Comparison
deep-research vs Agent-Reach
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.
Markdown twin · deep-research alternatives · Agent-Reach alternatives
GraphCanon updated 5d
vs
Trust & integrity
| Signal | deep-research | Agent-Reach |
|---|---|---|
| Maintenance | Slowing (129d since push) As of 5d · github_public_v1 | Very active (0d since push) As of 1mo · github_public_v1 |
| Provenance | Not a fork · Personal account As of 5d · github_public_v1 | Not a fork · Personal account As of 1mo · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- deep-research
- 20k
- Agent-Reach
- 61k
Forks
- deep-research
- 2.0k
- Agent-Reach
- 4.9k
Open issues
- deep-research
- 93
- Agent-Reach
- 168
Language
- deep-research
- TypeScript
- Agent-Reach
- Python
Adopt for
- deep-research
- 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
- Agent-Reach facilitates hands-off web and social media scraping via command line with no API costs for retrieving varied internet content.
Persona
- deep-research
- -
- Agent-Reach
- -
Runtime
- deep-research
- -
- Agent-Reach
- -
License
- deep-research
- MIT
- Agent-Reach
- MIT
Last pushed
- deep-research
- Apr 11, 2026
- Agent-Reach
- Jul 25, 2026
Categories
- deep-research
- AI Agents, Data & Retrieval
- Agent-Reach
- AI Agents, Data & Retrieval
Trust and health
Maintenance
- deep-research
- Slowing (36%)
- Agent-Reach
- Very active (96%)
Days since push
- deep-research
- 129d
- Agent-Reach
- 0d
Open issues (now)
- deep-research
- 93
- Agent-Reach
- 168
Stars delta
- deep-research
- +195 (30d)
- Agent-Reach
- Unknown
Open issues delta
- deep-research
- +3 (30d)
- Agent-Reach
- Unknown
OSV dependency advisories
- deep-research
- Published findings
- Agent-Reach
- No lockfile (source not queried)
Full report
- deep-research
- Trust report
- Agent-Reach
- Trust report
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.
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (dzhng/deep-research) · observed Aug 19, 2026
- GitHub forks (dzhng/deep-research) · observed Aug 19, 2026
- Last push (dzhng/deep-research) · observed Apr 11, 2026
- License file (MIT) · observed Aug 19, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Panniantong/Agent-Reach) · observed Jul 26, 2026
- GitHub forks (Panniantong/Agent-Reach) · observed Jul 26, 2026
- Last push (Panniantong/Agent-Reach) · observed Jul 25, 2026
- License file (MIT) · observed Jul 26, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: deep-research 20k · Agent-Reach 61k (synced Aug 19, 2026).
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 (60,828 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 and Agent-Reach alternatives (deep-research markdown twin, Agent-Reach markdown twin), 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 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; Agent-Reach trust report.