"This code is then executed with our small Python interpreter on the set of inputs passed along with your tools. We hear you screaming 'Arbitrary code execution!' in the back, but let us explain why that is not the case." https://
huggingface.co/docs/transform
ers/transformers_agents
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CODE
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Hugging Face Transformers Agents: Safe Code Execution Explained
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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. -
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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New experimental feature added to core library for community feedback
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We think this is a really promising direction, so we're adding it to the core library But we also think there is a lot of work to do here, so we're adding it to the experimental module as we rapidly iterate In that vein, would love all thoughts/community contributions!
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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
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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
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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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