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Sydney Runkle
2025-07-28 16:15:14 -04:00
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@@ -18,29 +18,20 @@ LangGraph provides **three** primary ways to manage context:
### Runtime Context
!!! note "`config['configurable']` -> `runtime.context`"
In LangGraph < v0.6, static runtime context was passed via the `config['configurable']` key, paired with a `config_schema` argument
to `StateGraph` or `Pregel`. This is now deprecated and will be removed in v2.0.
As of LangGraph v0.6, the `Runtime` object is recommended to access static context and runtime-specific information like the store and stream writer.
!!! warning "Context is an overloaded term"
In the world of LLMs, "context" is quite the overloaded term.
There are two main types of context that you will encounter:
1. Local context: data and dependencies your code needs to run. This might be helpful for tools, node invocations,
conditional branching, etc.
2. LLM context: often talked about regarding the "context window" of an LLM. This is the data the LLM sees when generating a response.
The field of "context engineering" refers to the practice of optimizing the content of the context window to improve the LLM's performance.
The context discussed in this section is the local context. As a developer, you might use the local context to eventually optimize
the LLM context (ex: use a user_id to fetch a user's name and information from a database to populate the context window with relevant memories).
Runtime context is for immutable data like user metadata, tools, db connections, etc. Use this when you have values that don't change mid-run.
!!! version-added "New in LangGraph v0.6: `Runtime.context` replaces config['configurable']"
The `Runtime` object is recommended to access static context and runtime-specific information like the store and stream writer.
!!! note "'Context' is an overloaded term"
Runtime context refers to local context: data and dependencies your code needs to run.
It does not refer to the LLM context, which is the data passed into the LLM's prompt.
You likely want to use the local context to optimize the llm's context window. For example, you
could use a user_id to fetch a user's name and information from a database to populate the context window with relevant memories.
Specify static context via the `context` argument to `invoke` / `stream`, which is reserved for this purpose:
```python