Hey HN! We are Windsor and Cathy of Dedalus Labs (https://www.dedaluslabs.ai/), a cloud platform for developers to build agentic AI applications. Our SDK allows you to connect any LLM to any MCP tools – local or hosted by us. No Dockerfiles or YAML configs required.Here’s a demo: https://youtu.be/s2khf1Monho?si=yiWnZh5OP4HQcAwL&t=11Last October, I (Windsor) was trying to build a stateful code execution sandbox in the cloud that LLMs could tool-call into. This was before MCP was released, and let’s just say it was super annoying to build… I was thinking to myself the entire time “Why can’t I just pass in `tools=code_execution` to the model and just have it…work?Even with MCP, you’re stuck running local servers and handwiring API auth and formatting across OpenAI, Anthropic, Google, etc. before you can ship anything. Every change means redeploys, networking configs, and hours lost wrangling AWS. Hours of reading docs and wrestling with cloud setup is not what you want when building your product!Dedalus simplifies this to just one API endpoint, so what used to take 2 weeks of setup can take 5 minutes. We allow you to upload streamable HTTP MCP servers to our platform. Once deployed, we offer OpenAI-compatible SDKs that you can drop into your codebase to use MCP-powered LLMs. The idea is to let anyone, anywhere, equip their LLMs with powerful tools for function calling.The code you write looks something like this: python client = Dedalus() runner = DedalusRunner(client) result = runner.run( input=prompt, tools=[tool_1, tool_2], mcp_servers=["author/server-1”, “author/server-2”], model=["openai/gpt-4.1”, “anthropic/claude-sonnet-4-20250514”], # Defaults to first model in list stream=True, ) stream_sync(result) # Streams result, supports tool calling too Our docs start at https://docs.dedaluslabs.ai. Here’s a simple Hello World example: https://docs.dedaluslabs.ai/examples/01-hello-world. For basic tool execution, see https://docs.dedaluslabs.ai/examples/02-basic-tools. T...
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