OpenAI Agents SDK: Clearly BEATS CrewAI & LangGraph?

Key Concepts

• Responses API (replaces Chat Completions API)
• Built-in Tools: Web Search, File Search, Computer Use
• Agents SDK (replaces Swarm Agents)
• Observability Tools
• Vector Store (for File Search)
• Agentic RAG (Retrieval Augmented Generation)

1. Responses API

• Main Point: The Responses API is a new API from OpenAI that replaces the Chat Completions API.
• Details:
  • It's designed to "superpower agentic behavior."
  • The core function is client.responses.create.
  • It takes parameters like model (model name) and messages (the question/prompt).
• Example: The video demonstrates creating a simple chatbot using the Responses API.
  • Code snippet:

    response = client.responses.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Write a meal plan for a week"}]
    )
    print(response.choices[0].message.content)
    

• UI Integration: The video shows how to integrate the Responses API with Gradio to create a simple chatbot UI with minimal code.
  • Gradio code snippet:

    import gradio as gr
    def respond(message, history):
        response = client.responses.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": message}]
        )
        return response.choices[0].message.content
    demo = gr.ChatInterface(respond)
    demo.launch()
    

2. Built-in Tools

• Main Point: OpenAI provides built-in tools that can be used with the Responses API to enhance agent capabilities.
• Types of Tools:
  • Web Search: Allows the agent to search the web for information.
    • Code: tools=[{"type": "web_search_preview"}]
    • Example: Asking the agent "Tell me about Mvin Praisen" uses web search to retrieve information.
  • File Search: Allows the agent to search files stored in a vector store.
    • Process:
      1. Create a vector store on platform.openai.com.
      2. Upload files to the vector store.
      3. Get the vector store ID.
      4. Use the file_search tool with the vector store ID.
    • Code: tools=[{"type": "file_search", "vector_store_id": "your_vector_store_id"}]
    • Example: Uploading a paper about Graph RAG and asking "Tell me about Graph RAG" uses file search to retrieve information from the paper.
  • Computer Use: Allows the agent to interact with a computer environment (e.g., a browser).
    • Code: tools=[{"type": "computer_use_preview", "width": 640, "height": 480, "environment": "browser"}]
    • Process:
      1. The agent generates a tool call with an ID, action, and other details.
      2. The data needs to be parsed.
      3. Playwright is used to execute the function.
      4. A screenshot is captured after execution.
      5. The process is repeated until the task is complete.
• Agentic RAG: The file search example demonstrates agentic RAG, where the agent decomposes the query into multiple sub-queries to improve search results.
  • Example sub-queries: "graph rag", "what is graph rag", "graph rag details", "graph rag summary".

3. Agents SDK

• Main Point: The Agents SDK is a new SDK from OpenAI that replaces Swarm Agents and provides a framework for building agents.
• Details:
  • It's considered production-ready.
  • The video suggests it could surpass LangGraph and other agentic frameworks.
• Code:

  from agents import agent, runner
  agent_name = "Assistant"
  agent_instructions = "Create a meal plan for a week"
  runner.run_sync(agent_name, agent_instructions)
  

• Comparison to Praise Agents: The video notes that the Agents SDK follows a similar format to Praise Agents.

4. Observability Tools

• Main Point: OpenAI provides observability tools to monitor agent activity.
• Details:
  • Available on platform.openai.com under "Traces."
  • Allows you to see the workflow of agents, including inputs, outputs, and intermediate steps.
  • Provides clarity on what each agent is doing behind the scenes.
• Example: The video shows traces for tasks like "create a meal plan for a week" and "triage agent with god rail responses handoff post salesforce."

5. Conclusion

• The video highlights the new tools and features from OpenAI for building agents, including the Responses API, built-in tools, Agents SDK, and observability tools.
• The Agents SDK is positioned as a potentially superior alternative to existing agentic frameworks like LangGraph.
• The presenter expresses excitement about these new features and their potential impact on the development of AI agents.