diff --git a/.github/workflows/pre-merge-checks.yml b/.github/workflows/pre-merge-checks.yml
new file mode 100644
index 0000000..83bec88
--- /dev/null
+++ b/.github/workflows/pre-merge-checks.yml
@@ -0,0 +1,46 @@
+name: Pre-merge Checks
+
+on:
+  pull_request:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  checks:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.9'
+
+      - name: Install Poetry 2.1.2
+        run: |
+          curl -sSL https://install.python-poetry.org | python3 - --version 2.1.2
+          poetry config virtualenvs.create true
+          poetry config virtualenvs.in-project true
+
+      - name: Load cached venv
+        id: cached-poetry-dependencies
+        uses: actions/cache@v3
+        with:
+          path: .venv
+          key: venv-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
+
+      - name: Install dependencies
+        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+        run: poetry install --no-interaction --no-root --all-groups
+
+      - name: Install pre-commit
+        run: |
+          pip install pre-commit
+          pre-commit install
+
+      - name: Run pre-commit hooks
+        run: pre-commit run --all-files --show-diff-on-failure
+
+      - name: Run tests
+        run: poetry run pytest tests/ -vvv
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..5b1a5a4
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,49 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+.ruff_cache/
+.mypy_cache/
+
+# Virtual Environment
+.env
+.venv
+env/
+venv/
+ENV/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store
+
+# Testing
+.coverage
+htmlcov/
+.pytest_cache/
+.tox/
+coverage.xml
+*.cover
+
+# Project specific
+*.log
+.cache/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..39ad370
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,42 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-added-large-files
+      - id: check-ast
+      - id: check-json
+      - id: check-merge-conflict
+      - id: detect-private-key
+
+  - repo: local
+    hooks:
+      - id: ruff
+        name: ruff
+        entry: poetry run ruff check . --fix
+        language: system
+        types: [python]
+        pass_filenames: false
+
+      - id: ruff-format
+        name: ruff-format
+        entry: poetry run ruff format .
+        language: system
+        types: [python]
+        pass_filenames: false
+
+      - id: mypy
+        name: mypy
+        entry: poetry run mypy .
+        language: system
+        types: [python]
+        pass_filenames: false
+
+      - id: poetry-lock
+        name: poetry lock check
+        entry: poetry check --lock
+        language: system
+        types: [toml]
+        pass_filenames: false
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..32ed615
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,112 @@
+# Contributing to AI Data Stream
+
+Thank you for your interest in contributing to AI Data Stream! This project implements the Vercel AI Data Stream protocol, which enables streaming AI chat responses to clients. There are two main types of contributions you can make:
+
+## Framework Requirements
+
+Regardless of whether you're implementing an agent framework or an API framework, you must:
+
+1. Add a dependency group in `pyproject.toml` for your framework:
+   ```toml
+   [tool.poetry.group.your-framework]
+   optional = true
+
+   [tool.poetry.group.your-framework.dependencies]
+   your-framework = ">=1.0.0,<2.0.0"
+   ```
+
+2. Create a README.md file in your framework's directory (e.g., `ai_datastream/agent/your-framework/README.md` or `ai_datastream/api/your-framework/README.md`) that includes:
+   - Installation instructions
+   - Basic usage examples
+   - Any framework-specific configuration or requirements
+   - Links to relevant documentation
+
+## 1. Agent Framework Contributions
+
+Agent frameworks are responsible for handling the AI agent's logic and streaming its responses. Currently, we support LangGraph as an agent framework.
+
+### Implementing a New Agent Framework
+
+To implement a new agent framework, you'll need to:
+
+1. Create a new directory under `ai_datastream/agent/` for your framework
+2. Implement at least one of the following interfaces (implementing both is highly preferred):
+   ```python
+   class Streamer(abc.ABC):
+       @abc.abstractmethod
+       def stream(self, prompt: str, messages: Sequence[ChatMessage]) -> Generator[DataStreamPart, None, None]:
+           pass
+
+   class AsyncStreamer(abc.ABC):
+       @abc.abstractmethod
+       def async_stream(self, prompt: str, messages: Sequence[ChatMessage]) -> AsyncGenerator[DataStreamPart, None]:
+           pass
+   ```
+
+Your implementation should handle:
+- Message parsing and conversion
+- Tool calls and their results
+- Streaming text responses
+- Proper start and finish steps for each message
+
+Note: While the LangGraph implementation uses additional classes like message parsers and stream converters, these are implementation details and not mandatory for new agent frameworks. You can structure your implementation in whatever way best suits your framework's needs, as long as you implement at least one of the above interfaces.
+
+## 2. API Framework Contributions
+
+API frameworks provide the web interface for the streaming functionality. Currently, we support FastAPI as an API framework.
+
+### Implementing a New API Framework
+
+To implement a new API framework, you'll need to implement all the necessary code to handle requests in the following format:
+
+```
+# Request
+POST <URL>
+Content-Type: application/json
+{
+    "messages": [{"role": "user", "content": "Hello, how are you?"}]
+}
+
+# Response
+Content-Type: text/event-stream
+Transfer-Encoding: chunked
+x-ai-data-stream: 1
+{type}:{data}
+{type}:{data}
+...
+```
+
+To do so, you'll need to:
+1. Offer a request type that converts from the framework's request format to our internal `ChatMessage` format
+2. Offer a way to stream the response of the `Streamer` or `AsyncStreamer` implementation (preferably both)
+3. Set the headers to the correct values
+
+## Development Setup
+
+1. Install dependencies:
+   ```bash
+   poetry install
+   ```
+
+2. Install development dependencies:
+   ```bash
+   poetry install --with dev
+   ```
+
+3. Run tests and linting:
+   ```bash
+   poetry run ruff check .
+   ```
+
+## Pull Request Process
+
+1. Fork the repository
+2. Create a new branch for your feature
+3. Make your changes
+4. Add tests for your changes
+5. Ensure all tests pass and linting is clean
+6. Submit a pull request with a clear description of your changes
+
+## License
+
+By contributing to this project, you agree that your contributions will be licensed under the project's Apache 2.0 license.
