Deep Agents are built on LangChain agents which you can also use LangChain directly.Use LangGraph, our low-level orchestration framework, for advanced needs combining deterministic and agentic workflows.Use LangSmith to trace, debug, and evaluate agents built with any of these frameworks.

June 15, 2025 - Imagine an incident response workflow that needs to decide on the next step based on the type of alert. langgraph's conditional edges make this straightforward. The following is a conceptual example of a graph for triaging alerts. 🧑🏻‍💻NOTE: This example is available at this GitHub repository. # Branching Conditional Logic # Branching conditional logic allows you to include conditional logic in a prompt template.

September 19, 2024 - We chose templates because this makes it easy to modify the inner functionality of the agents. With templates, you clone the repo - you then have access to all the code, so you can change prompts, chaining logic, and do anything else you want!

September 1, 2025 - We’ll use LangGraph for workflow management, LangChain for LLM interactions, and Groq’s LLama-3 model as our language model. The system starts by gathering information about the prompt requirements through a series of questions.

July 24, 2024 - In this tutorial, we will build a sophisticated tool for generating prompt templates tailored for AI language models. This project is particularly useful for AI developers and enthusiasts looking to optimize their models’ performance by creating well-structured prompts. By the end of this tutorial, you will have a comprehensive understanding of how to create, evaluate, and finalize prompts using state-of-the-art models like LLaMA and GPT, leveraging the powerful capabilities of Langchain and Langgraph.

Unified API reference documentation for LangChain, LangGraph, Deep Agents, LangSmith, and Integrations.

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April 14, 2024 - The variable parts in the template are surround by curly brackets { }, and to fill these parts we pass in a list of key-value pairs (kwargs in python) with the variable name and text they should be filled with to the format() method on the Prompt Template. prompt_template = PromptTemplate.from_template( 'Tell me a {adjective} joke about {content}' ) print(prompt_template.format(adjective='funny', content='chickens')) # -> 'Tell me a funny joke about chickens.'

Template represented by a dictionary. ... Prompt template for a language model.

June 27, 2025 - A prompt template is your agent’s “script”—a text blueprint with dynamic slots.

June 30, 2025 - Over time, **they also improve results by reducing randomness in how prompts are written**. A prompt template usually consists of two things: 1. A text prompt, which is just a chunk of natural language.

September 18, 2025 - In this post, we'll dive into several powerful prompting techniques you can leverage with LangChain, inspired by practical examples, to build more intelligent and reliable LLM applications. We'll cover everything from reusable templates to managing conversations and guiding complex reasoning.

1 week ago - AI agents prompting guide — node-level prompting patterns that drop straight into LangGraph workers. Context Engineering Maturity Model — staged framework for thinking about how state, retrieval, and prompts evolve as your agent stack grows. Use the Template Builder to customize 350+ expert templates with real-time preview, then export for any AI model.Open Template Builder