Type safety w generics

- rm langchain_core dependency
- replace callbacks w run tree
- rm Runnable dependency
- rm non-state Graph
- rm managed values
- rm entrypoint/task/call
- rm async methods
- rm shallow checkpointer
- rm messages stream mode
- rm debug flag
- rm remote graph

CLAUDE.md

@@ -0,0 +1,142 @@
+# LangGraph Coding Guide
+
+## Repository Structure
+
+LangGraph follows a monorepo organization, with the following structure:
+
+- `libs/langgraph` is the main Python library, published to pypi as `langgraph`. This contains the majority of the code for the framework, as well as the majority of the unit tests.
+- `libs/checkpoint` , published to pypi as `langgraph-checkpoint` contains the base classes for the persistence layer of langgraph. The two main abstractions are BaseCheckpointSaver (base class for persistence of workflow runs step-by-step) and BaseStore (base class for "long-term memory" operations, offering a key-value interface combined with semantic search over documents, used for persisting information across distinct workflow runs). This library is a dependency of both the main langgraph library, as well as implementations of these storage interfaces for specific databases. This library also contains reference implementations
+- `libs/checkpoint-postgres` published to pypi as langgraph-checkpoint-postgres, contains implementations of checkpoint and store backed by postgres. Majority of the test coverage is in `libs/langgraph` in the form of tests that run over all storage implementations in the repo.
+- `langgraph-java` contains a Java implementation of the langgraph framework, which is in the early stages of development.
+
+## Feature Overview
+
+langgraph is an orchestration framework (in the style of airflow or temporal) designed for LLM applications, with a focus on streaming output, cyclical and parallel workflows, and interrupt/resume capabilities. Applications built with langgraph are variously called workflows, graphs, cognitive architectures, agents. Key features:
+
+1. **Graph-based Architecture**: Build directed computation graphs with nodes and edges
+2. **State Management**: Type-safe state schema with custom reducers and transformations
+3. **Human-in-the-loop**: Support for interrupts, checkpoints, and tool call review
+4. **Persistence**: Save and resume execution with in-memory or database storage
+5. **Streaming**: Multiple modes (values, updates, custom) for real-time feedback
+6. **Multi-agent Patterns**: Support for network, supervisor, and hierarchical architectures
+
+## Python Development
+
+### Build/Test/Lint Commands
+
+(in the respective subdirectory)
+
+- Run all tests: `make test`
+- Run single test: `make test TEST=path/to/test_file.py::test_function`
+- Watch mode tests: `make test_watch`
+- Run tests in parallel: `make test_parallel`
+- Generate coverage report: `make coverage`
+- Format code: `make format`
+- Lint code: `make lint`
+- Check spelling: `make spell_check`
+- Fix spelling: `make spell_fix`
+- Build documentation: `make serve-docs` (from repo root)
+- Run benchmarks: `make benchmark` or `make benchmark-fast`
+
+### Code Style Guidelines
+
+- Follow [ruff](https://github.com/astral-sh/ruff) formatting/linting rules
+- Use [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html) for docstrings
+- Enforce type annotations with mypy (`disallow_untyped_defs = True`)
+- Use double quotes for strings
+- Maximum line length of 88 characters
+- Follow imports sorting with `ruff`
+- All functions/classes must have proper docstrings with args/returns
+- Write comprehensive unit tests for new features
+- Keep backward compatibility
+- PR scope should be isolated (changes shouldn't affect multiple packages)
+- Use descriptive variable names following Python conventions
+- Error handling should use appropriate exception types and messaging
+
+## Java Development
+
+(in the `langgraph-java` subdirectory)
+
+### Build/Test/Lint Commands
+
+- Build the project: `./gradlew build`
+- Run tests: `./gradlew test`
+- Run a specific test: `./gradlew test --tests "com.langgraph.package.TestClass.testMethod"`
+- Check formatting: `./gradlew spotlessCheck`
+- Apply formatting: `./gradlew spotlessApply`
+- Run all checks: `./gradlew check`
+- Generate Javadoc: `./gradlew javadoc`
+
+### Code Style Guidelines
+
+- Follow standard Java code style (Google Java Style Guide)
+- Use 4 spaces for indentation
+- Maximum line length of 100 characters
+- All public methods/classes must have proper Javadoc with @param/@return tags
+- Use descriptive variable names following Java conventions (camelCase)
+- Exception handling should use appropriate exception types with descriptive messages
+- Favor composition over inheritance
+- Use the Builder pattern for complex object creation
+- Write comprehensive unit tests for new features
+
+### Python Compatibility Guidelines
+
+- When implementing features from the Python version:
+  - Maintain semantic equivalence with the Python implementation
+  - Preserve the same behavior for all public APIs
+  - Document any intentional differences in behavior with comments
+  - Pay special attention to collections handling (Python lists vs Java Lists)
+  - Ensure that iteration order and value handling match Python where relevant
+- Use the same test cases as the Python version when possible
+- Do not introduce Java-specific shortcuts that would break Python compatibility
+- Never add test-specific code to source files - tests should adapt to implementation, not vice versa
+
+### Implementation Mapping
+
+- Always consult and update the `PYTHON_JAVA_MAPPING.md` file when:
+  - Adding new Java files or classes
+  - Updating existing Java implementations
+  - Fixing test failures in Java
+  - Implementing Python features in Java
+- This mapping file documents:
+  - Where to find equivalent functionality in Python and Java
+  - Any intentional deviations between implementations
+  - Implementation status and compatibility notes
+- When tests fail, check if the Java implementation matches Python behavior:
+  - Fix the implementation to match Python semantics whenever possible
+  - Update tests only if the Python version also differs
+  - Never create special cases or workarounds in Java just to make tests pass
+  - Document any implementation differences clearly in the mapping file
+- For new features, implement the Python behavior first, then adapt to Java idioms
+
+### Backward Compatibility and API Design
+
+- LangGraph Java has not been released publicly, so there is no need to maintain backward compatibility
+- When renaming methods, members, or classes:
+  - Use the clearest, most intuitive names that match Python semantics
+  - Remove old/deprecated methods completely rather than marking them as deprecated
+  - Update all tests and documentation to use the new names
+  - Do not leave deprecated methods or tests for backward compatibility
+
+### API Design Principles
+
+- Prefer a single, clear way to accomplish each task rather than multiple convenience methods
+- Prefer builder patterns over static factory methods where appropriate
+- For collections, prefer methods that operate on collections rather than having both single-item and collection variants
+- Choose method names that clearly express their purpose and align with Java conventions
+- Maintain consistent naming patterns across similar components
+- Document the recommended usage pattern in JavaDoc
+
+### Project Structure
+
+- `langgraph-core`: Core functionality of the framework
+- `langgraph-checkpoint`: Persistence layer for checkpoints and state management
+- `langgraph-examples`: Example applications and usage patterns
+
+### Error Handling
+
+- Use runtime exceptions for unexpected errors
+- Use checked exceptions for recoverable errors
+- Provide clear error messages that include context about what went wrong
+- Validate inputs early to prevent cascading errors
+- Ensure all resources are properly closed even in error conditions

docs/.gitignore

@@ -1,4 +0,0 @@
-site/
-docs/cloud/reference/sdk/js_ts_sdk_ref.md
-
-.vercel

docs/Makefile

@@ -1,66 +0,0 @@
-.PHONY: lint-docs format-docs build-docs serve-docs serve-clean-docs clean-docs codespell build-typedoc llms-text build-prebuilt tests
-
-build-typedoc:
-	cd ../libs/sdk-js && yarn install --include-dev && yarn typedoc
-	cd ../libs/sdk-js && yarn --silent concat-md --decrease-title-levels --ignore=js_ts_sdk_ref.md --start-title-level-at 2 docs > ../../docs/docs/cloud/reference/sdk/js_ts_sdk_ref.md 2>/dev/null
-	#  Add links to the monorepo
-	sed -e '1,10s|@langchain/langgraph-sdk|[@langchain/langgraph-sdk](https://github.com/langchain-ai/langgraph/tree/main/libs/sdk-js)|g' docs/cloud/reference/sdk/js_ts_sdk_ref.md > temp_file && mv temp_file docs/cloud/reference/sdk/js_ts_sdk_ref.md
-
-build-prebuilt:
-	# Use to create an update to date prebuilt page.
-	# Looks up download stats for each of the prebuilt packages and
-	# generates the final prebuilt page.
-	poetry run python -m _scripts.third_party_page.get_download_stats stats.yml
-	poetry run python -m _scripts.third_party_page.create_third_party_page stats.yml docs/prebuilt.md --language python
-
-build-docs: build-typedoc build-prebuilt
-	poetry run python -m mkdocs build --clean -f mkdocs.yml --strict
-
-llms-text:
-	poetry run python -m _scripts.generate_llms_text docs/llms-full.txt
-
-install-vercel-deps:
-	dnf install -y python3.11
-	curl -sSL https://install.python-poetry.org | python3 -
-	poetry self update 1.8.5
-	# don't use vercel's python - it wasn't compiled with sqlite support, and it fails when installing ipython's kernel
-	poetry env use /usr/bin/python3.11
-	poetry install --with docs --with test --no-root
-
-tests:
-	# Run unit tests
-	poetry run pytest tests/unit_tests
-
-
-vercel-build-docs: install-vercel-deps
-	make build-docs
-
-
-serve-clean-docs: clean-docs
-	poetry run python -m mkdocs serve -c -f mkdocs.yml --strict -w ../libs/langgraph
-
-serve-docs: build-typedoc
-	poetry run python -m mkdocs serve -f mkdocs.yml -w ../libs/langgraph  -w ../libs/checkpoint -w ../libs/sdk-py --dirty
-
-clean-docs:
-	find ./docs -name "*.ipynb" -type f -delete
-	rm -rf site
-
-## Run format against the project documentation.
-format-docs:
-	poetry run ruff format docs
-	poetry run ruff check --fix docs
-
-# Check the docs for linting violations
-lint-docs:
-	poetry run ruff format --check docs
-	poetry run ruff check docs
-
-codespell:
-	./codespell_notebooks.sh .
-
-start-services:
-	docker compose -f test-compose.yml up -V --force-recreate --wait --remove-orphans
-
-stop-services:
-	docker compose -f test-compose.yml down

