mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-30 13:35:09 +02:00
Harrison Chase
@@ -46,10 +46,25 @@ When creating complex graphs, leaving every decision up to the LLM can be danger
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## LangGraph Studio
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- [Test Cloud Deployment](https://langchain-ai.github.io/langgraph/cloud/how-tos/test_deployment/)
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- [Invoke graph in LangGraph Studio](https://langchain-ai.github.io/langgraph/cloud/how-tos/invoke_studio/)
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LangGraph Studio is a built-in UI for visualizing, testing, and debugging your agents.
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- [How to enter LangGraph Studio](https://langchain-ai.github.io/langgraph/cloud/how-tos/test_deployment/)
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- [How to test your graph in LangGraph Studio](https://langchain-ai.github.io/langgraph/cloud/how-tos/invoke_studio/)
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- [Interact with threads in LangGraph Studio](https://langchain-ai.github.io/langgraph/cloud/how-tos/threads_studio/)
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## And more!
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## Different Types of Runs:
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The four sections above don't cover everything that is possible with LangGraph cloud - make sure to check out our other how-to guides to learn even more!
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LangGraph Cloud supports multiple types of runs besides streaming runs.
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- [How to run an agent in the background](cloud_examples/background_run.ipynb)
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- [How to run multiple agents in the same thread](cloud_examples/same-thread.ipynb)
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- [How to create cron jobs](cloud_examples/cron_jobs.ipynb)
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- [How to create stateless runs](cloud_examples/stateless_runs.ipynb)
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## Other
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Other guides that may prove helpful!
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- [How to configure agents](cloud_examples/configuration_cloud.ipynb)
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- [How to convert LangGraph calls to LangGraph cloud calls](cloud_examples/langgraph_to_langgraph_cloud.ipynb)
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- [How to integrate webhooks](cloud_examples/webhooks.ipynb)
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@@ -23,15 +23,15 @@ This tutorial will use:
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2. The `agent.py` file should contain Python code for defining your graph. The following code is a simple example, the important thing is that at some point in your file you compile your graph and assign the compiled graph to a variable (in this case the `graph` variable). This example code uses `create_react_agent`, a prebuilt agent, read more about it [here](..//concepts/agentic_concepts.md#react-agent).
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```python
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from langchain_anthropic import ChatAnthropic
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from langchain_community.tools.tavily_search import TavilySearchResults
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from langgraph.prebuilt import create_react_agent
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from langchain_anthropic import ChatAnthropic
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from langchain_community.tools.tavily_search import TavilySearchResults
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from langgraph.prebuilt import create_react_agent
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model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
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model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
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tools = [TavilySearchResults(max_results=2)]
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tools = [TavilySearchResults(max_results=2)]
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graph = create_react_agent(model, tools)
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graph = create_react_agent(model, tools)
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```
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3. The `requirements.txt` file should contain any dependencies for your graph(s). In this case we only require four packages for our graph to run:
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@@ -231,8 +231,6 @@ async for chunk in client.runs.stream(
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{'agent': {'messages': [{'content': "Hi Bagatur! It's nice to meet you. How can I assist you today?", 'additional_kwargs': {}, 'response_metadata': {'finish_reason': 'stop', 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_9cb5d38cf7'}, 'type': 'ai', 'name': None, 'id': 'run-c89118b7-1b1e-42b9-a85d-c43fe99881cd', 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None}]}}
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You can learn more about the Python SDK in [this how-to guide](./sdk/python_sdk.ipynb), and read up on the Javascript SDK in [this how-to guide](./sdk/js_sdk.ipynb)
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## What's Next
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Congratulations! If you've worked your way through this tutorial you are well on your way to becoming a LangGraph Cloud expert. Here are some other resources to check out to help you out on the path to expertise:
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+19
-19
@@ -14,28 +14,28 @@ LangGraph for Agentic Applications
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Low Level Concepts
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- [Graphs](low_level.md#graphs)
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- [StateGraph](low_level.md#stategraph)
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- [MessageGraph](low_level.md#messagegraph)
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- [Compiling Your Graph](low_level.md#compiling-your-graph)
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- [StateGraph](low_level.md#stategraph)
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- [MessageGraph](low_level.md#messagegraph)
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- [Compiling Your Graph](low_level.md#compiling-your-graph)
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- [State](low_level.md#state)
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- [Schema](low_level.md#schema)
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- [Reducers](low_level.md#reducers)
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- [MessageState](low_level.md#messagestate)
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- [Schema](low_level.md#schema)
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- [Reducers](low_level.md#reducers)
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- [MessageState](low_level.md#messagestate)
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- [Nodes](low_level.md#nodes)
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- [`START` node](low_level.md#start-node)
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- [`END` node](low_level.md#end-node)
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- [`START` node](low_level.md#start-node)
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- [`END` node](low_level.md#end-node)
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- [Edges](low_level.md#edges)
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- [Normal Edges](low_level.md#normal-edges)
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- [Conditional Edges](low_level.md#conditional-edges)
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- [Entry Point](low_level.md#entry-point)
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- [Conditional Entry Point](low_level.md#conditional-entry-point)
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- [Normal Edges](low_level.md#normal-edges)
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- [Conditional Edges](low_level.md#conditional-edges)
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- [Entry Point](low_level.md#entry-point)
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- [Conditional Entry Point](low_level.md#conditional-entry-point)
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- [Send](low_level.md#send)
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- [Checkpointer](low_level.md#checkpointer)
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- [Threads](low_level.md#threads)
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- [Checkpointer states](low_level.md#checkpointer-state)
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- [Get state](low_level.md#get-state)
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- [Get state history](low_level.md#get-state-history)
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- [Update state](low_level.md#update-state)
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- [Get state](low_level.md#get-state)
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- [Get state history](low_level.md#get-state-history)
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- [Update state](low_level.md#update-state)
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- [Configuration](low_level.md#configuration)
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- [Visualization](low_level.md#visualization)
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- [Streaming](low_level.md#streaming)
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@@ -46,10 +46,10 @@ Common Agentic Patterns
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- [Tool calling](agentic_concepts.md#tool-calling)
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- [Memory](agentic_concepts.md#memory)
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- [Human in the loop](agentic_concepts.md#human-in-the-loop)
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- [Approval](agentic_concepts.md#approval)
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- [Wait for input](agentic_concepts.md#wait-for-input)
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- [Edit agent actions](agentic_concepts.md#edit-agent-actions)
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- [Time travel](agentic_concepts.md#time-travel)
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- [Approval](agentic_concepts.md#approval)
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- [Wait for input](agentic_concepts.md#wait-for-input)
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- [Edit agent actions](agentic_concepts.md#edit-agent-actions)
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- [Time travel](agentic_concepts.md#time-travel)
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- [Map-Reduce](agentic_concepts.md#map-reduce)
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- [Multi-agent](agentic_concepts.md#multi-agent)
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- [Planning](agentic_concepts.md#planning)
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@@ -7,11 +7,11 @@ hide:
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Welcome to the LangGraph Tutorials! These notebooks introduce LangGraph through building various language agents and applications.
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## Introduction to LangGraph
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## Quick Start
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Learn the basics of LangGraph through the onboarding tutorials.
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Learn the basics of LangGraph through a comprehensive quick start in which you will build an agent from scratch.
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- [Introduction to LangGraph](introduction.ipynb)
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- [Quick Start](introduction.ipynb)
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## Use cases
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