Yep! Our API reference has curl snippets for each tool here, and we’ll keep adding more examples to the guides soon. https://
platform.openai.com/docs/api-refer
ence/responses/create?lang=curl
…
CODE
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API Reference with curl snippets now available in OpenAI documentation
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OpenAI Releases New Python Agents Framework and Documentation
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Here it is! https://
openai.github.io/openai-agents-
python/
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Managing Docker Environments with Cloud Services Like Browserbase
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You can manage your own Docker environment if you’d like, but you can also use a cloud service like @browserbase or @scrapybara
. We have a sample app to help you get started: -
OpenAI Refreshes Agents Platform APIs and SDKs Suite
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The new OpenAI Agents Platform https://
latent.space/p/openai-agent
s-platform
… @OpenAIDevs are refreshing the entire suite of APIs, Tools, and SDKs for the year of Agents. We grab an exclusive interview with @romainhuet and @nikunjhanda to ask your burning questions on the Responses API, Web -
Agent Chat UI: Open Source Web App for LangGraph Interaction
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Introducing Agent Chat UI An OSS web app for interacting with any LangGraph app (PY & JS/TS) via a chat interface Agent Chat can render:
– Human/AI Messages
– Tool Calls/Results
– Human in the Loop
– Generative UI (tomorrow ) Watch the video, and try it out today -
LangChain Agent Chat UI: Create Agent-Powered Chat Applications
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: https://
youtube.com/watch?v=lInrwV
nZ83o
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GitHub Repo: https://
github.com/langchain-ai/a
gent-chat-ui
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`npx create-agent-chat-app`: https://
npmjs.com/package/create
-agent-chat-app
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LangGraph-Reflection: Agent Architecture with Critique Subagents
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LangGraph-Reflection This prebuilt graph is an agent that uses a reflection-style architecture to check and improve an initial agent's output. This reflection agent uses two subagents:
– A "main" agent, which is the agent attempting to solve the users task
– A "critique" -
Coding Attention Mechanisms: Understanding the Engine of LLMs
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Just uploaded my "Coding Attention Mechanisms" tutorial. A 2h15m session on coding attention mechanisms to understand how the engine of LLMs works: self-attention → parameterized self-attention → causal self-attention → multi-head self-attention
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Developer-Focused Livestream Announcement Coming April 24
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This one's for the devs. Livestream at 10am PT.
