{ "dependencies": ["langchain_openai", "./your_package"], // Required: Package dependencies "graphs": { "my_graph": "./your_package/file.py:graph" // Required: Graph definitions }, "env": "./.env", // Optional: Environment variables "python_version": "3.11", // Optional: Python version (3.11/3.12) "pip_config_file": "./pip.conf", // Optional: pip configuration "dockerfile_lines": [] // Optional: Additional Dockerfile commands } See the full documentation for detailed configuration options. ... # Run CLI commands directly uv run langgraph --help # Or use the examples cd examples uv pip install uv run langgraph dev # or other commands

3 days ago - reference.langchain.com/python/langgraph – API reference docs for LangGraph packages

# Run CLI commands directly uv run langgraph --help # Or use the examples cd examples uv pip install uv run langgraph dev # or other commands · This project is licensed under the terms specified in the repository's LICENSE file. Class · A container for referencing and managing local Python dependencies.

November 15, 2024 - LangGraph is a versatile Python ... This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph...

LangGraph is very low-level, and focused entirely on agent orchestration. Before using LangGraph, we recommend you familiarize yourself with some of the components used to build agents, starting with models and tools. We will commonly use LangChain components throughout the documentation to integrate models and tools, but you don’t need to use LangChain to use LangGraph.

from langgraph_sdk import get_client # If you're using a remote server, initialize the client with `get_client(url=REMOTE_URL)` client = get_client() # List all assistants assistants = await client.assistants.search() # We auto-create an assistant ...

July 22, 2024 - ---Final Answer--- To create a virtual environment in Python, you can use the following command: ... (agent-cli) exit Thank you for using Agent CLI. set_expertise <expertise>: Set the field of expertise. ... With these components integrated, our CLI application is now fully functional, providing a robust yet simple introduction to using Langchain and Langgraph for building AI-powered tools.

Design agents that reliably handle complex tasks with LangGraph, an agent runtime and low-level orchestration framework.

June 15, 2025 - This article will guide you through the fundamental steps of constructing a graph, including defining its state, adding simple Python functions as nodes, connecting them with edges, setting a starting point, and compiling it.

July 15, 2025 - from typing import TypedDict, List from langchain_core.messages import HumanMessage from langchain_openai import ChatOpenAI from langgraph.graph import StateGraph, START, END from dotenv import load_dotenv · The first line states TypedDict, which is a Type-Annotation that is defined like this in Python:

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Create a new LangGraph project at the specified PATH using the chosen TEMPLATE. Show warning if image_distro is not set to 'wolfi'. CLI entrypoint for running the LangGraph API server.

November 21, 2024 - pip install -U langgraph-cli[inmem] (Optional) Clone a starter template: langgraph new --template new-langgraph-project-python ./my-project cd my-project · (Recommended) Use a virtual environment and install dependencies: python -m venv .venv source .venv/bin/activate python -m pip install .

pip install --upgrade "langgraph-cli[inmem]" langgraph new path/to/your/app --template new-langgraph-project-python

1 month ago - Build a working AI research agent with LangGraph and Python. Step-by-step tutorial covering state, nodes, conditional routing, memory, and deployment — with complete, runnable code. Tagged with langgraph, python, ai, tutorial.

Learn the basics of LangGraph - our framework for building agentic and multi-agent applications. Separate from the LangChain package, LangGraph helps developers add better precision and control into agentic workflows.

January 23, 2025 - Learn LangGraph with this beginner-friendly tutorial featuring Python code examples.

May 20, 2025 - Now that we have seen the key components of LangGraph, let’s try to build a basic graph with three nodes and one conditional edge. This simple example shows how to invoke a graph involving the key concepts of State, Nodes, and Edges. The State defines the data structure which is shared between nodes. It acts like a shared memory that flows through the graph. from typing_extensions import TypedDict class State(TypedDict): graph_state: str · Here, we have used pythons TypeDict to declare that our state will have a single key called the graph_state, which stores a string.