docs/README.md

@@ -1,59 +0,0 @@
-# Setup
-
-To setup requirements for building docs you can run:
-
-```bash
-poetry install --with test
-```
-
-## Serving documentation locally
-
-To run the documentation server locally you can run:
-
-```bash
-make serve-docs
-```
-
-## Execute notebooks
-
-If you would like to automatically execute all of the notebooks, to mimic the "Run notebooks" GHA, you can run:
-
-```bash
-python _scripts/prepare_notebooks_for_ci.py
-./_scripts/execute_notebooks.sh
-```
-
-**Note**: if you want to run the notebooks without `%pip install` cells, you can run:
-
-```bash
-python _scripts/prepare_notebooks_for_ci.py --comment-install-cells
-./_scripts/execute_notebooks.sh
-```
-
-`prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
-* when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
-* when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
-
-## Adding new notebooks
-
-If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
-
-To record network requests, please make sure to first run `prepare_notebooks_for_ci.py` script.
-
-Then, run
-
-```bash
-jupyter execute <path_to_notebook>
-```
-
-Once the notebook is executed, you should see the new VCR cassettes recorded in `cassettes` directory and discard the updated notebook.
-
-## Updating existing notebooks
-
-If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
-
-To delete cassettes for a notebook, you can run:
-
-```bash
-rm cassettes/<notebook_name>*
-```

docs/_scripts/_patch.py

@@ -1,93 +0,0 @@
-import functools
-
-from urllib3 import __version__ as urllib3version  # type: ignore[import-untyped]
-from urllib3 import connection  # type: ignore[import-untyped]
-
-
-def _ensure_str(s, encoding="utf-8", errors="strict") -> str:
-    if isinstance(s, str):
-        return s
-
-    if isinstance(s, bytes):
-        return s.decode(encoding, errors)
-    return str(s)
-
-
-# Copied from https://github.com/urllib3/urllib3/blob/1c994dfc8c5d5ecaee8ed3eb585d4785f5febf6e/src/urllib3/connection.py#L231
-def request(self, method, url, body=None, headers=None):
-    """Make the request.
-
-    This function is based on the urllib3 request method, with modifications
-    to handle potential issues when using vcrpy in concurrent workloads.
-
-    Args:
-        self: The HTTPConnection instance.
-        method (str): The HTTP method (e.g., 'GET', 'POST').
-        url (str): The URL for the request.
-        body (Optional[Any]): The body of the request.
-        headers (Optional[dict]): Headers to send with the request.
-
-    Returns:
-        The result of calling the parent request method.
-    """
-    # Update the inner socket's timeout value to send the request.
-    # This only triggers if the connection is reused.
-    if getattr(self, "sock", None) is not None:
-        self.sock.settimeout(self.timeout)
-
-    if headers is None:
-        headers = {}
-    else:
-        # Avoid modifying the headers passed into .request()
-        headers = headers.copy()
-    if "user-agent" not in (_ensure_str(k.lower()) for k in headers):
-        headers["User-Agent"] = connection._get_default_user_agent()
-    # The above is all the same ^^^
-    # The following is different:
-    return self._parent_request(method, url, body=body, headers=headers)
-
-
-_PATCHED = False
-
-
-def patch_urllib3():
-    """Patch the request method of urllib3 to avoid type errors when using vcrpy.
-
-    In concurrent workloads (such as the tracing background queue), the
-    connection pool can get in a state where an HTTPConnection is created
-    before vcrpy patches the HTTPConnection class. In urllib3 >= 2.0 this isn't
-    a problem since they use the proper super().request(...) syntax, but in older
-    versions, super(HTTPConnection, self).request is used, resulting in a TypeError
-    since self is no longer a subclass of "HTTPConnection" (which at this point
-    is vcr.stubs.VCRConnection).
-
-    This method patches the class to fix the super() syntax to avoid mixed inheritance.
-    In the case of the LangSmith tracing logic, it doesn't really matter since we always
-    exclude cache checks for calls to LangSmith.
-
-    The patch is only applied for urllib3 versions older than 2.0.
-    """
-    global _PATCHED
-    if _PATCHED:
-        return
-    from packaging import version
-
-    if version.parse(urllib3version) >= version.parse("2.0"):
-        _PATCHED = True
-        return
-
-    # Lookup the parent class and its request method
-    parent_class = connection.HTTPConnection.__bases__[0]
-    parent_request = parent_class.request
-
-    def new_request(self, *args, **kwargs):
-        """Handle parent request.
-
-        This method binds the parent's request method to self and then
-        calls our modified request function.
-        """
-        self._parent_request = functools.partial(parent_request, self)
-        return request(self, *args, **kwargs)
-
-    connection.HTTPConnection.request = new_request
-    _PATCHED = True

docs/_scripts/add_translation.py

@@ -1,157 +0,0 @@
-"""Add typescript translation to a given markdown file."""
-
-import argparse
-import re
-
-import requests
-from langchain_anthropic import ChatAnthropic
-
-URL = "https://gist.githubusercontent.com/eyurtsev/e7486731415463a9bc5b4682358859c8/raw/b5a5fda9c7e3387cfcb781f25082814d43675d50/gistfile1.txt"
-response = requests.get(URL)
-response.raise_for_status()
-reference_snippets = response.text
-
-model = ChatAnthropic(model="claude-3-5-sonnet-latest")
-
-
-def _get_tqdm():
-    try:
-        from tqdm import tqdm
-    except ImportError:
-        # If not available return a simple identity function
-        def tqdm(iterable, *args, **kwargs):
-            return iterable
-
-    return tqdm
-
-
-_tqdm = _get_tqdm()
-
-opening_pattern = re.compile(r"^\s*```python(?:\s+.*)?\s*$")
-closing_pattern = re.compile(r"^\s*```\s*$")
-
-
-def extract_python_snippets(markdown: str) -> list[str]:
-    """
-    Extract all python code blocks (including their fence lines) from the markdown content.
-    A python block is defined as any block that starts with a line containing an opening fence
-    with '```python' (optionally with extra parameters) and ends with a closing fence '```'.
-    """
-    snippets = []
-    inside_block = False
-    current_snippet = []
-
-    for line in markdown.splitlines(keepends=True):
-        if not inside_block:
-            if opening_pattern.match(line):
-                inside_block = True
-                current_snippet = [line]
-        else:
-            current_snippet.append(line)
-            if closing_pattern.match(line):
-                inside_block = False
-                snippets.append("".join(current_snippet))
-                current_snippet = []
-    return snippets
-
-
-def translate_snippet(python_snippet: str) -> str:
-    """Translate a python code block into a TypeScript code block using Langchain.
-    The response is expected to be a properly fenced TypeScript code block (i.e.
-    starting with ```typescript and ending with ```).
-    """
-    ai_message = model.invoke(
-        [
-            {
-                "role": "system",
-                "content": (
-                    f"You have access to the following up-to-date example TypeScript code "
-                    f"snippets that show examples of building with langgraph "
-                    f"and langchain:\n\n{reference_snippets}\n\n"
-                    "Use this context to translate the following Python code to equivalent "
-                    "TypeScript. Ensure that your output is a valid fenced TypeScript "
-                    "code block (i.e. starts with ```typescript and ends with ```)."
-                ),
-            },
-            {
-                "role": "user",
-                "content": f"Translate this Python snippet to TypeScript:\n\n{python_snippet}",
-            },
-        ]
-    )
-
-    # Use a regular expression to search for a TypeScript code block in the response.
-    pattern = r"```typescript\s*(.*?)\s*```"
-    match = re.search(pattern, ai_message.content, re.DOTALL)
-    if match:
-        # Reconstruct the code block with proper fences.
-        typescript_code = match.group(1).strip()
-        return f"```typescript\n{typescript_code}\n```"
-    else:
-        raise ValueError("No TypeScript code block found in the model's response.")
-
-
-def insert_translations_into_markdown(
-    markdown: str, typescript_snippets: list[str]
-) -> str:
-    """Walks through the original markdown content and, after each
-    Python snippet block, inserts the corresponding translated TypeScript snippet.
-    It assumes that the ordering of the Python snippets
-    (from extract_python_snippets) matches the order they appear in the markdown.
-    """
-    output_lines = []
-    lines = markdown.splitlines(keepends=True)
-    inside_block = False
-    snippet_index = 0
-
-    for line in lines:
-        output_lines.append(line)
-        if not inside_block and opening_pattern.match(line):
-            # We've encountered the start of a python code block.
-            inside_block = True
-        elif inside_block:
-            if closing_pattern.match(line):
-                # End of a python snippet block.
-                inside_block = False
-                if snippet_index < len(typescript_snippets):
-                    # Insert an extra newline for clarity, then the translated TypeScript snippet.
-                    output_lines.append("\n")
-                    output_lines.append(typescript_snippets[snippet_index])
-                    output_lines.append("\n")
-                    snippet_index += 1
-    return "".join(output_lines)
-
-
-def main(file_path: str) -> None:
-    # Read the markdown file.
-    with open(file_path, "r") as f:
-        markdown_content = f.read()
-
-    # 1. Extract all Python snippets.
-    python_snippets = extract_python_snippets(markdown_content)[:1]
-
-    # 2. Translate each Python snippet to TypeScript.
-    typescript_snippets = []
-    # Replace with .batch() for faster translation
-    for python_snippet in _tqdm(python_snippets):
-        ts_snippet = translate_snippet(python_snippet)
-        typescript_snippets.append(ts_snippet)
-
-    # 3. Insert the TypeScript translations after their respective Python snippets.
-    updated_markdown = insert_translations_into_markdown(
-        markdown_content, typescript_snippets
-    )
-
-    # Overwrite the original markdown file with the updated content.
-    with open(file_path, "w") as f:
-        f.write(updated_markdown)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Translate Python snippets in a markdown file to TypeScript and insert them after each Python snippet."
-    )
-    parser.add_argument("file_path", type=str, help="Path to the markdown file.")
-    args = parser.parse_args()
-
-    main(args.file_path)

