Comparison
lagent vs LLMStack
Verdict
Pick lagent if lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents; pick LLMStack if lLMStack is a no-code framework designed for building LLM Agents and complex workflows with data integration, suitable for teams that want to leverage AI without deep coding expertise.
Markdown twin · lagent alternatives · LLMStack alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | lagent | LLMStack |
|---|---|---|
| Maintenance | Active (12d since push) As of 4d · github_public_v1 | Dormant (612d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- lagent
- A lightweight framework for building LLM-based agents
- LLMStack
- No-code multi-agent framework to build LLM Agents, workflows and applications with your data
Stars
- lagent
- 2.3k
- LLMStack
- 2.3k
Forks
- lagent
- 238
- LLMStack
- 347
Open issues
- lagent
- 24
- LLMStack
- 23
Language
- lagent
- Python
- LLMStack
- Python
Adopt for
- lagent
- lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents.
- LLMStack
- LLMStack is a no-code framework designed for building LLM Agents and complex workflows with data integration, suitable for teams that want to leverage AI without deep coding expertise.
Persona
- lagent
- -
- LLMStack
- -
Runtime
- lagent
- -
- LLMStack
- -
License
- lagent
- lagent is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution.
- LLMStack
- Other
Last pushed
- lagent
- Aug 3, 2026
- LLMStack
- Dec 11, 2024
Categories
- lagent
- AI Agents, LLM Frameworks
- LLMStack
- AI Agents, LLM Frameworks
Trust and health
Maintenance
- lagent
- Active (82%)
- LLMStack
- Dormant (18%)
Days since push
- lagent
- 12d
- LLMStack
- 612d
Open issues (now)
- lagent
- 24
- LLMStack
- 23
Stars delta
- lagent
- +8 (30d)
- LLMStack
- +2 (30d)
Open issues delta
- lagent
- +1 (30d)
- LLMStack
- -1 (30d)
Full report
- lagent
- Trust report
- LLMStack
- Trust report
Shared compatibility
- Python · lagent: Python runtime · LLMStack: Python runtime
Choose lagent if…
- License: lagent is Apache-2.0, LLMStack is Other.
- Pricing: Available freely due to its open-source nature, but customization or enterprise support might involve additional costs..
- Tags unique to lagent: agent, gpt, llm, transformers.
- When you need a streamlined approach to develop LLM-based agents with minimal overhead, lagent can be particularly advantageous due to its lightweight design.
When NOT to use lagent
- Avoid using lagent if your project necessitates integration with a broader set of tools that are not natively supported by this framework, as it offers limited out-of-the-box extensibility.
- Steer clear if you need robust scalability features right from the start. While lightweight, lagent may require additional custom work to handle more demanding scaling requirements.
Choose LLMStack if…
- License: LLMStack is Other, lagent is Apache-2.0.
- Tags unique to LLMStack: agents, ai-agents-framework, generative-ai, llm-agents.
- Use LLMStack when you need a no-code solution to develop multi-agent systems based on large language models.
When NOT to use LLMStack
- Avoid using LLMStack if your project necessitates heavy customization or fine-tuning at the coding level, as it may limit flexibility compared to code-based alternatives.
- Not recommended for teams with robust software engineering capabilities and a need for detailed control over underlying AI model architectures.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (InternLM/lagent) · observed Aug 16, 2026
- GitHub forks (InternLM/lagent) · observed Aug 16, 2026
- Last push (InternLM/lagent) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (trypromptly/LLMStack) · observed Aug 16, 2026
- GitHub forks (trypromptly/LLMStack) · observed Aug 16, 2026
- Last push (trypromptly/LLMStack) · observed Dec 11, 2024
- License file (Other) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: lagent 2.3k · LLMStack 2.3k (synced Aug 16, 2026).
Common questions
- What is the difference between lagent and LLMStack?
- lagent: A lightweight framework for building LLM-based agents. LLMStack: No-code multi-agent framework to build LLM Agents, workflows and applications with your data. See the comparison table for live GitHub stats and shared categories.
- When should I choose lagent over LLMStack?
- Choose lagent over LLMStack when License: lagent is Apache-2.0, LLMStack is Other; Pricing: Available freely due to its open-source nature, but customization or enterprise support might involve additional costs.; Tags unique to lagent: agent, gpt, llm, transformers; When you need a streamlined approach to develop LLM-based agents with minimal overhead, lagent can be particularly advantageous due to its lightweight design.
- When should I choose LLMStack over lagent?
- Choose LLMStack over lagent when License: LLMStack is Other, lagent is Apache-2.0; Tags unique to LLMStack: agents, ai-agents-framework, generative-ai, llm-agents; Use LLMStack when you need a no-code solution to develop multi-agent systems based on large language models.
- When should I avoid lagent?
- Avoid using lagent if your project necessitates integration with a broader set of tools that are not natively supported by this framework, as it offers limited out-of-the-box extensibility. Steer clear if you need robust scalability features right from the start. While lightweight, lagent may require additional custom work to handle more demanding scaling requirements.
- When should I avoid LLMStack?
- Avoid using LLMStack if your project necessitates heavy customization or fine-tuning at the coding level, as it may limit flexibility compared to code-based alternatives. Not recommended for teams with robust software engineering capabilities and a need for detailed control over underlying AI model architectures.
- Is lagent or LLMStack more popular on GitHub?
- LLMStack has more GitHub stars (2,309 vs 2,276). Stars measure visibility, not whether either tool fits your constraints.
- Are lagent and LLMStack open source?
- Yes - both are open-source projects on GitHub (lagent: Apache-2.0, LLMStack: Other).
- Where can I find alternatives to lagent or LLMStack?
- GraphCanon lists graph-backed alternatives at lagent alternatives and LLMStack alternatives (lagent markdown twin, LLMStack 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, lagent or LLMStack?
- lagent: Active. LLMStack: 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 lagent and LLMStack?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lagent trust report; LLMStack trust report.