--- title: "lagent vs LLMStack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lagent-vs-trypromptly-llmstack" tools: ["internlm-lagent", "trypromptly-llmstack"] --- # lagent vs LLMStack *GraphCanon updated Aug 16, 2026* ## 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. [lagent](https://github.com/InternLM/lagent) reports 2.3k GitHub stars, 238 forks, and 24 open issues, last pushed Aug 3, 2026. [LLMStack](https://llmstack.trypromptly.com) has 2.3k stars, 347 forks, and 23 open issues, last pushed Dec 11, 2024. Figures are from public GitHub metadata via [lagent's repository](https://github.com/InternLM/lagent) and [LLMStack's repository](https://github.com/trypromptly/LLMStack). | | [lagent](/tools/internlm-lagent.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | Tagline | A lightweight framework for building LLM-based agents | No-code multi-agent framework to build LLM Agents, workflows and applications with your data | | Stars | 2,276 | 2,309 | | Forks | 238 | 347 | | Open issues | 24 | 23 | | Language | Python | Python | | Adopt for | lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents. | 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 | - | - | | Runtime | - | - | | License | lagent is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution. | Other | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lagent](/tools/internlm-lagent.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 12d | 612d | | Open issues (now) | 24 | 23 | | Stars delta | +8 (30d) | +2 (30d) | | Open issues delta | +1 (30d) | -1 (30d) | | Full report | [trust report](/tools/internlm-lagent/trust.md) | [trust report](/tools/trypromptly-llmstack/trust.md) | ## Shared compatibility - **Python**: [lagent](/tools/internlm-lagent.md) - Python runtime; [LLMStack](/tools/trypromptly-llmstack.md) - Python runtime ## Decision facts: lagent - **Pricing:** freemium - Available freely due to its open-source nature, but customization or enterprise support might involve additional costs. - **Adopt for:** lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents. - **License detail:** lagent is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution. ## Decision facts: LLMStack - **Adopt for:** 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. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/internlm-lagent/alternatives) and [LLMStack alternatives](/tools/trypromptly-llmstack/alternatives) ([lagent markdown twin](/tools/internlm-lagent/alternatives.md), [LLMStack markdown twin](/tools/trypromptly-llmstack/alternatives.md)), 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](/compare/internlm-lagent-vs-trypromptly-llmstack.md) 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](/tools/internlm-lagent/trust); [LLMStack trust report](/tools/trypromptly-llmstack/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=internlm-lagent`](/api/graphcanon/graph?tool=internlm-lagent) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_