--- title: "lagent vs skyagi" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lagent-vs-litanlitudan-skyagi" tools: ["internlm-lagent", "litanlitudan-skyagi"] --- # lagent vs skyagi *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 skyagi if skyAGI is a development tool focusing on simulating human behavior using large language models and is available under the Apache-2.0 license. [lagent](https://github.com/InternLM/lagent) reports 2.3k GitHub stars, 238 forks, and 24 open issues, last pushed Aug 3, 2026. [skyagi](https://skyagi.ai) has 778 stars, 56 forks, and 42 open issues, last pushed Sep 21, 2023. Figures are from public GitHub metadata via [lagent's repository](https://github.com/InternLM/lagent) and [skyagi's repository](https://github.com/litanlitudan/skyagi). | | [lagent](/tools/internlm-lagent.md) | [skyagi](/tools/litanlitudan-skyagi.md) | | --- | --- | --- | | Tagline | A lightweight framework for building LLM-based agents | SkyAGI provides emerging human-behavior simulation capability in LLM. | | Stars | 2,276 | 778 | | Forks | 238 | 56 | | Open issues | 24 | 42 | | Language | Python | TypeScript | | Adopt for | lagent is a Python framework aimed at streamlining the creation of lightweight Large Language Model (LLM) agents. | SkyAGI is a development tool focusing on simulating human behavior using large language models and is available under the Apache-2.0 license. | | 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) | [skyagi](/tools/litanlitudan-skyagi.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 12d | 1058d | | Open issues (now) | 24 | 42 | | Stars delta | +8 (30d) | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/internlm-lagent/trust.md) | [trust report](/tools/litanlitudan-skyagi/trust.md) | ## Shared compatibility - **Python**: [lagent](/tools/internlm-lagent.md) - Python runtime; [skyagi](/tools/litanlitudan-skyagi.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: skyagi - **Requirements:** It requires a valid OpenAI API key for operational purposes. - **Adopt for:** SkyAGI is a development tool focusing on simulating human behavior using large language models and is available under the Apache-2.0 license. ## Choose when ### Choose lagent if… - lagent is primarily Python; skyagi is TypeScript. - 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 skyagi if… - skyagi is primarily TypeScript; lagent is Python. - Requirements: It requires a valid OpenAI API key for operational purposes.. - Tags unique to skyagi: ai-agent, aigc, langchain, language-model. - if you aim to integrate highly dynamic, human-like behavioral characteristics in AI agents within your application ## 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 skyagi - if your project does not need the complexity of simulating detailed human behavior through LLMs and prefers more straightforward automations - when you have constraints that do not allow for third-party API usage, as SkyAGI depends on an external OPENAI_API_KEY to function ## Common questions ### What is the difference between lagent and skyagi? lagent: A lightweight framework for building LLM-based agents. skyagi: SkyAGI provides emerging human-behavior simulation capability in LLM.. See the comparison table for live GitHub stats and shared categories. ### When should I choose lagent over skyagi? Choose lagent over skyagi when lagent is primarily Python; skyagi is TypeScript; 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 skyagi over lagent? Choose skyagi over lagent when skyagi is primarily TypeScript; lagent is Python; Requirements: It requires a valid OpenAI API key for operational purposes.; Tags unique to skyagi: ai-agent, aigc, langchain, language-model; if you aim to integrate highly dynamic, human-like behavioral characteristics in AI agents within your application. ### 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 skyagi? if your project does not need the complexity of simulating detailed human behavior through LLMs and prefers more straightforward automations when you have constraints that do not allow for third-party API usage, as SkyAGI depends on an external OPENAI_API_KEY to function ### Is lagent or skyagi more popular on GitHub? lagent has more GitHub stars (2,276 vs 778). Stars measure visibility, not whether either tool fits your constraints. ### Are lagent and skyagi open source? Yes - both are open-source projects on GitHub (lagent: Apache-2.0, skyagi: Apache-2.0). ### Where can I find alternatives to lagent or skyagi? GraphCanon lists graph-backed alternatives at [lagent alternatives](/tools/internlm-lagent/alternatives) and [skyagi alternatives](/tools/litanlitudan-skyagi/alternatives) ([lagent markdown twin](/tools/internlm-lagent/alternatives.md), [skyagi markdown twin](/tools/litanlitudan-skyagi/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-litanlitudan-skyagi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lagent or skyagi? lagent: Active. skyagi: 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 skyagi? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lagent trust report](/tools/internlm-lagent/trust); [skyagi trust report](/tools/litanlitudan-skyagi/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/_