Comparison
agentset vs awesome
Verdict
Pick agentset when license: agentset is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, agentset is MIT.
Markdown twin · agentset alternatives · awesome alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | agentset | awesome |
|---|---|---|
| Maintenance | Very active (5d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- agentset
- The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more.
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- agentset
- 2.0k
- awesome
- 484k
Forks
- agentset
- 182
- awesome
- 36k
Open issues
- agentset
- 12
- awesome
- 92
Language
- agentset
- TypeScript
- awesome
- -
Adopt for
- agentset
- -
- awesome
- -
Persona
- agentset
- -
- awesome
- -
Runtime
- agentset
- -
- awesome
- -
License
- agentset
- MIT
- awesome
- CC0-1.0
Last pushed
- agentset
- Jul 6, 2026
- awesome
- Jun 30, 2026
Categories
- agentset
- Vector Databases, LLM Frameworks, AI Agents
- awesome
- LLM Frameworks
Trust and health
Maintenance
- agentset
- Very active (96%)
- awesome
- Active (82%)
Days since push
- agentset
- 5d
- awesome
- 11d
Open issues (now)
- agentset
- 12
- awesome
- 92
Owner type
- agentset
- Organization
- awesome
- User
Full report
- agentset
- Trust report
- awesome
- Trust report
Choose agentset if…
- License: agentset is MIT, awesome is CC0-1.0.
- Tags unique to agentset: llms, ai-sdk, embeddings, genai.
- Also covers Vector Databases, AI Agents.
When NOT to use agentset
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
Choose awesome if…
- License: awesome is CC0-1.0, agentset is MIT.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 2.0k) - visibility, not fit.
When NOT to use awesome
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (agentset-ai/agentset) · observed Jul 11, 2026
- GitHub forks (agentset-ai/agentset) · observed Jul 11, 2026
- Last push (agentset-ai/agentset) · observed Jul 6, 2026
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentset 2.0k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between agentset and awesome?
- agentset: The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentset over awesome?
- Choose agentset over awesome when License: agentset is MIT, awesome is CC0-1.0; Tags unique to agentset: llms, ai-sdk, embeddings, genai; Also covers Vector Databases, AI Agents.
- When should I choose awesome over agentset?
- Choose awesome over agentset when License: awesome is CC0-1.0, agentset is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 2.0k) - visibility, not fit.
- When should I avoid agentset?
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- When should I avoid awesome?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is agentset or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 2,027). Stars measure visibility, not whether either tool fits your constraints.
- Are agentset and awesome open source?
- Yes - both are open-source projects on GitHub (agentset: MIT, awesome: CC0-1.0).
- Where can I find alternatives to agentset or awesome?
- GraphCanon lists graph-backed alternatives at agentset alternatives and awesome alternatives (agentset markdown twin, awesome 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, agentset or awesome?
- agentset: Very active. awesome: 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 agentset and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentset trust report; awesome trust report.