AI Agent 
How to Handle a Large Number of Tools The subset of tools available for invocation is typically determined by the model (although many providers also allow users to specify or limit the selection of tools). As the number of available tools increases, you may want to limit the LLM’s selection to reduce token consumption and … Read more
LangGraph Platform: Business Model Summary LangGraph is a platform for developing, deploying, and managing generative AI applications. Its core features revolve around the implementation of dynamic AI agents, including task queue management, API calls, storage and updating of conversation states and long-term memories. LangGraph supports everything from experimental applications by startup teams to enterprise-level large-scale … Read more
LangGraph provides a high level of control over the execution of charts. How to Create Parallel Execution Branches The parallel execution of nodes is crucial for speeding up overall graph operations. LangGraph offers native support for parallel execution of nodes, which can significantly enhance the performance of graph-based workflows. This parallelization is achieved through fan-out … Read more
Map-reduce operations are crucial for efficient task decomposition and parallel processing. This method involves breaking down tasks into smaller sub-tasks, processing each sub-task in parallel, and aggregating the results of all completed sub-tasks. Consider this example: Given a general topic from the user, generate a list of related topics, create a joke for each topic, … Read more
How to Update Graph State via Tool A common use case is to update the graph state from within the tool. For example, in a customer support application, you may want to look up the customer account or ID at the start of the conversation. To update the graph state from the tool, you can … Read more
LangGraph Persistence allows for easy state persistence between graph execution (thread-level persistence) and threads (cross-thread persistence). This tutorial demonstrates how to add persistence to a graph. LangGraph has a built-in persistence layer that is implemented through checkpoints. When compiling a graph with a checkpoint, the checkpoint saves a snapshot of the graph’s state at each … Read more
Overview When building AI applications, we often need to handle complex workflows, including serial and parallel execution of multiple steps, state management, event notifications, etc. LangGraph provides a powerful event stream mechanism and a flexible node return value system to support these needs. This article will delve into the event streams and node return value … Read more
An AI travel assistant built on LangGraph: ai-travel-agent, which can search for flights, book hotels, and organize itineraries to email you. It has a memory capability, able to remember user interaction history and continue from where the last conversation left off. It adopts a human-machine collaboration model, allowing users to control key operations, such as … Read more
Click the “Blue Word” to Follow Us In today’s rapidly evolving AI field, building Agents (Which Multi-Agent Framework is Better? A Deep Dive into Five Popular Options) has become an important means of achieving automation and decision optimization. LangGraph, as a powerful tool, provides developers with an intuitive and efficient way to build AI Agents … Read more
Andrew Ng recently said in his public lecture: I believe that AI Agent workflows will drive significant advances in artificial intelligence this year, possibly surpassing the next-generation foundational models. This is an important trend, and I urge everyone working in AI to pay attention to it. Coupled with the four paradigms for implementing Agent workflows … Read more