WEBDIENSTE/langgraph
1
0
Fork 0
mirror of https://github.com/langchain-ai/langgraph.git synced 2026-08-17 21:25:46 +02:00
Code Issues Packages Projects Releases Wiki Activity

notes on context

Browse Source
This commit is contained in:
Sydney Runkle
2025-07-28 15:17:05 -04:00
parent dba20d0577
commit 2115cffc94
1 changed files with 18 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/docs/agents/context.md
+18 -4
View File
@@ -8,7 +8,7 @@ Context includes *any* data outside the message list that can shape behavior. Th
- Internal state updated during a multi-step reasoning process.
- Persistent memory or facts from previous interactions.
LangGraph provides **three** primary ways to supply context:
LangGraph provides **three** primary ways to manage context:
| Type | Description | Mutable? | Lifetime |
|------------------------------------------------------------------------------|-----------------------------------------------|----------|-------------------------|
@@ -20,12 +20,26 @@ LangGraph provides **three** primary ways to supply context:
!!! note "`config['configurable']` -> `runtime.context`"
In LangGraph < v1.0, static runtime context was passed via the `config['configurable']` key, paired with a `config_schema` argument
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 v1.0, the Runtime object is recommended to access static context and runtime-specific information like the store and stream writer.
As of LangGraph v0.6, the `Runtime` object is recommended to access static context and runtime-specific information like the store and stream writer.
Runtime context is for immutable data like user metadata or API keys. Use this when you have values that don't change mid-run.
!!! 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.
Specify static context via the `context` argument to `invoke` / `stream`, which is reserved for this purpose: