Comparison
LazyLLM vs agentflow
Verdict
Pick LazyLLM if critical facts for LazyLLM; pick agentflow if agentflow simplifies the creation of complex workflows for large language models through simple JSON configurations.
Markdown twin · LazyLLM alternatives · agentflow alternatives
GraphCanon updated 6d
vs
Trust & integrity
| Signal | LazyLLM | agentflow |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Dormant (1100d since push) As of 6d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 6d · github_public_v1 |
| OSV dependency advisories | Published findings 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
- LazyLLM
- Easiest and laziest way for building multi-agent LLMs applications.
- agentflow
- Complex LLM Workflows from Simple JSON
Stars
- LazyLLM
- 3.9k
- agentflow
- 320
Forks
- LazyLLM
- 404
- agentflow
- 27
Open issues
- LazyLLM
- 41
- agentflow
- 13
Language
- LazyLLM
- Python
- agentflow
- Python
Adopt for
- LazyLLM
- Critical facts for LazyLLM
- agentflow
- Agentflow simplifies the creation of complex workflows for large language models through simple JSON configurations.
Persona
- LazyLLM
- -
- agentflow
- -
Runtime
- LazyLLM
- -
- agentflow
- -
License
- LazyLLM
- Apache-2.0
- agentflow
- MIT
Last pushed
- LazyLLM
- Aug 7, 2026
- agentflow
- Aug 11, 2023
Categories
- LazyLLM
- AI Agents, Model Training
- agentflow
- AI Agents, LLM Frameworks
Trust and health
Maintenance
- LazyLLM
- Very active (96%)
- agentflow
- Dormant (18%)
Days since push
- LazyLLM
- 0d
- agentflow
- 1100d
Open issues (now)
- LazyLLM
- 41
- agentflow
- 13
Stars delta
- LazyLLM
- Unknown
- agentflow
- -1 (30d)
Open issues delta
- LazyLLM
- Unknown
- agentflow
- 0 (30d)
Owner type
- LazyLLM
- Organization
- agentflow
- User
OSV dependency advisories
- LazyLLM
- Published findings
- agentflow
- No lockfile (source not queried)
Full report
- LazyLLM
- Trust report
- agentflow
- Trust report
Shared compatibility
- Python · LazyLLM: Python runtime · agentflow: Python runtime
Choose LazyLLM if…
- License: LazyLLM is Apache-2.0, agentflow 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: agents, ai-agent, deep-learning, framework.
- Also covers Model Training.
- - 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.
Choose agentflow if…
- License: agentflow is MIT, LazyLLM is Apache-2.0.
- Tags unique to agentflow: json, large language models, python, workflow-management.
- Also covers LLM Frameworks.
- When you need to rapidly prototype LLM workflows with minimal coding via JSON configs
When NOT to use agentflow
- Avoid if requiring advanced customization that goes beyond basic JSON configurations
- Not suitable for scenarios needing real-time dynamic changes in workflow setup during execution
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (LazyAGI/LazyLLM) · observed Aug 8, 2026
- GitHub forks (LazyAGI/LazyLLM) · observed Aug 8, 2026
- Last push (LazyAGI/LazyLLM) · observed Aug 7, 2026
- License file (Apache-2.0) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (simonmesmith/agentflow) · observed Aug 16, 2026
- GitHub forks (simonmesmith/agentflow) · observed Aug 16, 2026
- Last push (simonmesmith/agentflow) · observed Aug 11, 2023
- License file (MIT) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LazyLLM 3.9k · agentflow 320 (synced Aug 8, 2026).
Common questions
- What is the difference between LazyLLM and agentflow?
- LazyLLM: Easiest and laziest way for building multi-agent LLMs applications.. agentflow: Complex LLM Workflows from Simple JSON. See the comparison table for live GitHub stats and shared categories.
- When should I choose LazyLLM over agentflow?
- Choose LazyLLM over agentflow when License: LazyLLM is Apache-2.0, agentflow 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: agents, ai-agent, deep-learning, framework; Also covers Model Training; - When you need a highly user-friendly framework specifically designed for building multi-agent LLM applications, emphasizing simplicity and streamlined installation.
- When should I choose agentflow over LazyLLM?
- Choose agentflow over LazyLLM when License: agentflow is MIT, LazyLLM is Apache-2.0; Tags unique to agentflow: json, large language models, python, workflow-management; Also covers LLM Frameworks; When you need to rapidly prototype LLM workflows with minimal coding via JSON configs.
- 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.
- When should I avoid agentflow?
- Avoid if requiring advanced customization that goes beyond basic JSON configurations Not suitable for scenarios needing real-time dynamic changes in workflow setup during execution
- Is LazyLLM or agentflow more popular on GitHub?
- LazyLLM has more GitHub stars (3,866 vs 320). Stars measure visibility, not whether either tool fits your constraints.
- Are LazyLLM and agentflow open source?
- Yes - both are open-source projects on GitHub (LazyLLM: Apache-2.0, agentflow: MIT).
- Where can I find alternatives to LazyLLM or agentflow?
- GraphCanon lists graph-backed alternatives at LazyLLM alternatives and agentflow alternatives (LazyLLM markdown twin, agentflow 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, LazyLLM or agentflow?
- LazyLLM: Very active. agentflow: Dormant. 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 LazyLLM and agentflow?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LazyLLM trust report; agentflow trust report.