Breaking Down & Testing FIVE LLM Agent Architectures - (Reflexion, LATs, P&E, ReWOO, LLMCompiler)
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Published 2024-04-30
Resources -
@LangChain Agent Tutorials & Code - • LangGraph (Python)
Overview on LLM Agents - • AI Agents! Giving Reasoning and Tools...
Link to Miro Board PDF: drive.google.com/file/d/1ESnrIy4c5LPOhNHRnn87Cv7DU…
Papers:
Reflexion Paper: arxiv.org/abs/2303.11366
LATs Paper: arxiv.org/abs/2310.04406
Plan-And-Execute Paper: arxiv.org/abs/2305.04091
ReWOO Paper: arxiv.org/abs/2305.18323
LLMCompiler Paper: arxiv.org/abs/2312.04511
LangSmith Traces:
Basic Reflection: smith.langchain.com/public/604a986b-a751-48ed-9dda…
Reflexion: smith.langchain.com/public/83b0f3a3-b788-471b-ae1b…
LATs: smith.langchain.com/public/d4541960-3f1a-4e53-9ec7…
Plan-And-Execute: smith.langchain.com/public/c13392c5-e058-4bbe-b25e…
ReWOO: smith.langchain.com/public/ee636208-6914-4c97-9d29…
LLMCompiler: smith.langchain.com/public/28857a8a-b9b1-45d5-8403…
Chapters:
00:00 - Intro
01:08 - Basic Reflection
02:44 - Basic Reflection Testing
06:32 - Reflexion Actor
09:57 - Reflexion Action Testing
12:25 - Language Agent Tree Search (LATs)
17:04 - LATs Testing
20:54 - Plan And Execute
23:38 - Plan And Execute Testing
26:28 - Reasoning Without Observation (ReWOO)
29:26 - ReWOO Testing
31:11 - LLMCompiler
35:19 - LLMCompiler Testing
36:05 - Outro
All Comments (21)
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Loved this video. Great stuff.
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Your channel has been very valuable today to get me situated on how to get the hang of LLM use. I can now start thinking about project ideas to get some practice. Thank you very much !
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That's an amazing work we have here, guys. Cheers to you, bro. Thanks!
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outstanding overview of key the agentic architectures, I learned a ton, prob one of the best out atm - Thanks
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Thank you for sharing! Great summary.
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Very nicely done, thank you for such a good preseentation.
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Really good break down for folks building,thanks for putting this out
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This is really great info, thanks a bunch for sharing. What's really eye-opening is the run times and token counts.
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Well done! Thank you, sir
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Great work, thanks for this🙏. There is another agentic approach which is called self discovery. Would be cool if you cover that as well 😊.
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This breakdown is insanely helpful 👏 I’ve been working as a Web Engineer for > 10 yrs and recently started learning about AI/ML. I began my career as a self-taught dev in the good ol’ jQuery days, but my lack of CS fundamentals is starting to come back an bite me. These architectural diagrams are incredibly useful for breaking down high-level concepts.
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Good overview. It would be very interesting to see the answer quality benchmarks for these techniques. In a lot of real business cases the time and cost have much less importance than the quality.
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We made a 7th with output focused recursive events at my company :)
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Hi Adam, great work. I've been struggling trying to evaluate the different agent frameworks, autogen, crewai VRSEN and on and on. langchain etc. seems to be more logical as we can see what's happening and is more predictable. Would it be possible to get the Miro you built for this presentation? Greetings from France.
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Question: If I have a data preprocessing agent that has access to around 20 preprocessing tools, what is the best way to go about executing them on a pandas data frame? Do I have the data frame in the State and then pass that input in the function? Does the agent need to have access to that data frame or can we abstract that?
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Which would you say is more crucial to analyzing the "correctness" of the language agent tree search result: "blah blah blah" or "yada yada yada"?
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Hey can you please share the miro board link? Or drop it into a high res pdf? AWESOME work btw 👍👍👍
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what is used for scoring?
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which one do you prefer for the recommendation
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¿Puedes compartir con nosotros tu presentación de Miro?, Great Job
