Agency — AI Agent Review & Live Stats

Live GitHub stats, community sentiment, and trend data for Agency. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 1, 2026 • Sentiment updated: Mar 17, 2026

GitHub Statistics

Community Sentiment

Community Buzz: The operand/agency project has gained significant attention in the AI and machine learning communities for its minimalistic approach to building autonomous agents. Developers praise its ease of use and flexibility. The project's focus on artificial general intelligence has sparked interesting discussions.

Why Agency Stands Out

Agency stands out from alternative frameworks by providing a minimalistic and flexible approach to building autonomous agent systems. Its focus on artificial general intelligence and support for various communication protocols, such as AMQP, make it an attractive choice for developers. The project's emphasis on ease of use, scalability, and performance is evident in its straightforward API and support for multiprocessing and multithreading. By leveraging these features, developers can create complex agent-based systems with ease.

Built With

Build a decentralized autonomous organization (DAO) with multiple agents making decisions — Agency enables this by providing a flexible framework for agent communication and action invocation, Build a smart home system with agents controlling lighting, temperature, and security — Agency allows for easy integration of various agents and devices through its API, Build a research agent that analyzes large datasets and generates reports — Agency's support for multiprocessing and multithreading enables fast data processing, Build a customer service chatbot that routes inquiries to human agents — Agency's access policies and permission callbacks ensure secure and controlled communication, Build a multi-agent simulation for urban planning and traffic management — Agency's AMQP support allows for scalable and distributed simulations

Getting Started

  1. Install Agency using pip: `pip install agency`
  2. Create a new agent by subclassing the `Agent` class and defining actions using the `@action` decorator
  3. Configure a `LocalSpace` or `AMQPSpace` to connect your agents
  4. Add agents to the space using the `add` method
  5. Try sending a message to an agent using the `send` method to verify it works

About

A fast and minimal framework for building agentic systems

Official site: https://createwith.agency

Category & Tags

Category: infrastructure

Tags: actor, actor-model, agent, agents, agi, ai, api, artificial-general-intelligence, artificial-intelligence, autonomous-agent, autonomous-agents, framework, llm, llmops, llms, machine-learning, minimal, python

Market Context

The operand/agency project is part of the growing trend of open-source frameworks for building advanced AI models.