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..1585f0e
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
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+      the terms of any separate license agreement you may have executed
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+
+   6. Trademarks. This License does not grant permission to use the trade
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+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+   Copyright 2022 Elementary Data Inc.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/README.md b/README.md
index 80be200..9ce7456 100644
--- a/README.md
+++ b/README.md
@@ -76,11 +76,11 @@ const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat(
 
 ## Agent Frameworks
 
-- [LangGraph](/agent/langgraph)
+- [LangGraph](/ai_datastream/agent/langgraph)
 
-## Web Frameworks
+## API Frameworks
 
-- [FastAPI](/api/fastapi)
+- [FastAPI](/ai_datastream/api/fastapi)
 
 # Contributing
 
@@ -97,7 +97,7 @@ curl -sSL https://install.python-poetry.org | python3 - --version 2.1.2
 
 2. Install dependencies:
 ```bash
-poetry install
+poetry install --with dev
 ```
 
 3. Install pre-commit hooks:
@@ -124,4 +124,3 @@ Run the test suite:
 ```bash
 poetry run pytest
 ```
-
diff --git a/ai_datastream/__init__.py b/ai_datastream/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/ai_datastream/agent/__init__.py b/ai_datastream/agent/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/ai_datastream/agent/langgraph/README.md b/ai_datastream/agent/langgraph/README.md
new file mode 100644
index 0000000..5ce0087
--- /dev/null
+++ b/ai_datastream/agent/langgraph/README.md
@@ -0,0 +1,61 @@
+# LangGraph Framework
+
+This module provides integration between LangGraph and the Vercel AI Data Stream protocol, allowing you to stream LangGraph agent responses to clients.
+
+## Installation
+
+To use the LangGraph framework, install it with pip:
+
+```bash
+pip install ai-datastream[langgraph]
+```
+
+## Usage
+
+Here's a basic example of how to use the LangGraph framework:
+
+```python
+from langgraph.graph import StateGraph
+from langgraph.prebuilt import create_react_agent
+from langchain_openai import ChatOpenAI
+from ai_datastream.agent.langgraph import LanggraphStreamer
+from ai_datastream.messages import ChatMessage, MessageRole
+
+# Initialize your LangGraph agent
+model = ChatOpenAI(model="gpt-4")
+tools = [...]  # your tools
+prompt = "You are a helpful assistant."
+agent = create_react_agent(model, tools)
+
+# Create a streamer for the agent
+streamer = LanggraphStreamer(agent)
+
+# Create a message to send to the agent
+message = ChatMessage(
+    role=MessageRole.USER,
+    content="What's the weather like in San Francisco?"
+)
+
+# Stream the response
+for chunk in streamer.stream(prompt, [message]):
+    print(chunk)  # Each chunk will be in the Vercel AI Data Stream protocol format
+```
+
+## Features
+
+- Supports both synchronous and asynchronous streaming
+- Handles tool calls and their results
+- Converts LangGraph messages to the Vercel AI Data Stream protocol format
+- Maintains conversation state and message history
+
+## Requirements
+
+- Python 3.9+
+- LangGraph 0.3.0 or higher
+- LangChain (for the agent implementation)
+
+## Documentation
+
+- [LangGraph Documentation](https://python.langchain.com/docs/langgraph)
+- [Vercel AI Data Stream Protocol](https://sdk.vercel.ai/docs/ai-sdk-ui/stream-protocol#data-stream-protocol)
+- [LangChain Documentation](https://python.langchain.com/docs/get_started/introduction)
diff --git a/ai_datastream/agent/langgraph/__init__.py b/ai_datastream/agent/langgraph/__init__.py
new file mode 100644
index 0000000..d7f4196
--- /dev/null
+++ b/ai_datastream/agent/langgraph/__init__.py
@@ -0,0 +1,3 @@
+from .streamer import LanggraphStreamer
+
+__all__ = ["LanggraphStreamer"]
diff --git a/ai_datastream/agent/langgraph/message_parser.py b/ai_datastream/agent/langgraph/message_parser.py
new file mode 100644
index 0000000..afff439
--- /dev/null
+++ b/ai_datastream/agent/langgraph/message_parser.py
@@ -0,0 +1,64 @@
+from typing import Iterable, Iterator, Union
+
+from langchain_core.messages import (
+    AIMessage,
+    BaseMessage,
+    HumanMessage,
+    ToolCall,
+    ToolMessage,
+)
+
+from ai_datastream.messages import ChatMessage, MessageRole, ToolInvocation
+
+
+class LanggraphMessageParser:
+    def _parse_user_message(self, message: ChatMessage) -> Union[HumanMessage, None]:
+        if not message.content:
+            return None
+        return HumanMessage(content=message.content)
+
+    def _parse_tool_call(self, tool_invocation: ToolInvocation) -> ToolCall:
+        return ToolCall(
+            id=tool_invocation.tool_call_id,
+            name=tool_invocation.tool_name,
+            args=tool_invocation.args,
+        )
+
+    def _parse_ai_message(self, message: ChatMessage) -> Union[AIMessage, None]:
+        tool_calls = []
+        if message.tool_invocations:
+            tool_calls = [
+                self._parse_tool_call(tool_invocation)
+                for tool_invocation in message.tool_invocations
+            ]
+        if not tool_calls and not message.content:
+            return None
+        return AIMessage(
+            content=message.content or "",
+            tool_calls=tool_calls,
+        )
+
+    def _parse_tool_message(self, tool_invocation: ToolInvocation) -> ToolMessage:
+        return ToolMessage(
+            content=tool_invocation.result,
+            tool_call_id=tool_invocation.tool_call_id,
+        )
+
+    def parse(self, message: ChatMessage) -> Iterator[BaseMessage]:
+        if message.role == MessageRole.USER:
+            user_message = self._parse_user_message(message)
+            if user_message:
+                yield user_message
+        elif message.role == MessageRole.ASSISTANT:
+            ai_message = self._parse_ai_message(message)
+            if ai_message:
+                yield ai_message
+            if message.tool_invocations:
+                for tool_invocation in message.tool_invocations:
+                    yield self._parse_tool_message(tool_invocation)
+        else:
+            raise ValueError(f"Invalid message role: {message.role}")
+
+    def parse_many(self, messages: Iterable[ChatMessage]) -> Iterator[BaseMessage]:
+        for message in messages:
+            yield from self.parse(message)
diff --git a/ai_datastream/agent/langgraph/stream_converter.py b/ai_datastream/agent/langgraph/stream_converter.py
new file mode 100644
index 0000000..0e9e937
--- /dev/null
+++ b/ai_datastream/agent/langgraph/stream_converter.py
@@ -0,0 +1,99 @@
+import uuid
+from typing import Callable, Generator, Union
+
+from langchain_core.messages import AIMessageChunk
+from langgraph.types import StreamMode
+
+from ai_datastream.stream_parts import (
+    DataStreamFinishStep,
+    DataStreamFinishStepReason,
+    DataStreamPart,
+    DataStreamStartStep,
+    DataStreamText,
+    DataStreamToolCall,
+    DataStreamToolCallStart,
+    DataStreamToolResult,
+)
+
+
+class LanggraphStreamConverter:
+    def __init__(self) -> None:
+        self.current_message_id: Union[str, None] = None
+        self._running_tool_call_ids: set[str] = set()
+
+    def _iter_ai_message_chunk_content(
+        self, message: AIMessageChunk
+    ) -> Generator[DataStreamPart, None, None]:
+        if not isinstance(message.content, list):
+            return
+        for content in message.content:
+            if isinstance(content, str):
+                continue
+            elif content.get("type") == "tool_use":
+                tool_call_id = content.get("id")
+                tool_call_name = content.get("name")
+                if tool_call_id and tool_call_name:
+                    yield DataStreamToolCallStart(tool_call_id, tool_call_name)
+
+    def iter_message_step(
+        self, step: tuple[AIMessageChunk, dict]
+    ) -> Generator[DataStreamPart, None, None]:
+        message, metadata = step
+        if isinstance(message, AIMessageChunk):
+            if self.current_message_id is None:
+                self.current_message_id = uuid.uuid4().hex
+                yield DataStreamStartStep(self.current_message_id)
+            text = message.text()
+            if text:
+                yield DataStreamText(text)
+            yield from self._iter_ai_message_chunk_content(message)
+
+    def _iter_update_messages(
+        self, step: dict
+    ) -> Generator[DataStreamPart, None, None]:
+        messages = step.get("agent", {}).get("messages", [])
+        ai_message = messages[-1] if messages else None
+        if ai_message and ai_message.tool_calls:
+            for tool_call in ai_message.tool_calls:
+                tool_call_id = tool_call["id"]
+                self._running_tool_call_ids.add(tool_call_id)
+                yield DataStreamToolCall(
+                    tool_call_id,
+                    tool_call["name"],
+                    tool_call["args"],
+                )
+
+    def _iter_tool_results(self, step: dict) -> Generator[DataStreamPart, None, None]:
+        tool_messages = step.get("tools", {}).get("messages", [])
+        for tool_message in tool_messages:
+            if tool_message.tool_call_id in self._running_tool_call_ids:
+                self._running_tool_call_ids.remove(tool_message.tool_call_id)
+            yield DataStreamToolResult(
+                tool_message.tool_call_id,
+                tool_message.content,
+            )
+        if tool_messages:
+            yield DataStreamFinishStep(DataStreamFinishStepReason.TOOL_CALLS)
+            self.current_message_id = None
+
+    def iter_update_step(self, step: dict) -> Generator[DataStreamPart, None, None]:
+        yield from self._iter_update_messages(step)
+        yield from self._iter_tool_results(step)
+
+    def _get_stream_mode_map(self) -> dict[StreamMode, Callable]:
+        return {
+            "messages": self.iter_message_step,
+            "updates": self.iter_update_step,
+        }
+
+    def iter_step(
+        self, stream_mode: StreamMode, step: Union[tuple[AIMessageChunk, dict], dict]
+    ) -> Generator[DataStreamPart, None, None]:
+        stream_mode_map = self._get_stream_mode_map()
+        if stream_mode not in stream_mode_map:
+            raise ValueError(f"Unknown stream mode: {stream_mode}")
+        yield from stream_mode_map[stream_mode](step)
+
+    @property
+    def supported_stream_modes(self) -> list[StreamMode]:
+        return list(self._get_stream_mode_map().keys())
diff --git a/ai_datastream/agent/langgraph/streamer.py b/ai_datastream/agent/langgraph/streamer.py
new file mode 100644
index 0000000..4755748
--- /dev/null
+++ b/ai_datastream/agent/langgraph/streamer.py
@@ -0,0 +1,110 @@
+import uuid
+from enum import Enum
+from typing import Any, AsyncGenerator, Generator, Sequence, Union
+
+from langchain_core.messages import BaseMessage, SystemMessage
+from langchain_core.runnables import RunnableConfig
+from langgraph.graph.graph import CompiledGraph
+from langgraph.types import Command
+
+from ai_datastream.agent.langgraph.message_parser import LanggraphMessageParser
+from ai_datastream.agent.langgraph.stream_converter import LanggraphStreamConverter
+from ai_datastream.messages import ChatMessage
+from ai_datastream.stream_parts import (
+    DataStreamFinishRun,
+    DataStreamFinishStep,
+    DataStreamFinishStepReason,
+    DataStreamPart,
+)
+from ai_datastream.streamer import AsyncStreamer, Streamer
+
+
+class StreamStatus(Enum):
+    INIT = "init"
+    RUNNING = "running"
+    FINISHED = "finished"
+    INTERRUPTED = "interrupted"
+
+
+class LanggraphStreamer(Streamer, AsyncStreamer):
+    def __init__(self, agent: CompiledGraph, thread_id: Union[str, None] = None):
+        self.agent = agent
+        self.config = RunnableConfig(
+            configurable={"thread_id": thread_id or uuid.uuid4().hex}
+        )
+        self.converter = LanggraphStreamConverter()
