--- title: "agency vs agency" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/neurocult-agency-vs-operand-agency" tools: ["neurocult-agency", "operand-agency"] --- # agency vs agency Neutral, constraint-first comparison with live GitHub stats. | | [agency](/tools/neurocult-agency.md) | [agency](/tools/operand-agency.md) | | --- | --- | --- | | Tagline | Library for exploring LLMs and generative AI in a clean, effective, Go-idiomatic approach | A fast and minimal framework for building agentic systems. | | Stars | 512 | 487 | | Forks | 36 | 28 | | Open issues | 4 | 19 | | Language | Go | Python | | Adopt for | Agency is a Go-centric library that allows developers to explore Large Language Models (LLMs) and generative AI in a clean, effective manner. It provides OpenAI API bindings and supports custom operations. | 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 | Model Training, AI Agents, Developer Tools | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agency](/tools/neurocult-agency.md) | [agency](/tools/operand-agency.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 546d | 27d | | Open issues (now) | 4 | 19 | | Owner type | Organization | User | | Security scan | 23 low (23 low) | 5 low (5 low) | | Full report | [trust report](/tools/neurocult-agency/trust.md) | [trust report](/tools/operand-agency/trust.md) | **Typed relationship:** agency _(alternative)_ agency Both projects provide frameworks for building agentic systems, but 'agency' is in Go while 'agency' by operand-agency is likely in another language. They solve similar problems with different approaches. ## Shared compatibility - **Python**: [agency](/tools/neurocult-agency.md) - Python runtime; [agency](/tools/operand-agency.md) - Python runtime ## Decision facts: agency - **Pricing:** freemium - The core library is freely available under MIT license. Additional premium features might be introduced as the project evolves. - **Requirements:** Min 1 GB RAM - **Adopt for:** Agency is a Go-centric library that allows developers to explore Large Language Models (LLMs) and generative AI in a clean, effective manner. It provides OpenAI API bindings and supports custom operations. ## 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 agency if… - agency is primarily Go; agency is Python. - Pricing: The core library is freely available under MIT license. Additional premium features might be introduced as the project evolves.. - Requirements: Min 1 GB RAM. - Both projects provide frameworks for building agentic systems, but 'agency' is in Go while 'agency' by operand-agency is likely in another language. They solve similar problems with different approaches. - Tags unique to agency: go, artificial-intelligence, generative-ai, chatgpt. - Also covers Model Training, Developer Tools. - A situation where you prefer Go over other languages for implementing LLMs. ### Choose agency if… - agency is primarily Python; agency is Go. - 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 projects provide frameworks for building agentic systems, but 'agency' is in Go while 'agency' by operand-agency is likely in another language. They solve similar problems with different approaches. - Tags unique to agency: llmops, machine-learning, actor-model, framework. - - You need to develop a system where agents can be integrated in a concurrent or distributed environment using Python. ## When NOT to use agency - When your team prefers languages such as Python or JavaScript over Go for AI development tasks. - If there is a need for extensive machine learning libraries not supported by Go’s ecosystem, and Python's ecosystem suits better. - In scenarios where the project requires heavy use of external libraries that are poorly supported or not idiomatic in Go. - You prioritize rapid prototyping with less concern for performance and type safety, favoring dynamic languages like Python. ## 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 agency and agency? agency: Library for exploring LLMs and generative AI in a clean, effective, Go-idiomatic approach. 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 agency over agency? Choose agency over agency when agency is primarily Go; agency is Python; Pricing: The core library is freely available under MIT license. Additional premium features might be introduced as the project evolves.; Requirements: Min 1 GB RAM; Both projects provide frameworks for building agentic systems, but 'agency' is in Go while 'agency' by operand-agency is likely in another language. They solve similar problems with different approaches; Tags unique to agency: go, artificial-intelligence, generative-ai, chatgpt; Also covers Model Training, Developer Tools; A situation where you prefer Go over other languages for implementing LLMs. ### When should I choose agency over agency? Choose agency over agency when agency is primarily Python; agency is Go; 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 projects provide frameworks for building agentic systems, but 'agency' is in Go while 'agency' by operand-agency is likely in another language. They solve similar problems with different approaches; Tags unique to agency: llmops, machine-learning, actor-model, framework; - You need to develop a system where agents can be integrated in a concurrent or distributed environment using Python. ### When should I avoid agency? When your team prefers languages such as Python or JavaScript over Go for AI development tasks. If there is a need for extensive machine learning libraries not supported by Go’s ecosystem, and Python's ecosystem suits better. In scenarios where the project requires heavy use of external libraries that are poorly supported or not idiomatic in Go. You prioritize rapid prototyping with less concern for performance and type safety, favoring dynamic languages like Python. ### 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 agency or agency more popular on GitHub? agency has more GitHub stars (512 vs 487). Stars measure visibility, not whether either tool fits your constraints. ### Are agency and agency open source? Yes - both are open-source projects on GitHub (agency: MIT, agency: MIT). ### Where can I find alternatives to agency or agency? GraphCanon lists graph-backed alternatives at /tools/neurocult-agency/alternatives and /tools/operand-agency/alternatives (/tools/neurocult-agency/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/neurocult-agency-vs-operand-agency.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agency or agency? agency: Dormant. 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 agency and agency? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agency: /tools/neurocult-agency/trust; agency: /tools/operand-agency/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=neurocult-agency`](/api/graphcanon/graph?tool=neurocult-agency) - 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/_