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https://github.com/langchain-ai/langgraph.git
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William FHandGitHub
a734f5e6ce
Main jtbd here:
a) clarify who/how a graph is being accessed and make the factory aware
of the `context` where relevant (and make it obvious when it is
available)
b) make it more clear when you can bypass / defer resources with
expensive lifespans (like MCp connections)
c) make auth access more type-safe.
Gives us room to add other information, like:
- langsmith distributed tracing information
---- old
Can start doing things like this:
```
def my_graph(runtime: ServerRuntime):
if runtime.ensure_user().permissions not in ("foo"):
raise ValueError("bar")
```
etc.
Points of expected confusion:
- You won't have a stream_writer in this context.
- This won't be an accessible object within the graph, only the graph
factory.
For maintainers, related draft PR int he server
https://github.com/langchain-ai/langgraph/pull/6774
LangGraph Prebuilt
This library defines high-level APIs for creating and executing LangGraph agents and tools.
Important
This library is meant to be bundled with
langgraph, don't install it directly
Agents
langgraph-prebuilt provides an implementation of a tool-calling ReAct-style agent - create_react_agent:
pip install langchain-anthropic
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
# Define the tools for the agent to use
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-3-7-sonnet-latest")
app = create_react_agent(model, tools)
# run the agent
app.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
)
Tools
ToolNode
langgraph-prebuilt provides an implementation of a node that executes tool calls - ToolNode:
from langgraph.prebuilt import ToolNode
from langchain_core.messages import AIMessage
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."
tool_node = ToolNode([search])
tool_calls = [{"name": "search", "args": {"query": "what is the weather in sf"}, "id": "1"}]
ai_message = AIMessage(content="", tool_calls=tool_calls)
# execute tool call
tool_node.invoke({"messages": [ai_message]})
ValidationNode
langgraph-prebuilt provides an implementation of a node that validates tool calls against a pydantic schema - ValidationNode:
from pydantic import BaseModel, field_validator
from langgraph.prebuilt import ValidationNode
from langchain_core.messages import AIMessage
class SelectNumber(BaseModel):
a: int
@field_validator("a")
def a_must_be_meaningful(cls, v):
if v != 37:
raise ValueError("Only 37 is allowed")
return v
validation_node = ValidationNode([SelectNumber])
validation_node.invoke({
"messages": [AIMessage("", tool_calls=[{"name": "SelectNumber", "args": {"a": 42}, "id": "1"}])]
})
Agent Inbox
The library contains schemas for using the Agent Inbox with LangGraph agents. Learn more about how to use Agent Inbox here.
from langgraph.types import interrupt
from langgraph.prebuilt.interrupt import HumanInterrupt, HumanResponse
def my_graph_function():
# Extract the last tool call from the `messages` field in the state
tool_call = state["messages"][-1].tool_calls[0]
# Create an interrupt
request: HumanInterrupt = {
"action_request": {
"action": tool_call['name'],
"args": tool_call['args']
},
"config": {
"allow_ignore": True,
"allow_respond": True,
"allow_edit": False,
"allow_accept": False
},
"description": _generate_email_markdown(state) # Generate a detailed markdown description.
}
# Send the interrupt request inside a list, and extract the first response
response = interrupt([request])[0]
if response['type'] == "response":
# Do something with the response
...