--- title: "Agent-Reach vs llama-hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/panniantong-agent-reach-vs-run-llama-llama-hub" tools: ["panniantong-agent-reach", "run-llama-llama-hub"] --- # Agent-Reach vs llama-hub *GraphCanon updated Jul 12, 2026* ## Verdict Pick Agent-Reach when agent-Reach is primarily Python; llama-hub is Jupyter Notebook; pick llama-hub when llama-hub is primarily Jupyter Notebook; Agent-Reach is 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. [llama-hub](https://llamahub.ai/) has 3.5k stars, 719 forks, and 96 open issues, last pushed Mar 1, 2024. Figures are from public GitHub metadata via [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach) and [llama-hub's repository](https://github.com/run-llama/llama-hub). | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [llama-hub](/tools/run-llama-llama-hub.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. | A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain | | Stars | 54,715 | 3,473 | | Forks | 4,509 | 719 | | Open issues | 144 | 96 | | Language | Python | Jupyter Notebook | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Developer Tools, LLM Frameworks | Data & Retrieval, Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [llama-hub](/tools/run-llama-llama-hub.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 0d | 861d | | Archived on GitHub | No | Yes | | Open issues (now) | 144 | 96 | | Owner type | User | Organization | | Security scan | No MCP manifest | 121 low (121 low) | | Full report | [trust report](/tools/panniantong-agent-reach/trust.md) | [trust report](/tools/run-llama-llama-hub/trust.md) | ## Choose when ### Choose Agent-Reach if… - Agent-Reach is primarily Python; llama-hub is Jupyter Notebook. - Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation. - Also covers AI Agents, Developer Tools. ### Choose llama-hub if… - llama-hub is primarily Jupyter Notebook; Agent-Reach is Python. - Tags unique to llama-hub: jupyter notebook. - Also covers Data & Retrieval, Evaluation & Observability. ## 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. ## When NOT to use llama-hub - llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - 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. ## Common questions ### What is the difference between Agent-Reach and llama-hub? 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.. llama-hub: A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain. See the comparison table for live GitHub stats and shared categories. ### When should I choose Agent-Reach over llama-hub? Choose Agent-Reach over llama-hub when Agent-Reach is primarily Python; llama-hub is Jupyter Notebook; Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation; Also covers AI Agents, Developer Tools. ### When should I choose llama-hub over Agent-Reach? Choose llama-hub over Agent-Reach when llama-hub is primarily Jupyter Notebook; Agent-Reach is Python; Tags unique to llama-hub: jupyter notebook; Also covers Data & Retrieval, Evaluation & Observability. ### 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. ### When should I avoid llama-hub? llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. 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. ### Is Agent-Reach or llama-hub more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 3,473). Stars measure visibility, not whether either tool fits your constraints. ### Are Agent-Reach and llama-hub open source? Yes - both are open-source projects on GitHub (Agent-Reach: MIT, llama-hub: MIT). ### Where can I find alternatives to Agent-Reach or llama-hub? GraphCanon lists graph-backed alternatives at [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) and [llama-hub alternatives](/tools/run-llama-llama-hub/alternatives) ([Agent-Reach markdown twin](/tools/panniantong-agent-reach/alternatives.md), [llama-hub markdown twin](/tools/run-llama-llama-hub/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-run-llama-llama-hub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Agent-Reach or llama-hub? Agent-Reach: Very active. llama-hub: Archived. 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 llama-hub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Agent-Reach trust report](/tools/panniantong-agent-reach/trust); [llama-hub trust report](/tools/run-llama-llama-hub/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/_