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what is senior-swe-bench-v2026.06 fr?

snorkel-ai/senior-swe-bench-v2026.06 — explained in plain English

Analysis updated 2026-05-18

43PythonAudience · researcherComplexity · 3/5Setup · moderate

tl;dr

The dataset behind Senior SWE-Bench, a benchmark from Snorkel AI that measures how well AI coding agents handle real software engineering tasks.

vibe map

mindmap
  root((Senior SWE Bench))
    What it does
      Provides benchmark tasks
      Works with Harbor
    Tech stack
      Python
      Harbor
    Use cases
      Agent benchmarking
      Model comparison
    Audience
      Researchers
      AI teams

Code map

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what do people make with this?

VIBE 1

Benchmark an AI coding agent's performance on real software engineering tasks.

VIBE 2

Compare different AI models or agent frameworks using a shared evaluation set.

VIBE 3

Run automated grading of agent-generated code fixes.

what's the stack?

PythonHarbor

how it stacks up fr

snorkel-ai/senior-swe-bench-v2026.06alibaba/omnidoc-tokenbencharccalc/dwmfix
Stars434343
LanguagePythonPythonPython
Setup difficultymoderatemoderateeasy
Complexity3/53/52/5
Audienceresearcherresearchergeneral

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · moderate time til it works · 30min

Requires installing Harbor and an API key for the model being evaluated.

in plain english

This repository holds the dataset used by Senior SWE-Bench, a benchmark from Snorkel AI that tests how well AI coding agents perform on real software engineering tasks. The README itself is short and mainly points to a separate website and documentation for the actual details of what the benchmark measures and how the tasks were built. What this repo provides is the set of public test tasks in a format readable by a tool called Harbor, an evaluation framework also made by the same team. Because Harbor can read straight from this repository, someone wanting to run the benchmark does not need to download the dataset separately, only install Harbor itself. To actually run an evaluation, a user sets an API key for whichever AI model they want to test, such as Anthropic or OpenAI, picks an agent framework and a specific model, and then runs a single Harbor command pointing at this repository. The README shows both running it through something called the Harbor Hub and running it by pointing Harbor directly at the GitHub repo. It also lists a few optional environment variables for overriding which model is used for grading and classifying the agent's attempts, but does not explain the grading process in the README itself. Overall, this repo is a data and configuration companion to the Harbor benchmarking tool rather than a standalone application, and the README is sparse, so most of the actual explanation of how the benchmark works lives on the linked website and documentation pages instead of here.

prompts (copy fr)

prompt 1
Explain how to run this benchmark using the Harbor framework.
prompt 2
Walk me through setting the API keys and model overrides needed to evaluate an agent here.
prompt 3
What is the difference between running via Harbor Hub and running directly from this GitHub repo?

Frequently asked questions

what is senior-swe-bench-v2026.06 fr?

The dataset behind Senior SWE-Bench, a benchmark from Snorkel AI that measures how well AI coding agents handle real software engineering tasks.

What language is senior-swe-bench-v2026.06 written in?

Mainly Python. The stack also includes Python, Harbor.

How hard is senior-swe-bench-v2026.06 to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is senior-swe-bench-v2026.06 for?

Mainly researcher.

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