docs/_scripts/download_tiktoken.py

@@ -1,5 +0,0 @@
-import tiktoken
-
-# This will trigger the download and caching of the necessary files
-for encoding in ("gpt2", "gpt-3.5"):
-    tiktoken.encoding_for_model(encoding)

docs/_scripts/execute_notebooks.sh

@@ -1,36 +0,0 @@
-#!/bin/bash
-
-# Read the list of notebooks to skip from the JSON file
-SKIP_NOTEBOOKS=$(python -c "import json; print('\n'.join(json.load(open('notebooks_no_execution.json'))))")
-
-# Function to execute a single notebook
-execute_notebook() {
-    file="$1"
-    echo "Starting execution of $file"
-    start_time=$(date +%s)
-    if ! output=$(time poetry run jupyter execute "$file" 2>&1); then
-        end_time=$(date +%s)
-        execution_time=$((end_time - start_time))
-        echo "Error in $file. Execution time: $execution_time seconds"
-        echo "Error details: $output"
-        exit 1
-    fi
-    end_time=$(date +%s)
-    execution_time=$((end_time - start_time))
-    echo "Finished $file. Execution time: $execution_time seconds"
-}
-
-export -f execute_notebook
-
-# Check if custom notebook paths are provided
-if [ $# -gt 0 ]; then
-    notebooks=$(echo "$@" | tr ' ' '\n' | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
-else
-    # Find all notebooks and filter out those in the skip list
-    notebooks=$(find docs/tutorials docs/how-tos -name "*.ipynb" | grep -v ".ipynb_checkpoints" | grep -vFf <(echo "$SKIP_NOTEBOOKS"))
-fi
-
-# Execute notebooks sequentially
-for file in $notebooks; do
-    execute_notebook "$file"
-done

docs/_scripts/generate_api_reference_links.py

@@ -1,261 +0,0 @@
-import ast
-import importlib
-import logging
-import re
-from functools import lru_cache
-from typing import List, Optional
-
-from typing_extensions import TypedDict
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-# Base URL for all class documentation
-_LANGCHAIN_API_REFERENCE = "https://python.langchain.com/api_reference/"
-_LANGGRAPH_API_REFERENCE = "https://langchain-ai.github.io/langgraph/reference/"
-
-
-# (alias/re-exported modules, source module, class, docs namespace)
-MANUAL_API_REFERENCES_LANGGRAPH = [
-    (
-        ["langgraph.prebuilt"],
-        "langgraph.prebuilt.chat_agent_executor",
-        "create_react_agent",
-        "prebuilt",
-    ),
-    (["langgraph.prebuilt"], "langgraph.prebuilt.tool_node", "ToolNode", "prebuilt"),
-    (
-        ["langgraph.prebuilt"],
-        "langgraph.prebuilt.tool_node",
-        "tools_condition",
-        "prebuilt",
-    ),
-    (
-        ["langgraph.prebuilt"],
-        "langgraph.prebuilt.tool_node",
-        "InjectedState",
-        "prebuilt",
-    ),
-    # Graph
-    (["langgraph.graph"], "langgraph.graph.message", "add_messages", "graphs"),
-    (["langgraph.graph"], "langgraph.graph.state", "StateGraph", "graphs"),
-    (["langgraph.graph"], "langgraph.graph.state", "CompiledStateGraph", "graphs"),
-    (["langgraph.graph"], "langgraph.constants", "START", "constants"),
-    (["langgraph.graph"], "langgraph.constants", "END", "constants"),
-    (["langgraph.constants"], "langgraph.types", "Send", "types"),
-    (["langgraph.constants"], "langgraph.types", "Interrupt", "types"),
-    (["langgraph.constants"], "langgraph.types", "interrupt", "types"),
-    (["langgraph.constants"], "langgraph.types", "Command", "types"),
-    (["langgraph.func"], "langgraph.func", "entrypoint", "func"),
-    (["langgraph.func"], "langgraph.func", "task", "func"),
-    (["langgraph.types"], "langgraph.types", "RetryPolicy", "types"),
-    (["langgraph.types"], "langgraph.types", "StreamMode", "types"),
-    (["langgraph.types"], "langgraph.types", "StreamWriter", "types"),
-    ([], "langgraph.checkpoint.base", "Checkpoint", "checkpoints"),
-    ([], "langgraph.checkpoint.base", "CheckpointMetadata", "checkpoints"),
-    ([], "langgraph.checkpoint.base", "BaseCheckpointSaver", "checkpoints"),
-    ([], "langgraph.checkpoint.base", "SerializerProtocol", "checkpoints"),
-    ([], "langgraph.checkpoint.serde.jsonplus", "JsonPlusSerializer", "checkpoints"),
-    ([], "langgraph.checkpoint.memory", "MemorySaver", "checkpoints"),
-    ([], "langgraph.checkpoint.sqlite.aio", "AsyncSqliteSaver", "checkpoints"),
-    ([], "langgraph.checkpoint.sqlite", "SqliteSaver", "checkpoints"),
-    ([], "langgraph.checkpoint.postgres.aio", "AsyncPostgresSaver", "checkpoints"),
-    ([], "langgraph.checkpoint.postgres", "PostgresSaver", "checkpoints"),
-]
-
-WELL_KNOWN_LANGGRAPH_OBJECTS = {
-    (module_, class_): (source_module, namespace)
-    for (modules, source_module, class_, namespace) in MANUAL_API_REFERENCES_LANGGRAPH
-    for module_ in modules + [source_module]
-}
-
-
-@lru_cache(maxsize=10_000)
-def _get_full_module_name(module_path: str, class_name: str) -> Optional[str]:
-    """Get full module name using inspect, with LRU cache to memoize results."""
-    try:
-        module = importlib.import_module(module_path)
-        symbol = getattr(module, class_name)
-        # First check the __module__ attribute on the symbol.
-        mod_name = getattr(symbol, "__module__", None)
-        # If __module__ is not set or comes from typing,
-        # assume the definition is in module_path.
-        if mod_name is None or mod_name.startswith("typing"):
-            return module_path
-        return mod_name
-    except AttributeError as e:
-        logger.warning(f"API Reference: Could not find module for {class_name}, {e}")
-        return None
-    except ImportError as e:
-        logger.warning(f"API Reference: Failed to load for class {class_name}, {e}")
-        return None
-
-
-class ImportInformation(TypedDict):
-    imported: str  # The name of the class that was imported.
-    source: str  # The full module path from which the class was imported.
-    docs: str  # The URL pointing to the class's documentation.
-    path: str  # The path of the file where the markdown content originated.
-
-
-def get_imports(code: str, path: str) -> List[ImportInformation]:
-    """Retrieve all import references from the given code for specified ecosystems.
-
-    Args:
-        code: The source code from which to extract import references.
-        path: The path of the file where the markdown content originated.
-
-    Returns:
-        A list of import information for each import found.
-    """
-    # Parse the code into an AST.
-    try:
-        tree = ast.parse(code)
-    except SyntaxError:
-        return []
-
-    found_imports = []
-
-    # Walk through the AST and process ImportFrom nodes.
-    for node in ast.walk(tree):
-        if isinstance(node, ast.ImportFrom):
-            # node.module is the source module.
-            if node.module is None:
-                continue
-            for alias in node.names:
-                if not (
-                    node.module.startswith("langchain")
-                    or node.module.startswith("langgraph")
-                ):
-                    continue
-
-                found_imports.append(
-                    {
-                        "source": node.module,
-                        # alias.name is the original name even if an alias exists.
-                        "imported": alias.name,
-                    }
-                )
-
-    imports: list[ImportInformation] = []
-
-    for found_import in found_imports:
-        module = found_import["source"]
-
-        if module.startswith("langchain"):
-            # Handles things like `langchain` or `langchain_anthropic`
-            package_ecosystem = "langchain"
-        elif module.startswith("langgraph"):
-            package_ecosystem = "langgraph"
-        else:
-            continue
-
-        class_name = found_import["imported"]
-        module_path = _get_full_module_name(module, class_name)
-        if not module_path:
-            continue
-        if len(module_path.split(".")) < 2:
-            continue
-
-        if package_ecosystem == "langchain":
-            pkg = module_path.split(".")[0].replace("langchain_", "")
-            top_level_mod = module_path.split(".")[1]
-
-            url = (
-                _LANGCHAIN_API_REFERENCE
-                + pkg
-                + "/"
-                + top_level_mod
-                + "/"
-                + module_path
-                + "."
-                + class_name
-                + ".html"
-            )
-        elif package_ecosystem == "langgraph":
-            if (module, class_name) not in WELL_KNOWN_LANGGRAPH_OBJECTS:
-                # Likely not documented yet
-                continue
-
-            source_module, namespace = WELL_KNOWN_LANGGRAPH_OBJECTS[
-                (module, class_name)
-            ]
-            url = (
-                _LANGGRAPH_API_REFERENCE
-                + namespace
-                + "/#"
-                + source_module
-                + "."
-                + class_name
-            )
-        else:
-            raise ValueError(f"Invalid package ecosystem: {package_ecosystem}")
-
-        # Add the import information to our list
-        imports.append(
-            {
-                "imported": class_name,
-                "source": module,
-                "docs": url,
-                "path": path,
-            }
-        )
-
-    return imports
-
-
-def update_markdown_with_imports(markdown: str, path: str) -> str:
-    """Update markdown to include API reference links for imports in Python code blocks.
-
-    This function scans the markdown content for Python code blocks, extracts any
-    imports, and appends links to their API documentation.
-
-    Args:
-        markdown: The markdown content to process.
-        path: The path of the file where the markdown content originated.
-
-    Returns:
-        Updated markdown with API reference links appended to Python code blocks.
-
-    Example:
-        Given a markdown with a Python code block:
-
-        ```python
-        from langchain.nlp import TextGenerator
-        ```
-        This function will append an API reference link to the `TextGenerator` class
-        from the `langchain.nlp` module if it's recognized.
-    """
-    code_block_pattern = re.compile(
-        r"(?P<indent>[ \t]*)```(?P<language>python|py)\n(?P<code>.*?)\n(?P=indent)```",
-        re.DOTALL,
-    )
-
-    def replace_code_block(match: re.Match) -> str:
-        """Replace the matched code block with additional API reference links if imports are found.
-
-        Args:
-            match (re.Match): The regex match object containing the code block.
-
-        Returns:
-            str: The modified code block with API reference links appended if applicable.
-        """
-        indent = match.group("indent")
-        code_block = match.group("code")
-        # Retrieve import information from the code block
-        imports = get_imports(code_block, "__unused__")
-
-        original_code_block = match.group(0)
-        # If no imports are found, return the original code block
-        if not imports:
-            return original_code_block
-
-        # Generate API reference links for each import
-        api_links = " | ".join(
-            f'<a href="{imp["docs"]}">{imp["imported"]}</a>' for imp in imports
-        )
-        # Return the code block with appended API reference links
-        return f"{original_code_block}\n\n{indent}API Reference: {api_links}"
-
-    # Apply the replace_code_block function to all matches in the markdown
-    updated_markdown = code_block_pattern.sub(replace_code_block, markdown)
-    return updated_markdown

