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Use sandboxing and data-driven metrics to reproducibly benchmark AI coding agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "coder_eval" yet — see the docs or source repo.
Use coder_eval to benchmark Agent A and Agent B on the same coding tasks. Use a fixed test set, same sandbox environment, reproducible mode, and return ranking and failing cases.
Return pass rates per case, failure reasons, runtime environment details, and a reproducible scoring table.
Generate a reproducible evaluation report with coder_eval that includes execution steps, sandbox config, random seed, metric trends, and summarized analytics.
Produce a structured report suitable for re-run verification, including environment settings, per-run results, and traceable data-driven analysis.
Use coder_eval to run repeated evaluations of v1 and v2 on identical benchmarks, then provide data-backed conclusions and confidence in improvements.
Output a version comparison table and trend conclusions, flagging significant gains or regressions to guide iteration decisions.
Developers selecting an AI coding assistant can use coder_eval to compare multiple agents under the same sandboxed conditions, reducing subjective bias and improving stability checks.
Researchers testing new model versions need reproducible experiments and quantified metrics; coder_eval supports objective, data-driven comparisons for papers or internal reports.
Before rollout, product teams can rerun reproducible checks on agent behavior and success rates with coder_eval to detect regressions and reduce release risk.
coder_eval is a framework for evaluating AI coding agents and their skills, with sandboxed execution, reproducibility, and data-driven analysis.
Only a high-level description is provided here; installation steps, dependencies, or key requirements are not included. Please check the source repository for setup instructions.
Generic automated tests focus on functional correctness, while coder_eval targets AI coding-agent evaluation by combining sandboxed reproducibility with metric-based analysis for comparing agents or model versions.
Provide AI agents a local sandbox to run code and tasks safely.
Optimize AI coding context by sandboxing tool output and reducing token usage.
Guide coding agents to validate changes with evidence-first execution.
Use an auditable lightweight code agent for coding, planning, search, and debugging.
Coordinate coding agents locally with shared memory, tasks, and handoffs.
Gives coding agents a deterministic call graph to reduce breakage and token waste.