--- title: "deep-research vs Agent-Reach" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dzhng-deep-research-vs-panniantong-agent-reach" tools: ["dzhng-deep-research", "panniantong-agent-reach"] --- # deep-research vs Agent-Reach *GraphCanon updated Aug 25, 2026* ## Verdict Pick deep-research if deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics; 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. [deep-research](https://github.com/dzhng/deep-research) reports 20k GitHub stars, 2.0k forks, and 93 open issues, last pushed Apr 11, 2026. [Agent-Reach](https://github.com/Panniantong/Agent-Reach) has 75k stars, 6.4k forks, and 100 open issues, last pushed Aug 25, 2026. Figures are from public GitHub metadata via [deep-research's repository](https://github.com/dzhng/deep-research) and [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach). | | [deep-research](/tools/dzhng-deep-research.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Tagline | An AI-powered research assistant that refines its topic focus over time using search engines, web scraping, and large language models. | AI Agent for Automated Web and Social Media Data Extraction | | Stars | 19,571 | 75,209 | | Forks | 1,993 | 6,415 | | Open issues | 93 | 100 | | Language | TypeScript | Python | | Adopt for | Deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics. | 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 | MIT | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deep-research](/tools/dzhng-deep-research.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 129d | 0d | | Open issues (now) | 93 | 100 | | Stars delta | +195 (30d) | +14k (30d) | | Open issues delta | +3 (30d) | -68 (30d) | | Full report | [trust report](/tools/dzhng-deep-research/trust.md) | [trust report](/tools/panniantong-agent-reach/trust.md) | ## Decision facts: deep-research - **Requirements:** Requires Docker - **Adopt for:** Deep-research is an AI-powered research assistant that leverages search engines, web scraping, and large language models to conduct iterative and in-depth exploration of topics. ## 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 deep-research if… - deep-research is primarily TypeScript; Agent-Reach is Python. - Requirements: Requires Docker. - Tags unique to deep-research: agent, ai, gpt, o3-mini. - deep-research ships Docker support for self-hosted deployment. - When you need a tool that can refine its topic focus over time through repeated iterations. ### Choose Agent-Reach if… - Agent-Reach is primarily Python; deep-research is TypeScript. - Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation. - When needing to bypass costly API fees for extensive social media platform data extraction ## When NOT to use deep-research - When you prefer a language other than TypeScript, as deep-research specifically requires a Node.js environment. - If your use case does not necessitate the use of both Firecrawl and OpenAI APIs, preferring instead solutions with more API flexibility or that do not require API keys. ## 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 deep-research and Agent-Reach? deep-research: An AI-powered research assistant that refines its topic focus over time using search engines, web scraping, and large language models.. 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 deep-research over Agent-Reach? Choose deep-research over Agent-Reach when deep-research is primarily TypeScript; Agent-Reach is Python; Requirements: Requires Docker; Tags unique to deep-research: agent, ai, gpt, o3-mini; deep-research ships Docker support for self-hosted deployment; When you need a tool that can refine its topic focus over time through repeated iterations. ### When should I choose Agent-Reach over deep-research? Choose Agent-Reach over deep-research when Agent-Reach is primarily Python; deep-research is TypeScript; Tags unique to Agent-Reach: agent-infrastructure, ai-agent, ai-search, automation; When needing to bypass costly API fees for extensive social media platform data extraction. ### When should I avoid deep-research? When you prefer a language other than TypeScript, as deep-research specifically requires a Node.js environment. If your use case does not necessitate the use of both Firecrawl and OpenAI APIs, preferring instead solutions with more API flexibility or that do not require API keys. ### 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 deep-research or Agent-Reach more popular on GitHub? Agent-Reach has more GitHub stars (75,209 vs 19,571). Stars measure visibility, not whether either tool fits your constraints. ### Are deep-research and Agent-Reach open source? Yes - both are open-source projects on GitHub (deep-research: MIT, Agent-Reach: MIT). ### Where can I find alternatives to deep-research or Agent-Reach? GraphCanon lists graph-backed alternatives at [deep-research alternatives](/tools/dzhng-deep-research/alternatives) and [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) ([deep-research markdown twin](/tools/dzhng-deep-research/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/dzhng-deep-research-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, deep-research or Agent-Reach? deep-research: Slowing. 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 deep-research and Agent-Reach? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deep-research trust report](/tools/dzhng-deep-research/trust); [Agent-Reach trust report](/tools/panniantong-agent-reach/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dzhng-deep-research`](/api/graphcanon/graph?tool=dzhng-deep-research) - 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/_