Langchain multiple agents reddit. Or check it out in the app stores   .

Langchain multiple agents reddit 7 types of memory or 6 types of chains that sound different but NOWHERE do they make transparent what difference in outcome (or inner workings) it makes if i use a retrieval or retrievalqa or LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. To interact with external APIs, you can use the APIChain module in LangChain. Tag me too if you find something. 1 I think you should use a combination of two agents: an AgentExecutor which gathers the information (you can put a higher temperature on this one) and the agent which actually answers the question with all the information provided (put temperature less than 0. 22K subscribers in the LangChain community. I want to use an open source LLM as a RAG agent that also has memory of the current conversation (and eventually I want to work up to memory of previous conversations). For example: Get the Reddit app Scan this QR code to download the app now. Tracing stuff is valuable to check out what happened in every step during chain which is easier than putting bunch of print in between your chain or having langchain output verbose to terminal. I found 2nd one more useful, as ir creates sql query from useri put and we can manually add new step in our tool to run the generated sql query on database and the result is returned to If you have used tools or custom tools then scratchpad is where the tools descriptions are loaded for the agent to understand and use them properly. Debugging: Debugging was tough due to LangChain's complexity with many abstractions. If a helper agent can do a task, it might ask the user for more details to get the job done. I haven't yet tried Agency Swarm, which is another framework. For RAG you just need a vector database to store your source material. Can someone suggest me how can I plot charts using agents. On the other hand, Phidata make it so easy to create agents, set up tools and RAG and create multi-agent architectures that I’m leaning towards using it for the first version. AI agents Group Discussion using Autogen Tutorial Hey everyone, check out this tutorial on how to enable Multi-Agent conversations and group My agent writes queries to retrieve data from Sqlite Databases. LangChain (well, LangGraph actually) seems to be really working for me so far, and many others as well, to the point that even other such services (like Dify. Agreed. Having started playing with it in its relative infancy and watched it grow (growing pains included), I’ve come to believe langchain is really suited more to very rapid prototyping and an eclectic selection of helpers for testing different implementations. Reddit search functionality is also provided as a multi-input tool. Yes, the prompting in langchain is specifically tuned for OpenAI and assumes that the LLM is capable of at least that level of reasoning/instruction following. The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Unless things have changed since I last dug into LangChain there's lots of stuff it does poorly or in an non-optimized fashion. This was my first time writing an agent with a good and serious usecase. g. Whether this is true depends entirely on your use case. langchain already has a lot of adoption so you're fighting an uphill battle to begin with. I’m building an agent with custom tools with Langchain and wanna know how to use different llms within it. It occasionally picks the right tool but often chooses incorrectly. Get the Reddit app Scan this QR code to download the app now. LangChain is an open-source framework and developer toolkit that helps developers get LLM applications First, you can use a LangChain agent to dynamically call LLMs based on user input and access a suite of tools, such as external APIs. Some quick highlights: • ⁠works with practically any LLM, via api_base or using litellm • ⁠agents as first-class citizens from the start, not an afterthought • ⁠elegant multi-agent communication orchestration Get the Reddit app Scan this QR code to download the app now. 7K subscribers in the AI_Agents community. callbacks import StreamlitCallbackHandler from langchain. 5-16k for business tasks and have maybe 2-3 subtasks where I needed GPT4 for some academic reasoning/classification. As a tool dev, I was thinking that maybe we should focus more on making our real world APIs more understandable for LLM, rather than developing a langchain agent as a middleware.     TOPICS I an thinking in terms of software development if you have a multiple agents that send each other responses/review code and orchestrate tasks to write software or solve a problem? Has anyone attempted that? Let’s say you have two agents who both have access to python repl and bash. Langchain makes it fairly easy to do context augmented retrieval (i. reranking, two-stage retrieval, multi-modal agents, continuous learning/updating of db, cross-encoders, optimizing text splitters, etc. ) The relevant specialized agent then engages with the user to address their specific query Hii, I am trying to develop a data analysis agent, and using langchain CSV agent with local llm mistral through Ollama. 