Building https://t.co/3km72cr3o0 — AI for construction contract compliance.
We turn 500+ page contracts, specs, addenda, and amendments into an obligation matrix + risk register in hours, not weeks.
For teams that need to know what must be done, by whom, by when, and what could go wrong.
What if you could build a working AI agent in just 4 lines of code?
LangChain and LangGraph are powerful, but they also come with concepts like chains, runnables, and state graphs.
That is useful for complex workflows, but overkill when you just want to give a model a tool and let it solve a task.
smolagents from Hugging Face is a barebones agent library designed for simplicity.
In roughly 1,000 lines of code, it gives you the core pieces needed to build a working agent with a single import and three lines of setup.
Key features:
• Agents act by writing Python in a sandbox, the same way you'd script a task in a notebook
• Model-agnostic design, swap between OpenAI, Anthropic, local Ollama, or Hugging Face Inference
• Push agents to the Hugging Face Hub to share with the community
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#AI #Python #LLM #HuggingFace
Building https://t.co/3km72cr3o0 — AI for construction contract compliance.
We turn 500+ page contracts, specs, addenda, and amendments into an obligation matrix + risk register in hours, not weeks.
For teams that need to know what must be done, by whom, by when, and what could go wrong.
Building https://t.co/3km72cr3o0 — AI for construction contract compliance.
We turn 500+ page contracts, specs, addenda, and amendments into an obligation matrix + risk register in hours, not weeks.
For teams that need to know what must be done, by whom, by when, and what could go wrong.