

Llama Agents
Overview :
llama-agents is an asynchronous-first framework for building, iterating, and productionizing multi-agent systems. It encompasses functionalities like multi-agent communication, distributed tool execution, and human-in-the-loop (HITL) capabilities. Each agent is treated as a service, continually processing incoming tasks. Agents retrieve and publish messages from message queues. A control plane sits atop the system, tracking ongoing tasks, network services, and determining the next steps for task processing.
Target Users :
Targeted at software developers and system architects, particularly those building complex multi-agent systems or seeking to enhance their systems' automation and intelligence levels.
Use Cases
Build a customer service system with multiple agents collaborating to enhance response speed and service quality.
Develop a distributed data analysis platform where agents can concurrently process data, boosting analysis efficiency.
Implement a human-in-the-loop automated testing system where agents execute tests automatically, with human oversight for result verification.
Features
Supports multi-agent communication and distributed tool execution
Integrates human-in-the-loop functionality for more intelligent task processing
Top-level control plane tracks task and service status, enabling intelligent task handling decisions
Asynchronous design enhances system processing capacity and response speed
Supports Docker and Kubernetes deployment for easy scalability and integration
Provides a rich API and CLI tools for developers to monitor and interact with the system
How to Use
1. Install llama-agents using pip and its dependency llama-index-core.
2. Set up agents and initial components, creating AgentService and ControlPlaneServer, etc.
3. Write agent logic, defining how agents respond to and process tasks in the message queue.
4. Start the message queue and control plane, ensuring all system components operate correctly.
5. Register agent services to the message queue and control plane, allowing them to receive and send messages.
6. Launch the agent system using LocalLauncher or ServerLauncher for single or server mode operation.
7. Interact with the agent system using a client or CLI tools to create tasks and retrieve results.
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