--- title: "llm-lobbyist vs Agent-Reach" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/johnnay-llm-lobbyist-vs-panniantong-agent-reach" tools: ["johnnay-llm-lobbyist", "panniantong-agent-reach"] --- # llm-lobbyist vs Agent-Reach *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-lobbyist when llm-lobbyist is primarily Jupyter Notebook; Agent-Reach is Python; pick Agent-Reach when agent-Reach is primarily Python; llm-lobbyist is Jupyter Notebook. [llm-lobbyist](https://github.com/JohnNay/llm-lobbyist) reports 174 GitHub stars, 14 forks, and 0 open issues, last pushed Jan 13, 2023. [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 [llm-lobbyist's repository](https://github.com/JohnNay/llm-lobbyist) and [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach). | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Tagline | Code for the paper: "Large Language Models as Corporate Lobbyists" (2023). | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | | Stars | 174 | 54,715 | | Forks | 14 | 4,509 | | Open issues | 0 | 144 | | Language | Jupyter Notebook | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Vector Databases, LLM Frameworks, Evaluation & Observability | LLM Frameworks, AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-lobbyist](/tools/johnnay-llm-lobbyist.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1275d | 0d | | Open issues (now) | 0 | 144 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/johnnay-llm-lobbyist/trust.md) | [trust report](/tools/panniantong-agent-reach/trust.md) | ## Choose when ### Choose llm-lobbyist if… - llm-lobbyist is primarily Jupyter Notebook; Agent-Reach is Python. - Tags unique to llm-lobbyist: jupyter notebook. - Also covers Vector Databases, Evaluation & Observability. ### Choose Agent-Reach if… - Agent-Reach is primarily Python; llm-lobbyist is Jupyter Notebook. - Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code. - Also covers AI Agents, Developer Tools. ## When NOT to use llm-lobbyist - Last GitHub push was 1276 days ago (dormant maintenance, Jan 13, 2023). Validate activity before betting a new project on llm-lobbyist. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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. ## Common questions ### What is the difference between llm-lobbyist and Agent-Reach? llm-lobbyist: Code for the paper: "Large Language Models as Corporate Lobbyists" (2023).. 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 llm-lobbyist over Agent-Reach? Choose llm-lobbyist over Agent-Reach when llm-lobbyist is primarily Jupyter Notebook; Agent-Reach is Python; Tags unique to llm-lobbyist: jupyter notebook; Also covers Vector Databases, Evaluation & Observability. ### When should I choose Agent-Reach over llm-lobbyist? Choose Agent-Reach over llm-lobbyist when Agent-Reach is primarily Python; llm-lobbyist is Jupyter Notebook; Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; Also covers AI Agents, Developer Tools. ### When should I avoid llm-lobbyist? Last GitHub push was 1276 days ago (dormant maintenance, Jan 13, 2023). Validate activity before betting a new project on llm-lobbyist. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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. ### Is llm-lobbyist or Agent-Reach more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 174). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-lobbyist and Agent-Reach open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm-lobbyist or Agent-Reach? GraphCanon lists graph-backed alternatives at [llm-lobbyist alternatives](/tools/johnnay-llm-lobbyist/alternatives) and [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) ([llm-lobbyist markdown twin](/tools/johnnay-llm-lobbyist/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/johnnay-llm-lobbyist-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, llm-lobbyist or Agent-Reach? llm-lobbyist: 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 llm-lobbyist and Agent-Reach? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-lobbyist trust report](/tools/johnnay-llm-lobbyist/trust); [Agent-Reach trust report](/tools/panniantong-agent-reach/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=johnnay-llm-lobbyist`](/api/graphcanon/graph?tool=johnnay-llm-lobbyist) - 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/_