--- title: "lagent vs OmAgent" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lagent-vs-om-ai-lab-omagent" tools: ["internlm-lagent", "om-ai-lab-omagent"] --- # lagent vs OmAgent *GraphCanon updated Sep 20, 2026* ## Verdict Pick lagent if lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents; pick OmAgent if omAgent is a Python library for developing multimodal language agents that supports GPT, Gemini, LLaMA, and LLAVA models. [lagent](https://github.com/InternLM/lagent) reports 2.3k GitHub stars, 238 forks, and 24 open issues, last pushed Aug 3, 2026. [OmAgent](https://om-agent.com) has 2.7k stars, 292 forks, and 21 open issues, last pushed Mar 19, 2025. Figures are from public GitHub metadata via [lagent's repository](https://github.com/InternLM/lagent) and [OmAgent's repository](https://github.com/om-ai-lab/OmAgent). | | [lagent](/tools/internlm-lagent.md) | [OmAgent](/tools/om-ai-lab-omagent.md) | | --- | --- | --- | | Tagline | A lightweight framework for building LLM-based agents | Build multimodal language agents for fast prototype and production | | Stars | 2,276 | 2,667 | | Forks | 238 | 292 | | Open issues | 24 | 21 | | Language | Python | Python | | Adopt for | lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents. | OmAgent is a Python library for developing multimodal language agents that supports GPT, Gemini, LLaMA, and LLAVA models. | | Persona | - | - | | Runtime | - | - | | License | lagent is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution. | Apache-2.0 | | 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) | [OmAgent](/tools/om-ai-lab-omagent.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 12d | 545d | | Open issues (now) | 24 | 21 | | Stars delta | +8 (30d) | +2 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/internlm-lagent/trust.md) | [trust report](/tools/om-ai-lab-omagent/trust.md) | ## Shared compatibility - **Python**: [lagent](/tools/internlm-lagent.md) - Python runtime; [OmAgent](/tools/om-ai-lab-omagent.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: OmAgent - **Requirements:** Requires Python >= 3.10 - **Adopt for:** OmAgent is a Python library for developing multimodal language agents that supports GPT, Gemini, LLaMA, and LLAVA models. ## Choose when ### Choose lagent if… - Pricing: Available freely due to its open-source nature, but customization or enterprise support might involve additional costs.. - Tags unique to lagent: 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 OmAgent if… - Requirements: Requires Python >= 3.10. - Tags unique to OmAgent: chatbot, gemini, llama, multimodal-agent. - Use OmAgent if you are prototyping scenarios involving smart hardware and multimodal workflows. ## 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 OmAgent - Avoid using OmAgent if your project does not require support for multimodal models or specific integrations with GPT, Gemini, LLaMA, and LLAVA. - If a lightweight solution is required and advanced multimodal features are unnecessary, another tool might be more suited. ## Common questions ### What is the difference between lagent and OmAgent? lagent: A lightweight framework for building LLM-based agents. OmAgent: Build multimodal language agents for fast prototype and production. See the comparison table for live GitHub stats and shared categories. ### When should I choose lagent over OmAgent? Choose lagent over OmAgent when Pricing: Available freely due to its open-source nature, but customization or enterprise support might involve additional costs.; Tags unique to lagent: 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 OmAgent over lagent? Choose OmAgent over lagent when Requirements: Requires Python >= 3.10; Tags unique to OmAgent: chatbot, gemini, llama, multimodal-agent; Use OmAgent if you are prototyping scenarios involving smart hardware and multimodal workflows. ### 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 OmAgent? Avoid using OmAgent if your project does not require support for multimodal models or specific integrations with GPT, Gemini, LLaMA, and LLAVA. If a lightweight solution is required and advanced multimodal features are unnecessary, another tool might be more suited. ### Is lagent or OmAgent more popular on GitHub? OmAgent has more GitHub stars (2,667 vs 2,276). Stars measure visibility, not whether either tool fits your constraints. ### Are lagent and OmAgent open source? Yes - both are open-source projects on GitHub (lagent: Apache-2.0, OmAgent: Apache-2.0). ### Where can I find alternatives to lagent or OmAgent? GraphCanon lists graph-backed alternatives at [lagent alternatives](/tools/internlm-lagent/alternatives) and [OmAgent alternatives](/tools/om-ai-lab-omagent/alternatives) ([lagent markdown twin](/tools/internlm-lagent/alternatives.md), [OmAgent markdown twin](/tools/om-ai-lab-omagent/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-om-ai-lab-omagent.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lagent or OmAgent? lagent: Active. OmAgent: 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 OmAgent? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lagent trust report](/tools/internlm-lagent/trust); [OmAgent trust report](/tools/om-ai-lab-omagent/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/_