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feat(docs): add subsection on Tool Artifacts vs. Injected State (#32468)

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Clarify the differences between tool artifacts and injected state in
LangChain and LangGraph
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Mason Daugherty 2025-08-11 15:53:33 -04:00 committed by GitHub
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@ -171,6 +171,26 @@ Please see the [InjectedState](https://langchain-ai.github.io/langgraph/referenc
Please see the [InjectedStore](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.InjectedStore) documentation for more details. Please see the [InjectedStore](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.InjectedStore) documentation for more details.
## Tool Artifacts vs. Injected State
Although similar conceptually, tool artifacts in LangChain and [injected state in LangGraph](https://langchain-ai.github.io/langgraph/reference/agents/#langgraph.prebuilt.tool_node.InjectedState) serve different purposes and operate at different levels of abstraction.
**Tool Artifacts**
- **Purpose:** Store and pass data between tool executions within a single chain/workflow
- **Scope:** Limited to tool-to-tool communication
- **Lifecycle:** Tied to individual tool calls and their immediate context
- **Usage:** Temporary storage for intermediate results that tools need to share
**Injected State (LangGraph)**
- **Purpose:** Maintain persistent state across the entire graph execution
- **Scope:** Global to the entire graph workflow
- **Lifecycle:** Persists throughout the entire graph execution and can be saved/restored
- **Usage:** Long-term state management, conversation memory, user context, workflow checkpointing
Tool artifacts are ephemeral data passed between tools, while injected state is persistent workflow-level state that survives across multiple steps, tool calls, and even execution sessions in LangGraph.
## Best practices ## Best practices
When designing tools to be used by models, keep the following in mind: When designing tools to be used by models, keep the following in mind: