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docs: Delete LGP nav Items from docs (#5743)
Remove the files and nav for LGP docs in the mkdocs site. --------- Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
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Kathryn May
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Lauren Hirata Singh
Lauren Hirata Singh
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# How to Set Up a LangGraph Application with requirements.txt
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A LangGraph application must be configured with a [LangGraph configuration file](../reference/cli.md#configuration-file) in order to be deployed to LangGraph Platform (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using `requirements.txt` to specify project dependencies.
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This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment.
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!!! tip "Setup with pyproject.toml"
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If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Platform.
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!!! tip "Setup with a Monorepo"
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If you are interested in deploying a graph located inside a monorepo, take a look at [this repository](https://github.com/langchain-ai/langgraph-example-monorepo) for an example of how to do so.
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The final repository structure will look something like this:
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```bash
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my-app/
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├── my_agent # all project code lies within here
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│ ├── utils # utilities for your graph
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│ │ ├── __init__.py
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│ │ ├── tools.py # tools for your graph
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│ │ ├── nodes.py # node functions for you graph
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│ │ └── state.py # state definition of your graph
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│ ├── requirements.txt # package dependencies
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│ ├── __init__.py
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│ └── agent.py # code for constructing your graph
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├── .env # environment variables
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└── langgraph.json # configuration file for LangGraph
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```
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After each step, an example file directory is provided to demonstrate how code can be organized.
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## Specify Dependencies
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Dependencies can optionally be specified in one of the following files: `pyproject.toml`, `setup.py`, or `requirements.txt`. If none of these files is created, then dependencies can be specified later in the [LangGraph configuration file](#create-langgraph-configuration-file).
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The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
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```
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langgraph>=0.3.27
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langgraph-sdk>=0.1.66
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langgraph-checkpoint>=2.0.23
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langchain-core>=0.2.38
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langsmith>=0.1.63
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orjson>=3.9.7,<3.10.17
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httpx>=0.25.0
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tenacity>=8.0.0
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uvicorn>=0.26.0
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sse-starlette>=2.1.0,<2.2.0
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uvloop>=0.18.0
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httptools>=0.5.0
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jsonschema-rs>=0.20.0
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structlog>=24.1.0
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cloudpickle>=3.0.0
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```
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Example `requirements.txt` file:
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```
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langgraph
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langchain_anthropic
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tavily-python
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langchain_community
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langchain_openai
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```
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Example file directory:
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```bash
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my-app/
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├── my_agent # all project code lies within here
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│ └── requirements.txt # package dependencies
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```
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## Specify Environment Variables
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Environment variables can optionally be specified in a file (e.g. `.env`). See the [Environment Variables reference](../reference/env_var.md) to configure additional variables for a deployment.
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Example `.env` file:
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```
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MY_ENV_VAR_1=foo
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MY_ENV_VAR_2=bar
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OPENAI_API_KEY=key
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```
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Example file directory:
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```bash
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my-app/
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├── my_agent # all project code lies within here
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│ └── requirements.txt # package dependencies
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└── .env # environment variables
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```
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## Define Graphs
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Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each @[CompiledStateGraph][CompiledStateGraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph configuration file](../reference/cli.md#configuration-file).
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Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repository](https://github.com/langchain-ai/langgraph-example) to see their implementation):
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```python
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# my_agent/agent.py
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from typing import Literal
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from typing_extensions import TypedDict
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from langgraph.graph import StateGraph, END, START
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from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
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from my_agent.utils.state import AgentState # import state
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# Define the runtime context
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class GraphContext(TypedDict):
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model_name: Literal["anthropic", "openai"]
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workflow = StateGraph(AgentState, context_schema=GraphContext)
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workflow.add_node("agent", call_model)
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workflow.add_node("action", tool_node)
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workflow.add_edge(START, "agent")
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workflow.add_conditional_edges(
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"agent",
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should_continue,
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{
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"continue": "action",
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"end": END,
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},
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)
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workflow.add_edge("action", "agent")
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graph = workflow.compile()
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```
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Example file directory:
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```bash
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my-app/
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├── my_agent # all project code lies within here
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│ ├── utils # utilities for your graph
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│ │ ├── __init__.py
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│ │ ├── tools.py # tools for your graph
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│ │ ├── nodes.py # node functions for you graph
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│ │ └── state.py # state definition of your graph
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│ ├── requirements.txt # package dependencies
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│ ├── __init__.py
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│ └── agent.py # code for constructing your graph
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└── .env # environment variables
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```
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## Create LangGraph Configuration File
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Create a [LangGraph configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph configuration file reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.
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Example `langgraph.json` file:
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```json
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{
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"dependencies": ["./my_agent"],
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"graphs": {
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"agent": "./my_agent/agent.py:graph"
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},
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"env": ".env"
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}
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```
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Note that the variable name of the `CompiledGraph` appears at the end of the value of each subkey in the top-level `graphs` key (i.e. `:<variable_name>`).
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!!! warning "Configuration File Location"
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The LangGraph configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.
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Example file directory:
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```bash
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my-app/
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├── my_agent # all project code lies within here
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│ ├── utils # utilities for your graph
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│ │ ├── __init__.py
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│ │ ├── tools.py # tools for your graph
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│ │ ├── nodes.py # node functions for you graph
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│ │ └── state.py # state definition of your graph
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│ ├── requirements.txt # package dependencies
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│ ├── __init__.py
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│ └── agent.py # code for constructing your graph
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├── .env # environment variables
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└── langgraph.json # configuration file for LangGraph
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```
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## Next
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After you setup your project and place it in a GitHub repository, it's time to [deploy your app](./cloud.md).
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