+        self.message_parser = LanggraphMessageParser()
+        self.status = StreamStatus.INIT
+
+    def _handle_stream_finish(self) -> Generator[DataStreamPart, None, None]:
+        snapshot = self.agent.get_state(self.config)
+        if snapshot.next:
+            self.status = StreamStatus.INTERRUPTED
+            return
+        if self.converter.current_message_id is not None:
+            self.status = StreamStatus.FINISHED
+            if self.converter.current_message_id:
+                yield DataStreamFinishStep(DataStreamFinishStepReason.STOP)
+            yield DataStreamFinishRun()
+
+    def _parse_stream_input(self, prompt: str, messages: Sequence[ChatMessage]) -> dict:
+        return {
+            "messages": [
+                SystemMessage(prompt),
+                *self.message_parser.parse_many(messages),
+            ]
+        }
+
+    def _stream(self, input: Any) -> Generator[DataStreamPart, None, None]:
+        self.status = StreamStatus.RUNNING
+        for stream_mode, step in self.agent.stream(
+            input,
+            self.config,
+            stream_mode=self.converter.supported_stream_modes,
+        ):
+            for message in self.converter.iter_step(stream_mode, step):  # type: ignore[arg-type]
+                yield message
+        yield from self._handle_stream_finish()
+
+    async def _async_stream(self, input: Any) -> AsyncGenerator[DataStreamPart, None]:
+        self.status = StreamStatus.RUNNING
+        async for stream_mode, step in self.agent.astream(
+            input,
+            self.config,
+            stream_mode=self.converter.supported_stream_modes,
+        ):
+            for message in self.converter.iter_step(stream_mode, step):  # type: ignore[arg-type]
+                yield message
+        for finish_message in self._handle_stream_finish():
+            yield finish_message
+
+    def stream(
+        self, prompt: str, messages: Sequence[ChatMessage] = []
+    ) -> Generator[DataStreamPart, None, None]:
+        input = self._parse_stream_input(prompt, messages)
+        return self._stream(input)
+
+    async def async_stream(
+        self, prompt: str, messages: Sequence[ChatMessage] = []
+    ) -> AsyncGenerator[DataStreamPart, None]:
+        input = self._parse_stream_input(prompt, messages)
+        async for message in self._async_stream(input):
+            yield message
+
+    def continue_stream(
+        self, interrupt_response: Any
+    ) -> Generator[DataStreamPart, None, None]:
+        return self._stream(Command(resume=interrupt_response))
+
+    async def async_continue_stream(
+        self, interrupt_response: Any
+    ) -> AsyncGenerator[DataStreamPart, None]:
+        async for message in self._async_stream(Command(resume=interrupt_response)):
+            yield message
+
+    def get_messages(self) -> Sequence[BaseMessage]:
+        snapshot = self.agent.get_state(self.config)
+        messages = snapshot.values.get("messages", [])
+        if not isinstance(messages, Sequence):
+            return []
+        return messages
diff --git a/ai_datastream/api/__init__.py b/ai_datastream/api/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/ai_datastream/api/fastapi/README.md b/ai_datastream/api/fastapi/README.md
new file mode 100644
index 0000000..6c5644c
--- /dev/null
+++ b/ai_datastream/api/fastapi/README.md
@@ -0,0 +1,52 @@
+# FastAPI Framework
+
+This module provides integration between FastAPI and the Vercel AI Data Stream protocol, allowing you to create streaming AI chat endpoints.
+
+## Installation
+
+To use the FastAPI framework, install it with pip:
+
+```bash
+pip install ai-datastream[fastapi]
+```
+
+## Usage
+
+Here's how to use the FastAPI framework with a LangGraph agent:
+
+```python
+from fastapi import FastAPI
+from ai_datastream.api.fastapi import AiChatDataStreamAsyncResponse, FastApiDataStreamRequest
+from ai_datastream.streamer import AsyncStreamer
+
+app = FastAPI()
+
+@app.post("/ai/chat")
+async def chat(request: FastApiDataStreamRequest):
+    # Initialize a streamer for your framework
+    streamer: AsyncStreamer = ...
+
+    prompt = "You are a helpful assistant."
+
+    # Return the streaming response
+    return AiChatDataStreamAsyncResponse(streamer, prompt, request.messages)
+```
+
+## Features
+
+- Supports both synchronous and asynchronous streaming responses
+- Handles the Vercel AI Data Stream protocol format
+- Provides request and response types for FastAPI integration
+- Works with any agent framework that implements the Streamer interface
+
+## Requirements
+
+- Python 3.9+
+- FastAPI 0.113.0 or higher
+- Any agent framework (e.g., LangGraph)
+
+## Documentation
+
+- [FastAPI Documentation](https://fastapi.tiangolo.com/)
+- [Vercel AI Data Stream Protocol](https://sdk.vercel.ai/docs/ai-sdk-ui/stream-protocol#data-stream-protocol)
+- [LangGraph Framework Documentation](../agent/langgraph/README.md)
diff --git a/ai_datastream/api/fastapi/__init__.py b/ai_datastream/api/fastapi/__init__.py
new file mode 100644
index 0000000..962369c
--- /dev/null
+++ b/ai_datastream/api/fastapi/__init__.py
@@ -0,0 +1,9 @@
+from .response import AiChatDataStreamAsyncResponse, AiChatDataStreamSyncResponse
+from .types import FastApiChatMessage, FastApiDataStreamRequest
+
+__all__ = [
+    "AiChatDataStreamAsyncResponse",
+    "AiChatDataStreamSyncResponse",
+    "FastApiChatMessage",
+    "FastApiDataStreamRequest",
+]
diff --git a/ai_datastream/api/fastapi/response.py b/ai_datastream/api/fastapi/response.py
new file mode 100644
index 0000000..d8a42c6
--- /dev/null
+++ b/ai_datastream/api/fastapi/response.py
@@ -0,0 +1,48 @@
+from typing import AsyncGenerator, Generator, Sequence
+
+from starlette.responses import StreamingResponse
+
+from ai_datastream.consts import DATA_STREAM_HEADER, DATA_STREAM_HEADER_VALUE
+from ai_datastream.messages import ChatMessage
+from ai_datastream.streamer import AsyncStreamer, Streamer
+
+HEADERS = {
+    # Makes sure the GZip middleware is not applied.
