Breaking Down & Testing FIVE LLM Agent Architectures - (Reflexion, LATs, P&E, ReWOO, LLMCompiler)

Ғылым және технология

Large Language Model Agents have taken over LLM and Artificial Intelligence application design by storm, so this time we check out and simplify six main concepts and five popular papers documenting ways to set up language model based agents, as well as directly testing examples.
Resources -
@LangChain Agent Tutorials & Code - • LangGraph (Python)
Overview on LLM Agents - • AI Agents! Giving Reas...
Link to Miro Board PDF: drive.google.com/file/d/1ESnr...
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/60...
Reflexion: smith.langchain.com/public/83...
LATs: smith.langchain.com/public/d4...
Plan-And-Execute: smith.langchain.com/public/c1...
ReWOO: smith.langchain.com/public/ee...
LLMCompiler: smith.langchain.com/public/28...
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

Пікірлер: 37

  • @pinkmatter8488
    @pinkmatter848811 күн бұрын

    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 !

  • @MekMoney79
    @MekMoney7914 күн бұрын

    outstanding overview of key the agentic architectures, I learned a ton, prob one of the best out atm - Thanks

  • @PYETech
    @PYETech14 күн бұрын

    That's an amazing work we have here, guys. Cheers to you, bro. Thanks!

  • @kenchang3456
    @kenchang345612 күн бұрын

    This is really great info, thanks a bunch for sharing. What's really eye-opening is the run times and token counts.

  • @TheFocusedCoder
    @TheFocusedCoder13 күн бұрын

    Really good break down for folks building,thanks for putting this out

  • @cmthimmaiah
    @cmthimmaiah12 күн бұрын

    Very nicely done, thank you for such a good preseentation.

  • @caokang4957
    @caokang495711 күн бұрын

    Thank you for sharing! Great summary.

  • @GriffinBrown-tq9jz
    @GriffinBrown-tq9jz11 күн бұрын

    Well done! Thank you, sir

  • @sanesanyo
    @sanesanyo9 күн бұрын

    Great work, thanks for this🙏. There is another agentic approach which is called self discovery. Would be cool if you cover that as well 😊.

  • @user-gy7te1ql3g
    @user-gy7te1ql3g12 күн бұрын

    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.

  • @tyler-morrison
    @tyler-morrison11 күн бұрын

    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.

  • @AdamLucek

    @AdamLucek

    9 күн бұрын

    Glad you found this helpful! Everything I record and share is all self-taught as well, I've got no formal CS background- I just think the topic is interesting and worth sharing!

  • @tk0150

    @tk0150

    8 күн бұрын

    Would you share your slides? So helpful!

  • @Jandodev
    @Jandodev13 күн бұрын

    We made a 7th with output focused recursive events at my company :)

  • @MEvansMusic
    @MEvansMusic7 күн бұрын

    what is used for scoring?

  • @xollob
    @xollob8 күн бұрын

    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.

  • @AdamLucek

    @AdamLucek

    6 күн бұрын

    Here you go! drive.google.com/file/d/1ESnrIy4c5LPOhNHRnn87Cv7DU_i0-_J9/view?usp=sharing

  • @xollob

    @xollob

    3 сағат бұрын

    @@AdamLucek Thank you so much Adam.

  • @lavamonkeymc
    @lavamonkeymc8 күн бұрын

    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?

  • @AdamLucek

    @AdamLucek

    8 күн бұрын

    I imagine it could be abstracted out. A lot of the processing you can do with a langgraph setup similar to these doesn't necessarily need an LLM touch at the computation/function step- could use the LLM for logic based routing to the right node function that is already defined to affect a pre set dataframe

  • @geofffane5276
    @geofffane52766 күн бұрын

    Hey can you please share the miro board link? Or drop it into a high res pdf? AWESOME work btw 👍👍👍

  • @AdamLucek

    @AdamLucek

    6 күн бұрын

    Here you go! drive.google.com/file/d/1ESnrIy4c5LPOhNHRnn87Cv7DU_i0-_J9/view?usp=sharing

  • @ricardoaltamiranomarquez753
    @ricardoaltamiranomarquez7539 күн бұрын

    ¿Puedes compartir con nosotros tu presentación de Miro?, Great Job

  • @AdamLucek

    @AdamLucek

    6 күн бұрын

    Here you go! drive.google.com/file/d/1ESnrIy4c5LPOhNHRnn87Cv7DU_i0-_J9/view?usp=sharing

  • @ricardoaltamiranomarquez753

    @ricardoaltamiranomarquez753

    6 күн бұрын

    @@AdamLucek thank you very much, you are very good

  • @linuszhu
    @linuszhu13 күн бұрын

    which one do you prefer for the recommendation

  • @AdamLucek

    @AdamLucek

    12 күн бұрын

    I would say each have different applications, and are better used as parts of larger agent ecosystems. I.e. taking a reflection based approach to some end validation step would be useful, however a more plan-and-execute style approach to initial generation would likely be a better first step. As with most llm based apps, a lot depends on what data your using, the task/end goal you want, and your tolerance of processing time. Would more so apply the general concepts here rather than see them as strict end solutions 😁

  • @genXstream
    @genXstreamКүн бұрын

    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"?

  • @AdamLucek

    @AdamLucek

    6 сағат бұрын

    Im more partial to yada yada yada, but I can see the benefits of blah blah blah. Really comes down to your use case and desired blah to yada ratio

  • @matthewpublikum3114
    @matthewpublikum311413 күн бұрын

    Where's the code? It would be nice to know what is the smallest LLM capable of doing the planner/task decomposition and verification.

  • @AdamLucek

    @AdamLucek

    13 күн бұрын

    The code comes from LangChain's series on LangGraph, linked in the description. Here's a direct link to their repo github.com/langchain-ai/langgraph/tree/main/examples

  • @PRColacino
    @PRColacino11 күн бұрын

    Great video! Could you share the code?

  • @AdamLucek

    @AdamLucek

    9 күн бұрын

    Thanks! The code comes from LangChain's series on LangGraph, linked in the description. Here's a direct link to their repo github.com/langchain-ai/langgraph/tree/main/examples

  • @niftylius
    @niftylius14 күн бұрын

    Hello

  • @sharannagarajan4089
    @sharannagarajan408913 күн бұрын

    AlL of them suck

  • @readmarketings9061

    @readmarketings9061

    13 күн бұрын

    Do you have better solution?

  • @missigno42

    @missigno42

    12 күн бұрын

    Why?

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