mirror of
https://github.com/langchain-ai/langgraph.git
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51f6cee1b1
## Summary
Adds native uv workspace/lockfile support to the LangGraph CLI's Docker
build pipeline. Instead of listing dependencies manually, users can
point at their existing `uv.lock` and the CLI will:
1. Discover workspace packages and their dependency graph
2. Export locked requirements via `uv export --package <name> --frozen`
3. Copy only the necessary workspace closure into the container
4. Install packages in dependency order with `--no-deps` for
reproducibility
5. Rewrite all import paths (graphs, auth, encryption, etc.) to
container paths
### New config field: `source`
Rather than using `pip` or `uv pip`, we add a new `uv_lock` installer.
The previous installers should still remain unchanged.
To avoid ambiguity, we discriminate by "source" field and **do not
permit** other arbitrary "dependencies". In this mode, we will treat the
provided root (defaults to the current directory) as the source of
truth.
This also would natively support uv workspaces, so you can specify the
target package within a larger workspace.
**Simple single-package project:**
```json
{
"python_version": "3.11",
"graphs": {
"agent": "./agent.py:graph"
},
"source": {
"kind": "uv"
}
}
```
**Multi-package workspace with explicit package:**
```json
{
"python_version": "3.11",
"graphs": {
"agent": "../../apps/agent/src/agent/graph.py:graph"
},
"source": {
"kind": "uv",
"root": "../..",
"package": "agent"
}
}
```
**Traditional pip deployment (unchanged):**
```json
{
"python_version": "3.11",
"dependencies": ["langgraph", "my-package"],
"graphs": {
"agent": "./agent.py:graph"
}
}
```
Config validation enforces mutual exclusivity. you must use either
`dependencies` or `source`, not both.
---------
Co-authored-by: Will Fu-Hinthorn <will@langchain.dev>
30 lines
781 B
Python
30 lines
781 B
Python
from collections.abc import Sequence
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from typing import Annotated, TypedDict
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from langchain_core.messages import AIMessage, BaseMessage
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from langgraph.graph import END, START, StateGraph
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from langgraph.graph.message import add_messages
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class State(TypedDict):
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messages: Annotated[Sequence[BaseMessage], add_messages]
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def call_model(state: State) -> dict:
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message = AIMessage(content="Hello from simple uv agent!")
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return {"messages": [message]}
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def should_continue(state: State):
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if len(state["messages"]) > 0:
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return END
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return "call_model"
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workflow = StateGraph(State)
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workflow.add_node("call_model", call_model)
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workflow.add_edge(START, "call_model")
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workflow.add_conditional_edges("call_model", should_continue)
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graph = workflow.compile()
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