Insight-Driven System Design with Claude Code
/ 1 min read
I conducted an interesting exercise today. The goal was to take an accelerated path towards building an agent framework in Elixir based on insights gained from the most popular agent frameworks. I created an agentic system, using Claude Code, to study 14 popular agent frameworks, creating subject matter insights as context to facilitate an elixir architecture discussion.
How well did it do? Let me know on X
You can see the analysis findings for each framework in the repo.
| Framework | Source | Focus |
|---|---|---|
| autogen | Microsoft | Multi-agent conversation patterns |
| langgraph | LangChain | Graph-based agent orchestration |
| crewAI | CrewAI | Role-based multi-agent teams |
| openai-agents-python | OpenAI | Swarm-inspired lightweight agents |
| pydantic-ai | Pydantic | Type-safe agent interfaces |
| llama_index | LlamaIndex | Data-augmented agents |
| google-adk | Agent Development Kit | |
| aws-strands | AWS | Strands agent framework |
| ms-agent-framework | Microsoft | Semantic Kernel agents |
| MetaGPT | DeepWisdom | Software dev team simulation |
| camel | CAMEL-AI | Communicative agent framework |
| agno | Agno | Lightweight agent runtime |
| agent-zero | FrdLnd | Personal assistant framework |
| swarm | OpenAI | Educational multi-agent patterns |