5 is an idiot. It's for anyone interested in learning, sharing, and discussing how AI can be leveraged to optimize businesses or LangChain is an open-source framework and developer toolkit that helps developers get LLM applications Skip to main content Open menu Open navigation Go to Reddit Home I've tried using `JsonSpec`, `JsonToolkit`, and `create_json_agent` but I was able to apply this approach on a single JSON file, not multiple. LOL. Adding this to the prompt of the agent works sometimes but it is not consistent prompt = prompt+"Only output the tool response. I found langgraph very free to create the structures you want including users on the loop, state storage in database, co-pilot agents and once you understand the workflow you can do whatever you want, the most important is to think in the state and how each node alters the state. Langchain definitely needs an option that allows the agent to return the results from tools as is, especially tools Agreed. I played with agents about 9 months ago and it seemed to me like it's overhyped. So, I think you could build impressive showcases with AI agents, but generally they weren't useful in practice. The reason to use agents is sometimes users can ask a question which may need to use multiple tools to answer. My opinions of LlamaIndex are increasingly negative. this is a multi agent framework rather than a multi tool framework). However, I have found reading through the docs to be difficult - too many abstractions and many of them seem organically developed - lots of rapidly evolving code so hard to know if the API you are using will continue to be supported in a couple weeks. So I thought since Groq is ultra fast and rolled out the new tool calling feature, I’d give it a shot. Posts from users with negative karma are automoderated. Langchain seems pretty messed up. Use. What could be the drawbacks of such a system? One is timing, but this could be solved with two (or more been playing with agents for a while now, concretely via langchain tool calling agents/custom- is there a way to structure the (final) output to be schema tied (using pydantic as we have for llms)? all my searches so far found either utilizing some rural implementations like swarms or very simplistic ones (suited specifically for RAG) Langchain is not ai Langchain has nothing to do with chatgpt Langchain is a tool that makes Gpt4 and other language models more useful. Here's how it works: The user sends a message to the main agent (let's call it the "Planner I have multiple agents and I'm not sure if I should have multiple checkpoint tables, one for each of them, or only one table. You can create a custom agent that uses the ReAct (Reason + Act) framework to pick the most suitable tool based on the input query. There's too many similar ways to get to one outcome, and their interfaces are way too modular and obsfucated. you may have a lot of insightful and useful modifications in your design, but if you don't communicate what those are, you're just assuming everyone is as What are the pros/cons of using LangChain in January 2024 vs going vanilla? What does LangChain help you the most with vs going vanilla? Our use cases are: - Using multiple models using hosted and on-prem LLMs (both OSS and OpenAI/Anthropic/etc. But is there a way to allow the LLM in an agent setting to select up to 3 instead of using just one? I mean seems like the agent always chooses only one, not 2 or 3. Langchain tries to be a horizontal layer which works with everything underneath so langchain obfuscate lot of stuff. If the supervisor agent delegates to the API calling agent, and that agent responds with a follow-up question for more information, it goes back up the hierarchy to the supervisor agent and the follow-up question is returned as the response to the user. Initially, the agent was supposed to be training candidates for interview situations but based on the non-finetuned LLM appeared to work better as a junior recruiter. agents import initialize_agent, AgentType from langchain. Any agent can decide which other agent to call next. For example, I would say help me with Tesla information and choose 5-10 kpis from a predefined list such as valuation, assets, liability, share price , number of cars sold by agents and tools. Please check it out at https://getsparks. gguf file) The langchain agent currently fetches results from tools and runs another round of LLM on the tool’s results which changes the format (json for instance) and sometimes worsen the results before sending it as “final answer”. Internet Culture (Viral) Amazing So recently I was testing out the Multi-Agent Workflow of langchain with some budget constraints and hence I decided to use Llama 3 model from Ollama. We've added three separate example of multi-agent workflows to This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. Please share. Every framework is gonna be very young and suffering from the problems s langchain. Have you checked create_sql_agent and create_sql_query_chain . The nuances of RAG come later, e. You tell the main agent (Planner Agent) that you want to plan a trip to Paris. Or check it out in the app stores How to use agent with multiple vectorstores? I did it all with instruction, here's my piece of code, where agent is used Langchain Agent variables upvotes My issue was more around binding a tool to an agent_executor and then invoking it to just