docs/_scripts/generate_llms_text.py

@@ -1,90 +0,0 @@
-"""Experimental script to generate consolidated llms text from the docs."""
-
-import glob
-import os
-
-from mkdocs.structure.files import File
-from mkdocs.structure.pages import Page
-
-from _scripts.notebook_hooks import _on_page_markdown_with_config
-
-HERE = os.path.dirname(os.path.abspath(__file__))
-# Get source directory (parent of HERE / docs)
-SOURCE_DIR = os.path.abspath(os.path.join(os.path.dirname(HERE), "docs"))
-
-
-def _make_llms_text(output_file: str) -> str:
-    """Generate a consolidated text file from markdown/notebook files for LLM training.
-
-    Args:
-        output_file: Path to output the consolidated text file
-    """
-    # Collect all markdown and notebook files
-    relative_paths = [
-        # Files relative to docs/docs/
-        "tutorials/introduction.ipynb",
-    ]
-    all_files = [os.path.join(SOURCE_DIR, path) for path in relative_paths]
-
-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.md"), recursive=True)
-    )
-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "how-tos/*.ipynb"), recursive=True)
-    )
-    # Add all concepts
-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "concepts/*.md"), recursive=True)
-    )
-    all_files.extend(
-        glob.glob(os.path.join(SOURCE_DIR, "concepts/*.ipynb"), recursive=True)
-    )
-
-    all_content = []
-
-    # Process each file
-    for file_path in all_files:
-        print(f"Processing {file_path}")
-        rel_path = os.path.relpath(file_path, SOURCE_DIR)
-
-        # Create File and Page objects to match mkdocs structure
-        file_obj = File(
-            path=rel_path, src_dir=SOURCE_DIR, dest_dir="", use_directory_urls=True
-        )
-        page = Page(
-            title="",
-            file=file_obj,
-            config={},
-        )
-
-        # Read raw content
-        with open(file_path, "r", encoding="utf-8") as f:
-            content = f.read()
-
-        # Convert to markdown without logic to resolve API references
-        processed_content = _on_page_markdown_with_config(
-            content, page, add_api_references=False, remove_base64_images=True
-        )
-        if processed_content:
-            # Add file name
-            all_content.append(f"---\n{rel_path}\n---")
-            # Add content
-            all_content.append(processed_content)
-
-    # Write consolidated output
-    with open(output_file, "w", encoding="utf-8") as f:
-        f.write("\n\n".join(all_content))
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        description=(
-            "Generate consolidated text file from markdown/notebook files for LLMs."
-        )
-    )
-    parser.add_argument("output_file", help="Path to output the consolidated text file")
-
-    args = parser.parse_args()
-    _make_llms_text(args.output_file)

docs/_scripts/notebook_convert.py

@@ -1,363 +0,0 @@
-import ast
-import os
-import re
-from pathlib import Path
-from typing import Literal
-
-import nbformat
-from nbconvert.exporters import MarkdownExporter
-from nbconvert.preprocessors import Preprocessor
-
-
-def _uses_input(source: str) -> bool:
-    """Parse the source code to determine if it uses the input() function."""
-    try:
-        tree = ast.parse(source)
-    except SyntaxError:
-        # If there's a syntax error, assume input() might be present to be safe.
-        return False
-
-    for node in ast.walk(tree):
-        if isinstance(node, ast.Call):
-            # Check if the function called is named 'input'
-            if isinstance(node.func, ast.Name) and node.func.id == "input":
-                return True
-    return False
-
-
-def _rewrite_cell_magic(code: str) -> str:
-    """Process a code block that uses cell magic.:w
-
-    - Lines starting with "%%capture" are ignored.
-    - Lines starting with "%pip" are rewritten by removing the leading "%" character.
-    - Any other non-empty line causes a NotImplementedError.
-
-    Args:
-        code (str): The original code block.
-
-    Returns:
-        str: The transformed code block.
-
-    Raises:
-        NotImplementedError: If a line doesn't start with either "%%capture" or "%pip".
-    """
-    rewritten_lines = []
-
-    for line in code.splitlines():
-        stripped = line.strip()
-        # Skip empty lines
-        if not stripped:
-            continue
-        # Ignore %%capture lines
-        if stripped.startswith("%%capture"):
-            continue
-        # Rewrite %pip lines by dropping the '%'
-        elif stripped.startswith("%pip"):
-            # Drop the leading '%' character
-            rewritten_lines.append(stripped[1:])
-        # Anything else is not supported
-        else:
-            raise NotImplementedError(f"Unhandled line: {line}")
-
-    return "\n".join(rewritten_lines)
-
-
-class PrintCallVisitor(ast.NodeVisitor):
-    """
-    This visitor sets self.has_print to True if it encounters a call
-    to a print within the global scope.
-
-    This should catch calls to print(), print_stream(), etc. (Prefixed with "print").
-
-    May have some false positives, but it's not meant to be perfect.
-
-    Temporary code for notebook conversion.
-    """
-
-    def __init__(self):
-        self.has_print = False
-        self.scope_level = 0  # counter to track whether we're inside a def/lambda
-
-    def visit_FunctionDef(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_AsyncFunctionDef(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_Lambda(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_ClassDef(self, node):
-        self.scope_level += 1
-        self.generic_visit(node)
-        self.scope_level -= 1
-
-    def visit_Call(self, node):
-        # Only consider calls when not inside a function definition.
-        if self.scope_level == 0:
-            if isinstance(node.func, ast.Name) and node.func.id.startswith("print"):
-                self.has_print = True
-        self.generic_visit(node)
-
-
-def _has_output(source: str) -> bool:
-    """Determine if the code block is expected to produce output.
-
-    Args:
-        source (str): The source code of the code block.
-
-    Returns:
-        True if the code block is expected to produce output, False otherwise.
-
-        Must meet the following conditions:
-
-        1. There is a call to a printing function (name starts with "print")
-           that is not inside a function definition.
-        2. The last top-level statement is an expression that is valid if:
-         - It is any expression (including calls) AND
-         - It is NOT a call to `display(...)`.
-
-        `display` isn't handled currently by markdown-exec
-    """
-    try:
-        tree = ast.parse(source)
-    except SyntaxError:
-        return False
-
-    # Condition (1): Check for a global print-like call.
-    visitor = PrintCallVisitor()
-    visitor.visit(tree)
-    condition_a = visitor.has_print
-
-    # Condition (2): Check the last top-level statement.
-    condition_b = False
-    if tree.body:
-        last_stmt = tree.body[-1]
-        if isinstance(last_stmt, ast.Expr):
-            # If the expression is a call, ensure it's not a call to "display"
-            if isinstance(last_stmt.value, ast.Call):
-                if (
-                    isinstance(last_stmt.value.func, ast.Name)
-                    and last_stmt.value.func.id == "display"
-                ):
-                    condition_b = False  # exclude display-wrapped expressions
-                else:
-                    condition_b = True
-            else:
-                # Any other expression qualifies.
-                condition_b = True
-
-    return condition_a or condition_b
-
-
-def _convert_links_in_markdown(markdown: str) -> str:
-    """Convert links present in notebook markdown cells to standardized format.
-
-    We want to update markdown links code cells by linking to markdown
-    files rather than assuming that the link is to the finalized HTML.
-
