--- title: "Agent-Reach vs SAM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/panniantong-agent-reach-vs-syntheticautonomicmind-sam" tools: ["panniantong-agent-reach", "syntheticautonomicmind-sam"] --- # Agent-Reach vs SAM *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick SAM if sAM is an AI assistant for macOS systems with Apple Silicon built in Swift and supports local LLM models, offering installation via Homebrew. [Agent-Reach](https://github.com/Panniantong/Agent-Reach) reports 84k GitHub stars, 7.3k forks, and 153 open issues, last pushed Sep 15, 2026. [SAM](https://github.com/SyntheticAutonomicMind/SAM) has 130 stars, 6 forks, and 0 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach) and [SAM's repository](https://github.com/SyntheticAutonomicMind/SAM). | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [SAM](/tools/syntheticautonomicmind-sam.md) | | --- | --- | --- | | Tagline | AI Agent for Automated Web and Social Media Data Extraction | An AI assistant for everyone | | Stars | 83,516 | 130 | | Forks | 7,315 | 6 | | Open issues | 153 | 0 | | Language | Python | Swift | | Adopt for | Agent-Reach facilitates hands-off web and social media scraping via command line with no API costs for retrieving varied internet content. | SAM is an AI assistant for macOS systems with Apple Silicon built in Swift and supports local LLM models, offering installation via Homebrew. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-3.0 | | Categories | AI Agents, Data & Retrieval | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Agent-Reach](/tools/panniantong-agent-reach.md) | [SAM](/tools/syntheticautonomicmind-sam.md) | | --- | --- | --- | | Days since push | 4d | 1d | | Open issues (now) | 153 | 0 | | Stars delta | +23k (30d) | 0 (30d) | | Open issues delta | -15 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/panniantong-agent-reach/trust.md) | [trust report](/tools/syntheticautonomicmind-sam/trust.md) | ## 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. ## Decision facts: SAM - **Requirements:** Min 4 GB RAM; For optimal use with local AI models, a system with 16GB+ RAM is recommended.; The system should be equipped with macOS 14.0 (Sonoma) or later. - **Adopt for:** SAM is an AI assistant for macOS systems with Apple Silicon built in Swift and supports local LLM models, offering installation via Homebrew. ## Choose when ### Choose Agent-Reach if… - Agent-Reach is primarily Python; SAM is Swift. - License: Agent-Reach is MIT, SAM is GPL-3.0. - 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 ### Choose SAM if… - SAM is primarily Swift; Agent-Reach is Python. - License: SAM is GPL-3.0, Agent-Reach is MIT. - Requirements: Min 4 GB RAM; For optimal use with local AI models, a system with 16GB+ RAM is recommended.; The system should be equipped with macOS 14.0 (Sonoma) or later.. - Tags unique to SAM: ai-assistants, apple-silicon, open-source. - Also covers LLM Frameworks. - You should use SAM when you are working on a macOS system equipped with Apple Silicon as it is optimized for such hardware. ## 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 ## When NOT to use SAM - Avoid using SAM if you are developing on an Intel-based Mac or any system running a different operating system as it is specifically designed for macOS and Apple Silicon. - Do not opt for SAM if your project requires low memory consumption, because supporting local AI models typically demands high RAM (16GB+) and significant disk space. ## Common questions ### What is the difference between Agent-Reach and SAM? Agent-Reach: AI Agent for Automated Web and Social Media Data Extraction. SAM: An AI assistant for everyone. See the comparison table for live GitHub stats and shared categories. ### When should I choose Agent-Reach over SAM? Choose Agent-Reach over SAM when Agent-Reach is primarily Python; SAM is Swift; License: Agent-Reach is MIT, SAM is GPL-3.0; 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 choose SAM over Agent-Reach? Choose SAM over Agent-Reach when SAM is primarily Swift; Agent-Reach is Python; License: SAM is GPL-3.0, Agent-Reach is MIT; Requirements: Min 4 GB RAM; For optimal use with local AI models, a system with 16GB+ RAM is recommended.; The system should be equipped with macOS 14.0 (Sonoma) or later.; Tags unique to SAM: ai-assistants, apple-silicon, open-source; Also covers LLM Frameworks; You should use SAM when you are working on a macOS system equipped with Apple Silicon as it is optimized for such hardware. ### 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 ### When should I avoid SAM? Avoid using SAM if you are developing on an Intel-based Mac or any system running a different operating system as it is specifically designed for macOS and Apple Silicon. Do not opt for SAM if your project requires low memory consumption, because supporting local AI models typically demands high RAM (16GB+) and significant disk space. ### Is Agent-Reach or SAM more popular on GitHub? Agent-Reach has more GitHub stars (83,516 vs 130). Stars measure visibility, not whether either tool fits your constraints. ### Are Agent-Reach and SAM open source? Yes - both are open-source projects on GitHub (Agent-Reach: MIT, SAM: GPL-3.0). ### Where can I find alternatives to Agent-Reach or SAM? GraphCanon lists graph-backed alternatives at [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) and [SAM alternatives](/tools/syntheticautonomicmind-sam/alternatives) ([Agent-Reach markdown twin](/tools/panniantong-agent-reach/alternatives.md), [SAM markdown twin](/tools/syntheticautonomicmind-sam/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-syntheticautonomicmind-sam.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Agent-Reach or SAM? Agent-Reach: Very active. SAM: 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 Agent-Reach and SAM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Agent-Reach trust report](/tools/panniantong-agent-reach/trust); [SAM trust report](/tools/syntheticautonomicmind-sam/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/_