pass the tool output. Langchain offers tools for each of these steps, so it might be helpful to first do it in Langchain, and then build your own infrastructure that replaces each of these steps. Gpt3. So a bit more work up front for easier changes in the future. Both agents are asked to develop a simple ETL pipeline. The following blog shows how CodiumAI created the first open-source implementation - Cover-Agent, based I see LangChain at the moment as a quick and dirty solution for quick prototyping of very common use cases for LLMs. You can make certain parts or the whole agent workflow deterministic. Has anyone successfully used LM Studio with Langchain agents? If so hi all! we're gearing up for a release of langchain 0. I tried searching for what's the difference between chain and agent without getting a clear answer to it. That might be possible in this case, too. The agent then handles the subsequent interaction with the LLM and its different function calls. environ["OPENAI_API_KEY"]="sk-xxxxxxxx" agent_executor = create_python_agent( Assume agent that has two functions `say_yes(response)` and `say_no(response)` Isn’t that just plan-and-execute agents from the latest LangChain release? /r/StableDiffusion is back open after the protest of I'm exploring multi-agent systems and am curious about the role of an orchestrator in managing tasks among specialized agents. Decreasing the response time in Multi-Agent Workflow of LangGraph using Ollama - Llama 3 model So recently I was testing out the Multi-Agent Workflow of langchain with some budget constraints and hence I decided to use Llama If you have one agent with 3 tools you just need to create the tools. Other specialized agents include SQLChatAgent, Neo4jChatAgent, TableChatAgent (csv, etc). 5 was finetuned heavily on a type of answer that involves a lot of fluff. Lol Sorry I was in another Vibe So You can create a list empty Append the toolkit to list And Also Append your tool And Use Initialize_agent Within Initialize Agent you pass that list to the tools I'm developing an application using a large language model (LLM) and am in need of a robust core agent platform that supports multi-modal agent capabilities. It seems that loading several langchain agents takes quite a bit of time which means the client would have to wait quite a bit if I recretead the agent for every request. It allowed us to git rid of a lot of technical debt accumulated over the previous months of sub-classing different langchain agents. integration with ChainLit, lets you easily develop a chatGPT-like front end to visualize multi-agent chats. /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. r/ChatGPTCoding • I created GPT Pilot - a PoC for a dev tool that writes fully working apps from scratch while the developer oversees the implementation - it creates code and tests step by step as a human would, debugs the code, runs commands, and asks for feedback. It seems they have a goal to add as many features and build as many partnerships with random companies as possible. 2. Building agent from scratch, using langchain as inspiration. But, to use tools, I need to create an agent, via There are several ways to connect agents in a multi-agent system: Network: each agent can communicate with every other agent. But while implementing the same with an Agent based runnable I see that it gives 3 outputs in order, actions, steps and, output which contains answer. tool import PythonREPLTool from langchain. It misses opportunities for parallel processing, the way it handles stuffing of context leaves a lot to be desired and the way it loses information about formatting during the ingestion is pretty bad. I have build Openai based chatbot that uses Langchain agents - wiki, dolphin, etc. The only advantage is if you want to leverage Langsmith and can't orchestrate multiple agents on your own. The MCAT (Medical College Admission Test) is offered by the AAMC and is a required exam for admission to medical schools in the USA and Canada. I used LangSmith to trace requests and responses. In my case I needed exensive tooling support, RAG support, multi-agent support, multiple llm support, api access, persitence, etc. But in this jungle, how can you find some working stacks that uses OpenAI, LangChain and whatever else? Lets say I want an agent/bot that: * knows about my local workspace (git repo) but knows about it in REAL TIME * the agent or a sibling agent has access to all the latest documentation, say for example React Native a couple of bulletpoints of "here are the problems this solves that langchain doesn't" or "ways this is different from langchain" would go a long way. (Gpt4 is the engine that runs chatgpt) Basically a bunch of dudes were like. All the examples only pass in 1 API endpoint and its docs. Many times, we used langchain, set 'verbose' variable to true and directly took the resulting prompt in directly call to openai which provided better control and quality. I am ok with vendor lock-in for now, and function calling api + LangChain (I use elixir) is very straightforward, reliable and fast. Right now, i've managed to create a sort of router agent, which I want to create a chatbot that consists of several helper agents, each with its own specialty. Or check it out in the app stores Home LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. It is super impressive, the main difference I've seen with Haystack's history in comparison to langchain is the tight coupling with LLMs and generative applications. giving the agent a summary memory didn't help, did you find any solutions? SillyTavern is a fork of TavernAI 1. there’s nothing about LangChain/Graph that’s going to get in the way of that. So the memory is what you provide, the agent_scratchpad is where the tools are loaded for the intermediate steps. ). Or check it out in the app stores   but Langchain is more complete in terms of chains and agents. Agents, by those who bash them, often really mean "Super Agents" or drop in human replacements. I am a beginner in this field. However this documentation is referring to Claude 2 instead of Request update on particular AI agent If you have feedback, Im happy to hear it, because this is just a quick MVP. Discussion Hi, I've been using agents with autogen and crew, and now langgraph, mostly for learning and small/mid scale programs. (Little graph to illustrate the current state of LangChain) If you are restricted to use only open source then sure use Langchain until open source matures and rip it out once it does if u value flexibility and simplicity. It’s excellent for RAG use cases, but for large scale agent orchestration I find it limited. Or check it out in the app stores     TOPICS. I'm new to LangChain and I've been wondering how to achieve shared memory/session between independent agents, without using a graph with a supervisor. Hey r/LangChain. Treat other users the way you want to be treated. - Support chat and non-chat use cases. Much like how a project manager breaks a complex project into different tasks and assigns different individuals with different skills and trainings to manage each task, a multi-agent solution, where each agent has different capabilities and trainings, can be applied to a complex problem. It forces you to use a common set of inputs/outputs for all your steps, which means future changes are much simpler and more modular. All three come as a whole, one after the other, not word by word. When using LangChain agents it's down to the agent to decide which tool to use in response to the prompt. I’ve tried llamaindex, langchain, haystack, griptape, and I usually end up going back to langchain because it has much more functionality and keeps up with the updates. you can even create your own custom tool. Reading the documentation, it seems that the recommended Agent for Claude is the XML Agent. Then, the provide a lot of different but also same stuff, e. Get the Reddit app Scan this QR code to download the app now Currently, I am using an agent with several financial tools that call different financial API endpoints from the data provider. I'ts been the method that brings me the best results. Also, I would love to learn your experience with AI agents and frameworks, what actually worked or didn't work for you, or if Get the Reddit app Scan this QR code to download the app now. I developed a multi-tool agent with langchain. Most of these do support python natively, but if LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. ChatGPT seems to be the only zero shot agent capable of producing the correct Action, Action Input, Observation loop. Hello all again, I have a chromadb with thousands of images and documents, and i have some I noticed that in the langchain documentation there was no happy medium where it's explained how to add a memory to both the AgentExecutor and the chat itself. Check Crew AI, this the easiest put of these frameworks and it is based on langchain. Of course it does. Given the abundance of tools being developed nowadays, I conducted research but only found refining the tool descriptions as a potential solution. A subreddit for information and discussions related to the I2P (Cousin of R2D2) anonymous peer-to-peer network. After playing around with LangChain for a different purpose, I also found that having different models, some with memory and some without, increased performance on my goal, which was more human-like responses. I've tried many models ranging from 7B to 30B in langchain and found that none can perform tasks. Once all tasks are completed, the Planner Agent confirms with the user that everything went smoothly. Moreover i need some kind of an agent setup which can identify whether to respond with context from the codebase's vector files or from the confluence documentations vector files or an appropriate combination of both (that would be ideal We have a few companies using it in production (contact center agent productivity, resume ranking, policy compliance). It is aware other agents exist. In Feb 2024, Meta published a paper introducing TestGen-LLM, a tool for automated unit test generation using LLMs, but didn’t release the TestGen-LLM code. There are varying levels of abstraction for this, from using your own embeddings and setting up your own vector database, to using supporting frameworks i. openai import OpenAI import os os. llms. . it’s based on the mrkl paper. \n\nHowever stocks are only taxed on realised gains which is why they look more interesting to hold long term. Or check it out in the app stores after evaluating others including AutoGen. I