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fix tools

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Eugene Yurtsev
2025-06-20 16:45:24 -04:00
parent 0c0e5a299d
commit 3b59213311
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docs/docs/how-tos/tool-calling.md
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@@ -21,7 +21,7 @@ To create tools, you can use [@tool](https://python.langchain.com/api_reference/
=== "Python functions"
This requires using LangGraph's prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] or [agent](../../agents/agents), which automatically convert the functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
This requires using LangGraph's prebuilt [`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] or [agent](../agents/agents), which automatically convert the functions to [LangChain tools](https://python.langchain.com/docs/concepts/tools/#tool-interface).
```python
def multiply(a: int, b: int) -> int:
@@ -146,7 +146,7 @@ def get_user_info(
## Short-term memory
LangGraph allows agents to access and update their [short-term memory](../../concepts/memory#short-term-memory) (state) inside the tools.
LangGraph allows agents to access and update their [short-term memory](../concepts/memory.md#short-term-memory) (state) inside the tools.
### Read state
@@ -316,7 +316,7 @@ def update_user_info(
## Long-term memory
Use [long-term memory](../../concepts/memory#long-term-memory) to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information.
Use [long-term memory](../concepts/memory.md#long-term-memory) to store user-specific or application-specific data across conversations. This is useful for applications like chatbots, where you want to remember user preferences or other information.
To use long-term memory, you need to:
@@ -593,11 +593,11 @@ agent = create_react_agent(
graph.invoke({"messages": [{"role": "user", "content": "what's 42 x 7?"}]})
```
See this [guide](../../agents/overview) to learn more.
See this [guide](../agents/overview.md) to learn more.
## Use prebuilt `ToolNode`
[`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] is a prebuilt LangGraph [node](../../concepts/low_level#nodes) for executing tool calls.
[`ToolNode`][langgraph.prebuilt.tool_node.ToolNode] is a prebuilt LangGraph [node](../concepts/low_level.md#nodes) for executing tool calls.
**Why use `ToolNode`?**
@@ -605,14 +605,14 @@ See this [guide](../../agents/overview) to learn more.
* concurrent execution of the tools
* error handling during tool execution. You can enable / disable this by setting `handle_tool_errors=True` (enabled by default). See [this section](#handle-errors) for more details on handling errors
ToolNode operates on [MessagesState](../../concepts/low_level#messagesstate):
ToolNode operates on [MessagesState](../concepts/low_level.md#messagesstate):
* input: `MessagesState` where the last message is an `AIMessage` with `tool_calls` parameter
* output: `MessagesState` with [`ToolMessage`](https://python.langchain.com/docs/concepts/messages/#toolmessage) the result of tool calls
!!! tip
`ToolNode` is designed to work well out-of-box with LangGraph's prebuilt [agent](../../agents/agents), but can also work with any `StateGraph` that uses `MessagesState.`
`ToolNode` is designed to work well out-of-box with LangGraph's prebuilt [agent](../agents/agents), but can also work with any `StateGraph` that uses `MessagesState.`
```python
# highlight-next-line
@@ -762,7 +762,7 @@ tool_node.invoke({"messages": [...]})
??? example "Use in a tool-calling agent"
This is an example of creating a tool-calling agent from scratch using `ToolNode`. You can also use LangGraph's prebuilt [agent](../../agents/agents).
This is an example of creating a tool-calling agent from scratch using `ToolNode`. You can also use LangGraph's prebuilt [agent](../agents/agents).
```python
from langchain.chat_models import init_chat_model