-    This code is needed temporarily since the markdown links that are present
-    in ipython notebooks do not follow the same conventions as regular markdown
-    files in mkdocs (which should link to a .md file).
-    """
-
-    # Define the regex pattern in parts for clarity:
-    pattern = (
-        r"(?<!!)"  # Negative lookbehind: ensure the link is not an image (i.e., doesn't start with "!")
-        r"\["  # Literal '[' indicating the start of the link text.
-        r"(?P<text>[^\]]*)"  # Named group 'text': match any characters except ']', representing the link text.
-        r"\]"  # Literal ']' indicating the end of the link text.
-        r"\("  # Literal '(' indicating the start of the URL.
-        r"(?![^\)]*//)"  # Negative lookahead: ensure that the URL does not contain '//' (skip absolute URLs).
-        r"(?P<url>[^)]*)"  # Named group 'url': match any characters except ')', representing the URL.
-        r"\)"  # Literal ')' indicating the end of the URL.
-    )
-
-    def custom_replacement(match):
-        """logic will correct the link format used in ipython notebooks
-
-        Ipython notebooks were being converted directly into HTML links
-        instead of markdown links that retain the markdown extension.
-
-        It needs to handle the following cases:
-        - optional fragments (e.g., `#section`)
-            e.g., `[text](url/#section)` -> `[text](url.md#section)`
-            e.g., `[text](url#section)` -> `[text](url.md#section)`
-        - relative paths (e.g., `../path/to/file`) need to be denested by 1 level
-        """
-        text = match.group("text")
-        url = match.group("url")
-
-        if url.startswith("../"):
-            # we strip the "../" from the start of the URL
-            # We only need to denest one level.
-            url = url[3:]
-
-        url = url.rstrip("/")  # Strip `/` from the end of the URL
-
-        # if url has a fragment
-        if "#" in url:
-            url, fragment = url.split("#")
-            url = url.rstrip("/")
-            # Strip `/` from the end of the URL
-            return f"[{text}]({url}.md#{fragment})"
-        # Otherwise add the .md extension
-        return f"[{text}]({url}.md)"
-
-    return re.sub(
-        pattern,
-        custom_replacement,
-        markdown,
-    )
-
-
-class EscapePreprocessor(Preprocessor):
-    def __init__(self, markdown_exec_migration: bool = False, **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.markdown_exec_migration = markdown_exec_migration
-
-    def preprocess_cell(self, cell, resources, cell_index):
-        if cell.cell_type == "markdown":
-            if not self.markdown_exec_migration:
-                # Old logic is to convert ipynb links to HTML links
-                cell.source = re.sub(
-                    r"(?<!!)\[([^\]]*)\]\((?![^\)]*//)([^)]*)(?:\.ipynb)?\)",
-                    r'<a href="\2">\1</a>',
-                    cell.source,
-                )
-            else:
-                cell.source = _convert_links_in_markdown(cell.source)
-
-            # Fix image paths in <img> tags
-            cell.source = re.sub(
-                r'<img\s+src="\.?/img/([^"]+)"', r'<img src="../img/\1"', cell.source
-            )
-
-        elif cell.cell_type == "code":
-            # Determine if the cell has bash or cell magic
-            source = cell.source
-            is_exec = not (
-                source.startswith("%") or source.startswith("!") or _uses_input(source)
-            )
-            cell.metadata["exec"] = is_exec
-
-            if self.markdown_exec_migration:
-                # For markdown exec migration we'll re-write cell magic as bash commands
-                if source.startswith("%%"):
-                    cell.source = _rewrite_cell_magic(source)
-                    cell.metadata["language"] = "shell"
-
-                cell.metadata["has_output"] = _has_output(source)
-
-            # Remove noqa comments
-            cell.source = re.sub(r"#\s*noqa.*$", "", cell.source, flags=re.MULTILINE)
-            # escape ``` in code
-            # This is needed because the markdown exporter will wrap code blocks in
-            # triple backticks, which will break the markdown output if the code block
-            # contains triple backticks.
-            cell.source = cell.source.replace("```", r"\`\`\`")
-            # escape ``` in output
-            if "outputs" in cell:
-                filter_out = set()
-                for i, output in enumerate(cell["outputs"]):
-                    if "text" in output:
-                        if not output["text"].strip():
-                            filter_out.add(i)
-                            continue
-
-                        value = output["text"].replace("```", r"\`\`\`")
-                        # handle a funky case w/ references in text
-                        value = re.sub(r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value)
-                        output["text"] = value
-                    elif "data" in output:
-                        for key, value in output["data"].items():
-                            if isinstance(value, str):
-                                value = value.replace("```", r"\`\`\`")
-                                # handle a funky case w/ references in text
-                                output["data"][key] = re.sub(
-                                    r"\[(\d+)\](?=\[(\d+)\])", r"[\1]\\", value
-                                )
-                cell["outputs"] = [
-                    output
-                    for i, output in enumerate(cell["outputs"])
-                    if i not in filter_out
-                ]
-
-        return cell, resources
-
-
-class ExtractAttachmentsPreprocessor(Preprocessor):
-    """
-    Extracts all of the outputs from the notebook file.  The extracted
-    outputs are returned in the 'resources' dictionary.
-    """
-
-    def preprocess_cell(self, cell, resources, cell_index):
-        """
-        Apply a transformation on each cell,
-        Parameters
-        ----------
-        cell : NotebookNode cell
-            Notebook cell being processed
-        resources : dictionary
-            Additional resources used in the conversion process.  Allows
-            preprocessors to pass variables into the Jinja engine.
-        cell_index : int
-            Index of the cell being processed (see base.py)
-        """
-
-        # Get files directory if it has been specified
-
-        # Make sure outputs key exists
-        if not isinstance(resources["outputs"], dict):
-            resources["outputs"] = {}
-
-        # Loop through all of the attachments in the cell
-        for name, attach in cell.get("attachments", {}).items():
-            for mime, data in attach.items():
-                if mime not in {
-                    "image/png",
-                    "image/jpeg",
-                    "image/svg+xml",
-                    "application/pdf",
-                }:
-                    continue
-
-                # attachments are pre-rendered. Only replace markdown-formatted
-                # images with the following logic
-                attach_str = f"({name})"
-                if attach_str in cell.source:
-                    data = f"(data:{mime};base64,{data})"
-                    cell.source = cell.source.replace(attach_str, data)
-
-        return cell, resources
-
-
-exporter = MarkdownExporter(
-    preprocessors=[
-        EscapePreprocessor,
-        ExtractAttachmentsPreprocessor,
-    ],
-    template_name="mdoutput",
-    extra_template_basedirs=[
-        os.path.join(os.path.dirname(__file__), "notebook_convert_templates")
-    ],
-)
-
-
-def convert_notebook(
-    notebook_path: Path,
-    mode: Literal["markdown", "exec"] = "markdown",
-) -> str:
-    with open(notebook_path) as f:
-        nb = nbformat.read(f, as_version=4)
-
-    nb.metadata.mode = mode
-    body, _ = exporter.from_notebook_node(nb)
-    return body

docs/_scripts/notebook_convert_templates/mdoutput/conf.json

@@ -1,5 +0,0 @@
-{
-  "mimetypes": {
-    "text/markdown": true
-  }
-}

docs/_scripts/notebook_convert_templates/mdoutput/index.md.j2

@@ -1,33 +0,0 @@
-{% extends 'markdown/index.md.j2' %}
-
-{%- block traceback_line -%}
-```output
-{{ line.rstrip() | strip_ansi }}
-```
-{%- endblock traceback_line -%}
-
-{%- block stream -%}
-```output
-{{ output.text.rstrip() }}
-```
-{%- endblock stream -%}
-
-{%- block data_text scoped -%}
-```output
-{{ output.data['text/plain'].rstrip() }}
-```
-{%- endblock data_text -%}
-
-{%- block data_html scoped -%}
-```html
-{{ output.data['text/html'] | safe }} 
-```
-{%- endblock data_html -%}
-
-{%- block data_jpg scoped -%}
-![](data:image/jpg;base64,{{ output.data['image/jpeg'] }})
-{%- endblock data_jpg -%}
-
-{%- block data_png scoped -%}
-![](data:image/png;base64,{{ output.data['image/png'] }})
-{%- endblock data_png -%}

docs/_scripts/notebook_hooks.py

@@ -1,255 +0,0 @@
-import logging
-import os
-import posixpath
-import re
-from typing import Any, Dict
-
-from mkdocs.structure.files import Files, File
-from mkdocs.structure.pages import Page
-
-from _scripts.generate_api_reference_links import update_markdown_with_imports
-from _scripts.notebook_convert import convert_notebook
-
-logger = logging.getLogger(__name__)
-logging.basicConfig()
-logger.setLevel(logging.INFO)
-DISABLED = os.getenv("DISABLE_NOTEBOOK_CONVERT") in ("1", "true", "True")
-
-
-REDIRECT_MAP = {
-    # lib redirects
-    "how-tos/stream-values.ipynb": "how-tos/streaming.ipynb#values",
-    "how-tos/stream-updates.ipynb": "how-tos/streaming.ipynb#updates",
-    "how-tos/streaming-content.ipynb": "how-tos/streaming.ipynb#custom",
-    "how-tos/stream-multiple.ipynb": "how-tos/streaming.ipynb#multiple",
-    "how-tos/streaming-tokens-without-langchain.ipynb": "how-tos/streaming-tokens.ipynb#example-without-langchain",
-    "how-tos/streaming-from-final-node.ipynb": "how-tos/streaming-specific-nodes.ipynb",
-    "how-tos/streaming-events-from-within-tools-without-langchain.ipynb": "how-tos/streaming-events-from-within-tools.ipynb#example-without-langchain",
-    # cloud redirects
-    "cloud/index.md": "concepts/index.md#langgraph-platform",
-    "cloud/how-tos/index.md": "how-tos/index.md#langgraph-platform",
-    "cloud/concepts/api.md": "concepts/langgraph_server.md",
-    "cloud/concepts/cloud.md": "concepts/langgraph_cloud.md",
-    "cloud/faq/studio.md": "concepts/langgraph_studio.md#studio-faqs",
-}
-
-
-class NotebookFile(File):
-    def is_documentation_page(self):
-        return True
-
-
-def on_files(files: Files, **kwargs: Dict[str, Any]):
-    if DISABLED:
-        return files
-    new_files = Files([])
-    for file in files:
-        if file.src_path.endswith(".ipynb"):
-            new_file = NotebookFile(
-                path=file.src_path,
-                src_dir=file.src_dir,
-                dest_dir=file.dest_dir,
-                use_directory_urls=file.use_directory_urls,
-            )
-            new_files.append(new_file)
-        else:
-            new_files.append(file)
-    return new_files
-
-
-def _add_path_to_code_blocks(markdown: str, page: Page) -> str:
-    """Add the path to the code blocks."""
-    code_block_pattern = re.compile(
-        r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
-        r"(?P<code>((?:.*\n)*?))"  # Capture the code inside the block using named group
-        r"(?P=indent)```"  # Match closing backticks with the same indentation
-    )
-
-    def replace_code_block_header(match: re.Match) -> str:
-        indent = match.group("indent")
-        language = match.group("language")
-        attributes = match.group("attributes").rstrip()
-
-        if 'exec="on"' not in attributes:
-            # Return original code block
-            return match.group(0)
-
-        code = match.group("code")
-        return f'{indent}```{language} {attributes} path="{page.file.src_path}"\n{code}{indent}```'
-
-    return code_block_pattern.sub(replace_code_block_header, markdown)
-
-
-def _highlight_code_blocks(markdown: str) -> str:
-    """Find code blocks with highlight comments and add hl_lines attribute.
-
-    Args:
-        markdown: The markdown content to process.
-
-    Returns:
-        updated Markdown code with code blocks containing highlight comments
-        updated to use the hl_lines attribute.
-    """
-    # Pattern to find code blocks with highlight comments and without
-    # existing hl_lines for Python and JavaScript
-    # Pattern to find code blocks with highlight comments, handling optional indentation
-    code_block_pattern = re.compile(
-        r"(?P<indent>[ \t]*)```(?P<language>\w+)[ ]*(?P<attributes>[^\n]*)\n"
-        r"(?P<code>((?:.*\n)*?))"  # Capture the code inside the block using named group
-        r"(?P=indent)```"  # Match closing backticks with the same indentation
-    )
-
-    def replace_highlight_comments(match: re.Match) -> str:
-        indent = match.group("indent")
-        language = match.group("language")
-        code_block = match.group("code")
-        attributes = match.group("attributes").rstrip()
-
-        # Account for a case where hl_lines is manually specified
-        if "hl_lines" in attributes:
-            # Return original code block
-            return match.group(0)
-
-        lines = code_block.split("\n")
-        highlighted_lines = []
-
-        # Skip initial empty lines
-        while lines and not lines[0].strip():
-            lines.pop(0)
-
-        lines_to_keep = []
-
-        comment_syntax = (
-            "# highlight-next-line"
-            if language in ["py", "python"]
-            else "// highlight-next-line"
-        )
-
-        for line in lines:
-            if comment_syntax in line:
-                count = len(lines_to_keep) + 1
-                highlighted_lines.append(str(count))
-            else:
-                lines_to_keep.append(line)
-
-        # Reconstruct the new code block
-        new_code_block = "\n".join(lines_to_keep)
-
-        # Construct the full code block that also includes
-        # the fenced code block syntax.
-        opening_fence = f"```{language}"
-
-        if attributes:
-            opening_fence += f" {attributes}"
-
-        if highlighted_lines:
-            opening_fence += f" hl_lines=\"{' '.join(highlighted_lines)}\""
-
-        return (
-            # The indent and opening fence
-            f"{indent}{opening_fence}\n"
-            # The indent and terminating \n is already included in the code block
-            f"{new_code_block}"
-            f"{indent}```"
-        )
-
-    # Replace all code blocks in the markdown
-    markdown = code_block_pattern.sub(replace_highlight_comments, markdown)
-    return markdown
-
-
-def _on_page_markdown_with_config(
-    markdown: str,
-    page: Page,
-    *,
-    add_api_references: bool = True,
-    remove_base64_images: bool = False,
-    **kwargs: Any,
-) -> str:
-    if DISABLED:
-        return markdown
-
-    if page.file.src_path.endswith(".ipynb"):
-        # logger.info("Processing Jupyter notebook: %s", page.file.src_path)
-        markdown = convert_notebook(page.file.abs_src_path)
-
-    # Append API reference links to code blocks
-    if add_api_references:
-        markdown = update_markdown_with_imports(markdown, page.file.abs_src_path)
-    # Apply highlight comments to code blocks
-    markdown = _highlight_code_blocks(markdown)
-
-    # Add file path as an attribute to code blocks that are executable.
-    # This file path is used to associate fixtures with the executable code
-    # which can be used in CI to test the docs without making network requests.
-    markdown = _add_path_to_code_blocks(markdown, page)
-
-    if remove_base64_images:
-        # Remove base64 encoded images from markdown
-        markdown = re.sub(r"!\[.*?\]\(data:image/+;base64,[^\)]+\)", "", markdown)
-
-    return markdown
-
-
-def on_page_markdown(markdown: str, page: Page, **kwargs: Dict[str, Any]):
-    return _on_page_markdown_with_config(
-        markdown,
-        page,
-        add_api_references=True,
-        **kwargs,
-    )
-
-
-# redirects
-
-HTML_TEMPLATE = """
-<!doctype html>
-<html lang="en">
-<head>
-    <meta charset="utf-8">
-    <title>Redirecting...</title>
-    <link rel="canonical" href="{url}">
-    <meta name="robots" content="noindex">
-    <script>var anchor=window.location.hash.substr(1);location.href="{url}"+(anchor?"#"+anchor:"")</script>
-    <meta http-equiv="refresh" content="0; url={url}">
-</head>
-<body>
-Redirecting...
-</body>
-</html>
-"""
-
-
-def write_html(site_dir, old_path, new_path):
-    """Write an HTML file in the site_dir with a meta redirect to the new page"""
-    # Determine all relevant paths
-    old_path_abs = os.path.join(site_dir, old_path)
-    old_dir_abs = os.path.dirname(old_path_abs)
-
-    # Create parent directories if they don't exist
-    if not os.path.exists(old_dir_abs):
-        os.makedirs(old_dir_abs)
-
-    # Write the HTML redirect file in place of the old file
-    content = HTML_TEMPLATE.format(url=new_path)
-    with open(old_path_abs, "w", encoding="utf-8") as f:
-        f.write(content)
-
-
-# Create HTML files for redirects after site dir has been built
-def on_post_build(config):
-    use_directory_urls = config.get("use_directory_urls")
-    for page_old, page_new in REDIRECT_MAP.items():
-        page_old = page_old.replace(".ipynb", ".md")
-        page_new = page_new.replace(".ipynb", ".md")
-        page_new_before_hash, hash, suffix = page_new.partition("#")
-        old_html_path = File(page_old, "", "", use_directory_urls).dest_path.replace(
-            os.sep, "/"
-        )
-        new_html_path = File(page_new_before_hash, "", "", True).url
-        new_html_path = (
-            posixpath.relpath(new_html_path, start=posixpath.dirname(old_html_path))
-            + hash
-            + suffix
-        )
-        write_html(config["site_dir"], old_html_path, new_html_path)

docs/_scripts/prepare_notebooks_for_ci.py

@@ -1,237 +0,0 @@
-"""Preprocess notebooks for CI. Currently adds VCR cassettes and optionally removes pip install cells."""
-
-import logging
-import os
-import json
-import click
-import nbformat
-
-logger = logging.getLogger(__name__)
-NOTEBOOK_DIRS = ("docs/how-tos","docs/tutorials")
-DOCS_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-CASSETTES_PATH = os.path.join(DOCS_PATH, "cassettes")
-
-BLOCKLIST_COMMANDS = (
-    # skip if has WebBaseLoader to avoid caching web pages
-    "WebBaseLoader",
-    # skip if has draw_mermaid_png to avoid generating mermaid images via API
-    "draw_mermaid_png",
-)
-
-NOTEBOOKS_NO_CASSETTES = (
-    "docs/how-tos/visualization.ipynb",
-    "docs/how-tos/many-tools.ipynb"
-)
-
-NOTEBOOKS_NO_EXECUTION = [
-    # this uses a user provided project name for langsmith
-    "docs/tutorials/tnt-llm/tnt-llm.ipynb",
-    # this uses langsmith datasets
-    "docs/tutorials/chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb",
-    # this uses browser APIs
-    "docs/tutorials/web-navigation/web_voyager.ipynb",
-    # these RAG guides use an ollama model
-    "docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb",
-    "docs/tutorials/rag/langgraph_crag_local.ipynb",
-    "docs/tutorials/rag/langgraph_self_rag_local.ipynb",
-    # this loads a massive dataset from gcp
-    "docs/tutorials/usaco/usaco.ipynb",
-    # TODO: figure out why autogen notebook is not runnable (they are just hanging. possible due to code execution?)
-    "docs/how-tos/autogen-integration.ipynb",
-    "docs/how-tos/autogen-integration-functional.ipynb",
-    # TODO: need to update these notebooks to make sure they are runnable in CI
-    "docs/tutorials/storm/storm.ipynb",  # issues only when running with VCR
-    "docs/tutorials/lats/lats.ipynb",  # issues only when running with VCR
-    "docs/tutorials/rag/langgraph_crag.ipynb",  # flakiness from tavily
-    "docs/tutorials/rag/langgraph_adaptive_rag.ipynb",  # flakiness only when running in GHA 
-    "docs/tutorials/rag/langgraph_self_rag.ipynb",  # flakiness only when running in GHA
-    "docs/tutorials/rag/langgraph_agentic_rag.ipynb",  # flakiness only when running in GHA
-    "docs/how-tos/map-reduce.ipynb",  # flakiness from structured output, only when running with VCR
-    "docs/tutorials/tot/tot.ipynb",
-    "docs/how-tos/visualization.ipynb",
-    "docs/tutorials/llm-compiler/LLMCompiler.ipynb"
-]
-
-
-def comment_install_cells(notebook: nbformat.NotebookNode) -> nbformat.NotebookNode:
-    for cell in notebook.cells:
-        if cell.cell_type != "code":
-            continue
-
-        if "pip install" in cell.source:
-            # Comment out the lines in cells containing "pip install"
-            cell.source = "\n".join(
-                f"# {line}" if line.strip() else line
-                for line in cell.source.splitlines()
-            )
-
-    return notebook
-
-
-def is_magic_command(code: str) -> bool:
-    return code.strip().startswith("%") or code.strip().startswith("!")
-
-
-def is_comment(code: str) -> bool:
-    return code.strip().startswith("#")
-
-
-def has_blocklisted_command(code: str, metadata: dict) -> bool:
-    if 'hide_from_vcr' in metadata:
-        return True
-    
-    code = code.strip()
-    for blocklisted_pattern in BLOCKLIST_COMMANDS:
-        if blocklisted_pattern in code:
-            return True
-    return False
-
-
-def add_vcr_to_notebook(
-    notebook: nbformat.NotebookNode, cassette_prefix: str
-) -> nbformat.NotebookNode:
-    """Inject `with vcr.cassette` into each code cell of the notebook."""
-
-    uses_langsmith = False
-    # Inject VCR context manager into each code cell
-    for idx, cell in enumerate(notebook.cells):
-        if cell.cell_type != "code":
-            continue
-
-        lines = cell.source.splitlines()
-        # skip if empty cell
-        if not lines:
-            continue
-
-        are_magic_lines = [is_magic_command(line) for line in lines]
-
-        # skip if all magic
-        if all(are_magic_lines):
-            continue
-
-        if any(are_magic_lines):
-            raise ValueError(
-                "Cannot process code cells with mixed magic and non-magic code."
-            )
-
-        # skip if just comments
-        if all(is_comment(line) or not line.strip() for line in lines):
-            continue
-
-        if has_blocklisted_command(cell.source, cell.metadata):
-            continue
-
-        cell_id = cell.get("id", idx)
-        cassette_name = f"{cassette_prefix}_{cell_id}.msgpack.zlib"
-        cell.source = f"with custom_vcr.use_cassette('{cassette_name}', filter_headers=['x-api-key', 'authorization'], record_mode='once', serializer='advanced_compressed'):\n" + "\n".join(
-            f"    {line}" for line in lines
-        )
-
-        if any("hub.pull" in line or "from langsmith import" in line for line in lines):
-            uses_langsmith = True
-
-    # Add import statement
-    vcr_import_lines = []
-    if uses_langsmith:
-        vcr_import_lines.extend([
-            # patch urllib3 to handle vcr errors, see more here:
-            # https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/_internal/_patch.py
-            "import sys",
-            f"sys.path.insert(0, '{os.path.join(DOCS_PATH, '_scripts')}')",
-            "import _patch as patch_urllib3",
-            "patch_urllib3.patch_urllib3()",
-        ])
-
-    vcr_import_lines.extend([
-        "import nest_asyncio",
-        "nest_asyncio.apply()",
-        "import vcr",
-        "import msgpack",
-        "import base64",
-        "import zlib",
-        "import os",
-        "os.environ.pop(\"LANGCHAIN_TRACING_V2\", None)",
-        "custom_vcr = vcr.VCR()",
-        "",
-        "def compress_data(data, compression_level=9):",
-        "    packed = msgpack.packb(data, use_bin_type=True)",
-        "    compressed = zlib.compress(packed, level=compression_level)",
-        "    return base64.b64encode(compressed).decode('utf-8')",
-        "",
-        "def decompress_data(compressed_string):",
-        "    decoded = base64.b64decode(compressed_string)",
-        "    decompressed = zlib.decompress(decoded)",
-        "    return msgpack.unpackb(decompressed, raw=False)",
-        "",
-        "class AdvancedCompressedSerializer:",
-        "    def serialize(self, cassette_dict):",
-        "        return compress_data(cassette_dict)",
-        "",
-        "    def deserialize(self, cassette_string):",
-        "        return decompress_data(cassette_string)",
-        "",
-        "custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
-        "custom_vcr.serializer = 'advanced_compressed'",
-    ])
-
-    import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
-    import_cell.pop("id", None)
-    notebook.cells.insert(0, import_cell)
-    return notebook
-
-
-def process_notebooks(should_comment_install_cells: bool) -> None:
-    for directory in NOTEBOOK_DIRS:
-        for root, _, files in os.walk(directory):
-            for file in files:
-                if not file.endswith(".ipynb") or "ipynb_checkpoints" in root:
-                    continue
-
-                notebook_path = os.path.join(root, file)
-                try:
-                    notebook = nbformat.read(notebook_path, as_version=4)
-
-                    if should_comment_install_cells:
-                        notebook = comment_install_cells(notebook)
-
-                    base_filename = os.path.splitext(os.path.basename(file))[0]
-                    cassette_prefix = os.path.join(CASSETTES_PATH, base_filename)
-                    if notebook_path not in NOTEBOOKS_NO_CASSETTES:
-                        notebook = add_vcr_to_notebook(
-                            notebook, cassette_prefix=cassette_prefix
-                        )
-
-                    if notebook_path in NOTEBOOKS_NO_EXECUTION:
-                        # Add a cell at the beginning to indicate that this notebook should not be executed
-                        warning_cell = nbformat.v4.new_markdown_cell(
-                            source="**Warning:** This notebook is not meant to be executed automatically."
-                        )
-                        notebook.cells.insert(0, warning_cell)
-
-                        # Add a special tag to the first code cell
-                        if notebook.cells and notebook.cells[1].cell_type == "code":
-                            notebook.cells[1].metadata["tags"] = notebook.cells[1].metadata.get("tags", []) + ["no_execution"]
-
-                    nbformat.write(notebook, notebook_path)
-                    logger.info(f"Processed: {notebook_path}")
-                except Exception as e:
-                    logger.error(f"Error processing {notebook_path}: {e}")
-    
-    with open("notebooks_no_execution.json", "w") as f:
-        json.dump(NOTEBOOKS_NO_EXECUTION, f)
-
-
-@click.command()
-@click.option(
-    "--comment-install-cells",
-    is_flag=True,
-    default=False,
-    help="Whether to comment out install cells",
-)
-def main(comment_install_cells):
-    process_notebooks(should_comment_install_cells=comment_install_cells)
-    logger.info("All notebooks processed successfully.")
-
-
-if __name__ == "__main__":
-    main()

docs/_scripts/third_party_page/create_third_party_page.py

@@ -1,141 +0,0 @@
-#!/usr/bin/env python
-"""Create the third party page for the documentation."""
-
-import argparse
-from typing import List
-from typing import TypedDict
-
-import yaml
-
-MARKDOWN = """\
-[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-# 🚀 Prebuilt Agents
-
-LangGraph includes a prebuilt React agent. For more information on how to use it, 
-check out our [how-to guides](https://langchain-ai.github.io/langgraph/how-tos/#prebuilt-react-agent).
-
-If you’re looking for other prebuilt libraries, explore the community-built options 
-below. These libraries can extend LangGraph's functionality in various ways.
-
-## 📚 Available Libraries
-
-[//]: # (This file is automatically generated using a script in docs/_scripts. Do not edit this file directly!)
-{library_list}
-
-## ✨ Contributing Your Library
-
-Have you built an awesome open-source library using LangGraph? We'd love to feature 
-your project on the official LangGraph documentation pages! 🏆
-
-To share your project, simply open a Pull Request adding an entry for your package in our [packages.yml]({langgraph_url}) file.
-
-**Guidelines**
-
-- Your repo must be distributed as an installable package (e.g., PyPI for Python, npm 
-  for JavaScript/TypeScript, etc.) 📦
-- The repo should either use the Graph API (exposing a `StateGraph` instance) or 
-  the Functional API (exposing an `entrypoint`).
-- The package must include documentation (e.g., a `README.md` or docs site) 
-  explaining how to use it.
-  
-We'll review your contribution and merge it in!
-
-Thanks for contributing! 🚀
-"""
-
-
-class ResolvedPackage(TypedDict):
-    name: str
-    """The name of the package."""
-    repo: str
-    """Repository ID within github. Format is: [orgname]/[repo_name]."""
-    weekly_downloads: int | None
-    """The weekly download count of the package."""
-    description: str
-    """A brief description of what the package does."""
-
-
-def generate_markdown(resolved_packages: List[ResolvedPackage], language: str) -> str:
-    """Generate the markdown content for the third party page.
-
-    Args:
-        resolved_packages: A list of resolved package information.
-        language: str
-
-    Returns:
-        The markdown content as a string.
-    """
-    # Update the URL to the actual file once the initial version is merged
-    if language == "python":
-        langgraph_url = (
-            "https://github.com/langchain-ai/langgraph/blob/main/docs"
-            "/_scripts/third_party_page/packages.yml"
-        )
-    elif language == "js":
-        langgraph_url = (
-            "https://github.com/langchain-ai/langgraphjs/blob/main/docs"
-            "/_scripts/third_party/packages.yml"
-        )
-    else:
-        raise ValueError(f"Invalid language '{language}'. Expected 'python' or 'js'.")
-
-    sorted_packages = sorted(
-        resolved_packages, key=lambda p: p["weekly_downloads"] or 0, reverse=True
-    )
-    rows = [
-        "| Name | GitHub URL | Description | Weekly Downloads | Stars |",
-        "| --- | --- | --- | --- | --- |",
-    ]
-    for package in sorted_packages:
-        name = f"**{package['name']}**"
-        repo_url = f"[{package['repo']}](https://github.com/{package['repo']})"
-        stars_badge = (
-            f"https://img.shields.io/github/stars/{package['repo']}?style=social"
-        )
-        stars = f"![GitHub stars]({stars_badge})"
-        downloads = package["weekly_downloads"] or "-"
-        row = f"| {name} | {repo_url} | {package['description']} | {downloads} | {stars}"
-        rows.append(row)
-    markdown_content = MARKDOWN.format(
-        library_list="\n".join(rows), langgraph_url=langgraph_url
-    )
-    return markdown_content
-
-
-def main(input_file: str, output_file: str, language: str) -> None:
-    """Main function to create the third party page.
-
-    Args:
-        input_file: Path to the input YAML file containing resolved package information.
-        output_file: Path to the output file for the third party page.
-        language: The language for which to generate the third party page.
-    """
-    # Parse the input YAML file
-    with open(input_file, "r") as f:
-        resolved_packages: List[ResolvedPackage] = yaml.safe_load(f)
-
-    markdown_content = generate_markdown(resolved_packages, language)
-
-    # Write the markdown content to the output file
-    with open(output_file, "w", encoding="utf-8") as f:
-        f.write(markdown_content)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Create the third party page.")
-    parser.add_argument(
-        "input_file",
-        help="Path to the input YAML file containing resolved package information.",
-    )
-    parser.add_argument(
-        "output_file", help="Path to the output file for the third party page."
-    )
-    parser.add_argument(
-        "--language",
-        choices=["python", "js"],
-        default="python",
-        help="The language for which to generate the third party page. Defaults to 'python'.",
-    )
-    args = parser.parse_args()
-
-    main(args.input_file, args.output_file, args.language)