+    "Content-Type": "text/event-stream",
+    "Transfer-Encoding": "chunked",
+    DATA_STREAM_HEADER: DATA_STREAM_HEADER_VALUE,
+}
+
+
+class AiChatDataStreamSyncResponse(StreamingResponse):
+    def __init__(
+        self, streamer: Streamer, prompt: str, messages: Sequence[ChatMessage]
+    ):
+        self.streamer = streamer
+        self.prompt = prompt
+        self.messages = messages
+        super().__init__(
+            self._stream(),
+            headers=HEADERS,
+        )
+
+    def _stream(self) -> Generator[str, None, None]:
+        for message in self.streamer.stream(self.prompt, self.messages):
+            yield message.format()
+
+
+class AiChatDataStreamAsyncResponse(StreamingResponse):
+    def __init__(
+        self, streamer: AsyncStreamer, prompt: str, messages: Sequence[ChatMessage]
+    ):
+        self.streamer = streamer
+        self.prompt = prompt
+        self.messages = messages
+        super().__init__(
+            self._stream(),
+            headers=HEADERS,
+        )
+
+    async def _stream(self) -> AsyncGenerator[str, None]:
+        async for message in self.streamer.async_stream(self.prompt, self.messages):
+            yield message.format()
diff --git a/ai_datastream/api/fastapi/types.py b/ai_datastream/api/fastapi/types.py
new file mode 100644
index 0000000..fd85e38
--- /dev/null
+++ b/ai_datastream/api/fastapi/types.py
@@ -0,0 +1,11 @@
+from typing import Annotated
+
+from pydantic import BaseModel, BeforeValidator
+
+from ai_datastream.messages import ChatMessage
+
+FastApiChatMessage = Annotated[ChatMessage, BeforeValidator(ChatMessage.from_dict)]
+
+
+class FastApiDataStreamRequest(BaseModel):
+    messages: list[FastApiChatMessage]
diff --git a/ai_datastream/consts.py b/ai_datastream/consts.py
new file mode 100644
index 0000000..aa4b082
--- /dev/null
+++ b/ai_datastream/consts.py
@@ -0,0 +1,2 @@
+DATA_STREAM_HEADER = "X-Vercel-AI-Data-Stream"
+DATA_STREAM_HEADER_VALUE = "v1"
diff --git a/ai_datastream/messages.py b/ai_datastream/messages.py
new file mode 100644
index 0000000..c48baa0
--- /dev/null
+++ b/ai_datastream/messages.py
@@ -0,0 +1,48 @@
+from dataclasses import dataclass
+from enum import Enum
+from typing import List, Union
+
+DEFAULT_TOOL_RESULT = """
+Tool call does not contain a result.
+This might indicate that the user has interrupted the conversation.
+"""
+
+
+class MessageRole(str, Enum):
+    USER = "user"
+    ASSISTANT = "assistant"
+
+
+@dataclass
+class ToolInvocation:
+    tool_call_id: str
+    tool_name: str
+    args: dict
+    result: str
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "ToolInvocation":
+        return cls(
+            tool_call_id=data["toolCallId"],
+            tool_name=data["toolName"],
+            args=data["args"],
+            result=data.get("result") or DEFAULT_TOOL_RESULT,
+        )
+
+
+@dataclass
+class ChatMessage:
+    role: MessageRole
+    content: Union[str, None] = None
+    tool_invocations: Union[List[ToolInvocation], None] = None
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "ChatMessage":
+        return cls(
+            role=MessageRole(data["role"]),
+            content=data["content"],
+            tool_invocations=[
+                ToolInvocation.from_dict(tool_invocation)
+                for tool_invocation in data.get("toolInvocations", [])
+            ],
+        )
diff --git a/ai_datastream/stream_parts.py b/ai_datastream/stream_parts.py
new file mode 100644
index 0000000..0e6a710
--- /dev/null
+++ b/ai_datastream/stream_parts.py
@@ -0,0 +1,104 @@
+"""
+Implements vercel ai data stream protocol.
+Documentation: https://sdk.vercel.ai/docs/ai-sdk-ui/stream-protocol#data-stream-protocol
+NOTE - Not all part types are implemented, for full list of available part types see the documentation.
+"""  # noqa: E501
+
+import json
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Union
+
+
+# todo - tool call start
+class DataStreamType:
+    START_STEP = "f"
+    FINISH_STEP = "e"
+    FINISH_RUN = "d"
+    TOOL_CALL_START = "b"
+    TOOL_CALL = "9"
+    TOOL_RESULT = "a"
+    TEXT = "0"
+
+
+@dataclass
+class DataStreamPart:
+    type: str
+    body: Any
+
+    def format(self) -> str:
+        return f"{self.type}:{json.dumps(self.body)}\n"
+
+
+class DataStreamStartStep(DataStreamPart):
+    def __init__(self, message_id: str):
+        super().__init__(DataStreamType.START_STEP, {"messageId": message_id})
+
+
+class DataStreamFinishStepReason(Enum):
+    STOP = "stop"
+    TOOL_CALLS = "tool-calls"
+
+
+class DataStreamFinishStep(DataStreamPart):
+    def __init__(
+        self,
+        finish_reason: DataStreamFinishStepReason,
+        usage: Union[dict, None] = None,
+        is_continued: bool = False,
+    ):
+        super().__init__(
+            DataStreamType.FINISH_STEP,
+            {
+                "finishReason": finish_reason.value,
+                "usage": usage or {"promptTokens": None, "completionTokens": None},
+                "isContinued": is_continued,
+            },
+        )
+
+
+class DataStreamFinishRun(DataStreamPart):
+    def __init__(self, usage: Union[dict, None] = None):
+        super().__init__(
+            DataStreamType.FINISH_RUN,
+            {
+                "finishReason": "stop",
+                "usage": usage or {"promptTokens": None, "completionTokens": None},
+            },
+        )
+
+
+class DataStreamToolCallStart(DataStreamPart):
+    def __init__(self, tool_call_id: str, tool_name: str):
+        super().__init__(
+            DataStreamType.TOOL_CALL_START,
+            {"toolCallId": tool_call_id, "toolName": tool_name},
