Cool competition with @langchain
AGENTS
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Building and Deploying Custom Tools for AI Agents
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Tools are elementary: a name, a description, a function. Designing a tool and pushing it to the Hub can be done in a few lines of code. The toolkit of the agent serves as a base: extend it with your tools, or with other community-contributed tools: https://
huggingface.co/docs/transform
ers/custom_tools
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Building Super-Powerful AI Agents Together
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Please play with it, add your tools, and let's create *super-powerful agents* together. Here's a notebook to get started:
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How AI Agents Work: Prompt Building and Chain-of-Thought
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How does it work in practice? It's straightforward prompt-building:
• Tell the agent what it aims to do
• Give it tools
• Show examples
• Give it a task The agent uses chain-of-thought reasoning to identify its task and outputs Python code using the tools. -
Transformers Agents: Democratizing ML with Multimodal Control
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We just released Transformers' boldest feature: Transformers Agents. This removes the barrier of entry to machine learning Control 100,000+ HF models by talking to Transformers and Diffusers Fully multimodal agent: text, images, video, audio, docs… https://
huggingface.co/docs/transform
ers/transformers_agents
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Building AI Agents with LLMs for Multimodal Tasks
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Create an agent using LLMs (OpenAssistant, StarCoder, OpenAI …) and start talking to transformers and diffusers It responds to complex queries and offers a chat mode. Create images using your words, have the agent read the summary of websites out loud, read through a PDF
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LangChain Plan and Execute Agent Documentation
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Python Docs: https://
python.langchain.com/en/latest/modu
les/agents/plan_and_execute.html
… JS/TS Docs: https://
js.langchain.com/docs/modules/a
gents/agents/examples/plan_and_execute_agent
… Blog: -
LangChain Agents Enhance Focus on Complex Task
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Up until now, agents in LangChain have followed the algorithm of: – take user input
– think about action to take
– take action and observe response
– repeat until done This is great for simple tasks, but for more complex tasks we've noticed the agent losing focus -
BabyAGI and Plan-and-Solve Pattern Recognition in AI
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We first noticed this pattern in @yoheinakajima
's BabyAGI project, and then more recently saw it in the Plan-and-Solve paper Paper: https://
arxiv.org/abs/2305.04091
Code: -
LangChain Introduces Plan-and-Execute Agents for Complex Tasks
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Plan-and-Execute Agents Inspired by BabyAGI and the recent Plan-and-Solve paper, we're introducing a new type of @langchain agent We think these are better for more complex tasks, at the cost of more calls to the LLM Blog: https://
blog.langchain.dev/plan-and-execu
te-agents/
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