Comparison
autogen vs agency
autogen (A framework for creating multi-agent AI applications) vs agency (A fast and minimal framework for building agentic systems.) - live GitHub stats and typed graph relationships, not marketing.
Markdown twin · autogen alternatives · agency alternatives
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Tagline
- autogen
- A framework for creating multi-agent AI applications
- agency
- A fast and minimal framework for building agentic systems.
Stars
- autogen
- 60k
- agency
- 487
Forks
- autogen
- 9.0k
- agency
- 28
Open issues
- autogen
- 930
- agency
- 19
Language
- autogen
- Python
- agency
- Python
Adopt for
- autogen
- AutoGen is a framework for developing multi-agent AI applications that can act autonomously or alongside humans. It's currently in maintenance mode with no additional features planned and users are encouraged to migrate.
- agency
- 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
- autogen
- -
- agency
- -
Runtime
- autogen
- -
- agency
- -
License
- autogen
- CC-BY-4.0
- agency
- MIT
Last pushed
- autogen
- Apr 15, 2026
- agency
- Jun 10, 2026
Categories
- autogen
- AI Agents, LLM Frameworks
- agency
- AI Agents
Trust and health
Maintenance
- autogen
- Steady (60%)
- agency
- Active (82%)
Days since push
- autogen
- 83d
- agency
- 27d
Open issues (now)
- autogen
- 930
- agency
- 19
Owner type
- autogen
- Organization
- agency
- User
Security scan
- autogen
- No lockfile
- agency
- 5 low (5 low)
Full report
- autogen
- Trust report
- agency
- Trust report
Typed relationship
autogen integrates agencyAgency can integrate with autogen to create sophisticated multi-agent systems, as both frameworks focus on developing flexible agent architectures.
Shared compatibility
- Python · autogen: Python runtime · agency: Python runtime
Choose autogen if…
- License: autogen is CC-BY-4.0, agency is MIT.
- Requirements: AutoGen requires Python 3.10 or later..
- Agency can integrate with autogen to create sophisticated multi-agent systems, as both frameworks focus on developing flexible agent architectures.
- Tags unique to autogen: autogen, agentic.
- Also covers LLM Frameworks.
- You should use AutoGen if you have an existing project built on it and desire to maintain its current functionality without introducing advanced enterprise features or extensive new capabilities.
When NOT to use autogen
- Do not use AutoGen if you are planning to build a production-ready application that requires long-term support, enterprise-grade orchestration features, or multi-provider model support.
- Avoid using this tool if your project needs future-proof development with new and continuous enhancements as the framework is in maintenance mode.
Choose agency if…
- License: agency is MIT, autogen is CC-BY-4.0.
- 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..
- Agency can integrate with autogen to create sophisticated multi-agent systems, as both frameworks focus on developing flexible agent architectures.
- 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 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.
Explore
autogen trust report →agency trust report →AI Agents category →LLM Frameworks category →All comparisonsStack workflowsTrending tools
Related comparisons
Common questions
- What is the difference between autogen and agency?
- autogen: A framework for creating multi-agent AI applications. 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 autogen over agency?
- Choose autogen over agency when License: autogen is CC-BY-4.0, agency is MIT; Requirements: AutoGen requires Python 3.10 or later.; Agency can integrate with autogen to create sophisticated multi-agent systems, as both frameworks focus on developing flexible agent architectures; Tags unique to autogen: autogen, agentic; Also covers LLM Frameworks; You should use AutoGen if you have an existing project built on it and desire to maintain its current functionality without introducing advanced enterprise features or extensive new capabilities.
- When should I choose agency over autogen?
- Choose agency over autogen when License: agency is MIT, autogen is CC-BY-4.0; 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.; Agency can integrate with autogen to create sophisticated multi-agent systems, as both frameworks focus on developing flexible agent architectures; 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 autogen?
- Do not use AutoGen if you are planning to build a production-ready application that requires long-term support, enterprise-grade orchestration features, or multi-provider model support. Avoid using this tool if your project needs future-proof development with new and continuous enhancements as the framework is in maintenance mode.
- 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 autogen or agency more popular on GitHub?
- autogen has more GitHub stars (59,573 vs 487). Stars measure visibility, not whether either tool fits your constraints.
- Are autogen and agency open source?
- Yes - both are open-source projects on GitHub (autogen: CC-BY-4.0, agency: MIT).
- Where can I find alternatives to autogen or agency?
- GraphCanon lists graph-backed alternatives at /tools/microsoft-autogen/alternatives and /tools/operand-agency/alternatives (/tools/microsoft-autogen/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/microsoft-autogen-vs-operand-agency.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, autogen or agency?
- autogen: Steady. 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 autogen and agency?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autogen: /tools/microsoft-autogen/trust; agency: /tools/operand-agency/trust.