have an agent which is responsible for breaking down complex question to steps that can be executed by other agents. I am trying to switch to Open source LLM for this chatbot, has anyone used Langchain with LM studio? I was facing some issues using open source LLM from LM Studio for this task. user_agent = "extractor by u/Master_Ocelot8179", categories long term effects of compounding interest. Agents actually think about how to solve a problem (based on the user‘s query), pick the right tools for the job (tool could be non-LLM functions), and by default answer the user back in natural language. e. Very easy to implement: Its pretty convenient to import LLMs Gpt, Langchain went very early into agents and has assembled a truly impressive variety of features there by now. I have an application that is currently based on 3 agents using LangChain and GPT4-turbo. I've seen many that claim that Langchain isn't worth it because you can recode what you need faster than you can learn it. Transformers Agent is an experimental API, meaning it is subject to change at any point. The agent was instructed like this Understand its task and role definition (prompt) I want my app to be able to chat with multiple APIs. Agent who will be the supervisor and will decide which agent ang tool to use each time I’m trying to figure out if it’s possible to create a Multi Agent application with LangGraph, where the agents can work in parallel (if needed). set_page_config(page_title="LangChain Agents + MRKL", page_icon="🐦") Retrievals need work, but it’s mainly because of the limits of LLMs and summary and extraction refined chains seem clunky, there almost needs to be a domain breakdown of memory where an agent has the full context and is within its token limit and then engage a vote based gymnasium system so the full context can exist together in a way that chains will never do. This agent chain is able to pull information from Reddit and use these posts to respond to subsequent input. The latter seems more reasonable, even more if I had another table To achieve concurrent execution of multiple tools in a custom agent using AgentExecutor with LangChain, you can modify the agent's execution logic to utilize This project explores multiple multi-agent architectures using Langchain (LangGraph), focusing on agent collaboration to solve complex problems. python. ai and provide your thoughts. I'd like to test Claude 3 in this context. If you have 3 agents check on Lang graph, there you will need one more. Multi-Agent chat using Autogen AI tech team using CrewAI Autogen using HuggingFace and local LLMs Langchain is probably the issue here, not the embeddings. Most answers follow a really annoying and easily spotted pattern - well, it depends but here are some things, one, two, lastly, in conclusion. Please follow Reddit's Content Policy. We’ve given this considerable thought in the Langroid multi-agent framework from ex-CMU/UW-Madison researchers (it is NOT built on top of LangChain). For instance, imagine a scenario where there are four agents, each designed to perform one of the basic mathematical operations: addition, subtraction, multiplication, and division. ChromaDb with multiple collections and Agents . tool import DuckDuckGoSearchRun import streamlit as st load_dotenv() st. Langchain is a good concept but poorly executed. As your need for more in depth features grows you will eventually realise how badly designed and full of bugs the library is. Currently I've set up a chatbot that uses Langchain, OpenAI embedding, Deeplake as a vector database. If you don't have it in the AgentExecutor, it doesn't see previous steps. Let’s say I have three agents (stupid example): Supervisor Agent specialized in tech conferences Agent specialized in Hey everyone, check out how I built a Multi-Agent Debate app which intakes a debate topic, creates 2 opponents, have a debate and than comes a jury who decide which party wins. answering questions on the basis of documents, websites, repositories etc. The issue I ran into with assistant API from OpenAI is that it’s super slow. Or check it out in the app stores LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. Hello. Agents, by those who promote them, are units of abstraction used to break a big problem into multiple small problems. ddg_search. LLM grows very very fast. However, if you have agents—prompts, tools, orchestration via the graph—and some tracing, retry and failure mechanisms, etc. Interesting! How have you set up your LangGraph to get structured outputs? This is one thing I've found finicky in Langchain and have been considering if LangGraph will make easier to achieve/more deterministically a specified schema. I want to get word by word streaming for the agent's final answer. ⛓🦜It's now possible to trace LangChain agents and chains with Aim, using just a few lines An agent usually refers to a wrapper around a bare LLM, with optionally access to tools, external data (for RAG), and some type of orchestration/loop mechanism. After almost 12 hours understanding langchain Framewrok practicing with multiple applicatiosn in js and python I see these post which brings finally my quest an end to understand the development for more deeply commands oriented or dependent GPT applications like the ones seen in the