--- title: "lagent vs ReAct" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lagent-vs-ysymyth-react" tools: ["internlm-lagent", "ysymyth-react"] --- # lagent vs ReAct *GraphCanon updated Aug 17, 2026* ## Verdict Pick lagent if lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents; pick ReAct if reAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3. [lagent](https://github.com/InternLM/lagent) reports 2.3k GitHub stars, 238 forks, and 24 open issues, last pushed Aug 3, 2026. [ReAct](https://github.com/ysymyth/ReAct) has 4.1k stars, 396 forks, and 5 open issues, last pushed Feb 6, 2024. Figures are from public GitHub metadata via [lagent's repository](https://github.com/InternLM/lagent) and [ReAct's repository](https://github.com/ysymyth/ReAct). | | [lagent](/tools/internlm-lagent.md) | [ReAct](/tools/ysymyth-react.md) | | --- | --- | --- | | Tagline | A lightweight framework for building LLM-based agents | ReAct Prompting for decision-making with language models | | Stars | 2,276 | 4,109 | | Forks | 238 | 396 | | Open issues | 24 | 5 | | Language | Python | Jupyter Notebook | | Adopt for | lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents. | ReAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3. | | 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, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lagent](/tools/internlm-lagent.md) | [ReAct](/tools/ysymyth-react.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 12d | 923d | | Open issues (now) | 24 | 5 | | Stars delta | +8 (30d) | +50 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/internlm-lagent/trust.md) | [trust report](/tools/ysymyth-react/trust.md) | ## Shared compatibility - **Python**: [lagent](/tools/internlm-lagent.md) - Python runtime; [ReAct](/tools/ysymyth-react.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: ReAct - **Adopt for:** ReAct enhances large language models by improving reasoning and executing actions through specific tasks using GPT-3. ## Choose when ### Choose lagent if… - lagent is primarily Python; ReAct is Jupyter Notebook. - License: lagent is Apache-2.0, ReAct 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, gpt, 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 ReAct if… - ReAct is primarily Jupyter Notebook; lagent is Python. - License: ReAct is MIT, lagent is Apache-2.0. - Tags unique to ReAct: decision-making, large language models, prompting, reasoning. - When aiming for better decision-making in HotpotQA, alfworld environments, or WebShop scenarios with GPT-3 ## 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 ReAct - If requiring extensive custom task integration beyond provided notebooks, LangChain's zero-shot ReAct agent may be more preferable - When PaLM outperforms GPT-3 on specific tasks or if an alternative model is preferred ## Common questions ### What is the difference between lagent and ReAct? lagent: A lightweight framework for building LLM-based agents. ReAct: ReAct Prompting for decision-making with language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose lagent over ReAct? Choose lagent over ReAct when lagent is primarily Python; ReAct is Jupyter Notebook; License: lagent is Apache-2.0, ReAct 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, gpt, 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 ReAct over lagent? Choose ReAct over lagent when ReAct is primarily Jupyter Notebook; lagent is Python; License: ReAct is MIT, lagent is Apache-2.0; Tags unique to ReAct: decision-making, large language models, prompting, reasoning; When aiming for better decision-making in HotpotQA, alfworld environments, or WebShop scenarios with GPT-3. ### 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 ReAct? If requiring extensive custom task integration beyond provided notebooks, LangChain's zero-shot ReAct agent may be more preferable When PaLM outperforms GPT-3 on specific tasks or if an alternative model is preferred ### Is lagent or ReAct more popular on GitHub? ReAct has more GitHub stars (4,109 vs 2,276). Stars measure visibility, not whether either tool fits your constraints. ### Are lagent and ReAct open source? Yes - both are open-source projects on GitHub (lagent: Apache-2.0, ReAct: MIT). ### Where can I find alternatives to lagent or ReAct? GraphCanon lists graph-backed alternatives at [lagent alternatives](/tools/internlm-lagent/alternatives) and [ReAct alternatives](/tools/ysymyth-react/alternatives) ([lagent markdown twin](/tools/internlm-lagent/alternatives.md), [ReAct markdown twin](/tools/ysymyth-react/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-ysymyth-react.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lagent or ReAct? lagent: Active. ReAct: 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 ReAct? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lagent trust report](/tools/internlm-lagent/trust); [ReAct trust report](/tools/ysymyth-react/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/_