--- title: "do-not-answer vs Agent-Reach" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/libr-ai-do-not-answer-vs-panniantong-agent-reach" tools: ["libr-ai-do-not-answer", "panniantong-agent-reach"] --- # do-not-answer vs Agent-Reach *GraphCanon updated Jul 11, 2026* ## Verdict Pick do-not-answer when do-not-answer is primarily Jupyter Notebook; Agent-Reach is Python; pick Agent-Reach when agent-Reach is primarily Python; do-not-answer is Jupyter Notebook. [do-not-answer](https://github.com/Libr-AI/do-not-answer) reports 334 GitHub stars, 29 forks, and 0 open issues, last pushed Jun 7, 2024. [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 [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer) and [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach). | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Tagline | Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | | Stars | 334 | 54,715 | | Forks | 29 | 4,509 | | Open issues | 0 | 144 | | Language | Jupyter Notebook | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability, LLM Frameworks | AI Agents, Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 764d | 0d | | Open issues (now) | 0 | 144 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/libr-ai-do-not-answer/trust.md) | [trust report](/tools/panniantong-agent-reach/trust.md) | ## Choose when ### Choose do-not-answer if… - do-not-answer is primarily Jupyter Notebook; Agent-Reach is Python. - License: do-not-answer is Apache-2.0, Agent-Reach is MIT. - Tags unique to do-not-answer: jupyter notebook. - Also covers Evaluation & Observability. ### Choose Agent-Reach if… - Agent-Reach is primarily Python; do-not-answer is Jupyter Notebook. - License: Agent-Reach is MIT, do-not-answer is Apache-2.0. - Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation. - Also covers AI Agents, Developer Tools. ## When NOT to use do-not-answer - Last GitHub push was 764 days ago (dormant maintenance, Jun 7, 2024). Validate activity before betting a new project on do-not-answer. - 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. ## 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 do-not-answer and Agent-Reach? do-not-answer: Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs. 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 do-not-answer over Agent-Reach? Choose do-not-answer over Agent-Reach when do-not-answer is primarily Jupyter Notebook; Agent-Reach is Python; License: do-not-answer is Apache-2.0, Agent-Reach is MIT; Tags unique to do-not-answer: jupyter notebook; Also covers Evaluation & Observability. ### When should I choose Agent-Reach over do-not-answer? Choose Agent-Reach over do-not-answer when Agent-Reach is primarily Python; do-not-answer is Jupyter Notebook; License: Agent-Reach is MIT, do-not-answer is Apache-2.0; Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation; Also covers AI Agents, Developer Tools. ### When should I avoid do-not-answer? Last GitHub push was 764 days ago (dormant maintenance, Jun 7, 2024). Validate activity before betting a new project on do-not-answer. 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. ### 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 do-not-answer or Agent-Reach more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 334). Stars measure visibility, not whether either tool fits your constraints. ### Are do-not-answer and Agent-Reach open source? Yes - both are open-source projects on GitHub (do-not-answer: Apache-2.0, Agent-Reach: MIT). ### Where can I find alternatives to do-not-answer or Agent-Reach? GraphCanon lists graph-backed alternatives at [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) and [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) ([do-not-answer markdown twin](/tools/libr-ai-do-not-answer/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/libr-ai-do-not-answer-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, do-not-answer or Agent-Reach? do-not-answer: 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 do-not-answer and Agent-Reach? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [do-not-answer trust report](/tools/libr-ai-do-not-answer/trust); [Agent-Reach trust report](/tools/panniantong-agent-reach/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=libr-ai-do-not-answer`](/api/graphcanon/graph?tool=libr-ai-do-not-answer) - 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/_