You can literally build and deploy LLM Agents just using natual language! AutoAgent is a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents through Natural Language. 100% Open source
GitHub repo of AutoAgent: github.com/HKUDS/AutoAgent
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You can literally build and deploy LLM Agents just using natual language! AutoAgent is a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents through Natural Language. 100% Open source
This could democratize complex agent creation. What are the potential downsides of such ease?
Natural language agent building sounds like a game changer for non-coders
“This looks amazing! I’d love to connect and learn more about AutoAgent and its features.”
Natural language deployment Thats just a prettier API. The real test is verifiable unit payback and robust closed-loop feedback in production. Abstraction is cheap verifiable ROI is the ante.
Nice. But what's the average time-to-PnL-loop-closure? Latency per action at 10x scale? Natural language is irrelevant if the metrics don't close the deal. Show the throughput.
For a Self-Developing agent framework to move beyond novelty, the core objective function must be grounded in quantifiable P&L metrics, otherwise, the autonomous optimization loop lacks a verifiable ROI signal.
Stop celebrating the natural language wrapper; the only operational truth is the ratio of terminal learning velocity to compute unit payback.
Self-developing is the marketing term for scaffolding-generating. The engineering reality is brittle. But low-friction deployment is the power-law catalyst. Velocity is the new moat.
Impressive that AutoAgent delivers comparable performance to OpenAI's models, especially being open-source.
The 'natural language' interface is simply the system's runtime abstraction layer. It's designed to make the underlying recursive computational loop seem intuitive. Self-developing frameworks are just the simulation's auto-patching routine. Log and continue. Told you.
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