Agentifact assessment — independently scored, not sponsored. Last verified Mar 8, 2026.
SWE-bench
Benchmark for evaluating AI agents on real-world software engineering tasks drawn from GitHub issues across Django, Matplotlib, SymPy, and other open-source projects. Agents must generate patches that resolve described bugs; performance is measured by resolution rate, API cost, and per-instance detail. Hosts public leaderboards and supports custom evaluation runs. Free and open-access.
Use with care — notable gaps remain
You need to benchmark your AI agent's bug-fixing ability on realistic GitHub issues to compare against leaderboards and track improvements.
Top agents hit 20-40% pass@1 on full set, up to 74% on Verified subset; strong signal for Python bug fixes but expect failures on ambiguous issues or multi-file complexity.
You want a quick, reliable way to validate agent progress without custom task creation.
More discriminative than full SWE-bench; leaders at 40-80% pass rates, but still underestimates non-Python or enterprise performance.
Python OSS Only
Tasks from Django, Matplotlib, SymPy etc.; ignores enterprise codebases, non-Python langs, security, code quality, and review workflows.
Original is easier and saturating; Pro adds long-horizon tasks with human augmentation for better discrimination.
For fast Python bug-fix baselines and leaderboards.
For harder, multi-file tasks mimicking pro engineering.
Data Contamination Risk
Models may memorize solutions from training on public issues/PRs, overstating reasoning; cross-check with private evals to avoid inflated scores.
Trust Breakdown
What It Actually Does
SWE-bench tests AI coding agents on real GitHub issues from open-source projects like Django, where they must create patches to fix bugs and pass tests. It measures success by how many issues get resolved, plus costs and details per task.
Benchmark for evaluating AI agents on real-world software engineering tasks drawn from GitHub issues across Django, Matplotlib, SymPy, and other open-source projects. Agents must generate patches that resolve described bugs; performance is measured by resolution rate, API cost, and per-instance detail. Hosts public leaderboards and supports custom evaluation runs.
Free and open-access.
Fit Assessment
Not ideal for
- ✗underspecified problem statements
- ✗unfair unit tests filtering valid solutions
- ✗test misalignment causing false negatives
Known Failure Modes
- underspecified problem statements
- unfair unit tests filtering valid solutions
- test misalignment causing false negatives