Inside Google’s New Agent Development Kit An AutoGen Developer’s Perspective
AI Summary
Overview
- Google announced the multi-agent open-source SDK for building agents, showing similarities with the autogen framework previously developed.
- This video compares Google’s SDK documentation with the autogen framework, exploring similarities and differences.
Similarities
- Hello World Implementation
- Objective: Create a simple agent with access to a language model (LM) and a calculator tool.
- Both frameworks use high-level abstractions for agents, with autogen using an “assistant agent” and Google using an “LLM agent.”
- Initialization of tools and setup processes share common steps.
- Tool Setup
- Both frameworks allow Python functions to be passed as tools.
- Clear structure for defining agents, instructions, and tools in both SDKs.
Differences
- Setup Complexity
- Google API requires more setup details (app name, user ID, session ID) and integrates with Google Cloud services.
- Agent Definition & Execution
- Google requires defining more session-related structures.
- Execution uses a different flow, emphasizing integration with cloud services.
- Deployment
- Google ADK offers a clear deployment strategy, allowing easy deployment through their agent engine or containerization.
- Callback support to implement control flow modifications and evaluate agents is strong.
Areas for Improvement
- Lack of scalability in distributed runtime environments.
- Task management abstractions and clear termination conditions are not specified.
- Limited low-code tooling and declarative specifications for agent configurations.
- Observability and tracing methods are unclear in the Google ADK.
Conclusion
- The Google SDK is a promising solution for multi-agent systems, mirroring many design ideas from existing frameworks like autogen.
- The space of multi-agent systems is still developing, focusing on interoperability across frameworks and environments.