--- title: "lagent vs langroid" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lagent-vs-langroid-langroid" tools: ["internlm-lagent", "langroid-langroid"] --- # lagent vs langroid *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 langroid if langroid specializes in multi-agent systems with large language models, providing a Python framework for function-calling and chat integrations. [lagent](https://github.com/InternLM/lagent) reports 2.3k GitHub stars, 238 forks, and 24 open issues, last pushed Aug 3, 2026. [langroid](https://langroid.github.io/langroid/) has 4.1k stars, 390 forks, and 75 open issues, last pushed Jul 29, 2026. Figures are from public GitHub metadata via [lagent's repository](https://github.com/InternLM/lagent) and [langroid's repository](https://github.com/langroid/langroid). | | [lagent](/tools/internlm-lagent.md) | [langroid](/tools/langroid-langroid.md) | | --- | --- | --- | | Tagline | A lightweight framework for building LLM-based agents | Harness LLMs with Multi-Agent Programming | | Stars | 2,276 | 4,090 | | Forks | 238 | 390 | | Open issues | 24 | 75 | | Language | Python | Python | | Adopt for | lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents. | Langroid specializes in multi-agent systems with large language models, providing a Python framework for function-calling and chat integrations. | | Persona | - | - | | Runtime | - | - | | License | lagent is open-source under the Apache-2.0 license, allowing for broad use and modification with attribution. | MIT | | Categories | AI Agents, LLM Frameworks | AI Agents, Data & Retrieval, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lagent](/tools/internlm-lagent.md) | [langroid](/tools/langroid-langroid.md) | | --- | --- | --- | | Days since push | 12d | 8d | | Open issues (now) | 24 | 75 | | Stars delta | +8 (30d) | Unknown | | Open issues delta | +1 (30d) | Unknown | | Full report | [trust report](/tools/internlm-lagent/trust.md) | [trust report](/tools/langroid-langroid/trust.md) | ## Shared compatibility - **Python**: [lagent](/tools/internlm-lagent.md) - Python runtime; [langroid](/tools/langroid-langroid.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: langroid - **Adopt for:** Langroid specializes in multi-agent systems with large language models, providing a Python framework for function-calling and chat integrations. ## Choose when ### Choose lagent if… - License: lagent is Apache-2.0, langroid is MIT. - Pricing: Available freely due to its open-source nature, but customization or enterprise support might involve additional costs.. - Tags unique to lagent: agent, llm, transformers. - Also covers LLM Frameworks. - 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 langroid if… - License: langroid is MIT, lagent is Apache-2.0. - Tags unique to langroid: agents, ai, chatgpt, function-calling. - Also covers Data & Retrieval, Inference & Serving, Model Training. - langroid ships Docker support for self-hosted deployment. - You need to integrate multiple agents working with 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 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. ## Common questions ### What is the difference between lagent and langroid? lagent: A lightweight framework for building LLM-based agents. langroid: Harness LLMs with Multi-Agent Programming. See the comparison table for live GitHub stats and shared categories. ### When should I choose lagent over langroid? Choose lagent over langroid when License: lagent is Apache-2.0, langroid is MIT; Pricing: Available freely due to its open-source nature, but customization or enterprise support might involve additional costs.; Tags unique to lagent: agent, llm, transformers; Also covers LLM Frameworks; 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 langroid over lagent? Choose langroid over lagent when License: langroid is MIT, lagent is Apache-2.0; Tags unique to langroid: agents, ai, chatgpt, function-calling; Also covers Data & Retrieval, Inference & Serving, Model Training; langroid ships Docker support for self-hosted deployment; You need to integrate multiple agents working with 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 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. ### Is lagent or langroid more popular on GitHub? langroid has more GitHub stars (4,090 vs 2,276). Stars measure visibility, not whether either tool fits your constraints. ### Are lagent and langroid open source? Yes - both are open-source projects on GitHub (lagent: Apache-2.0, langroid: MIT). ### Where can I find alternatives to lagent or langroid? GraphCanon lists graph-backed alternatives at [lagent alternatives](/tools/internlm-lagent/alternatives) and [langroid alternatives](/tools/langroid-langroid/alternatives) ([lagent markdown twin](/tools/internlm-lagent/alternatives.md), [langroid markdown twin](/tools/langroid-langroid/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-langroid-langroid.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lagent or langroid? lagent: Active. langroid: 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 lagent and langroid? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lagent trust report](/tools/internlm-lagent/trust); [langroid trust report](/tools/langroid-langroid/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/_