docs/_scripts/third_party_page/get_download_stats.py

@@ -1,120 +0,0 @@
-#!/usr/bin/env python
-"""Retrieve download count for a list of Python packages from PyPI."""
-
-import argparse
-from datetime import datetime
-from typing import TypedDict
-import pathlib
-
-import requests
-import yaml
-
-
-class Package(TypedDict):
-    """A TypedDict representing a package"""
-
-    name: str
-    """The name of the package."""
-    repo: str
-    """Repository ID within github. Format is: [orgname]/[repo_name]."""
-    description: str
-    """A brief description of what the package does."""
-
-
-class ResolvedPackage(Package):
-    weekly_downloads: int | None
-
-
-HERE = pathlib.Path(__file__).parent
-PACKAGES_FILE = HERE / "packages.yml"
-PACKAGES = yaml.safe_load(PACKAGES_FILE.read_text())['packages']
-
-
-def _get_weekly_downloads(packages: list[Package]) -> list[ResolvedPackage]:
-    """Retrieve the monthly download count for a list of packages from PyPIStats."""
-    resolved_packages: list[ResolvedPackage] = []
-
-    for package in packages:
-        # First check if package exists on PyPI
-        pypi_url = f"https://pypi.org/pypi/{package['name']}/json"
-        try:
-            pypi_response = requests.get(pypi_url)
-            pypi_response.raise_for_status()
-        except requests.exceptions.HTTPError:
-            raise AssertionError(f"Package {package['name']} does not exist on PyPI")
-
-        # Get first release date
-        pypi_data = pypi_response.json()
-        releases = pypi_data["releases"]
-        first_release_date = None
-        for version_releases in releases.values():
-            if version_releases:  # Some versions may be empty lists
-                upload_time = datetime.fromisoformat(version_releases[0]["upload_time"])
-                if first_release_date is None or upload_time < first_release_date:
-                    first_release_date = upload_time
-
-        if first_release_date is None:
-            raise AssertionError(f"Package {package['name']} has no releases yet")
-
-        # If package was published in last 48 hours, skip download stats
-        if (datetime.now() - first_release_date).total_seconds() >= 48 * 3600:
-            url = f"https://pypistats.org/api/packages/{package['name']}/overall"
-
-            response = requests.get(url)
-            response.raise_for_status()
-            data = response.json()
-
-            sorted_data = sorted(
-                data["data"],
-                key=lambda x: datetime.strptime(x["date"], "%Y-%m-%d"),
-                reverse=True,
-            )
-
-            # Sum the last 7 days of downloads
-            num_downloads = sum(entry["downloads"] for entry in sorted_data[:7])
-        else:
-            num_downloads = None
-
-        resolved_packages.append(
-            {
-                "name": package["name"],
-                "repo": package["repo"],
-                "weekly_downloads": num_downloads,
-                "description": package["description"],
-            }
-        )
-
-    return resolved_packages
-
-
-
-def main(output_file: str) -> None:
-    """Main function to generate package download information.
-
-    Args:
-        output_file: Path to the output YAML file.
-    """
-    resolved_packages: list[ResolvedPackage] = _get_weekly_downloads(PACKAGES)
-
-    if not output_file.endswith(".yml"):
-        raise ValueError("Output file must have a .yml extension")
-
-    with open(output_file, "w") as f:
-        f.write("# This file is auto-generated. Do not edit.\n")
-        yaml.dump(resolved_packages, f)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Generate package download information."
-    )
-    parser.add_argument(
-        "output_file",
-        help=(
-            "Path to the output YAML file. Example: python generate_downloads.py "
-            "downloads.yml"
-        ),
-    )
-    args = parser.parse_args()
-
-    main(args.output_file)

docs/_scripts/third_party_page/packages.yml

@@ -1,26 +0,0 @@
-#A list of third-party packages to surface on the third-party page.
-packages:
-  - name: "trustcall"
-    repo: "hinthornw/trustcall"
-    description: "Tenacious tool calling built on LangGraph."
-  - name: "breeze-agent"
-    repo: "andrestorres123/breeze-agent"
-    description: "A streamlined research system built inspired on STORM and built on LangGraph."
-  - name: "langgraph-supervisor"
-    repo: "langchain-ai/langgraph-supervisor-py"
-    description: "Build supervisor multi-agent systems with LangGraph."
-  - name: "langmem"
-    repo: "langchain-ai/langmem"
-    description: "Build agents that learn and adapt from interactions over time."
-  - name: "langchain-mcp-adapters"
-    repo: "langchain-ai/langchain-mcp-adapters"
-    description: "Make Anthropic Model Context Protocol (MCP) tools compatible with LangGraph agents."
-  - name: "open-deep-research"
-    repo: "langchain-ai/open_deep_research"
-    description: "Open source assistant for iterative web research and report writing."
-  - name: "langgraph-swarm"
-    repo: "langchain-ai/langgraph-swarm-py"
-    description: "Build swarm-style multi-agent systems using LangGraph."
-  - name: "delve-taxonomy-generator"
-    repo: "andrestorres123/delve"
-    description: "A taxonomy generator for unstructured data"

docs/cassettes/add-summary-conversation-history_048805a4-3d97-4e76-ac45-8d80d4364c46.msgpack.zlib

@@ -1 +0,0 @@
-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

docs/cassettes/add-summary-conversation-history_0a1a0fda-5309-45f0-9465-9f3dff604d74.msgpack.zlib

@@ -1 +0,0 @@
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docs/cassettes/agent-simulation-evaluation_32848c2e-be82-46f3-81db-b23fea45461c.msgpack.zlib

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docs/cassettes/agent-simulation-evaluation_6f80669e-aa78-4666-b67c-a539366d5aab.msgpack.zlib

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docs/cassettes/agent_supervisor_45a92dfd-0e11-47f5-aad4-b68d24990e34.msgpack.zlib

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docs/cassettes/agent_supervisor_56ba78e9-d9c1-457c-a073-d606d5d3e013.msgpack.zlib

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docs/cassettes/async_4b369a6f.msgpack.zlib

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truncated

docs/cassettes/async_8edb04b9-40b6-46f1-a7a8-4b2d8aba7752.msgpack.zlib

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docs/cassettes/async_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib

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docs/cassettes/async_f544977e-31f7-41f0-88c4-ec9c27b8cecb.msgpack.zlib

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docs/cassettes/branching_1d0e6c56.msgpack.zlib

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docs/cassettes/branching_66f52a20.msgpack.zlib

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docs/cassettes/branching_83320227-8ab3-44c0-b6cf-064a7a425b9f.msgpack.zlib

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docs/cassettes/branching_932c497e.msgpack.zlib

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truncated

docs/cassettes/breakpoints_51923913-20f7-4ee1-b9ba-d01f5fb2869b.msgpack.zlib

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docs/cassettes/breakpoints_6098e5cb.msgpack.zlib

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docs/cassettes/configuration_718685f7-4cdd-4181-9fc8-e7762d584727.msgpack.zlib

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docs/cassettes/configuration_e043a719-f197-46ef-9d45-84740a39aeb0.msgpack.zlib

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docs/cassettes/configuration_f2f7c74b-9fb0-41c6-9728-dcf9d8a3c397.msgpack.zlib

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docs/cassettes/create-react-agent-hitl_740bbaeb.msgpack.zlib

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docs/cassettes/create-react-agent-hitl_83148e08-63e8-49e5-a08b-02dc907bed1d.msgpack.zlib

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docs/cassettes/create-react-agent-hitl_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

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docs/cassettes/create-react-agent-memory_187479f9-32fa-4611-9487-cf816ba2e147.msgpack.zlib

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docs/cassettes/create-react-agent-memory_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

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docs/cassettes/create-react-agent-structured-output_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

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docs/cassettes/create-react-agent-structured-output_d1386f99-ffd1-4b36-86ec-cabb3357d929.msgpack.zlib

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docs/cassettes/create-react-agent-system-prompt_9ffff6c3-a4f5-47c9-b51d-97caaee85cd6.msgpack.zlib

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docs/cassettes/create-react-agent_fa16de4c-aac0-4ff4-ab69-60d399f75423.msgpack.zlib

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docs/cassettes/cross-thread-persistence-functional_c871a073-a466-46ad-aafe-2b870831057e.msgpack.zlib

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docs/cassettes/cross-thread-persistence-functional_d362350b-d730-48bd-9652-983812fd7811.msgpack.zlib

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docs/cassettes/cross-thread-persistence-functional_d862be40-1f8a-4057-81c4-b7bf073dc4c1.msgpack.zlib

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docs/cassettes/cross-thread-persistence_c871a073-a466-46ad-aafe-2b870831057e.msgpack.zlib

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docs/cassettes/cross-thread-persistence_d362350b-d730-48bd-9652-983812fd7811.msgpack.zlib

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Line 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docs/cassettes/cross-thread-persistence_d862be40-1f8a-4057-81c4-b7bf073dc4c1.msgpack.zlib

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docs/cassettes/customer-support_b7443751.msgpack.zlib

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docs/cassettes/delete-messages_3975f34c-c243-40ea-b9d2-424d50a48dc9.msgpack.zlib

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docs/cassettes/delete-messages_57b27553-21be-43e5-ac48-d1d0a3aa0dca.msgpack.zlib

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docs/cassettes/disable-streaming_2.msgpack.zlib

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docs/cassettes/disable-streaming_4.msgpack.zlib

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docs/cassettes/disable-streaming_8.msgpack.zlib

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docs/cassettes/dynamic_breakpoints_9a14c8b2-5c25-4201-93ea-e5358ee99bcb.msgpack.zlib

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docs/cassettes/edit-graph-state_51923913-20f7-4ee1-b9ba-d01f5fb2869b.msgpack.zlib

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docs/cassettes/edit-graph-state_85e452f8-f33a-4ead-bb4d-7386cdba8edc.msgpack.zlib

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docs/cassettes/edit-graph-state_cfd140f0-a5a6-4697-8115-322242f197b5.msgpack.zlib

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docs/cassettes/hierarchical_agent_teams_6b8badbf-d728-44bd-a2a7-5b4e587c92fe.msgpack.zlib

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docs/cassettes/hierarchical_agent_teams_9860fd46-c24d-40a5-a6ba-e8fddcd43369.msgpack.zlib

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docs/cassettes/how-tos_create-react-agent_1_python.msgpack.zlib

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docs/cassettes/information-gather-prompting_25793988-45a2-4e65-b33c-64e72aadb10e.msgpack.zlib

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