webinars from youtube channel of langchain. I'm also working on evluations for GenAI stuff. I've now worked with it for a few days. If we use example from your link, what if user asks - How do I use anthropic and langchain? With agents, it can utilize Anthropic tool to get information on using anthropic and langchain tool to get information on using Langchain. The #1 social media platform for MCAT advice. look it up. Hi there! Today, the LangChain team released what they call: LangChain Templates. You'd have to find and rewrite every one of langchain's dozens of backend prompts, or at least every one that is used by the agent/chain you're working with. Make a Reddit Application and initialize the loader with with your Reddit API credentials. For the many times I had solutions, I just moved on with development, with no need to talk about my successes. I also use „only“ Gpt3. I ve been using several frameworks like autogen, crewai, agent_swarm and langraph. In the custom agent example, it has you managing the chat history manually. IMO given that there is abstraction with LangGraph, when simple steps go wrong (multi agent web browsing, for instance), debugging is much less about software development and more about getting langchain to work. The Second Intelligent Species is Marshall Brain's latest book on Earth's robotic future and how it will affect the human race. Moreover, `create_json_agent` it's using Q&A agent not the chatting agent. Router chains route things, aka passing the user’s query to the right chain. More frequently used for end to end applications than llamaindex. a math agent for solving math problems, a history agent for discussing historical topics, etc. We now have a few folks using it in production (who were similarly frustrated with the bloat/kitchen-sink approach of other frameworks) especially for RAG but also you get seamless LangChain is great when you are first starting out playing with LLMs. faiss, to a fully managed solution like pinecone. There are so many places on Reddit to discuss LangChain and other What practical applications for langchain based agents have you been having success with? Of particular interests, what foundational models have you been seeing perform best as agents? What size of datasets do you have it reasoning through? I have created a chatbot that uses RetrievalQAwithSourcesChain to answer questions, however if I ask the chatbot a question (refer 1 in image), it runs the AgentExecutor, gives the answer and automatically creates another AgentExecutor chain with the same query (refer 2 in image), this happens even when I have asked the question just once. I have googled around for this but can't seem to find any. Questions: Q1. If I combine multiple json files into a single file and try the above approach, it's not able to find the answer. Hello r/Langchain, we have been building an Autopilot AI tool called Sparks AI for the past 5 months that combines web search, external app integrations and Langchain to performs complex multi-step tasks in the background. The SQLDB agent within LangChain is highly impressive, as it has the ability to communicate with multiple tables and perform join operations in order to construct comprehensive responses. My first thought was to use the tool decorator Multi-agent designs allow you to divide complicated problems into tractable units of work that can be targeted by specialized agents and LLM programs. Consequently, the results returned by the agents can vary as the APIs or underlying models evolve. However, the agent struggles to select suitable tools for the task consistently. Help I am new to building AI agents (robotics background) and I was curious to learn about the most As a result, it is easier to customize and more transparent. - The dis-cord community is pretty inactive honestly so many unclosed queries still in the chat. Their implementation of agents are also fairly easy and robust, with a lot of tools you can integrate into an agent and seamless usage between them, unlike ChatGPT with plugins. Langchain executes multiple prompts one after the other. Companies using it in production, after evaluating CrewAI, Autogen, Langgraph, LangChain, etc. Also, it’s open source, if you don’t like how something is being done instead of writing your own framework just open a PR with how you see it better. Intuitively, one would assume the agent will invoke the "uber_10k" tool. It's great as it is for getting things out fast right out of the box, but once you go to prod that gets a bit slow, and it also use way more tokens that it should. ) - Support for complex RAG. huggingface_hub import HuggingFaceHub from langchain. Pros: Multi-Agent Interview Panel using LangGraph by LangChain Check out this demo on how I developed a Multi-Agent system to first generate an Interview panel given job role and than these interviewers interview the candidate one by one (sequentially) , give feedback and eventually all the feedbacks are combined to select the candidate. There is an agent for SQLDatabase in langchain, https://python This is the agents final reply: As an AI developed by OpenAI, I'm unable to directly modify files or execute code, including applying changes to API specifications or saving files. Thanks for sharing. Hi folks, I am fairly new to LangChain and I am trying to: Create a LangChain Agent with a custom LLM model (from HuggingFace, . AI) utilizes LangChain under the hood. The first framework i used for this was Langchain. Perhaps their docs and real-world use cases articles helped make LangChain more relatable to me. These APIs cover almost all fundamental financial data for a particular stock. If you are open to explore an alternative to LC, you can look at this colab I made which is a walk-through of how you can build a multi-agent system with Langroid (which has tools, retrieval, I'm trying to create a conversational chatbot, using multiple agents who specialise in certain sections of the conversation. 