llm-as-a-verifier
Fine-grained evaluation framework to score, select, and track AI agent outputs without retraining.
Rank candidate agent outputs and monitor execution progress using logprob-based scoring models.
LLM-as-a-Verifier is a Python framework that uses logprob distribution scoring to evaluate and rank AI agent trajectories. It suits developers building agentic workflows who need test-time scaling, output selection, and step-by-step progress tracking without fine-tuning new models. Medical AI researchers can use it to rank clinical agent trajectories on benchmarks like MedAgentBench.
What it is
The tool evaluates multi-step agent trajectories using pairwise reward models, single-step progress tracking, and Probabilistic Pivot Tournaments. It calculates fine-grained score expectations from LLM logprobs across custom evaluation criteria. You can use weaker models like DeepSeek V4.1 Flash to verify trajectories generated by larger systems.
Who it's for
This tool suits AI developers and medical informatics researchers who build multi-step agent systems and want automated test-time verification. It is ideal for teams running agent benchmarks like MedAgentBench or SWE-Bench Verified. It is not intended for end-user clinical decision support without integration into a larger developer workflow.
Key features
Probabilistic Pivot Tournament
Ranks N agent trajectories using reduced pairwise comparisons instead of full round-robin matrices.
Fine-grained Logprob Scoring
Calculates expectation scores over the full logprob distribution of evaluation tokens for candidate selection.
Real-time Progress Tracking
Monitors agent step executions live with ProgressTracker to identify failed runs early.
Custom Criteria Evaluation
Accepts user-defined criteria dictionaries to evaluate domains like clinical reasoning or code fixes.
Up and running in 3 steps
Our honest verdict
What's great
- Ranks agent trajectories efficiently using O(Nk) pairwise comparisons.
- Provides live step-by-step tracking to halt failing agent rollouts early.
- Works across proprietary model APIs and self-hosted vLLM endpoints.
- Requires no additional training or fine-tuning of verifier models.
Where it falls short
- Requires access to external model APIs or local GPU setups that return token logprobs.
- Repeated candidate evaluations incur significant API token usage or compute overhead.
- Requires Python coding to integrate scoring workflows into existing agent pipelines.
Pricing & license
LLM-as-a-Verifier is free and open-source under the MIT license. The primary cost comes from model API token usage or running your own GPU server to generate logprobs.
Open remote AI-training roles
880 open remote AI-training roles, updated November 15, 2026; most pay $20–$175 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
Use it, or have us set it up
FAQ
Can I run this with a locally hosted LLM?
Yes, you can connect it to an OpenAI-compatible server such as vllm serve provided it returns token logprobs.
How does it reduce evaluation costs for best-of-N selection?
It uses a Probabilistic Pivot Tournament to rank candidates in O(Nk) comparisons rather than full O(N^2) round-robin checks.
Can a smaller model verify a larger model's output?
Yes, benchmarks in the README show smaller verifiers like DeepSeek V4.1 Flash successfully ranking rollouts from larger models like Claude Opus.