+        )
+
+
+class DataStreamToolCall(DataStreamPart):
+    def __init__(self, tool_call_id: str, tool_name: str, args: Any):
+        super().__init__(
+            DataStreamType.TOOL_CALL,
+            {"toolCallId": tool_call_id, "toolName": tool_name, "args": args},
+        )
+
+
+class DataStreamToolResult(DataStreamPart):
+    def __init__(self, tool_call_id: str, result: Any):
+        try:
+            result = json.loads(result)
+        except json.JSONDecodeError:
+            pass
+        super().__init__(
+            DataStreamType.TOOL_RESULT,
+            {
+                "toolCallId": tool_call_id,
+                "result": result,
+            },
+        )
+
+
+class DataStreamText(DataStreamPart):
+    def __init__(self, text: str):
+        super().__init__(DataStreamType.TEXT, text)
diff --git a/ai_datastream/streamer.py b/ai_datastream/streamer.py
new file mode 100644
index 0000000..a14e16c
--- /dev/null
+++ b/ai_datastream/streamer.py
@@ -0,0 +1,21 @@
+import abc
+from typing import AsyncGenerator, Generator, Sequence
+
+from ai_datastream.messages import ChatMessage
+from ai_datastream.stream_parts import DataStreamPart
+
+
+class Streamer(abc.ABC):
+    @abc.abstractmethod
+    def stream(
+        self, prompt: str, messages: Sequence[ChatMessage]
+    ) -> Generator[DataStreamPart, None, None]:
+        pass
+
+
+class AsyncStreamer(abc.ABC):
+    @abc.abstractmethod
+    def async_stream(
+        self, prompt: str, messages: Sequence[ChatMessage]
+    ) -> AsyncGenerator[DataStreamPart, None]:
+        pass
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..bb88927
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1574 @@
+# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+groups = ["fastapi", "langgraph"]
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
+[[package]]
+name = "anyio"
+version = "4.9.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.9"
+groups = ["fastapi", "langgraph"]
+files = [
+    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
+    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
+
+[package.extras]
+doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""]
+trio = ["trio (>=0.26.1)"]
+
+[[package]]
+name = "certifi"
+version = "2025.4.26"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+groups = ["langgraph"]
+files = [
+    {file = "certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3"},
+    {file = "certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6"},
+]
+
+[[package]]
+name = "cffi"
+version = "1.17.1"
+description = "Foreign Function Interface for Python calling C code."
+optional = false
+python-versions = ">=3.8"
+groups = ["langgraph"]
+markers = "platform_python_implementation == \"PyPy\""
+files = [
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+]
+
+[package.dependencies]
+cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""}
+
+[package.extras]
+cffi = ["cffi (>=1.11)"]
+
+[metadata]
+lock-version = "2.1"
+python-versions = ">=3.9,<4.0"
+content-hash = "6c9946051574e14c89a8dd48aa9b488c8b251fe81254fadbef29fba2b301bd1e"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..febef75
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,62 @@
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "ai-datastream"
+version = "0.1.0"
+description = "A Python implementation of the Vercel AI Data Stream protocol"
+readme = "README.md"
+authors = ["Elementary"]
+license = "Apache-2.0"
+keywords = ["ai", "data", "stream", "vercel"]
+packages = [{include = "ai_datastream"}]
+
+[tool.poetry.dependencies]
+python = ">=3.9,<4.0"
+
+[tool.poetry.group.langgraph]
+optional = true
+
+[tool.poetry.group.langgraph.dependencies]
+langgraph = ">=0.3.0,<1.0.0"
+
+[tool.poetry.group.fastapi]
+optional = true
+
+[tool.poetry.group.fastapi.dependencies]
+fastapi = ">=0.113.0,<1.0.0"
+
+[tool.poetry.group.dev.dependencies]
+ruff = "^0.11.9"
+mypy = "^1.9.0"
+pytest = "^8.3.4"
+pre-commit = "^3.6.0"
+pytest-asyncio = "^0.26.0"
+
+[tool.ruff]
+line-length = 88
+target-version = "py39"
+
+[tool.ruff.lint]
+select = [
+    "E",  # pycodestyle errors
+    "W",  # pycodestyle warnings
+    "F",  # pyflakes
+    "I",  # isort
+    "B",  # flake8-bugbear
+]
+
+[tool.mypy]
+python_version = "3.9"
+warn_return_any = true
+warn_unused_configs = true
+disallow_untyped_defs = true
+disallow_incomplete_defs = true
+check_untyped_defs = true
+disallow_untyped_decorators = true
+no_implicit_optional = true
+warn_redundant_casts = true
+warn_unused_ignores = true
+warn_no_return = true
+warn_unreachable = true
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/compare.py b/tests/compare.py
new file mode 100644
index 0000000..f1eb80b
--- /dev/null
+++ b/tests/compare.py
@@ -0,0 +1,18 @@
+from typing import Callable
+
+from ai_datastream.stream_parts import DataStreamPart, DataStreamStartStep
+
+_COMPARER_MAP: dict[
+    type[DataStreamPart], Callable[[DataStreamPart, DataStreamPart], bool]
+] = {
+    # skips comparing randomly generated message_id
+    DataStreamStartStep: lambda a, b: True,
+}
+
+
+def compare_stream_parts(a: DataStreamPart, b: DataStreamPart) -> bool:
+    if type(a) != type(b):  # noqa: E721
+        return False
+    if type(a) in _COMPARER_MAP:
+        return _COMPARER_MAP[type(a)](a, b)
+    return a == b
diff --git a/tests/fastapi/__init__.py b/tests/fastapi/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/fastapi/test_api.py b/tests/fastapi/test_api.py
new file mode 100644
index 0000000..5109aeb
--- /dev/null
+++ b/tests/fastapi/test_api.py
@@ -0,0 +1,65 @@
+import pytest
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+from ai_datastream.api.fastapi import (
+    AiChatDataStreamAsyncResponse,
+    FastApiDataStreamRequest,
+)
+from ai_datastream.api.fastapi.response import AiChatDataStreamSyncResponse
+from ai_datastream.stream_parts import (
+    DataStreamFinishRun,
+    DataStreamFinishStep,
+    DataStreamFinishStepReason,
+    DataStreamStartStep,
+    DataStreamText,
+)
+from tests.mock_streamer import MockStreamer
+
+app = FastAPI()
+
+STREAMER = MockStreamer(
+    [
+        DataStreamStartStep(message_id="1"),
+        DataStreamText(text="Hello, world!"),
+        DataStreamFinishStep(
+            finish_reason=DataStreamFinishStepReason.STOP,
+        ),
+        DataStreamFinishRun(),
+    ]
+)
+
+
+@app.post("/ai/chat-async")
+async def chat_async(
+    request: FastApiDataStreamRequest,
+) -> AiChatDataStreamAsyncResponse:
+    return AiChatDataStreamAsyncResponse(
+        streamer=STREAMER,
+        prompt="You are a helpful assistant.",
+        messages=request.messages,
+    )
+
+
+@app.post("/ai/chat-sync")
+def chat_sync(request: FastApiDataStreamRequest) -> AiChatDataStreamSyncResponse:
+    return AiChatDataStreamSyncResponse(
+        streamer=STREAMER,
+        prompt="You are a helpful assistant.",
+        messages=request.messages,
+    )
+
+
+@pytest.mark.parametrize("endpoint", ["/ai/chat-async", "/ai/chat-sync"])
+def test_ai_chat_data_stream_endpoint(endpoint: str) -> None:
+    client = TestClient(app)
+    response = client.post(
+        endpoint,
+        json={"messages": [{"role": "user", "content": "Hello, world!"}]},
+    )
+    assert response.status_code == 200
+    assert response.headers["content-type"] == "text/event-stream"
+    assert response.headers["transfer-encoding"] == "chunked"
+    assert response.headers["x-vercel-ai-data-stream"] == "v1"
+    for line, expected_part in zip(response.iter_lines(), STREAMER.stream_parts):
+        assert line + "\n" == expected_part.format()
diff --git a/tests/langchain/__init__.py b/tests/langchain/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/langchain/generate_mock_stream.py b/tests/langchain/generate_mock_stream.py
new file mode 100644
index 0000000..64cca45
--- /dev/null
+++ b/tests/langchain/generate_mock_stream.py
@@ -0,0 +1,43 @@
+"""
+This is a script to generate a mock stream for testing the LangGraph agent.
+This script requres the langchain-openai package in addition to the regular dependencies.
+In order for this script to work, you must have a valid OpenAI API key set as an environment variable, in OPENAI_API_KEY.
+"""  # noqa: E501
+
+from langchain_core.messages import HumanMessage
+from langchain_core.tools import tool
+from langchain_openai.chat_models import ChatOpenAI  # type: ignore
+from langgraph.prebuilt import create_react_agent
+
+
+@tool
+def get_weather(city: str) -> str:
+    """
+    Get the weather in a city
+    """
+    return f"The weather in {city} is sunny"
+
+
+def main() -> None:
+    stream = []
+    model = ChatOpenAI(model="gpt-4o")
+    agent = create_react_agent(model, tools=[get_weather])
+    for mode, step in agent.stream(
+        {
+            "messages": [
+                HumanMessage(
+                    content=(
+                        "What is the weather in Tokyo? great me and tell me what "
+                        "you are doing before berforming tool calls"
+                    )
+                )
+            ]
+        },
+        stream_mode=["messages", "updates"],
+    ):
+        stream.append({"mode": mode, "step": step})
+    print(stream)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/langchain/test_message_parser.py b/tests/langchain/test_message_parser.py
new file mode 100644
index 0000000..4ba8918
--- /dev/null
+++ b/tests/langchain/test_message_parser.py
@@ -0,0 +1,112 @@
+import pytest
+from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
+
+from ai_datastream.agent.langgraph.message_parser import LanggraphMessageParser
+from ai_datastream.messages import ChatMessage, MessageRole, ToolInvocation
+
+
+@pytest.fixture
+def parser() -> LanggraphMessageParser:
+    return LanggraphMessageParser()
+
+
+def test_parse_user_message(parser: LanggraphMessageParser) -> None:
+    message = ChatMessage(role=MessageRole.USER, content="Hello, how are you?")
+
+    messages = list(parser.parse(message))
+    assert len(messages) == 1
+    assert isinstance(messages[0], HumanMessage)
+    assert messages[0].content == "Hello, how are you?"
+
+
+def test_parse_ai_message_without_tools(parser: LanggraphMessageParser) -> None:
+    message = ChatMessage(
+        role=MessageRole.ASSISTANT, content="I'm doing well!", tool_invocations=[]
+    )
+
+    messages = list(parser.parse(message))
+    assert len(messages) == 1
+    assert isinstance(messages[0], AIMessage)
+    assert messages[0].content == "I'm doing well!"
+    assert messages[0].tool_calls == []
+
+
+def test_parse_ai_message_with_tools(parser: LanggraphMessageParser) -> None:
+    message = ChatMessage(
+        role=MessageRole.ASSISTANT,
+        content="Let me help you with that.",
+        tool_invocations=[
+            ToolInvocation(
+                tool_call_id="call_1",
+                tool_name="search",
+                args={"query": "test"},
+                result="Search results",
+            )
+        ],
+    )
+
+    messages = list(parser.parse(message))
+    assert len(messages) == 2
+
+    # Check AI message
+    assert isinstance(messages[0], AIMessage)
+    assert messages[0].content == "Let me help you with that."