2 for a more deterministic approach). These templates are downloadable customizable components and are directly accessible within your codebase which allows for quick and easy customization wherever needed. And pass them to the agent x then barsd on the user question the agent will figure out which tool to. It is a bit more effort to get things done, but in the long term this saves time as you will want to customize things. Ie, your full time dev or customer service replacement. Say I have swagger docs for 5-50 endpoints, whats the best way to make it work. For the models I modified There’s been a bit of time now for a few alternatives to come out to langchain. It likely performs Hey guys, using Langchain, does anyone have any example Python scripts of a central agent coordinating multi agents (ie. In a few months maybe LLM can understand the whole workflow by accessing the API documentations only, without any extra agents. We're also adding in a new docs structure and highlighting a bunch of the changes we made as part of 0. However all my agents are created using the function create_openai_tools_agent(). It's more about making all of this more accessible imo, which is severely needed. Or check it out in the app stores   LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. If langchain can improve their documentation and consistency of APIs with important features exposed as parameters I'll go back to them. It is because the term "Agent" is still being defined. Also anyone using the JS version of LangGraph? Is it operationally the same/up to speed with the python version? Has anyone created a langchain and/or autogen web scraping and crawling agent that on given a key word or series of keywords could scrape the web based on certain kpis. Currently, I'm utilizing LLM for intent recognition and named entity recognition, and then I do backend workflow orchestration without LLM or Agents. The author explains that: Since this information is out-of-scope for any of the retriever tools, the agent correctly decided to invoke the external search tool. I2P provides applications and tooling for communicating on a privacy-aware, self-defensed, distributed network. The memory contains all the conversions or previously generated values. Langraph gives you more control allowing you to create a whole agentic workflow graph. When the user asks a question or makes a request, the conversational AI analyzes the input to determine which specialized agent is best suited to assist (e. I spent the last weekend building an AI Agent with Memory and Human Feedback. as well as using an agent and make tools for the different retrievals from langchain. In all seriousness though, there's a lot of people who aren't pro coders and need an abstraction layer to take advantage of all of the models and tools available in the ML landscape. I was looking into conversational retrieval agents from Langchain (linked below), but it seems they only work with OpenAI models. \n\nI do not like the lack of RAG (and agents generally) don't require langchain. Damn, gpt4 is cool but like it’s kind of dumb that it can’t store any memory for like long term use. agents. Unity is the ultimate entertainment development platform. Supervisor: each agent communicates with a single There seem to be multiple ways to accomplish the same tasks with langchain, so I'm just trying to get an idea of what is working best for everyone. Would really appreciate any help on this. tools. I am running the following in a Jupyter Notebook: from langchain. Example Use Case: Imagine you're using a travel planning chat agent. Some have endorsed us publicly. However, the agent invokes "DuckDuckGoSearch". But lately, when running the agent I been running with the token limit error: This model's maximum context length is 4097 tokens. Observability, lineage: All multi-agent chats are logged, and lineage of messages is tracked. As of now we have tried langsmith evluations. Using tool with an agent chain Reddit search functionality is also provided as a multi-input tool. individuals are welcome to boycott Get the Reddit app Scan this QR code to download the app now. Langchain and others, Llamaindex, make this simple to get up and going fast. First you need to understand their weird concepts of agents, tools, chains, memory. Yes, I've seen people ordering 100 Starbucks latte from DoorDash using agent on a hackaton and it was the best demo I've seen deploying agents. This agent chain is able to pull information from Reddit and use these posts to Get the Reddit app Scan this QR code to download the app now. In this example, we adapt existing code from the docs , and use ChatOpenAI to create an agent chain with memory. Langroid is a multi-agent LLM framework from ex-CMU and UW Madison researchers: https: I find it frustrating to use langchain with azure openai, as many unexpected errors take place when I set the I feel like LangChain is much more comprehensive and will be useful for improving my application. This group focuses on using AI tools like ChatGPT, OpenAI API, and other automated code generators for Ai programming & prompt engineering. In case you're still curious, check out Langchain's SQL agent. 8 which is under more active development, and has added many major features. Reddit is an American social news aggregation, content rating, and discussion website. Building that from scratch would've been a huge pain in my ass but Langchain made it shockingly easy. Overall though, I dislike the product, funny enough. The more I use them the more I'm confused about the purpose of the agent I've been using langchain's csv_agent to ask questions about my csv files or to make request to the agent. Or check it out in the app stores Using LangChain agents to create a multi-agent platform that creates robot softwares 🖲️Apps a multi-agent solution, where each agent has different capabilities and trainings, can be applied to a complex problem. LangChain-free, unlike CrewAI which is built on top of LC Reply reply The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. In general, as a rule, GPT 3. Or check it out in the app stores Using LangChain agents to create a multi-agent platform that creates robot softwares Resources a multi-agent solution, where each agent has different capabilities and trainings, can be applied to a complex problem. I am looking use a Router that can initiate different chains and agents based on the inquiry that the user is inputting So far, It doesn't look like a router can initiate agent? Am i right? Also it doesn't look like chain can use tools? Am I right? I would like Chains and Agents to have tools. agent_toolkits import create_python_agent from langchain. python import PythonREPL from langchain. The main change is no longer depending on langchain-community (this will increase modularity, decrease size of package, make more secure). Any feedback & ideas are welcome. Use Unity to build high-quality 3D and 2D games and experiences. I implement and compare three main I would like to use a MultiRootChain to use one QA chain, and an "agents" with tools. 1. What are the limitations of sending in multiple API endpoints and It is a lightweight, principled agent-oriented framework (in fact Agent was the first class I wrote), unlike LangChain which added agents as a late afterthought. It's not that hard, less than 100 line of code for a basic Agent, and you can customize it as you want, add every layer of protection you want. tools allows the llm to do stuff that it cannot do or suck at e. calculator, access a sql database and do sql statements while users ask questions about the db data in natural language, answer questions past it’s sept 2021 training data by googling the answer. This capability allows for natural language communication with databases. This loader fetches the text from the Posts of Subreddits or Reddit users, using the praw Python package. Get the Reddit app Scan this QR code to download the app now LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. Agent and Tools: LangChain’s unified interface for adding tools and building agents is great. I myself tried generating the answers by manually querying the DB, but the answer are like to the point, ie when the Agent thing worked for me, which was very rarely, it gave the answer more like a conversational manner whereas when I used Langchain to make an query and then run it on the DB manually myself, I got the answer which was just the fact. In this example, we adapt existing code from the docs, and use ChatOpenAI to create an agent chain with memory. Deploy them across mobile, desktop, VR/AR, consoles or the Web and connect with people globally. - The documentation is subpar compared to what one can expect from a tool that can be used in production. If you’re looking to implement cached datastores for user convo’s or biz specific knowledge or implementing multiple agents in a chain or mid-stream re-context actions etc, use Langchain. Two types of agents are provided: HfAgent, which uses inference endpoints for open-source models, and OpenAiAgent, which uses OpenAI's proprietary models. A true software engineering nightmare IMHO. Or check it out in the app stores     TOPICS LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. At this moment, millions of engineers, scientists, corporations, universities and entrepreneurs are racing to create the LangChain is an open-source framework and developer toolkit that helps developers get LLM applications from prototype to production. A place for discussion around the use of AI Agents and related tools such as in Auto-GPT, LangChain Tribe is a low-code platform built on top of langgraph to simplify building and coordinating these multi-agent teams! Recently, I added tool calling to allow agents to browse the web, support for Anthropic models, and the ability to Reddit. fkwjri vspbxd taygh moeh roea rqcma rrkxcrv dpjhbu qqewron stjey