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docs: add high level example into the readme, make examples collapsible (#3052)

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Co-authored-by: Chester Curme <chester.curme@gmail.com>
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Vadym Barda
2025-01-17 17:10:22 +00:00
committed by GitHub
co-authored by Chester Curme
parent 7db29042b4
commit 9912ae1053
2 changed files with 114 additions and 28 deletions
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README.md
+57 -14
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@@ -47,9 +47,7 @@ pip install -U langgraph
## Example
One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
Let's take a look at a simple example of an agent that can use a search tool.
Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
```shell
pip install langchain-anthropic
@@ -66,10 +64,58 @@ export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=lsv2_sk_...
```
```python
from typing import Annotated, Literal, TypedDict
The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
<details open>
<summary>High-level implementation</summary>
```python
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
# Define the tools for the agent to use
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
tools = [search]
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
# Initialize memory to persist state between graph runs
checkpointer = MemorySaver()
app = create_react_agent(model, tools, checkpointer=checkpointer)
# Use the agent
final_state = app.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
```
</details>
> [!TIP]
> LangGraph is a **low-level** framework that allows you to implement any custom agent
architectures. Click on the low-level implementation below to see how to implement a
tool-calling agent from scratch.
<details>
<summary>Low-level implementation</summary>
```python
from typing import Literal
from langchain_core.messages import HumanMessage
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
@@ -91,7 +137,7 @@ tools = [search]
tool_node = ToolNode(tools)
model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
# Define the function that determines whether to continue or not
def should_continue(state: MessagesState) -> Literal["tools", END]:
@@ -145,23 +191,20 @@ checkpointer = MemorySaver()
# Note that we're (optionally) passing the memory when compiling the graph
app = workflow.compile(checkpointer=checkpointer)
# Use the Runnable
# Use the agent
final_state = app.invoke(
{"messages": [HumanMessage(content="what is the weather in sf")]},
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
```
</details>
Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
```python
final_state = app.invoke(
{"messages": [HumanMessage(content="what about ny")]},
{"messages": [{"role": "user", "content": "what about ny"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
libs/langgraph/README.md
+57 -14
View File
@@ -47,9 +47,7 @@ pip install -U langgraph
## Example
One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
Let's take a look at a simple example of an agent that can use a search tool.
Let's build a tool-calling [ReAct-style](https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation) agent that uses a search tool!
```shell
pip install langchain-anthropic
@@ -66,10 +64,58 @@ export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=lsv2_sk_...
```
```python
from typing import Annotated, Literal, TypedDict
The simplest way to create a tool-calling agent in LangGraph is to use `create_react_agent`:
<details open>
<summary>High-level implementation</summary>
```python
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import MemorySaver
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
# Define the tools for the agent to use
@tool
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
tools = [search]
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0)
# Initialize memory to persist state between graph runs
checkpointer = MemorySaver()
app = create_react_agent(model, tools, checkpointer=checkpointer)
# Use the agent
final_state = app.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
```
</details>
> [!TIP]
> LangGraph is a **low-level** framework that allows you to implement any custom agent
architectures. Click on the low-level implementation below to see how to implement a
tool-calling agent from scratch.
<details>
<summary>Low-level implementation</summary>
```python
from typing import Literal
from langchain_core.messages import HumanMessage
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
@@ -91,7 +137,7 @@ tools = [search]
tool_node = ToolNode(tools)
model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0).bind_tools(tools)
model = ChatAnthropic(model="claude-3-5-sonnet-latest", temperature=0).bind_tools(tools)
# Define the function that determines whether to continue or not
def should_continue(state: MessagesState) -> Literal["tools", END]:
@@ -145,23 +191,20 @@ checkpointer = MemorySaver()
# Note that we're (optionally) passing the memory when compiling the graph
app = workflow.compile(checkpointer=checkpointer)
# Use the Runnable
# Use the agent
final_state = app.invoke(
{"messages": [HumanMessage(content="what is the weather in sf")]},
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content
```
```
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
```
</details>
Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
```python
final_state = app.invoke(
{"messages": [HumanMessage(content="what about ny")]},
{"messages": [{"role": "user", "content": "what about ny"}]},
config={"configurable": {"thread_id": 42}}
)
final_state["messages"][-1].content