Comparison
AutoAgent vs LLMStack
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.
Markdown twin · AutoAgent alternatives · LLMStack alternatives
GraphCanon updated Sep 20, 2026
6views this month
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Trust & integrity
| Signal | AutoAgent | LLMStack |
|---|---|---|
| Maintenance | Slowing (307d since push) As of Aug 19, 2026 · github_public_v1 | Dormant (612d since push) As of Aug 16, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 19, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 16, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- AutoAgent
- 9.7k
- LLMStack
- 2.3k
Forks
- AutoAgent
- 1.4k
- LLMStack
- 347
Open issues
- AutoAgent
- 68
- LLMStack
- 23
Language
- AutoAgent
- Python
- LLMStack
- Python
Adopt for
- AutoAgent
- 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
- 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
- AutoAgent
- -
- LLMStack
- -
Runtime
- AutoAgent
- -
- LLMStack
- -
License
- AutoAgent
- MIT
- LLMStack
- Other
Last pushed
- AutoAgent
- Oct 16, 2025
- LLMStack
- Dec 11, 2024
Categories
- AutoAgent
- AI Agents
- LLMStack
- AI Agents, LLM Frameworks
Trust and health
Maintenance
- AutoAgent
- Slowing (36%)
- LLMStack
- Dormant (18%)
Days since push
- AutoAgent
- 307d
- LLMStack
- 612d
Open issues (now)
- AutoAgent
- 68
- LLMStack
- 23
Stars delta
- AutoAgent
- +242 (30d)
- LLMStack
- +2 (30d)
Full report
- AutoAgent
- Trust report
- LLMStack
- Trust report
Shared compatibility
- Python · AutoAgent: Python runtime · LLMStack: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (HKUDS/AutoAgent) · observed Sep 20, 2026
- GitHub forks (HKUDS/AutoAgent) · observed Sep 20, 2026
- Last push (HKUDS/AutoAgent) · observed Oct 16, 2025
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (trypromptly/LLMStack) · observed Sep 20, 2026
- GitHub forks (trypromptly/LLMStack) · observed Sep 20, 2026
- Last push (trypromptly/LLMStack) · observed Dec 11, 2024
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: AutoAgent 9.7k · LLMStack 2.3k (synced Sep 20, 2026).
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 and LLMStack alternatives (AutoAgent markdown twin, LLMStack markdown twin), 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 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; LLMStack trust report.