence-backed swap plan that is reviewed and approved by the engineering team. The planned agent product is intended to go further by automatically opening pull requests when new model opportunities are identified.
Software Category
Developer & Software Development
Pricing Model
Open Source / Free
Product Type
saas
Starting Price
USD $0.00
rightmodeler Features
✓
Key Feature
Uses real production agent traces rather than generic benchmarks to evaluate model substitutions, ensuring decisions are grounded in actual workflow performance data
✓
Key Feature
Supports multiple trace formats including Claude Code, Codex, LangSmith/LangGraph, OpenAI SDK, and Langfuse, providing broad compatibility with existing development toolchains
✓
Key Feature
Provides quantified audit reports including model cost, candidate savings, quality agreement rates, evidence samples, sample size, and abstentions for comprehensive decision-making
✓
Key Feature
Zero cost barrier with open-source availability enables teams to self-host and audit the tooling itself
rightmodeler Pricing
Billing Model: Open Source / Free
View Official Pricing →
USD $0.00 / starting
Check the official vendor site for volume discounts, regional tiers, and enterprise terms.
rightmodeler Pros and Cons
✓ Key Strengths (Pros)
- • Uses real production agent traces rather than generic benchmarks to evaluate model substitutions, ensuring decisions are grounded in actual workflow performance data
- • Supports multiple trace formats including Claude Code, Codex, LangSmith/LangGraph, OpenAI SDK, and Langfuse, providing broad compatibility with existing development toolchains
- • Provides quantified audit reports including model cost, candidate savings, quality agreement rates, evidence samples, sample size, and abstentions for comprehensive decision-making
- • Zero cost barrier with open-source availability enables teams to self-host and audit the tooling itself
⚠ Considerations & Limitations (Cons)
- • Requires teams to have pre-existing agent traces from production workflows, limiting applicability for teams just starting AI agent implementations
- • No user reviews or ratings available (0 reviews, null average rating), making it difficult to assess real-world user satisfaction or common implementation challenges
- • Open-source model typically demands technical expertise for deployment, configuration, and maintenance compared to fully managed alternatives
- • Only evaluates model substitution viability against previously accepted outputs, meaning it cannot identify if the original accepted outputs themselves had quality issues