--- title: "agno vs oneflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agno-agi-agno-vs-oneflow-inc-oneflow" tools: ["agno-agi-agno", "oneflow-inc-oneflow"] --- # agno vs oneflow *GraphCanon updated Jul 12, 2026* ## Verdict Pick agno if use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions; pick oneflow if oneFlow is a deep learning framework built for user-friendly, scalable, and efficient performance in model training, with support via CUDA installations. [agno](https://docs.agno.com) reports 41k GitHub stars, 5.6k forks, and 1.0k open issues, last pushed Jul 11, 2026. [oneflow](http://www.oneflow.org) has 9.4k stars, 1.0k forks, and 645 open issues, last pushed Dec 4, 2025. Figures are from public GitHub metadata via [agno's repository](https://github.com/agno-agi/agno) and [oneflow's repository](https://github.com/Oneflow-Inc/oneflow). | | [agno](/tools/agno-agi-agno.md) | [oneflow](/tools/oneflow-inc-oneflow.md) | | --- | --- | --- | | Tagline | Build, run, and manage your own agent platform. | OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. | | Stars | 41,090 | 9,409 | | Forks | 5,627 | 1,013 | | Open issues | 1,009 | 645 | | Language | Python | C++ | | Adopt for | Use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions. | OneFlow is a deep learning framework built for user-friendly, scalable, and efficient performance in model training, with support via CUDA installations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | AI Agents, Developer Tools | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agno](/tools/agno-agi-agno.md) | [oneflow](/tools/oneflow-inc-oneflow.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 219d | | Open issues (now) | 1.0k | 645 | | Full report | [trust report](/tools/agno-agi-agno/trust.md) | [trust report](/tools/oneflow-inc-oneflow/trust.md) | ## Decision facts: agno - **Adopt for:** Use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions. ## Decision facts: oneflow - **Adopt for:** OneFlow is a deep learning framework built for user-friendly, scalable, and efficient performance in model training, with support via CUDA installations. ## Choose when ### Choose agno if… - agno is primarily Python; oneflow is C++. - Tags unique to agno: agents, python, developer-tools, ai-agents. - Also covers AI Agents, Developer Tools. - Use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions. ### Choose oneflow if… - oneflow is primarily C++; agno is Python. - Tags unique to oneflow: neural-networks, deep-learning, distributed, machine-learning. - Also covers Model Training. - OneFlow is preferable when you need a user-friendly framework for both CPU and CUDA installations, aiming to streamline the deep learning workflow. ## When NOT to use agno - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## When NOT to use oneflow - Avoid OneFlow if your project requires extensive customization features not natively supported, as switching to another framework might offer better flexibility. - If the development environment lacks support for CUDA or Python3-based installation methods, consider an alternative framework that suits your hardware and software environment more closely. - OneFlow may not be ideal when working in regions with difficulty accessing external libraries due to dependency management tailored towards certain geographic locations. ## Common questions ### What is the difference between agno and oneflow? agno: Build, run, and manage your own agent platform.. oneflow: OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.. See the comparison table for live GitHub stats and shared categories. ### When should I choose agno over oneflow? Choose agno over oneflow when agno is primarily Python; oneflow is C++; Tags unique to agno: agents, python, developer-tools, ai-agents; Also covers AI Agents, Developer Tools; Use agno for a high level of control over your AI agent platform, including 50+ production API endpoints and support for 100+ integrations. Ideal if you need to customize data ownership and permissions. ### When should I choose oneflow over agno? Choose oneflow over agno when oneflow is primarily C++; agno is Python; Tags unique to oneflow: neural-networks, deep-learning, distributed, machine-learning; Also covers Model Training; OneFlow is preferable when you need a user-friendly framework for both CPU and CUDA installations, aiming to streamline the deep learning workflow. ### When should I avoid agno? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### When should I avoid oneflow? Avoid OneFlow if your project requires extensive customization features not natively supported, as switching to another framework might offer better flexibility. If the development environment lacks support for CUDA or Python3-based installation methods, consider an alternative framework that suits your hardware and software environment more closely. OneFlow may not be ideal when working in regions with difficulty accessing external libraries due to dependency management tailored towards certain geographic locations. ### Is agno or oneflow more popular on GitHub? agno has more GitHub stars (41,090 vs 9,409). Stars measure visibility, not whether either tool fits your constraints. ### Are agno and oneflow open source? Yes - both are open-source projects on GitHub (agno: Apache-2.0, oneflow: Apache-2.0). ### Where can I find alternatives to agno or oneflow? GraphCanon lists graph-backed alternatives at [agno alternatives](/tools/agno-agi-agno/alternatives) and [oneflow alternatives](/tools/oneflow-inc-oneflow/alternatives) ([agno markdown twin](/tools/agno-agi-agno/alternatives.md), [oneflow markdown twin](/tools/oneflow-inc-oneflow/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/agno-agi-agno-vs-oneflow-inc-oneflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agno or oneflow? agno: Very active. oneflow: Slowing. 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 agno and oneflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agno trust report](/tools/agno-agi-agno/trust); [oneflow trust report](/tools/oneflow-inc-oneflow/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agno-agi-agno`](/api/graphcanon/graph?tool=agno-agi-agno) - 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/_