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docs: cross link working w/ memory in tools (#4464)

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Vadym Barda
2025-04-30 13:52:10 -04:00
committed by GitHub
parent aa9651910c
commit ce2ba47aff
4 changed files with 116 additions and 70 deletions
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docs/docs/agents/agents.md
+1 -1
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@@ -112,7 +112,7 @@ from langgraph.prebuilt import create_react_agent
# highlight-next-line
def prompt(state: AgentState, config: RunnableConfig) -> list[AnyMessage]: # (1)!
user_name = config.get("configurable", {}).get("user_name")
user_name = config["configurable"].get("user_name")
system_msg = f"You are a helpful assistant. Address the user as {user_name}."
return [{"role": "system", "content": system_msg}] + state["messages"]
docs/docs/agents/context.md
+5 -65
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@@ -107,7 +107,7 @@ Common use cases:
config: RunnableConfig,
) -> list[AnyMessage]:
# highlight-next-line
user_name = config.get("configurable", {}).get("user_name")
user_name = config["configurable"].get("user_name")
system_msg = f"You are a helpful assistant. User's name is {user_name}"
return [{"role": "system", "content": system_msg}] + state["messages"]
@@ -162,7 +162,7 @@ Common use cases:
})
```
## Tools
## Accessing Context in Tools
Tools can access context through special parameter **annotations**.
@@ -183,7 +183,7 @@ Tools can access context through special parameter **annotations**.
) -> str:
"""Look up user info."""
# highlight-next-line
user_id = config.get("configurable", {}).get("user_id")
user_id = config["configurable"].get("user_id")
return "User is John Smith" if user_id == "user_123" else "Unknown user"
agent = create_react_agent(
@@ -231,66 +231,6 @@ Tools can access context through special parameter **annotations**.
})
```
### Update Context from Tools
## Update context from tools
Tools can modify the agent's state during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
```python
from typing import Annotated
from langchain_core.tools import InjectedToolCallId
from langchain_core.messages import ToolMessage
from langgraph.prebuilt import InjectedState
from langgraph.types import Command
class CustomState(AgentState):
# highlight-next-line
user_name: str
def get_user_info(
# highlight-next-line
tool_call_id: Annotated[str, InjectedToolCallId],
# highlight-next-line
config: RunnableConfig
) -> Command:
"""Look up user info."""
# highlight-next-line
user_id = config.get("configurable", {}).get("user_id")
name = "John Smith" if user_id == "user_123" else "Unknown user"
return Command(update={
# highlight-next-line
"user_name": name,
# update the message history
# highlight-next-line
"messages": [
ToolMessage(
"Successfully looked up user information",
# highlight-next-line
tool_call_id=tool_call_id
)
]
})
def greet(
# highlight-next-line
state: Annotated[CustomState, InjectedState]
) -> str:
"""Use this to greet the user once you found their info."""
user_name = state["user_name"]
return f"Hello {user_name}!"
agent = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=[get_user_info, greet],
# highlight-next-line
state_schema=CustomState
)
agent.invoke(
{"messages": [{"role": "user", "content": "greet the user"}]},
# highlight-next-line
config={"configurable": {"user_id": "user_123"}}
)
```
For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).
Tools can update agent's context (state and long-term memory) during execution. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts. See [Memory](./memory.md#read-short-term) guide for more information.
docs/docs/agents/memory.md
+103 -4
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@@ -149,6 +149,104 @@ agent = create_react_agent(
To learn more about using `pre_model_hook` for managing message history, see this [how-to guide](../how-tos/create-react-agent-manage-message-history.ipynb)
### Read in tools { #read-short-term }
LangGraph allows agent to access its short-term memory (state) inside the tools.
```python
from typing import Annotated
from langgraph.prebuilt import InjectedState, create_react_agent
class CustomState(AgentState):
# highlight-next-line
user_id: str
def get_user_info(
# highlight-next-line
state: Annotated[CustomState, InjectedState]
) -> str:
"""Look up user info."""
# highlight-next-line
user_id = state["user_id"]
return "User is John Smith" if user_id == "user_123" else "Unknown user"
agent = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=[get_user_info],
# highlight-next-line
state_schema=CustomState,
)
agent.invoke({
"messages": "look up user information",
# highlight-next-line
"user_id": "user_123"
})
```
See the [Context](./context.md#__tabbed_2_2) guide for more information.
