The Building Blocks of Agentic Systems with Harrison Chase - #698
Digest
This podcast episode features Harrison Chase, co-founder and CEO of Langchain, discussing the framework's development, its growing popularity, and its role in building AI-powered applications. The episode begins with an overview of Langchain's origins, highlighting the need for an abstraction layer to simplify building with LLMs. Chase then discusses Langchain's rapid growth, attributing its success to its ability to simplify development and its strong community support. The episode delves into Langchain's product portfolio, including Langchain (the open-source package), LangSmith (for observability and testing), LangGraph (for orchestration), and LangGraph Cloud (a hosted runtime for LangGraph). Chase explains the purpose and features of each product, emphasizing their role in bridging the gap from prototype to production. The podcast then explores the concept of agents in AI, discussing their potential and limitations. Chase traces the evolution of agents from early research to the rise of AutoGPT and the subsequent skepticism. He highlights successful agent applications in customer support and data enrichment, emphasizing the importance of focused workflows and cognitive architectures. The episode delves deeper into the agentic aspects of data enrichment systems, introducing the concept of "agenticness" as a spectrum rather than a binary definition. Chase discusses how LLMs contribute to agentic behavior by making decisions about control flow, handling ambiguity, and adapting to edge cases. The podcast addresses the argument that current agent architectures might become obsolete as LLMs evolve. Chase acknowledges that some tasks might be handled directly by future LLMs, but argues that the need for communication and external knowledge access will persist, requiring some form of agentic framework. The episode discusses the key challenges faced when deploying agentic systems, emphasizing the importance of effective communication with LLMs, ensuring proper context and input. Chase also mentions cost and latency as potential issues, although recent advancements in model efficiency have mitigated some concerns. The podcast explores how LangSmith helps developers build better agents through observability. Chase highlights the importance of tracing agent steps and understanding input/output at each stage. He explains how LangSmith integrates with frameworks like Langchain and LangGraph, providing valuable insights for debugging and optimization. The episode compares LangGraph to other agent frameworks, emphasizing its low-level nature and flexibility. Chase explains how LangGraph addresses limitations of Langchain by providing greater control and customization. He highlights LangGraph's support for streaming, looping, conditional edges, and human-in-the-loop interactions. The podcast discusses RAG (Retrieval-Augmented Generation) and its applications. Chase describes RAG as a way to bring external knowledge to LLMs, highlighting its use in customer support and data enrichment. He emphasizes the importance of indexing and retrieval in RAG systems, and how they can be integrated with agents for more complex workflows. The episode focuses on LangSmith's role in evaluation and its contribution to moving LLM applications from prototype to production. Chase explains the concept of a data flywheel, where user feedback is used to improve the application through testing, evaluation, and continual learning. He highlights the importance of custom data sets and metrics for effective evaluation. The episode compares LangSmith to other evaluation tools like Weights & Biases. Chase emphasizes LangSmith's focus on textual evaluations, tracing, and pairwise comparisons, which are crucial for debugging and understanding LLM performance. He highlights LangSmith's emphasis on data management and user experience. The episode concludes with Chase's predictions for the future of agentic applications. He expresses optimism about the potential of streamlined workflows and the continued importance of orchestration frameworks like LangGraph. He highlights the significance of few-shot prompting and multimodal models, particularly those involving speech.
Outlines
Langchain: Building AI-Powered Applications with LLMs
This episode explores Langchain, a framework for building AI-powered applications, focusing on its growth, product portfolio, and the future of agentic systems powered by LLMs.
Langchain's Growth and Popularity
The episode discusses Langchain's rapid growth, with impressive statistics like 15 million monthly downloads and 2000 contributors. Harrison attributes this success to the right time, right place, and the framework's ability to simplify building with LLMs. He emphasizes the importance of community contributions in expanding Langchain's integrations.
Langchain's Product Portfolio
The episode delves into Langchain's product portfolio, including Langchain (the open-source package), LangSmith (for observability and testing), LangGraph (for orchestration), and LangGraph Cloud (a hosted runtime for LangGraph). Harrison explains the purpose and features of each product, highlighting their role in bridging the gap from prototype to production.
Agents in AI: Potential and Challenges
The episode explores the concept of agents in AI, discussing their potential and limitations. Harrison traces the evolution of agents from early research to the rise of AutoGPT and the subsequent skepticism. He highlights successful agent applications in customer support and data enrichment, emphasizing the importance of focused workflows and cognitive architectures.
Agentic Systems and the Spectrum of Agenticness
The episode delves deeper into the agentic aspects of data enrichment systems. Harrison introduces the concept of "agenticness" as a spectrum rather than a binary definition. He discusses how LLMs contribute to agentic behavior by making decisions about control flow, handling ambiguity, and adapting to edge cases.
The Future of Agents and the Role of LLMs
The episode addresses the argument that current agent architectures might become obsolete as LLMs evolve. Harrison acknowledges that some tasks might be handled directly by future LLMs, but argues that the need for communication and external knowledge access will persist, requiring some form of agentic framework.
Challenges in Deploying Agentic Systems
The episode discusses the key challenges faced when deploying agentic systems. Harrison emphasizes the importance of effective communication with LLMs, ensuring proper context and input. He also mentions cost and latency as potential issues, although recent advancements in model efficiency have mitigated some concerns.
LangSmith and Observability for Agents
The episode explores how LangSmith helps developers build better agents through observability. Harrison highlights the importance of tracing agent steps and understanding input/output at each stage. He explains how LangSmith integrates with frameworks like Langchain and LangGraph, providing valuable insights for debugging and optimization.
Keywords
Langchain
An open-source framework for building AI-powered applications, particularly those involving LLMs. It provides tools for orchestration, data retrieval, and integration with various LLM providers and vector stores.
LangSmith
A platform for observability, testing, and evaluation of LLM applications. It helps developers understand agent behavior, track performance metrics, and improve model accuracy through continual learning.
LangGraph
A low-level orchestration framework for building agentic systems. It provides a flexible and customizable approach to defining and managing complex workflows involving LLMs and external tools.
Agents
AI systems that can reason, act, and interact with the world autonomously. They often involve LLMs, external tools, and a cognitive architecture that guides their behavior.
RAG (Retrieval-Augmented Generation)
A technique for enhancing LLM responses by incorporating external knowledge from a knowledge base or search engine. It involves retrieving relevant information and providing it as context to the LLM.
Few-Shot Prompting
A prompting technique where the LLM is provided with a few examples of desired input-output pairs to guide its behavior. It allows for personalization and adaptation to specific tasks or user preferences.
Multimodal Models
AI models that can process and understand multiple types of data, such as text, images, and speech. They offer the potential for more natural and intuitive interactions with AI systems.
Agenticness
A spectrum that describes the degree to which an AI system exhibits autonomous behavior, decision-making, and interaction with the world. It is a measure of how much control the system has over its own actions and responses.
Data Flywheel
A process for continuously improving LLM applications by collecting user feedback, evaluating performance, and using that information to refine the model or its prompts. It involves a cycle of data collection, analysis, and feedback.
Q&A
What are the key benefits of using Langchain for building AI-powered applications?
Langchain simplifies building with LLMs by providing an abstraction layer, a wide range of integrations, and a framework for orchestration. It makes it easier to connect different components and build complex applications.
How does LangSmith help developers improve the performance of their agents?
LangSmith provides observability tools that allow developers to trace agent steps, understand input/output at each stage, and identify potential issues. It helps developers debug and optimize their agents for better accuracy and reliability.
What are some of the challenges faced when deploying agentic systems?
Effective communication with LLMs is crucial, as they need to be provided with the right context and input. Cost and latency can also be issues, although recent advancements in model efficiency have mitigated some concerns.
What are some of the key trends that Harrison Chase sees shaping the future of agentic applications?
Harrison believes that streamlined workflows and the importance of orchestration frameworks will continue. He also highlights the significance of few-shot prompting and multimodal models, particularly those involving speech.
How does LangSmith differ from other evaluation tools like Weights & Biases?
LangSmith focuses on textual evaluations, tracing, and pairwise comparisons, which are crucial for debugging and understanding LLM performance. It also emphasizes data management and user experience, tailored specifically for LLM applications.
What is the concept of "agenticness" and how does it apply to AI systems?
Agenticness is a spectrum that describes the degree to which an AI system exhibits autonomous behavior, decision-making, and interaction with the world. It is a measure of how much control the system has over its own actions and responses.
Show Notes
Today, we're joined by Harrison Chase, co-founder and CEO of LangChain to discuss LLM frameworks, agentic systems, RAG, evaluation, and more. We dig into the elements of a modern LLM framework, including the most productive developer experiences and appropriate levels of abstraction. We dive into agents and agentic systems as well, covering the “spectrum of agenticness,” cognitive architectures, and real-world applications. We explore key challenges in deploying agentic systems, and the importance of agentic architectures as a means of communication in system design and operation. Additionally, we review evolving use cases for RAG, and the role of observability, testing, and evaluation tools in moving LLM applications from prototype to production. Lastly, Harrison shares his hot takes on prompting, multi-modal models, and more!
The complete show notes for this episode can be found at https://twimlai.com/go/698.