Comparison
langroid vs LazyLLM
Verdict
Pick langroid if langroid specializes in multi-agent systems with large language models, providing a Python framework for function-calling and chat integrations; pick LazyLLM if critical facts for LazyLLM.
Markdown twin · langroid alternatives · LazyLLM alternatives
GraphCanon updated Sep 20, 2026
9views this month
Trust & integrity
| Signal | langroid | LazyLLM |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 7, 2026 · github_public_v1 | Very active (3d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 7, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- langroid
- Harness LLMs with Multi-Agent Programming
- LazyLLM
- Easiest and laziest way for building multi-agent LLMs applications.
Stars
- langroid
- 4.1k
- LazyLLM
- 3.9k
Forks
- langroid
- 399
- LazyLLM
- 411
Open issues
- langroid
- 53
- LazyLLM
- 47
Language
- langroid
- Python
- LazyLLM
- Python
Adopt for
- langroid
- Langroid specializes in multi-agent systems with large language models, providing a Python framework for function-calling and chat integrations.
- LazyLLM
- Critical facts for LazyLLM
Persona
- langroid
- -
- LazyLLM
- -
Runtime
- langroid
- -
- LazyLLM
- -
License
- langroid
- MIT
- LazyLLM
- Apache-2.0
Last pushed
- langroid
- Sep 5, 2026
- LazyLLM
- Sep 4, 2026
Categories
- langroid
- AI Agents, Data & Retrieval, Inference & Serving, Model Training
- LazyLLM
- AI Agents, Model Training
Trust and health
Days since push
- langroid
- 1d
- LazyLLM
- 3d
Open issues (now)
- langroid
- 53
- LazyLLM
- 47
Stars delta
- langroid
- +12 (30d)
- LazyLLM
- +14 (30d)
Open issues delta
- langroid
- -22 (30d)
- LazyLLM
- +6 (30d)
OSV dependency advisories
- langroid
- No lockfile (source not queried)
- LazyLLM
- Published findings
Full report
- langroid
- Trust report
- LazyLLM
- Trust report
Shared compatibility
- Python · langroid: Python runtime · LazyLLM: Python runtime
Choose langroid if…
- License: langroid is MIT, LazyLLM is Apache-2.0.
- Tags unique to langroid: ai, chatgpt, function-calling, gpt.
- Also covers Data & Retrieval, Inference & Serving.
- langroid ships Docker support for self-hosted deployment.
- You need to integrate multiple agents working with large language models.
When NOT to use langroid
- You require support for a broad range of programming languages beyond Python.
- The project scope does not include multi-agent interactions or function-calling capabilities.
Choose LazyLLM if…
- License: LazyLLM is Apache-2.0, langroid is MIT.
- Pricing: LazyLLM is open-source under the Apache-2.0 license, making it free to use for both personal and commercial projects..
- Requirements: Min 8 GB RAM; Installation can be done via pip or from source. No Docker required, but a Python environment is necessary..
- Tags unique to LazyLLM: ai-agent, deep-learning, framework, llm.
- - When you need a highly user-friendly framework specifically designed for building multi-agent LLM applications, emphasizing simplicity and streamlined installation.
When NOT to use LazyLLM
- - Avoid if you require extensive customization options or a more complex framework; LazyLLM's focus on being the 'laziest' way may mean it lacks advanced or specialized features found in other tools.
- - If you are working with non-Python environments, as LazyLLM is specifically language-oriented towards Python. Users needing cross-language support might not find LazyLLM suitable.
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 Sep 20, 2026
- GitHub forks (langroid/langroid) · observed Sep 20, 2026
- Last push (langroid/langroid) · observed Sep 5, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (LazyAGI/LazyLLM) · observed Sep 20, 2026
- GitHub forks (LazyAGI/LazyLLM) · observed Sep 20, 2026
- Last push (LazyAGI/LazyLLM) · observed Sep 4, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: langroid 4.1k · LazyLLM 3.9k (synced Sep 20, 2026).
Common questions
- What is the difference between langroid and LazyLLM?
- langroid: Harness LLMs with Multi-Agent Programming. LazyLLM: Easiest and laziest way for building multi-agent LLMs applications.. See the comparison table for live GitHub stats and shared categories.
- When should I choose langroid over LazyLLM?
- Choose langroid over LazyLLM when License: langroid is MIT, LazyLLM is Apache-2.0; Tags unique to langroid: ai, chatgpt, function-calling, gpt; Also covers Data & Retrieval, Inference & Serving; langroid ships Docker support for self-hosted deployment; You need to integrate multiple agents working with large language models.
- When should I choose LazyLLM over langroid?
- Choose LazyLLM over langroid when License: LazyLLM is Apache-2.0, langroid is MIT; Pricing: LazyLLM is open-source under the Apache-2.0 license, making it free to use for both personal and commercial projects.; Requirements: Min 8 GB RAM; Installation can be done via pip or from source. No Docker required, but a Python environment is necessary.; Tags unique to LazyLLM: ai-agent, deep-learning, framework, llm; - When you need a highly user-friendly framework specifically designed for building multi-agent LLM applications, emphasizing simplicity and streamlined installation.
- When should I avoid langroid?
- You require support for a broad range of programming languages beyond Python. The project scope does not include multi-agent interactions or function-calling capabilities.
- When should I avoid LazyLLM?
- - Avoid if you require extensive customization options or a more complex framework; LazyLLM's focus on being the 'laziest' way may mean it lacks advanced or specialized features found in other tools. - If you are working with non-Python environments, as LazyLLM is specifically language-oriented towards Python. Users needing cross-language support might not find LazyLLM suitable.
- Is langroid or LazyLLM more popular on GitHub?
- langroid has more GitHub stars (4,102 vs 3,880). Stars measure visibility, not whether either tool fits your constraints.
- Are langroid and LazyLLM open source?
- Yes - both are open-source projects on GitHub (langroid: MIT, LazyLLM: Apache-2.0).
- Where can I find alternatives to langroid or LazyLLM?
- GraphCanon lists graph-backed alternatives at langroid alternatives and LazyLLM alternatives (langroid markdown twin, LazyLLM 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 LazyLLM?
- langroid: Very active. LazyLLM: 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 langroid and LazyLLM?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: langroid trust report; LazyLLM trust report.