--- title: "AutoAgent vs LLMStack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hkuds-autoagent-vs-trypromptly-llmstack" tools: ["hkuds-autoagent", "trypromptly-llmstack"] --- # AutoAgent vs LLMStack *GraphCanon updated Sep 20, 2026* ## Verdict Pick AutoAgent if autoAgent is a framework designed for creating automated AI agents using large language models in a zero-code, fully-automated environment. It operates with Python and uses Docker to manage agent-interaction environments; 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. [AutoAgent](https://arxiv.org/abs/2502.05957) reports 9.7k GitHub stars, 1.4k forks, and 68 open issues, last pushed Oct 16, 2025. [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 [AutoAgent's repository](https://github.com/HKUDS/AutoAgent) and [LLMStack's repository](https://github.com/trypromptly/LLMStack). | | [AutoAgent](/tools/hkuds-autoagent.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | Tagline | Fully-Automated and Zero-Code LLM Agent Framework | No-code multi-agent framework to build LLM Agents, workflows and applications with your data | | Stars | 9,738 | 2,309 | | Forks | 1,357 | 347 | | Open issues | 68 | 23 | | Language | Python | Python | | Adopt for | AutoAgent is a framework designed for creating automated AI agents using large language models in a zero-code, fully-automated environment. It operates with Python and uses Docker to manage agent-interaction environments | 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 | MIT | Other | | Categories | AI Agents | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AutoAgent](/tools/hkuds-autoagent.md) | [LLMStack](/tools/trypromptly-llmstack.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 307d | 612d | | Open issues (now) | 68 | 23 | | Stars delta | +242 (30d) | +2 (30d) | | Full report | [trust report](/tools/hkuds-autoagent/trust.md) | [trust report](/tools/trypromptly-llmstack/trust.md) | ## Shared compatibility - **Python**: [AutoAgent](/tools/hkuds-autoagent.md) - Python runtime; [LLMStack](/tools/trypromptly-llmstack.md) - Python runtime ## Decision facts: AutoAgent - **Adopt for:** AutoAgent is a framework designed for creating automated AI agents using large language models in a zero-code, fully-automated environment. It operates with Python and uses Docker to manage agent-interaction environments ## 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 AutoAgent if… - License: AutoAgent is MIT, LLMStack is Other. - Tags unique to AutoAgent: agent, llms. - Use AutoAgent when you aim to leverage LLMs in an automation setting without needing any coding experience. ### Choose LLMStack if… - License: LLMStack is Other, AutoAgent is MIT. - Tags unique to LLMStack: agents, ai-agents-framework, generative-ai, llm-agents. - Also covers LLM Frameworks. - Use LLMStack when you need a no-code solution to develop multi-agent systems based on large language models. ## When NOT to use AutoAgent - Avoid AutoAgent if your project requires customization or modification of the underlying agent framework code directly. - Do not use AutoAgent when you require real-time performance and low latency operation since its automatic Docker image handling can cause delays in deployment. ## 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 AutoAgent and LLMStack? AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework. 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 AutoAgent over LLMStack? Choose AutoAgent over LLMStack when License: AutoAgent is MIT, LLMStack is Other; Tags unique to AutoAgent: agent, llms; Use AutoAgent when you aim to leverage LLMs in an automation setting without needing any coding experience. ### When should I choose LLMStack over AutoAgent? Choose LLMStack over AutoAgent when License: LLMStack is Other, AutoAgent is MIT; Tags unique to LLMStack: agents, ai-agents-framework, generative-ai, llm-agents; Also covers LLM Frameworks; Use LLMStack when you need a no-code solution to develop multi-agent systems based on large language models. ### When should I avoid AutoAgent? Avoid AutoAgent if your project requires customization or modification of the underlying agent framework code directly. Do not use AutoAgent when you require real-time performance and low latency operation since its automatic Docker image handling can cause delays in deployment. ### 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 AutoAgent or LLMStack more popular on GitHub? AutoAgent has more GitHub stars (9,738 vs 2,309). Stars measure visibility, not whether either tool fits your constraints. ### Are AutoAgent and LLMStack open source? Yes - both are open-source projects on GitHub (AutoAgent: MIT, LLMStack: Other). ### Where can I find alternatives to AutoAgent or LLMStack? GraphCanon lists graph-backed alternatives at [AutoAgent alternatives](/tools/hkuds-autoagent/alternatives) and [LLMStack alternatives](/tools/trypromptly-llmstack/alternatives) ([AutoAgent markdown twin](/tools/hkuds-autoagent/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/hkuds-autoagent-vs-trypromptly-llmstack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AutoAgent or LLMStack? AutoAgent: Slowing. 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 AutoAgent and LLMStack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AutoAgent trust report](/tools/hkuds-autoagent/trust); [LLMStack trust report](/tools/trypromptly-llmstack/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hkuds-autoagent`](/api/graphcanon/graph?tool=hkuds-autoagent) - 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/_