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EvaluationExperimental
coder_eval
by UiPath
Sandboxed, reproducible evaluation framework for coding agents
Python
Updated Aug 16, 2026
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Summary
Evaluates AI coding agents by running reproducible, sandboxed coding tasks and measuring skill-level performance. Uses containerized sandboxes, fixed seeds, and data-driven metrics to ensure reproducibility and safe execution. Includes CLI tooling and extensible task/metric hooks for custom benchmarks. See how this aligns with the Tool Use Pattern and the Evaluation-Driven Development (EDDOps) approaches.
Why It Matters
As agentic systems are trusted to write and modify code, objective, reproducible evaluation becomes essential to compare capabilities and detect failure modes. coder_eval provides a consistent way to log skill-level outcomes and reproduce runs, which helps establish reliable agent track records and supports continual assessment. Having sandboxed benchmarks reduces risk while producing the data needed for reputation and trust systems. This fits with guardrails practices like Guardrails Pattern.
Best For
Teams benchmarking code-generation agents or researchers needing reproducible, data-driven assessments of agent coding skills. For practical orchestration and pattern guidance, consider the Role-Based Agent Pattern.
Applications
- Benchmarking code-generation skills across LLMs (e.g., codex, claude) with reproducible runs
- Running sandboxed tests to detect agent failure modes and unsafe code generation
- Generating repeatable datasets and metrics for agent track records and longitudinal comparison
Works With
openaianthropiccodex
Topics
agent-evaluationagentic-aiai-agentsbenchmarkbenchmarkingclaudeclaude-codeclaude-code-skillclaude-skillscli+8 more
Similar Tools
evalsagent-playground
Keywords
agent-evaluationcoding-agentsreproducible-benchmarks