How to MAKE AI Agents MORE SUCCESSFUL!!!
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- Опубликовано: 2 окт 2024
- This work proposes to use executable Python code to consolidate
LLM agents’ actions into a unified action space (CodeAct). Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions.
🔗 Links 🔗
Executable Code Actions Elicit Better LLM Agents
arxiv.org/pdf/...
CodeAct Project - github.com/xin...
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🎯 Key points for quick navigation:
00:00 *🎯 Introduction to improving AI agent communication for enhanced effectiveness.*
00:13 *📄 Overview of the paper "Executable Code Actions Elicit Better LLM Agents."*
00:42 *🛠️ Traditional AI agents use text or JSON for communication with tools.*
01:26 *💡 Paper proposes using executable Python code instead of text/JSON for better effectiveness.*
01:55 *🚀 Introduction of "CodeAct" system for creating executable Python code by LLM agents.*
02:08 *🖥️ Shift from human-readable text to code language for tool communication.*
02:36 *⚙️ CodeAct consolidates LLM agent actions into a unified action space, improving success rate by 20%.*
03:33 *🌍 Example of calculating the most cost-effective country to buy a smartphone using Python code.*
04:41 *🔄 CodeAct allows simultaneous actions and control/data flow in Python, enhancing efficiency.*
05:35 *🛡️ Benefits of using Python: access to extensive libraries, automatic feedback, and better productionization.*
06:46 *📈 Higher success rate and efficiency in using CodeAct compared to JSON or text communication.*
07:53 *🔍 Wide availability of pre-trained data and tools in Python simplifies implementation.*
08:38 *🔧 Python's programming features like looping and data flow enhance LLM agent capabilities.*
09:22 *🧪 Live demo of CodeAct's capabilities in a Jupyter notebook environment.*
10:58 *📊 Framework overview: CodeAct executes code and interacts with tools, providing effective results.*
12:19 *🔝 CodeAct shows higher success rates and improves open model performance.*
13:31 *🔍 AI communication using code can be more efficient than human language due to reduced ambiguity.*
14:00 *🧠 Empirical proof that using code for LLM actions improves agent performance.*
Made with HARPA AI
Yeah I think the people excited about using only english for agents don't realize how inefficient english is for communicating instructions. Thanks for highlighting the paper! great video
basically code interpreter is the best agent. I thought this was obvious
AI research prompts
AI takes care of the knowledge and conceptual frameworks for you (second brain). Your job is to build projects.
excellent take!
i myself have been thinking about this exact idea. But the issue is dont have a good way to overcome is how to handle the generated code
This part of codeACT Agents is somewhat acheived by the Autogen team, since they have code interpreter in built and user_proxy executes it to get the desired output to move on to the further steps. BTW This paper is quite interesting.
Doesn't this effectively open up the possibility of remote code execution via prompt injection now for every layer in an agent? I get code can be executed in an isolated container but it still can hit external APIs and still needs to return some output to an agent orchestrator. I was thinking a safer approach may be a combination of CodeAct (for dynamic/complex, code interpreter type of tasks) and standard text/JSON for statically defined python actions.
So this is basicaly a code interpreter. Which have been around quite some time now.
Some very nice work! Thanks.
Glad you liked it!
Split on the thought of arbitrary code execution
Now this is very _Informative_ and meaningful AI content for those who actually _use_ AI systems. Thanks!
Thank you very much. This is the kind of content that usually doesn't do well but I can't stop making :) I find these kind of paper work with practical implications extremely helpful to stay ahead of the game!
I think it's because of the niche topics presented and the relatively low interest from the general population. Or maybe it is influence by the less established community of your channel. Or maybe idk!?😂@@1littlecoder
Mostly it's the latter :)
I’ve been doing this frame work for my agents for about a year now, I thought it was an obvious solution instead of having the LLM try to communicate in some obscure JSON language you define, its communicating in a language it natively understands, (and is extremely good at)
One major issue, is that it is executing code… that is particularly dangerous because if you ask it to make a fork bomb… it becomes a suicidal agent very quickly lol. there are a lot of serious security risks in that.
This video arrives in Brazil. Please do more of this. You are get a big bubble of IA here.
Thank you 🙏🏾
Dope channel
❤
Can you create a video on how to build functions that are useable for ai agents😊
Good content. Thank you
Glad you liked it!
But we can easily use langchain tool call python repl
That is an interesting point. This paper particularly talks about the communication channel than adding an extra tool which is python repl like you gave an example
Great idea to easily implement this pattern. Thanks for the idea! About the pattern itself now : must evaluate to check the success rates of this technique, and think to dockerize the code execution to avoid sick security hacks.
Your thought creation in these videos are awesome! I am so impressed with your ability and the fact you also provide the article also. Nice work!
Thank you. Curious what do you mean by Thought creation?
@@1littlecoder Simply the flow of your entire videos, information, examples, and explanations seems to be well put together. You also reference the paper which isn't something a lot of youtubers are doing. I am looking forward to more of your videos to learn about how I can launch my AI agency for local businesses in my area. If you ever start creating specific use cases this would go next level with all your knowledge. Also I watched you speak with another guest speaker and just from his 'time-boxing' comment it's game changing for someone who wants to start learning somewhere about AI
This a great idea, however letting the AI loose to execute code can be dangerous. At least with tools, the AI is restricted to what it can execute.
There’s a great Jupyter server docker image in the Autogen repo that’s extremely lightweight and setup to act as a code execution environment. I take that image and provide some additional packages and make some tools permanent over the existing execution services like code interpreter or Google’s new extensions environment service.