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William Fu-Hinthorn
2024-12-17 17:43:09 -08:00
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docs/docs/tutorials/auth/getting_started.md
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# Setting up custom authentication
Let's learn how to add custom authentication to a LangGraph Platform deployment. We'll cover the core concepts of token-based authentication and show how to integrate with an authentication server.
Let's add custom authentication to a LangGraph template. This lets users interact with our bot making their conversations accessible to other users. This tutorial covers the core concepts of token-based authentication and show how to integrate with an authentication server.
??? note "Default authentication"
When deploying to LangGraph Cloud, requests are authenticated using LangSmith API keys by default. This gates access to the server but doesn't provide fine-grained access control over threads. Self-hosted LangGraph platform has no default authentication. This guide shows how to add custom authentication handlers that work in both cases, to provide fine-grained access control over threads, runs, and other resources.
When deploying to LangGraph Cloud, requests are authenticated using LangSmith API keys by default. This gates access to the server but doesn't provide fine-grained access control over threads. Self-hosted LangGraph platform has no default authentication. This guide shows how to add custom authentication handlers that work in both cases, to provide fine-grained access control over threads, runs, and other resources.
!!! note "Prerequisites"
@@ -21,58 +21,35 @@ The key components in a token-based authentication system are:
2. **Client**: gets tokens from auth server and includes them in requests. This is typically the user's browser or mobile app.
3. **LangGraph backend**: validates tokens and enforces access control to control access to your agents and data.
After implementing the following steps, when a user's client application (such as their web browser or mobile app) wants to access resources in LangGraph, the following steps occur:
```mermaid
sequenceDiagram
participant User
participant AuthServer as Auth Server
participant LangGraph
User->>AuthServer: 1. Authenticate (username/password)
AuthServer-->>User: 2. Return signed JWT token
User->>LangGraph: 3. Request with JWT in header
LangGraph->>AuthServer: 4a. Validate token
AuthServer-->>LangGraph: 4b. Confirm token validity
Note over LangGraph: 4c. Apply access filters
LangGraph-->>User: Return authorized resources
```
Here's how it typically works:
1. User authenticates with the auth server (username/password, OAuth, "Sign in with Google", etc.)
2. Auth server returns a signed JWT token attesting "I am user X with claims/roles Y"
3. User includes this token in request headers to LangGraph
4. LangGraph validates token signature and checks claims against the auth server. If valid, it allows the request, using custom filters to restrict access only to the user's resources.
In this tutorial, we'll implement password-based authentication using Supabase as our auth server.
## 1. Clone the template
## Project Structure
After cloning, you'll see these key files:
Clone the [LangGraph template](https://github.com/langchain-ai/new-langgraph-project) to get started.
```shell
custom-auth/
├── src/
│ └── security/
│ └── auth.py # We'll create this
├── langgraph.json # We'll update this
└── .env.example # Environment variables template
pip install -U "langgraph-cli[inmem]"
langgraph new --template=new-langgraph-project-python custom-auth
cd custom-auth
```
## Setting up authentication
### 2. Create the auth handler
### 1. Create the auth handler
First, let's create our authentication handler. Create a new file at `src/security/auth.py`:
Next, let's create our authentication handler. Create a new file at `src/security/auth.py`:
```python
import os
import httpx
import jwt
from langgraph_sdk import Auth
# Load from your .env file
SUPABASE_URL = os.environ["SUPABASE_URL"]
SUPABASE_SERVICE_KEY = os.environ["SUPABASE_SERVICE_KEY"]
SUPABASE_JWT_SECRET = os.environ["SUPABASE_JWT_SECRET"]
# Create the auth object we'll use to protect our endpoints
auth = Auth()
@@ -90,20 +67,14 @@ async def get_current_user(
)
try:
# Extract and verify JWT token
token = authorization.split(" ", 1)[1]
payload = jwt.decode(
token,
SUPABASE_JWT_SECRET,
algorithms=["HS256"],
audience="authenticated",
)
# Double-check with Supabase that token is still valid
# Fetch the user info from Supabase
async with httpx.AsyncClient() as client:
response = await client.get(
f"{SUPABASE_URL}/auth/v1/user",
headers={"Authorization": f"Bearer {token}"},
headers={
"Authorization": authorization,
"apiKey": SUPABASE_SERVICE_KEY,
},
)
if response.status_code != 200:
raise Auth.exceptions.HTTPException(
@@ -112,7 +83,7 @@ async def get_current_user(
)
user_data = response.json()
return [], {
return {
"identity": user_data["id"],
"display_name": user_data.get("name"),
"is_authenticated": True,
@@ -147,9 +118,9 @@ Next, tell LangGraph about our auth handler. Open `langgraph.json` and add:
```json
{
"auth": {
"path": "src/security/auth.py:auth"
}
"auth": {
"path": "src/security/auth.py:auth"
}
}
```
@@ -168,21 +139,21 @@ cp .env.example .env
```
Add to your `.env`:
```bash
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-key # aka the service_role secret
SUPABASE_JWT_SECRET=your-jwt-secret
ANTHROPIC_API_KEY=your-anthropic-key # For the LLM in our chatbot
```
Also note down your project's "anon public" key - we'll use this for client authentication.
Also note down your project's "anon public" key - we'll use this for client authentication below.
### 4. Start the server
Install dependencies and start LangGraph:
```bash
pip install -U "langgraph-cli[inmem]" && pip install -e .
pip install -e .
langgraph dev --no-browser
```
docs/docs/tutorials/index.md
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@@ -8,14 +8,15 @@ title: Tutorials
New to LangGraph or LLM app development? Read this material to get up and running building your first applications.
## Get Started
## Get Started 🚀 {#quick-start}
- [LangGraph Quickstart](introduction.ipynb): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
- [LangGraph Server Quickstart](langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
- [LangGraph Template Quickstart](../concepts/template_applications.md): Start building with LangGraph Platform using a template application.
- [Deploy with LangGraph Cloud Quickstart](../cloud/quick_start.md): Deploy a LangGraph app using LangGraph Cloud.
## Use cases
## Use cases 🛠️ {#use-cases}
Explore practical implementations tailored for specific scenarios:
@@ -72,10 +73,10 @@ Explore practical implementations tailored for specific scenarios:
- [Competitive Programming](usaco/usaco.ipynb): Build an agent with few-shot "episodic memory" and human-in-the-loop collaboration to solve problems from the USA Computing Olympiad; adapted from the ["Can Language Models Solve Olympiad Programming?"](https://arxiv.org/abs/2404.10952v1) paper by Shi, Tang, Narasimhan, and Yao.
- [Complex data extraction](extraction/retries.ipynb): Build an agent that can use function calling to do complex extraction tasks
## LangGraph Platform
## LangGraph Platform 🧱 {#platform}
### Authentication & Access Control
Learn how to secure your LangGraph applications:
Add custom authentication and authorization to your LangGraph Platform deployment.
- [Setting Up Custom Authentication](./auth/getting_started.md): Implement OAuth2 authentication to authorize users on your deployment