Comparison
llm_agents vs awesome
Verdict
Pick llm_agents when license: llm_agents is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, llm_agents is MIT.
Markdown twin · llm_agents alternatives · awesome alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | llm_agents | awesome |
|---|---|---|
| Maintenance | Dormant (382d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | 32 low (32 low) As of today · osv@v1 | No lockfile As of today · none |
Tagline
- llm_agents
- Build agents which are controlled by LLMs
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- llm_agents
- 1.1k
- awesome
- 484k
Forks
- llm_agents
- 85
- awesome
- 36k
Open issues
- llm_agents
- 3
- awesome
- 92
Language
- llm_agents
- Python
- awesome
- -
Adopt for
- llm_agents
- -
- awesome
- -
Persona
- llm_agents
- -
- awesome
- -
Runtime
- llm_agents
- -
- awesome
- -
License
- llm_agents
- MIT
- awesome
- CC0-1.0
Last pushed
- llm_agents
- Jun 23, 2025
- awesome
- Jun 30, 2026
Categories
- llm_agents
- LLM Frameworks, AI Agents
- awesome
- LLM Frameworks
Trust and health
Maintenance
- llm_agents
- Dormant (18%)
- awesome
- Active (82%)
Days since push
- llm_agents
- 382d
- awesome
- 11d
Open issues (now)
- llm_agents
- 3
- awesome
- 92
Security scan
- llm_agents
- 32 low (32 low)
- awesome
- No lockfile
Full report
- llm_agents
- Trust report
- awesome
- Trust report
Choose llm_agents if…
- License: llm_agents is MIT, awesome is CC0-1.0.
- Tags unique to llm_agents: llms, deep-learning, machine-learning, python.
- Also covers AI Agents.
When NOT to use llm_agents
- Last GitHub push was 383 days ago (dormant maintenance, Jun 23, 2025). Validate activity before betting a new project on llm_agents.
- 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, llm_agents is MIT.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 1.1k) - 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 (mpaepper/llm_agents) · observed Jul 11, 2026
- GitHub forks (mpaepper/llm_agents) · observed Jul 11, 2026
- Last push (mpaepper/llm_agents) · observed Jun 23, 2025
- 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: llm_agents 1.1k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between llm_agents and awesome?
- llm_agents: Build agents which are controlled by LLMs. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose llm_agents over awesome?
- Choose llm_agents over awesome when License: llm_agents is MIT, awesome is CC0-1.0; Tags unique to llm_agents: llms, deep-learning, machine-learning, python; Also covers AI Agents.
- When should I choose awesome over llm_agents?
- Choose awesome over llm_agents when License: awesome is CC0-1.0, llm_agents is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 1.1k) - visibility, not fit.
- When should I avoid llm_agents?
- Last GitHub push was 383 days ago (dormant maintenance, Jun 23, 2025). Validate activity before betting a new project on llm_agents. 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 llm_agents or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 1,050). Stars measure visibility, not whether either tool fits your constraints.
- Are llm_agents and awesome open source?
- Yes - both are open-source projects on GitHub (llm_agents: MIT, awesome: CC0-1.0).
- Where can I find alternatives to llm_agents or awesome?
- GraphCanon lists graph-backed alternatives at llm_agents alternatives and awesome alternatives (llm_agents 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, llm_agents or awesome?
- llm_agents: Dormant. 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 llm_agents and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm_agents trust report; awesome trust report.