Comparison
LaVague vs Agent-Reach
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.
Markdown twin · LaVague alternatives · Agent-Reach alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | LaVague | Agent-Reach |
|---|---|---|
| Maintenance | Dormant (574d since push) As of today · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- LaVague
- Large Action Model framework to develop AI Web Agents
- Agent-Reach
- AI Agent for Automated Web and Social Media Data Extraction
Stars
- LaVague
- 6.4k
- Agent-Reach
- 61k
Forks
- LaVague
- 573
- Agent-Reach
- 4.9k
Open issues
- LaVague
- 104
- Agent-Reach
- 168
Language
- LaVague
- Python
- Agent-Reach
- Python
Adopt for
- LaVague
- 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
- Agent-Reach facilitates hands-off web and social media scraping via command line with no API costs for retrieving varied internet content.
Persona
- LaVague
- -
- Agent-Reach
- -
Runtime
- LaVague
- -
- Agent-Reach
- -
License
- LaVague
- LaVague's license (Apache-2.0) allows free use in both open and closed-source applications, provided that copyright notices are preserved.
- Agent-Reach
- MIT
Last pushed
- LaVague
- Jan 21, 2025
- Agent-Reach
- Jul 25, 2026
Categories
- LaVague
- AI Agents
- Agent-Reach
- AI Agents, Data & Retrieval
Trust and health
Maintenance
- LaVague
- Dormant (18%)
- Agent-Reach
- Very active (96%)
Days since push
- LaVague
- 574d
- Agent-Reach
- 0d
Open issues (now)
- LaVague
- 104
- Agent-Reach
- 168
Stars delta
- LaVague
- +6 (30d)
- Agent-Reach
- Unknown
Open issues delta
- LaVague
- 0 (30d)
- Agent-Reach
- Unknown
Owner type
- LaVague
- Organization
- Agent-Reach
- User
Full report
- LaVague
- Trust report
- Agent-Reach
- Trust report
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.
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.
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 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 (lavague-ai/LaVague) · observed Aug 18, 2026
- GitHub forks (lavague-ai/LaVague) · observed Aug 18, 2026
- Last push (lavague-ai/LaVague) · observed Jan 21, 2025
- License file (Apache-2.0) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 12, 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: LaVague 6.4k · Agent-Reach 61k (synced Aug 18, 2026).
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-oor 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 and Agent-Reach alternatives (LaVague 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, 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; Agent-Reach trust report.