--- title: "LaVague vs Agent-Reach" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lavague-ai-lavague-vs-panniantong-agent-reach" tools: ["lavague-ai-lavague", "panniantong-agent-reach"] --- # LaVague vs Agent-Reach *GraphCanon updated Aug 18, 2026* ## Verdict Pick LaVague if large Action Model (LaVague) framework supports developing AI web agents using customizable Large Language Models (LLMs), offering tracking of token usage for cost estimation purposes; pick Agent-Reach if agent-Reach facilitates hands-off web and social media scraping via command line with no API costs for retrieving varied internet content. [LaVague](https://docs.lavague.ai/en/latest/) reports 6.4k GitHub stars, 573 forks, and 104 open issues, last pushed Jan 21, 2025. [Agent-Reach](https://github.com/Panniantong/Agent-Reach) has 61k stars, 4.9k forks, and 168 open issues, last pushed Jul 25, 2026. Figures are from public GitHub metadata via [LaVague's repository](https://github.com/lavague-ai/LaVague) and [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach). | | [LaVague](/tools/lavague-ai-lavague.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Tagline | Large Action Model framework to develop AI Web Agents | AI Agent for Automated Web and Social Media Data Extraction | | Stars | 6,386 | 60,828 | | Forks | 573 | 4,921 | | Open issues | 104 | 168 | | Language | Python | Python | | Adopt for | Large Action Model (LaVague) framework supports developing AI web agents using customizable Large Language Models (LLMs), offering tracking of token usage for cost estimation purposes. | Agent-Reach facilitates hands-off web and social media scraping via command line with no API costs for retrieving varied internet content. | | Persona | - | - | | Runtime | - | - | | License | LaVague's license (Apache-2.0) allows free use in both open and closed-source applications, provided that copyright notices are preserved. | MIT | | Categories | AI Agents | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LaVague](/tools/lavague-ai-lavague.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 574d | 0d | | Open issues (now) | 104 | 168 | | Stars delta | +6 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/lavague-ai-lavague/trust.md) | [trust report](/tools/panniantong-agent-reach/trust.md) | ## Decision facts: LaVague - **Pricing:** freemium - Free to use with customizable LLMs; actual costs depend on the specific LLM used and operational complexity. - **Requirements:** Min 2 GB RAM; Requires Python for development. - **Adopt for:** Large Action Model (LaVague) framework supports developing AI web agents using customizable Large Language Models (LLMs), offering tracking of token usage for cost estimation purposes. - **License detail:** LaVague's license (Apache-2.0) allows free use in both open and closed-source applications, provided that copyright notices are preserved. ## Decision facts: Agent-Reach - **Adopt for:** Agent-Reach facilitates hands-off web and social media scraping via command line with no API costs for retrieving varied internet content. ## Choose when ### Choose LaVague if… - License: LaVague is Apache-2.0, Agent-Reach is MIT. - Pricing: Free to use with customizable LLMs; actual costs depend on the specific LLM used and operational complexity.. - Requirements: Min 2 GB RAM; Requires Python for development.. - Tags unique to LaVague: ai, browser, large-action-model, llm. - When you need a flexible framework that allows customization of LLMs, particularly if your primary model is OpenAI's `gpt4-o` or similar models. ### Choose Agent-Reach if… - License: Agent-Reach is MIT, LaVague is Apache-2.0. - Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation. - Also covers Data & Retrieval. - When needing to bypass costly API fees for extensive social media platform data extraction ## When NOT to use LaVague - Avoid using LaVague if you are working on projects that do not involve AI web agent development, as the framework is specifically designed around this use-case. - Do not opt for LaVague if you require a non-Python environment for your development since the framework is Python-based. ## When NOT to use Agent-Reach - If strict compliance with website scraping policies is critical due to its use of scraping techniques - When direct interaction through APIs for precision and reliability is preferred over scraping ## Common questions ### What is the difference between LaVague and Agent-Reach? LaVague: Large Action Model framework to develop AI Web Agents. Agent-Reach: AI Agent for Automated Web and Social Media Data Extraction. See the comparison table for live GitHub stats and shared categories. ### When should I choose LaVague over Agent-Reach? Choose LaVague over Agent-Reach when License: LaVague is Apache-2.0, Agent-Reach is MIT; Pricing: Free to use with customizable LLMs; actual costs depend on the specific LLM used and operational complexity.; Requirements: Min 2 GB RAM; Requires Python for development.; Tags unique to LaVague: ai, browser, large-action-model, llm; When you need a flexible framework that allows customization of LLMs, particularly if your primary model is OpenAI's `gpt4-o` or similar models. ### When should I choose Agent-Reach over LaVague? Choose Agent-Reach over LaVague when License: Agent-Reach is MIT, LaVague is Apache-2.0; Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation; Also covers Data & Retrieval; When needing to bypass costly API fees for extensive social media platform data extraction. ### When should I avoid LaVague? Avoid using LaVague if you are working on projects that do not involve AI web agent development, as the framework is specifically designed around this use-case. Do not opt for LaVague if you require a non-Python environment for your development since the framework is Python-based. ### When should I avoid Agent-Reach? If strict compliance with website scraping policies is critical due to its use of scraping techniques When direct interaction through APIs for precision and reliability is preferred over scraping ### Is LaVague or Agent-Reach more popular on GitHub? Agent-Reach has more GitHub stars (60,828 vs 6,386). Stars measure visibility, not whether either tool fits your constraints. ### Are LaVague and Agent-Reach open source? Yes - both are open-source projects on GitHub (LaVague: Apache-2.0, Agent-Reach: MIT). ### Where can I find alternatives to LaVague or Agent-Reach? GraphCanon lists graph-backed alternatives at [LaVague alternatives](/tools/lavague-ai-lavague/alternatives) and [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) ([LaVague markdown twin](/tools/lavague-ai-lavague/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/lavague-ai-lavague-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, LaVague or Agent-Reach? LaVague: 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 LaVague and Agent-Reach? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LaVague trust report](/tools/lavague-ai-lavague/trust); [Agent-Reach trust report](/tools/panniantong-agent-reach/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lavague-ai-lavague`](/api/graphcanon/graph?tool=lavague-ai-lavague) - 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/_