--- title: "Agent-Reach vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/panniantong-agent-reach-vs-rlancemartin-auto-evaluator" tools: ["panniantong-agent-reach", "rlancemartin-auto-evaluator"] --- # Agent-Reach vs auto-evaluator *GraphCanon updated Jul 11, 2026* ## Verdict Pick Agent-Reach when tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; pick auto-evaluator when tags unique to auto-evaluator: python. [Agent-Reach](https://github.com/Panniantong/Agent-Reach) reports 55k GitHub stars, 4.5k forks, and 144 open issues, last pushed Jul 10, 2026. [auto-evaluator](https://autoevaluator.langchain.com/) has 1.1k stars, 92 forks, and 3 open issues, last pushed May 10, 2023. Figures are from public GitHub metadata via [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach) and [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator). | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Tagline | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | Evaluation tool for LLM QA chains | | Stars | 54,715 | 1,102 | | Forks | 4,509 | 92 | | Open issues | 144 | 3 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | LLM Frameworks, AI Agents, Developer Tools | LLM Frameworks, Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1158d | | Open issues (now) | 144 | 3 | | Security scan | No MCP manifest | 118 low (118 low) | | Full report | [trust report](/tools/panniantong-agent-reach/trust.md) | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | ## Choose when ### Choose Agent-Reach if… - Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code. - Also covers AI Agents, Developer Tools. - More GitHub stars (55k vs 1.1k) - visibility, not fit. ### Choose auto-evaluator if… - Tags unique to auto-evaluator: python. - Also covers Data & Retrieval, Vector Databases. - Leaner open-issue backlog (3). ## When NOT to use Agent-Reach - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - 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. ## When NOT to use auto-evaluator - Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between Agent-Reach and auto-evaluator? 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.. auto-evaluator: Evaluation tool for LLM QA chains. See the comparison table for live GitHub stats and shared categories. ### When should I choose Agent-Reach over auto-evaluator? Choose Agent-Reach over auto-evaluator when Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; Also covers AI Agents, Developer Tools; More GitHub stars (55k vs 1.1k) - visibility, not fit. ### When should I choose auto-evaluator over Agent-Reach? Choose auto-evaluator over Agent-Reach when Tags unique to auto-evaluator: python; Also covers Data & Retrieval, Vector Databases; Leaner open-issue backlog (3). ### When should I avoid Agent-Reach? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. 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. ### When should I avoid auto-evaluator? Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is Agent-Reach or auto-evaluator more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 1,102). Stars measure visibility, not whether either tool fits your constraints. ### Are Agent-Reach and auto-evaluator open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Agent-Reach or auto-evaluator? GraphCanon lists graph-backed alternatives at [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) and [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) ([Agent-Reach markdown twin](/tools/panniantong-agent-reach/alternatives.md), [auto-evaluator markdown twin](/tools/rlancemartin-auto-evaluator/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/panniantong-agent-reach-vs-rlancemartin-auto-evaluator.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Agent-Reach or auto-evaluator? Agent-Reach: Very active. auto-evaluator: Dormant. 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 Agent-Reach and auto-evaluator? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Agent-Reach trust report](/tools/panniantong-agent-reach/trust); [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=panniantong-agent-reach`](/api/graphcanon/graph?tool=panniantong-agent-reach) - 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/_