A CLI tool that benchmarks AI coding agents against a team's own real production tasks and codebase
Mainstream AI coding agent benchmarks (SWE-bench, HumanEval, MMLU) use synthetic quiz tasks that do not predict real-world performance on a team's actual codebase, stack, and ticket types. Engineering teams waste weeks building one-off evaluation harnesses from scratch, then lack a repeatable way to compare agents as new models ship. This CLI tool lets a team point at their own repo and task history, auto-generate a real-task eval suite scoped to their domain, run any agent against it, and get a reproducible pass/fail scorecard they can re-run on every new model release.
Demand Breakdown
Social Proof 2 sources
Gap Assessment
4 tools exist (Braintrust, Confident AI (DeepEval), PromptFoo, LangSmith) but gaps remain: Requires teams to manually author eval datasets from scratch; no auto-generation of eval tasks from an existing production repo or ticket history; domain-specific suites cannot be cloned and parameterized in under 1 hour; Designed for LLM output quality (text, RAG, agents) not coding-agent task completion on a team's own codebase; no repo-aware task harvesting or agent-vs-agent coding benchmarks on production code.
Features7 agent-ready prompts
Competitive LandscapeFREE
| Product | Does | Missing |
|---|---|---|
| Braintrust | AI observability, eval, and logging platform with prompt engineering, dataset versioning, and automated scoring; raised $80M Series B at $800M valuation, Feb 2026, led by ICONIQ Capital with a16z and Greylock | Requires teams to manually author eval datasets from scratch; no auto-generation of eval tasks from an existing production repo or ticket history; domain-specific suites cannot be cloned and parameterized in under 1 hour |
| Confident AI (DeepEval) | Open-source LLM eval framework (YC W25); comprehensive test metrics, regression testing, red teaming for LLM outputs | Designed for LLM output quality (text, RAG, agents) not coding-agent task completion on a team's own codebase; no repo-aware task harvesting or agent-vs-agent coding benchmarks on production code |
| PromptFoo | Open-source LLM testing and red teaming tool; 300k+ developers, 127 Fortune 500 companies; acquired by OpenAI March 2026 after $18.4M Series A | Focused on prompt regression and security/red teaming, not agent-level coding task benchmarks; no production codebase ingestion to auto-generate domain-specific eval tasks |
| LangSmith | LLM observability, tracing, and eval platform by LangChain; logging, dataset management, human annotation, automated scoring | Tightly coupled to LangChain ecosystem; no coding-agent-specific benchmarking or automatic task harvest from a team's git history and issue tracker |
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