--- title: "human-eval vs Agent-Reach" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openai-human-eval-vs-panniantong-agent-reach" tools: ["openai-human-eval", "panniantong-agent-reach"] --- # human-eval vs Agent-Reach *GraphCanon updated Jul 11, 2026* ## Verdict Pick human-eval when tags unique to human-eval: python; pick Agent-Reach when tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation. [human-eval](https://github.com/openai/human-eval) reports 3.3k GitHub stars, 449 forks, and 42 open issues, last pushed Jan 17, 2025. [Agent-Reach](https://github.com/Panniantong/Agent-Reach) has 55k stars, 4.5k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [human-eval's repository](https://github.com/openai/human-eval) and [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach). | | [human-eval](/tools/openai-human-eval.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Tagline | Code for the paper "Evaluating Large Language Models Trained on Code" | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | | Stars | 3,294 | 54,715 | | Forks | 449 | 4,509 | | Open issues | 42 | 144 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Evaluation & Observability, LLM Frameworks, Model Training | AI Agents, Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [human-eval](/tools/openai-human-eval.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 540d | 0d | | Open issues (now) | 42 | 144 | | Owner type | Organization | User | | Security scan | No criticals | No MCP manifest | | Full report | [trust report](/tools/openai-human-eval/trust.md) | [trust report](/tools/panniantong-agent-reach/trust.md) | ## Choose when ### Choose human-eval if… - Tags unique to human-eval: python. - Also covers Evaluation & Observability, Model Training. - Leaner open-issue backlog (42). ### Choose Agent-Reach if… - Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation. - Also covers AI Agents, Developer Tools. - More GitHub stars (55k vs 3.3k) - visibility, not fit. ## When NOT to use human-eval - Last GitHub push was 540 days ago (dormant maintenance, Jan 17, 2025). Validate activity before betting a new project on human-eval. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use Agent-Reach - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between human-eval and Agent-Reach? human-eval: Code for the paper "Evaluating Large Language Models Trained on Code". Agent-Reach: Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.. See the comparison table for live GitHub stats and shared categories. ### When should I choose human-eval over Agent-Reach? Choose human-eval over Agent-Reach when Tags unique to human-eval: python; Also covers Evaluation & Observability, Model Training; Leaner open-issue backlog (42). ### When should I choose Agent-Reach over human-eval? Choose Agent-Reach over human-eval when Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation; Also covers AI Agents, Developer Tools; More GitHub stars (55k vs 3.3k) - visibility, not fit. ### When should I avoid human-eval? Last GitHub push was 540 days ago (dormant maintenance, Jan 17, 2025). Validate activity before betting a new project on human-eval. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid Agent-Reach? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is human-eval or Agent-Reach more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 3,294). Stars measure visibility, not whether either tool fits your constraints. ### Are human-eval and Agent-Reach open source? Yes - both are open-source projects on GitHub (human-eval: MIT, Agent-Reach: MIT). ### Where can I find alternatives to human-eval or Agent-Reach? GraphCanon lists graph-backed alternatives at [human-eval alternatives](/tools/openai-human-eval/alternatives) and [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) ([human-eval markdown twin](/tools/openai-human-eval/alternatives.md), [Agent-Reach markdown twin](/tools/panniantong-agent-reach/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/openai-human-eval-vs-panniantong-agent-reach.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, human-eval or Agent-Reach? human-eval: Dormant. Agent-Reach: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for human-eval and Agent-Reach? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [human-eval trust report](/tools/openai-human-eval/trust); [Agent-Reach trust report](/tools/panniantong-agent-reach/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openai-human-eval`](/api/graphcanon/graph?tool=openai-human-eval) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_