Comparison
langroid vs awesome
Verdict
Pick langroid when license: langroid is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, langroid is MIT.
Markdown twin · langroid alternatives · awesome alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | langroid | awesome |
|---|---|---|
| Maintenance | Very active (3d since push) As of 1d · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | 2 low (2 low) As of 1d · mcp_manifest@v1 | No lockfile As of today · none |
Tagline
- langroid
- Harness LLMs with Multi-Agent Programming
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- langroid
- 4.1k
- awesome
- 484k
Forks
- langroid
- 381
- awesome
- 36k
Open issues
- langroid
- 74
- awesome
- 92
Language
- langroid
- Python
- awesome
- -
Adopt for
- langroid
- -
- awesome
- -
Persona
- langroid
- -
- awesome
- -
Runtime
- langroid
- -
- awesome
- -
License
- langroid
- MIT
- awesome
- CC0-1.0
Last pushed
- langroid
- Jul 7, 2026
- awesome
- Jun 30, 2026
Categories
- langroid
- AI Agents, LLM Frameworks, Vector Databases
- awesome
- LLM Frameworks
Trust and health
Maintenance
- langroid
- Very active (96%)
- awesome
- Active (82%)
Days since push
- langroid
- 3d
- awesome
- 11d
Open issues (now)
- langroid
- 74
- awesome
- 92
Owner type
- langroid
- Organization
- awesome
- User
Security scan
- langroid
- 2 low (2 low)
- awesome
- No lockfile
Full report
- langroid
- Trust report
- awesome
- Trust report
Choose langroid if…
- License: langroid is MIT, awesome is CC0-1.0.
- Tags unique to langroid: agents, ai, chatgpt, function-calling.
- Also covers AI Agents, Vector Databases.
- langroid ships Docker support for self-hosted deployment.
When NOT to use langroid
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Choose awesome if…
- License: awesome is CC0-1.0, langroid is MIT.
- Tags unique to awesome: awesome-list, resources.
- More GitHub stars (484k vs 4.1k) - 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 (langroid/langroid) · observed Jul 11, 2026
- GitHub forks (langroid/langroid) · observed Jul 11, 2026
- Last push (langroid/langroid) · observed Jul 7, 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: langroid 4.1k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between langroid and awesome?
- langroid: Harness LLMs with Multi-Agent Programming. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose langroid over awesome?
- Choose langroid over awesome when License: langroid is MIT, awesome is CC0-1.0; Tags unique to langroid: agents, ai, chatgpt, function-calling; Also covers AI Agents, Vector Databases; langroid ships Docker support for self-hosted deployment.
- When should I choose awesome over langroid?
- Choose awesome over langroid when License: awesome is CC0-1.0, langroid is MIT; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 4.1k) - visibility, not fit.
- When should I avoid langroid?
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- 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 langroid or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 4,056). Stars measure visibility, not whether either tool fits your constraints.
- Are langroid and awesome open source?
- Yes - both are open-source projects on GitHub (langroid: MIT, awesome: CC0-1.0).
- Where can I find alternatives to langroid or awesome?
- GraphCanon lists graph-backed alternatives at langroid alternatives and awesome alternatives (langroid 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, langroid or awesome?
- langroid: 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 langroid and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: langroid trust report; awesome trust report.