--- title: "openai-agents-python vs agency" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openai-openai-agents-python-vs-operand-agency" tools: ["openai-openai-agents-python", "operand-agency"] --- # openai-agents-python vs agency Neutral, constraint-first comparison with live GitHub stats. | | [openai-agents-python](/tools/openai-openai-agents-python.md) | [agency](/tools/operand-agency.md) | | --- | --- | --- | | Tagline | A lightweight, powerful framework for multi-agent workflows | A fast and minimal framework for building agentic systems. | | Stars | 27,732 | 487 | | Forks | 4,277 | 28 | | Open issues | 65 | 19 | | Language | Python | Python | | Adopt for | A flexible and lightweight framework for building complex multi-agent systems capable of integrating with multiple LLMs. | Agency provides an Actor model framework that enables the development of custom agent-based applications with support for concurrency, networked agents through AMQP, and detailed observability features. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [openai-agents-python](/tools/openai-openai-agents-python.md) | [agency](/tools/operand-agency.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 27d | | Open issues (now) | 65 | 19 | | Owner type | Organization | User | | Security scan | No lockfile | 5 low (5 low) | | Full report | [trust report](/tools/openai-openai-agents-python/trust.md) | [trust report](/tools/operand-agency/trust.md) | **Typed relationship:** openai-agents-python _(alternative)_ agency Both Agency and OpenAI Agents Python address multi-agent workflows but offer different design approaches, making them alternative frameworks for similar purposes. ## Shared compatibility - **Python**: [openai-agents-python](/tools/openai-openai-agents-python.md) - Python runtime; [agency](/tools/operand-agency.md) - Python runtime ## Decision facts: openai-agents-python - **Adopt for:** A flexible and lightweight framework for building complex multi-agent systems capable of integrating with multiple LLMs. ## Decision facts: agency - **Pricing:** freemium - The framework is available under an MIT license, making it free to use. However, advanced services like detailed support or commercial customizations might require a contract with additional costs. - **Requirements:** Min 1 GB RAM; Requires Docker; Docker is used for deployment but not mandatory; the framework can be set up in any environment that supports Python and potentially RabbitMQ for networked use. - **Adopt for:** Agency provides an Actor model framework that enables the development of custom agent-based applications with support for concurrency, networked agents through AMQP, and detailed observability features. ## Choose when ### Choose openai-agents-python if… - Both Agency and OpenAI Agents Python address multi-agent workflows but offer different design approaches, making them alternative frameworks for similar purposes. - Tags unique to openai-agents-python: llm, python, openai. - - When you need a provider-agnostic solution that supports over 100 different Language Models including OpenAI's. ### Choose agency if… - Pricing: The framework is available under an MIT license, making it free to use. However, advanced services like detailed support or commercial customizations might require a contract with additional costs.. - Requirements: Min 1 GB RAM; Requires Docker; Docker is used for deployment but not mandatory; the framework can be set up in any environment that supports Python and potentially RabbitMQ for networked use.. - Both Agency and OpenAI Agents Python address multi-agent workflows but offer different design approaches, making them alternative frameworks for similar purposes. - Tags unique to agency: llmops, machine-learning, actor-model, agi. - - You need to develop a system where agents can be integrated in a concurrent or distributed environment using Python. ## When NOT to use openai-agents-python - - When you prefer a more specialized solution specifically tailored for simpler agent workflows without comprehensive multi-agent capabilities. - - If you are solely working with frameworks or LLMs that do not integrate well or at all with the OpenAI responses and Chat Completions APIs. - - For projects requiring minimalistic setup; if your project can be handled efficiently by a less complex tool or framework. ## When NOT to use agency - - If your primary requirement is to implement generic AI functionalities without focusing on agent interaction and custom architectures, preferring simplicity over flexibility. - - For systems requiring extremely high performance for processing large datasets where overhead from the agentic model might introduce unacceptable latency. ## Common questions ### What is the difference between openai-agents-python and agency? openai-agents-python: A lightweight, powerful framework for multi-agent workflows. agency: A fast and minimal framework for building agentic systems.. See the comparison table for live GitHub stats and shared categories. ### When should I choose openai-agents-python over agency? Choose openai-agents-python over agency when Both Agency and OpenAI Agents Python address multi-agent workflows but offer different design approaches, making them alternative frameworks for similar purposes; Tags unique to openai-agents-python: llm, python, openai; - When you need a provider-agnostic solution that supports over 100 different Language Models including OpenAI's. ### When should I choose agency over openai-agents-python? Choose agency over openai-agents-python when Pricing: The framework is available under an MIT license, making it free to use. However, advanced services like detailed support or commercial customizations might require a contract with additional costs.; Requirements: Min 1 GB RAM; Requires Docker; Docker is used for deployment but not mandatory; the framework can be set up in any environment that supports Python and potentially RabbitMQ for networked use.; Both Agency and OpenAI Agents Python address multi-agent workflows but offer different design approaches, making them alternative frameworks for similar purposes; Tags unique to agency: llmops, machine-learning, actor-model, agi; - You need to develop a system where agents can be integrated in a concurrent or distributed environment using Python. ### When should I avoid openai-agents-python? - When you prefer a more specialized solution specifically tailored for simpler agent workflows without comprehensive multi-agent capabilities. - If you are solely working with frameworks or LLMs that do not integrate well or at all with the OpenAI responses and Chat Completions APIs. - For projects requiring minimalistic setup; if your project can be handled efficiently by a less complex tool or framework. ### When should I avoid agency? - If your primary requirement is to implement generic AI functionalities without focusing on agent interaction and custom architectures, preferring simplicity over flexibility. - For systems requiring extremely high performance for processing large datasets where overhead from the agentic model might introduce unacceptable latency. ### Is openai-agents-python or agency more popular on GitHub? openai-agents-python has more GitHub stars (27,732 vs 487). Stars measure visibility, not whether either tool fits your constraints. ### Are openai-agents-python and agency open source? Yes - both are open-source projects on GitHub (openai-agents-python: MIT, agency: MIT). ### Where can I find alternatives to openai-agents-python or agency? GraphCanon lists graph-backed alternatives at /tools/openai-openai-agents-python/alternatives and /tools/operand-agency/alternatives (/tools/openai-openai-agents-python/alternatives.md, /tools/operand-agency/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 /compare/openai-openai-agents-python-vs-operand-agency.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, openai-agents-python or agency? openai-agents-python: Very active. agency: 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 openai-agents-python and agency? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: openai-agents-python: /tools/openai-openai-agents-python/trust; agency: /tools/operand-agency/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openai-openai-agents-python`](/api/graphcanon/graph?tool=openai-openai-agents-python) - 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/_