--- title: "aqueduct vs AutoGPT" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/runllm-aqueduct-vs-significant-gravitas-autogpt" tools: ["runllm-aqueduct", "significant-gravitas-autogpt"] --- # aqueduct vs AutoGPT *GraphCanon updated Jul 11, 2026* ## Verdict Pick aqueduct when aqueduct is primarily Go; AutoGPT is Python; pick AutoGPT when autoGPT is primarily Python; aqueduct is Go. [aqueduct](https://aqueducthq.com) reports 517 GitHub stars, 20 forks, and 11 open issues, last pushed Jun 7, 2023. [AutoGPT](https://agpt.co) has 185k stars, 46k forks, and 494 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [aqueduct's repository](https://github.com/RunLLM/aqueduct) and [AutoGPT's repository](https://github.com/Significant-Gravitas/AutoGPT). | | [aqueduct](/tools/runllm-aqueduct.md) | [AutoGPT](/tools/significant-gravitas-autogpt.md) | | --- | --- | --- | | Tagline | Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure. | AutoGPT is the vision of accessible AI for everyone, to use and to build on. | | Stars | 517 | 185,464 | | Forks | 20 | 46,111 | | Open issues | 11 | 494 | | Language | Go | Python | | Adopt for | - | AutoGPT is a Python-based tool for creating accessible autonomous AI agents that can leverage various LLM APIs including OpenAI's GPT and Anthropic's Claude. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | AI Agents, LLM Frameworks, Model Training | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aqueduct](/tools/runllm-aqueduct.md) | [AutoGPT](/tools/significant-gravitas-autogpt.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1130d | 0d | | Open issues (now) | 11 | 494 | | Full report | [trust report](/tools/runllm-aqueduct/trust.md) | [trust report](/tools/significant-gravitas-autogpt/trust.md) | ## Decision facts: AutoGPT - **Adopt for:** AutoGPT is a Python-based tool for creating accessible autonomous AI agents that can leverage various LLM APIs including OpenAI's GPT and Anthropic's Claude. ## Choose when ### Choose aqueduct if… - aqueduct is primarily Go; AutoGPT is Python. - License: aqueduct is Apache-2.0, AutoGPT is Other. - Tags unique to aqueduct: data, data-science, kubernetes, llms. - Also covers Model Training. ### Choose AutoGPT if… - AutoGPT is primarily Python; aqueduct is Go. - License: AutoGPT is Other, aqueduct is Apache-2.0. - Tags unique to AutoGPT: agentic-ai, agents, artificial-intelligence, autonomous-agents. - When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise. ## When NOT to use aqueduct - Last GitHub push was 1130 days ago (dormant maintenance, Jun 7, 2023). Validate activity before betting a new project on aqueduct. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use AutoGPT - Avoid if you require absolute control over the underlying AI infrastructure and APIs used by your autonomous agents, as AutoGPT imposes its own framework. - If your project demands proprietary or specialized models that aren't supported by AutoGPT's API ecosystem (e.g., custom TensorFlow or PyTorch models), consider other tools. ## Common questions ### What is the difference between aqueduct and AutoGPT? aqueduct: Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.. AutoGPT: AutoGPT is the vision of accessible AI for everyone, to use and to build on.. See the comparison table for live GitHub stats and shared categories. ### When should I choose aqueduct over AutoGPT? Choose aqueduct over AutoGPT when aqueduct is primarily Go; AutoGPT is Python; License: aqueduct is Apache-2.0, AutoGPT is Other; Tags unique to aqueduct: data, data-science, kubernetes, llms; Also covers Model Training. ### When should I choose AutoGPT over aqueduct? Choose AutoGPT over aqueduct when AutoGPT is primarily Python; aqueduct is Go; License: AutoGPT is Other, aqueduct is Apache-2.0; Tags unique to AutoGPT: agentic-ai, agents, artificial-intelligence, autonomous-agents; When you need to rapidly prototype or deploy an autonomous agent using existing language models without deep AI expertise. ### When should I avoid aqueduct? Last GitHub push was 1130 days ago (dormant maintenance, Jun 7, 2023). Validate activity before betting a new project on aqueduct. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid AutoGPT? Avoid if you require absolute control over the underlying AI infrastructure and APIs used by your autonomous agents, as AutoGPT imposes its own framework. If your project demands proprietary or specialized models that aren't supported by AutoGPT's API ecosystem (e.g., custom TensorFlow or PyTorch models), consider other tools. ### Is aqueduct or AutoGPT more popular on GitHub? AutoGPT has more GitHub stars (185,464 vs 517). Stars measure visibility, not whether either tool fits your constraints. ### Are aqueduct and AutoGPT open source? Yes - both are open-source projects on GitHub (aqueduct: Apache-2.0, AutoGPT: Other). ### Where can I find alternatives to aqueduct or AutoGPT? GraphCanon lists graph-backed alternatives at [aqueduct alternatives](/tools/runllm-aqueduct/alternatives) and [AutoGPT alternatives](/tools/significant-gravitas-autogpt/alternatives) ([aqueduct markdown twin](/tools/runllm-aqueduct/alternatives.md), [AutoGPT markdown twin](/tools/significant-gravitas-autogpt/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/runllm-aqueduct-vs-significant-gravitas-autogpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aqueduct or AutoGPT? aqueduct: Dormant. AutoGPT: Very 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 aqueduct and AutoGPT? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aqueduct trust report](/tools/runllm-aqueduct/trust); [AutoGPT trust report](/tools/significant-gravitas-autogpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=runllm-aqueduct`](/api/graphcanon/graph?tool=runllm-aqueduct) - 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/_