llm-as-a-verifier/llm-as-a-verifier
LLM-as-a-Verifier is a general-purpose framework that provides fine-grained feedback for any agent without requiring additional training. It achieves SOTA performance across coding, robotics, and medical agentic benchmarks. observed · 2026-08-28
Health v2 · maintenance only
58/100
- Activity 98
- Release rhythm 35
- Longevity 10
Flags: no_releases young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 146
- days_rel: n/a
- days_push: 13
- n_releases_24m: 0
Adoption not part of the score
2901 stars · 229 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Python framework that uses LLM token logprobs to produce fine-grained verification scores for agent trajectories without additional training. It provides best-of-N selection, pairwise comparison, and progress tracking APIs, and ships agentic benchmarks like Terminal-Bench and SWE-Bench Verified.
Use cases
- rank candidate solutions from an LLM agent with best-of-N selection
- score agent trajectories step by step for progress tracking
- generate dense reward signals for reinforcement learning
- verify coding agent outputs against custom criteria
- evaluate robotics or medical agent rollouts with image inputs
- run agentic benchmarks like Terminal-Bench and SWE-Bench Verified
- compare two candidate answers pairwise with an LLM judge
When to choose
- you need fine-grained, calibrated verification scores for agent outputs without training a reward model
- you want test-time scaling via best-of-N selection with a cost-efficient tournament
- you need dense rewards for RLHF-style training of agents
- you want multimodal verification of image or video agent trajectories
When to avoid
- you need a simple pass/fail test suite rather than probabilistic scoring
- you cannot access an LLM backend that returns token logprobs
- you need fully offline verification with no API or GPU costs
- your evaluation requires deterministic, reproducible ground-truth checks only
Facets
library · maturity active
llm-inference machine-learning agent-framework benchmarking rag large-language-models machine-learning developer-tools python cross-platform cli llm-as-a-judge reward-model best-of-n-selection reinforcement-learning-rewards test-time-scaling pairwise-comparison progress-tracking multimodal-verification ai-agents
10 sources
- readme: https://github.com/llm-as-a-verifier/llm-as-a-verifier · fetched 2026-08-28 · 0c1da9471a6c
- homepage: https://llm-as-a-verifier.com/docs/ · fetched 2026-08-29 · de979941de69
- site_page: https://llm-as-a-verifier.com/docs/get_started/installation.html · fetched 2026-08-29 · 15065a241cdd
- site_page: https://llm-as-a-verifier.com/docs/multimodal/image_inputs.html · fetched 2026-08-29 · 72d5df7d74ea
- site_page: https://llm-as-a-verifier.com/docs/basic_usage/best_of_n_selection.html · fetched 2026-08-29 · 478a6ec842df
- site_page: https://llm-as-a-verifier.com/docs/basic_usage/pairwise_comparison.html · fetched 2026-08-29 · 847be5422938
- site_page: https://llm-as-a-verifier.com/docs/basic_usage/progress_tracking.html · fetched 2026-08-29 · c7a2e4efc440
- site_page: https://llm-as-a-verifier.com/docs/basic_usage/criteria.html · fetched 2026-08-29 · af5a4eeaa18f
- site_page: https://llm-as-a-verifier.com/docs/benchmarks/running_benchmarks.html · fetched 2026-08-29 · 3199fe85b96b
- site_page: https://llm-as-a-verifier.com/docs/benchmarks/add_new_benchmark.html · fetched 2026-08-29 · faa05618530e
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| llm-as-a-verifier/llm-as-a-verifier | main | 58 |
For agents
markdown · JSON · MCP: product_card(name="llm-as-a-verifier/llm-as-a-verifier")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem