mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-17 21:25:46 +02:00
## 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>
297 lines
10 KiB
Python
297 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Script to generate a JSON schema for the langgraph-cli Config class.
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This script creates a schema.json file that can be referenced in langgraph.json files
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to provide IDE autocompletion and validation.
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"""
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import inspect
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import json
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import textwrap
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from pathlib import Path
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import msgspec
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from langgraph_cli.schemas import (
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AuthConfig,
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CacheConfig,
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CheckpointerConfig,
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Config,
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ConfigurableHeaderConfig,
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CorsConfig,
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GraphDef,
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HttpConfig,
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IndexConfig,
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SecurityConfig,
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SerdeConfig,
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StoreConfig,
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ThreadTTLConfig,
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TTLConfig,
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WebhooksConfig,
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WebhookUrlPolicy,
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)
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def add_descriptions_to_schema(schema, cls):
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"""Add docstring descriptions to the schema properties."""
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if schema.get("description"):
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schema["description"] = inspect.cleandoc(schema["description"])
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elif class_doc := inspect.getdoc(cls):
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schema["description"] = inspect.cleandoc(class_doc)
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# Get attribute docstrings from the class
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attr_docs = {}
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# Also check class annotations for docstrings
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source_lines = inspect.getsourcelines(cls)[0]
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current_attr = None
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docstring_lines = []
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for line in source_lines:
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line = line.strip()
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# Check for attribute definition (TypedDict style)
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if ":" in line and not line.startswith("#") and not line.startswith('"""'):
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parts = line.split(":", 1)
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if len(parts) == 2 and parts[0].strip().isidentifier():
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# If we were collecting a docstring, save it for the previous attribute
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if current_attr and docstring_lines:
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attr_docs[current_attr] = "\n".join(docstring_lines).strip('"')
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docstring_lines = []
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current_attr = parts[0].strip()
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# Check for docstring after attribute
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elif line.startswith('"""') and current_attr:
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# Start or end of a docstring
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if len(line) > 3 and line.endswith('"""'):
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# Single line docstring
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attr_docs[current_attr] = line.strip('"')
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current_attr = None
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elif docstring_lines:
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# End of multi-line docstring
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docstring_lines.append(line.rstrip('"'))
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attr_docs[current_attr] = "\n".join(docstring_lines).strip('"')
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docstring_lines = []
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current_attr = None
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else:
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# Start of multi-line docstring
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docstring_lines.append(line.lstrip('"'))
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# Continue multi-line docstring
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elif docstring_lines and current_attr:
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docstring_lines.append(line.strip('"'))
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# Add the last docstring if there is one
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if current_attr and docstring_lines:
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attr_docs[current_attr] = "\n".join(docstring_lines).strip('"')
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# Add descriptions to properties
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if "properties" in schema:
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for prop_name, prop_schema in schema["properties"].items():
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# First try to get from attribute docstrings
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if prop_name in attr_docs and "description" not in prop_schema:
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prop_schema["description"] = textwrap.dedent(attr_docs[prop_name])
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# Fall back to class docstring parsing
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elif class_doc:
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for line in class_doc.split("\n"):
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if line.strip().startswith(
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f"{prop_name}:"
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) or line.strip().startswith(f'"{prop_name}"'):
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description = line.split(":", 1)[1].strip()
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if description and "description" not in prop_schema:
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prop_schema["description"] = description
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break
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# Recursively process nested definitions
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if "$defs" in schema:
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for def_name, def_schema in schema["$defs"].items():
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# Find the class that corresponds to this definition
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for potential_cls in [
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Config,
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GraphDef,
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StoreConfig,
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IndexConfig,
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AuthConfig,
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SecurityConfig,
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HttpConfig,
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CorsConfig,
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CacheConfig,
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ThreadTTLConfig,
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CheckpointerConfig,
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SerdeConfig,
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TTLConfig,
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ConfigurableHeaderConfig,
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WebhooksConfig,
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WebhookUrlPolicy,
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]:
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if potential_cls.__name__ == def_name:
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add_descriptions_to_schema(def_schema, potential_cls)
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break
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return schema
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def generate_schema():
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"""Generate a JSON schema for the Config class using msgspec."""
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# Generate the basic schema
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schema = msgspec.json.schema(Config)
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# Add title and description
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schema["title"] = "LangGraph CLI Configuration"
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schema["description"] = "Configuration schema for langgraph-cli"
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# Add docstring descriptions
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schema = add_descriptions_to_schema(schema, Config)
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# Add constraint that only one of python_version or node_version should be specified
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config_schema = schema["$defs"]["Config"]
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# Create two subschemas: one with python_version and one with node_version
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# Define properties specific to Python projects
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python_specific_props = ["python_version", "pip_config_file"]
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# Define properties specific to Node.js projects
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node_specific_props = ["node_version"]
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# Define properties common to both project types
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common_props = [
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k
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for k in config_schema["properties"]
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if k not in python_specific_props and k not in node_specific_props
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]
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# Create legacy Python schema with python_version and pip_config_file
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legacy_python_schema = {
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"type": "object",
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"properties": {
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# Include Python-specific properties
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**{k: config_schema["properties"][k].copy() for k in python_specific_props},
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# Include common properties
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**{k: config_schema["properties"][k].copy() for k in common_props},
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},
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"required": ["dependencies", "graphs"],
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}
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legacy_python_schema["properties"]["pip_installer"] = {
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"anyOf": [
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{"type": "string", "enum": ["auto", "pip", "uv"]},
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{"type": "null"},
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]
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}
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uv_source_python_schema = {
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"type": "object",
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"properties": {
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**{
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k: config_schema["properties"][k].copy()
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for k in python_specific_props + common_props
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},
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},
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"required": ["graphs", "source"],
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}
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# source must be a UvSource object (not null)
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uv_source_python_schema["properties"]["source"] = {"$ref": "#/$defs/UvSource"}
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uv_source_python_schema["properties"]["pip_installer"] = {
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"anyOf": [
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{"type": "string", "enum": ["auto", "pip", "uv"]},
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{"type": "null"},
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]
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}
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# Add enum constraint for python_version
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if "python_version" in legacy_python_schema["properties"]:
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legacy_python_schema["properties"]["python_version"]["enum"] = [
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"3.11",
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"3.12",
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"3.13",
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]
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if "python_version" in uv_source_python_schema["properties"]:
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uv_source_python_schema["properties"]["python_version"]["enum"] = [
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"3.11",
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"3.12",
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"3.13",
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]
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# Create Node.js schema with node_version
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node_schema = {
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"type": "object",
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"properties": {
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# Include Node-specific properties
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**{k: config_schema["properties"][k].copy() for k in node_specific_props},
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# Include common properties
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**{k: config_schema["properties"][k].copy() for k in common_props},
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},
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"required": ["node_version", "graphs"],
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}
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node_schema["properties"]["pip_installer"] = {
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"anyOf": [
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{"type": "string", "enum": ["auto", "pip", "uv"]},
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{"type": "null"},
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]
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}
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# Add enum constraint for node_version
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if "node_version" in node_schema["properties"]:
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node_schema["properties"]["node_version"]["anyOf"] = [
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{"type": "string", "enum": ["20"]},
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{"type": "null"},
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]
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# Add enum constraint for image_distro
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if "image_distro" in node_schema["properties"]:
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node_schema["properties"]["image_distro"]["anyOf"] = [
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{"type": "string", "enum": ["debian", "wolfi"]},
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{"type": "null"},
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]
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# Replace the Config schema with a oneOf constraint
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config_schema["oneOf"] = [
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legacy_python_schema,
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uv_source_python_schema,
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node_schema,
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]
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# Remove the properties field as it's now defined in the oneOf subschemas
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if "properties" in config_schema:
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del config_schema["properties"]
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return schema
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def main():
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"""Generate the schema and write it to a file."""
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schema = generate_schema()
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# Add versioning to the schema
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import importlib.metadata
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try:
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version = importlib.metadata.version("langgraph_cli").split(".")
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schema_version = f"v{version[0]}"
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except importlib.metadata.PackageNotFoundError:
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schema_version = "v1"
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# Add version to schema
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schema["version"] = schema_version
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config_dir = Path(__file__).parent / "schemas"
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# Create versioned schema file
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versioned_path = config_dir / f"schema.{schema_version}.json"
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with open(versioned_path, "w") as f:
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json.dump(schema, f, indent=2)
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# Also create a latest version
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latest_path = config_dir / "schema.json"
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with open(latest_path, "w") as f:
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json.dump(schema, f, indent=2)
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print(f"Schema written to {versioned_path} and {latest_path}")
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print(
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f"You can now add '$schema: https://raw.githubusercontent.com/langchain-ai/langgraph/refs/heads/main/libs/cli/schemas/schema.json'"
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f" or '$schema: https://raw.githubusercontent.com/langchain-ai/langgraph/refs/heads/main/libs/cli/schemas/schema.{schema_version}.json'"
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" to your langgraph.json files"
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)
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if __name__ == "__main__":
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main()
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