mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-31 12:19:58 +02:00
Eugene YurtsevandGitHub
50601dc02c
* Add structured output tools to ToolNode
* Fix default tool node name to match the actual default ('tools')
* Update doc-strings to explain what inputs/outputs are for the ToolNode.
* Mark internal attributes as private (potentially breaking -- although hopefully users aren't accessing these)
## Decisions points
* OK with two properties? Done since users may be relying on
`tools_by_name` and expanding the return type will break user code.
## Changes in public/private interface
### Marked as public
* Make `tools_by_name` an official public property
* Make `structured_output_tools` a public property
### Marked as private
There should be no reason why users are accessing these attributes
```python
_tool_to_state_args
_tool_to_store_arg
_handle_tool_errors
_messages_key
```
### Usage
```python
class OutputSchema(BaseModel):
name: str
age: int
location: str
tool_node = ToolNode([OutputSchema])
# Test that the structured output tool is registered correctly
assert "OutputSchema" in tool_node.structured_output_tools
# Create a tool call that matches the schema
tool_call = {
"name": "OutputSchema",
"args": {"name": "Alice", "age": 30, "location": "NYC"},
"id": "call_123",
"type": "tool_call",
}
# Test sync execution
result = tool_node.invoke(
{"messages": [AIMessage(content="", tool_calls=[tool_call])]}
)
# Should return a Command with structured response
assert isinstance(result, list)
assert len(result) == 1
command = result[0]
assert isinstance(command, Command)
# Check the update structure
assert "messages" in command.update
assert "structured_response" in command.update
# Check the tool message
tool_message = command.update["messages"][0]
assert isinstance(tool_message, ToolMessage)
assert tool_message.name == "OutputSchema"
assert tool_message.tool_call_id == "call_123"
# Check the structured response
structured_response = command.update["structured_response"]
assert isinstance(structured_response, OutputSchema)
assert structured_response.name == "Alice"
assert structured_response.age == 30
assert structured_response.location == "NYC"
```
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
Get started
Install LangGraph:
pip install -U langgraph
Then, create an agent using prebuilt components:
# pip install -qU "langchain[anthropic]" to call the model
from langgraph.prebuilt import create_react_agent
def get_weather(city: str) -> str:
"""Get weather for a given city."""
return f"It's always sunny in {city}!"
agent = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=[get_weather],
prompt="You are a helpful assistant"
)
# Run the agent
agent.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
For more information, see the Quickstart. Or, to learn how to build an agent workflow with a customizable architecture, long-term memory, and other complex task handling, see the LangGraph basics tutorials.
Core benefits
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
- Durable execution: Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop: Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory: Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- Debugging with LangSmith: Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- Production-ready deployment: Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
LangGraph’s ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
- LangSmith — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangGraph Platform — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.
- LangChain – Provides integrations and composable components to streamline LLM application development.
Note
Looking for the JS version of LangGraph? See the JS repo and the JS docs.
Additional resources
- Guides: Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- Reference: Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- Examples: Guided examples on getting started with LangGraph.
- LangChain Forum: Connect with the community and share all of your technical questions, ideas, and feedback.
- LangChain Academy: Learn the basics of LangGraph in our free, structured course.
- Templates: Pre-built reference apps for common agentic workflows (e.g. ReAct agent, memory, retrieval etc.) that can be cloned and adapted.
- Case studies: Hear how industry leaders use LangGraph to ship AI applications at scale.
Acknowledgements
LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.