--- title: "agent-control vs agency" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentcontrol-agent-control-vs-operand-agency" tools: ["agentcontrol-agent-control", "operand-agency"] --- # agent-control vs agency *GraphCanon updated Aug 21, 2026* ## Verdict Pick agent-control if agent-control is a highly configurable and extensible centralized agent control system that governs runtime behavior of agents at scale via UI or SDK/API; pick agency if agency is a fast and minimal Python-based framework for developing agentic systems that excels in its streamlined approach to creating autonomous agents. [agent-control](https://agentcontrol.dev) reports 289 GitHub stars, 44 forks, and 36 open issues, last pushed Aug 6, 2026. [agency](https://createwith.agency) has 489 stars, 28 forks, and 19 open issues, last pushed Jun 10, 2026. Figures are from public GitHub metadata via [agent-control's repository](https://github.com/agentcontrol/agent-control) and [agency's repository](https://github.com/operand/agency). | | [agent-control](/tools/agentcontrol-agent-control.md) | [agency](/tools/operand-agency.md) | | --- | --- | --- | | Tagline | Centralized agent control plane for governing runtime agent behavior at scale | A fast and minimal framework for building agentic systems | | Stars | 289 | 489 | | Forks | 44 | 28 | | Open issues | 36 | 19 | | Language | Python | Python | | Adopt for | agent-control is a highly configurable and extensible centralized agent control system that governs runtime behavior of agents at scale via UI or SDK/API. | Agency is a fast and minimal Python-based framework for developing agentic systems that excels in its streamlined approach to creating autonomous agents. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-control](/tools/agentcontrol-agent-control.md) | [agency](/tools/operand-agency.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 2d | 71d | | Open issues (now) | 36 | 19 | | Stars delta | Unknown | +2 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/agentcontrol-agent-control/trust.md) | [trust report](/tools/operand-agency/trust.md) | ## Shared compatibility - **Python**: [agent-control](/tools/agentcontrol-agent-control.md) - Python runtime; [agency](/tools/operand-agency.md) - Python runtime ## Decision facts: agent-control - **Adopt for:** agent-control is a highly configurable and extensible centralized agent control system that governs runtime behavior of agents at scale via UI or SDK/API. ## Decision facts: agency - **Adopt for:** Agency is a fast and minimal Python-based framework for developing agentic systems that excels in its streamlined approach to creating autonomous agents. ## Choose when ### Choose agent-control if… - License: agent-control is Apache-2.0, agency is MIT. - Tags unique to agent-control: agentic-workflow, ai safety, guardrails, llm. - agent-control ships Docker support for self-hosted deployment. - Use agent-control if you need to centrally manage the behavior of multiple AI agents in production environments. ### Choose agency if… - License: agency is MIT, agent-control is Apache-2.0. - Tags unique to agency: actor-model, agent-framework, autonomous-agents. - When you prefer a lightweight solution for building autonomous agent systems without the need for extensive configuration or complex dependencies. ## When NOT to use agent-control - Avoid using agent-control when your project does not require centralized control for large-scale AI agent management. - Not recommended if your setup is simplistic or relies solely on languages other than Python or TypeScript, since the tool primarily supports these two. ## When NOT to use agency - If your development requires deeply integrated functionalities that would necessitate a heavier, more feature-rich framework. - In scenarios where you need robust tooling for large-scale deployment and management as Agency does not offer extensive LLMOps (LLM Operations) capabilities beyond its minimalistic design. ## Common questions ### What is the difference between agent-control and agency? agent-control: Centralized agent control plane for governing runtime agent behavior at scale. 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 agent-control over agency? Choose agent-control over agency when License: agent-control is Apache-2.0, agency is MIT; Tags unique to agent-control: agentic-workflow, ai safety, guardrails, llm; agent-control ships Docker support for self-hosted deployment; Use agent-control if you need to centrally manage the behavior of multiple AI agents in production environments. ### When should I choose agency over agent-control? Choose agency over agent-control when License: agency is MIT, agent-control is Apache-2.0; Tags unique to agency: actor-model, agent-framework, autonomous-agents; When you prefer a lightweight solution for building autonomous agent systems without the need for extensive configuration or complex dependencies. ### When should I avoid agent-control? Avoid using agent-control when your project does not require centralized control for large-scale AI agent management. Not recommended if your setup is simplistic or relies solely on languages other than Python or TypeScript, since the tool primarily supports these two. ### When should I avoid agency? If your development requires deeply integrated functionalities that would necessitate a heavier, more feature-rich framework. In scenarios where you need robust tooling for large-scale deployment and management as Agency does not offer extensive LLMOps (LLM Operations) capabilities beyond its minimalistic design. ### Is agent-control or agency more popular on GitHub? agency has more GitHub stars (489 vs 289). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-control and agency open source? Yes - both are open-source projects on GitHub (agent-control: Apache-2.0, agency: MIT). ### Where can I find alternatives to agent-control or agency? GraphCanon lists graph-backed alternatives at [agent-control alternatives](/tools/agentcontrol-agent-control/alternatives) and [agency alternatives](/tools/operand-agency/alternatives) ([agent-control markdown twin](/tools/agentcontrol-agent-control/alternatives.md), [agency markdown twin](/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 [this comparison](/compare/agentcontrol-agent-control-vs-operand-agency.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agent-control or agency? agent-control: Very active. agency: Steady. 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 agent-control and agency? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-control trust report](/tools/agentcontrol-agent-control/trust); [agency trust report](/tools/operand-agency/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agentcontrol-agent-control`](/api/graphcanon/graph?tool=agentcontrol-agent-control) - 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/_