### Write from tools { #write-short-term }
To modify the agent's short-term memory (state) during execution, you can return state updates directly from the tools. This is useful for persisting intermediate results or making information accessible to subsequent tools or prompts.
```python
from typing import Annotated
from langchain_core.tools import InjectedToolCallId
from langchain_core.runnables import RunnableConfig
from langchain_core.messages import ToolMessage
from langgraph.prebuilt import InjectedState, create_react_agent
from langgraph.prebuilt.chat_agent_executor import AgentState
from langgraph.types import Command
class CustomState(AgentState):
# highlight-next-line
user_name: str
def update_user_info(
tool_call_id: Annotated[str, InjectedToolCallId],
config: RunnableConfig
) -> Command:
"""Look up and update user info."""
user_id = config["configurable"].get("user_id")
name = "John Smith" if user_id == "user_123" else "Unknown user"
# highlight-next-line
return Command(update={
# highlight-next-line
"user_name": name,
# update the message history
"messages": [
ToolMessage(
"Successfully looked up user information",
tool_call_id=tool_call_id
)
]
})
def greet(
# highlight-next-line
state: Annotated[CustomState, InjectedState]
) -> str:
"""Use this to greet the user once you found their info."""
user_name = state["user_name"]
return f"Hello {user_name}!"
agent = create_react_agent(
model="anthropic:claude-3-7-sonnet-latest",
tools=[get_user_info, greet],
# highlight-next-line
state_schema=CustomState
)
agent.invoke(
{"messages": [{"role": "user", "content": "greet the user"}]},
# highlight-next-line
config={"configurable": {"user_id": "user_123"}}
)
```
For more details, see [how to update state from tools](../how-tos/update-state-from-tools.ipynb).
## Long-term memory
Use 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.
@@ -158,9 +256,10 @@ To use long-term memory, you need to:
1. [Configure a store](../how-tos/cross-thread-persistence.ipynb) to persist data across invocations.
2. Use the [`get_store`][langgraph.config.get_store] function to access the store from within tools or prompts.
### Reading
### Read { #read-long-term }
```python title="A tool the agent can use to look up user information"
from langchain_core.runnables import RunnableConfig
from langgraph.config import get_store
from langgraph.prebuilt import create_react_agent
from langgraph.store.memory import InMemoryStore
@@ -183,7 +282,7 @@ def get_user_info(config: RunnableConfig) -> str:
# Same as that provided to `create_react_agent`
# highlight-next-line
store = get_store() # (6)!
user_id = config.get("configurable", {}).get("user_id")
user_id = config["configurable"].get("user_id")
# highlight-next-line
user_info = store.get(("users",), user_id) # (7)!
return str(user_info.value) if user_info else "Unknown user"
@@ -212,7 +311,7 @@ agent.invoke(
7. The `get` method is used to retrieve data from the store. The first argument is the namespace, and the second argument is the key. This will return a `StoreValue` object, which contains the value and metadata about the value.
8. The `store` is passed to the agent. This enables the agent to access the store when running tools. You can also use the `get_store` function to access the store from anywhere in your code.
### Writing
### Write { #write-long-term }
```python title="Example of a tool that updates user information"
from typing_extensions import TypedDict
@@ -231,7 +330,7 @@ def save_user_info(user_info: UserInfo, config: RunnableConfig) -> str: # (3)!
# Same as that provided to `create_react_agent`
# highlight-next-line
store = get_store() # (4)!
user_id = config.get("configurable", {}).get("user_id")
user_id = config["configurable"].get("user_id")
# highlight-next-line
store.put(("users",), user_id, user_info) # (5)!
return "Successfully saved user info."
docs/docs/agents/tools.md
+7
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@@ -271,6 +271,13 @@ By default, the agent will catch all exceptions raised during tool calls and wil
See [API reference][langgraph.prebuilt.tool_node.ToolNode] for more information on different tool error handling options.
## Working with memory
LangGraph allows access to short-term and long-term memory from tools. See [Memory](./memory.md) guide for more information on:
* how to [read](./memory.md#read-short-term) from and [write](./memory.md#write-short-term) to **short-term** memory
* how to [read](./memory.md#read-long-term) from and [write](./memory.md#write-long-term) to **long-term** memory
## Prebuilt tools
LangChain supports a wide range of prebuilt tool integrations for interacting with APIs, databases, file systems, web data, and more. These tools extend the functionality of agents and enable rapid development.