LangChain AgentEvals Harness Model Card
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LangChain AgentEvals Harness Model Card
| Field | Value |
|---|---|
| Repository | langchain-ai/agentevals |
| Category | Agent Evaluation Harness with LangGraph Integrations |
| Stars / forks snapshot | 76 / 9 |
| Language | Python |
| License | MIT |
| Raw capture | raw-github/langchain-ai_agentevals.md |
| Updated by | hourly public metadata update, 2026-06-02 07:54 +0800 |
1. Role in Self Evolve
langchain-ai/agentevals is a LangChain ecosystem harness for running repeatable agent evaluation suites. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
define executable eval datasets and scoring rules -> run deterministic and model-graded checks against agent trajectories -> surface regressions through repeatable evaluation loops -> harden agent releases with test-like quality gates
3. Evidence Path
web-observed GitHub page showed 76 stars, 9 forks, 81 commits, MIT license, and explicit AgentEvals framing. Shell GitHub API access remained blocked by DNS and local gh auth was invalid, so this card treats the snapshot as web-observed rather than API-verified.
4. Teaching Use
Use this card to explain Agent Evaluation Harness with LangGraph Integrations: it shows how harness/runtime/benchmark layers convert agent behavior into reproducible and auditable engineering workflows.
5. Limits
The repository was not cloned in this iteration; no benchmark run, plugin install, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.