+    assert len(messages[0].tool_calls) == 1
+    tool_call = messages[0].tool_calls[0]
+    assert tool_call["id"] == "call_1"
+    assert tool_call["name"] == "search"
+    assert tool_call["args"] == {"query": "test"}
+
+    # Check tool message
+    assert isinstance(messages[1], ToolMessage)
+    assert messages[1].content == "Search results"
+    assert messages[1].tool_call_id == "call_1"
+
+
+def test_parse_ai_message_multiple_tools(parser: LanggraphMessageParser) -> None:
+    message = ChatMessage(
+        role=MessageRole.ASSISTANT,
+        content="Processing multiple tools",
+        tool_invocations=[
+            ToolInvocation(
+                tool_call_id="call_1",
+                tool_name="tool1",
+                args={"arg1": "value1"},
+                result="Result 1",
+            ),
+            ToolInvocation(
+                tool_call_id="call_2",
+                tool_name="tool2",
+                args={"arg2": "value2"},
+                result="Result 2",
+            ),
+        ],
+    )
+
+    messages = list(parser.parse(message))
+    assert len(messages) == 3  # 1 AI message + 2 tool messages
+
+    assert isinstance(messages[0], AIMessage)
+    assert len(messages[0].tool_calls) == 2
+
+    assert isinstance(messages[1], ToolMessage)
+    assert messages[1].tool_call_id == "call_1"
+    assert messages[1].content == "Result 1"
+
+    assert isinstance(messages[2], ToolMessage)
+    assert messages[2].tool_call_id == "call_2"
+    assert messages[2].content == "Result 2"
+
+
+def test_parse_user_message_without_content(parser: LanggraphMessageParser) -> None:
+    message = ChatMessage(role=MessageRole.USER, content="")
+
+    messages = list(parser.parse(message))
+    assert len(messages) == 0
+
+
+def test_parse_ai_message_without_content(parser: LanggraphMessageParser) -> None:
+    message = ChatMessage(role=MessageRole.ASSISTANT, content="", tool_invocations=[])
+
+    messages = list(parser.parse(message))
+    assert len(messages) == 0
diff --git a/tests/langchain/test_streamer.py b/tests/langchain/test_streamer.py
new file mode 100644
index 0000000..287367d
--- /dev/null
+++ b/tests/langchain/test_streamer.py
@@ -0,0 +1,2570 @@
+from typing import Any, AsyncGenerator, Generator
+from unittest.mock import MagicMock
+
+import pytest
+from langchain_core.messages import AIMessage, AIMessageChunk, ToolMessage
+
+from ai_datastream.agent.langgraph.streamer import LanggraphStreamer
+from ai_datastream.stream_parts import (
+    DataStreamFinishRun,
+    DataStreamFinishStep,
+    DataStreamFinishStepReason,
+    DataStreamStartStep,
+    DataStreamText,
+    DataStreamToolCall,
+    DataStreamToolResult,
+)
+from tests.compare import compare_stream_parts
+
+# Generated using generate_mock_stream.py
+_MOCK_STREAM: list[dict[str, Any]] = [
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content="",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content="Got",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" it",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content="!",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" Before",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" I",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" fetch",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" the",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" current",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" weather",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" in",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" Tokyo",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" for",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" you",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=",",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" I'm",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" going",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" to",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" use",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" a",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" tool",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "checkpoint_ns": "agent:a0bf1dda-c767-626d-28ba-21475a2b6a2b",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" call",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+            ),
+            {
+                "langgraph_step": 1,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
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+                    AIMessage(
+                        content=(
+                            "Got it! Before I fetch the current weather in Tokyo for"
+                            " you, I'm going to use a tool call to obtain the "
+                            "latest data. This involves connecting to a service "
+                            "that provides weather information, specifically for "
+                            "the city you're interested in. Now, I'll perform the "
+                            "tool call to get the weather in Tokyo."
+                        ),
+                        additional_kwargs={
+                            "tool_calls": [
+                                {
+                                    "index": 0,
+                                    "id": "call_TeWMRmlCYAjYehoHxliW1jPS",
+                                    "function": {
+                                        "arguments": '{"city":"Tokyo"}',
+                                        "name": "get_weather",
+                                    },
+                                    "type": "function",
+                                }
+                            ]
+                        },
+                        response_metadata={
+                            "finish_reason": "tool_calls",
+                            "model_name": "gpt-4o-2024-08-06",
+                            "system_fingerprint": "fp_90122d973c",
+                        },
+                        id="run-a2cddfba-ee24-429f-ad83-f9c789eb6dc0",
+                        tool_calls=[
+                            {
+                                "name": "get_weather",
+                                "args": {"city": "Tokyo"},
+                                "id": "call_TeWMRmlCYAjYehoHxliW1jPS",
+                                "type": "tool_call",
+                            }
+                        ],
+                    )
+                ]
+            }
+        },
+    },
+    {
+        "mode": "messages",
+        "step": (
+            ToolMessage(
+                content="The weather in Tokyo is sunny",
+                name="get_weather",
+                id="c34cb25d-a1cb-4ec5-923b-acbe9eb191f4",
+                tool_call_id="call_TeWMRmlCYAjYehoHxliW1jPS",
+            ),
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+                "langgraph_node": "tools",
+                "langgraph_triggers": ("branch:to:tools",),
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+                "langgraph_checkpoint_ns": "tools:13f38b4b-78a1-0615-ee16-cc461a990435",
+            },
+        ),
+    },
+    {
+        "mode": "updates",
+        "step": {
+            "tools": {
+                "messages": [
+                    ToolMessage(
+                        content="The weather in Tokyo is sunny",
+                        name="get_weather",
+                        id="c34cb25d-a1cb-4ec5-923b-acbe9eb191f4",
+                        tool_call_id="call_TeWMRmlCYAjYehoHxliW1jPS",
+                    )
+                ]
+            }
+        },
+    },
+    {
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+        "step": (
+            AIMessageChunk(
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+                additional_kwargs={},
+                response_metadata={},
+                id="run-56b61190-e66d-49d8-bc7e-f0d939f78cc0",
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+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
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+            },
+        ),
+    },
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+        "step": (
+            AIMessageChunk(
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+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
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+                response_metadata={},
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+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" in",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-56b61190-e66d-49d8-bc7e-f0d939f78cc0",
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+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
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+                "ls_provider": "openai",
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+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content=" Tokyo",
+                additional_kwargs={},
+                response_metadata={},
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+            ),
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+                "ls_model_type": "chat",
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+            },
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+        "step": (
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+            },
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+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
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+            },
+        ),
+    },
+    {
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+            AIMessageChunk(
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+                response_metadata={},
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+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content="!",
+                additional_kwargs={},
+                response_metadata={},
+                id="run-56b61190-e66d-49d8-bc7e-f0d939f78cc0",
+            ),
+            {
+                "langgraph_step": 3,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:779c70f5-55ff-d18d-0502-ecf7ed827954",
+                "checkpoint_ns": "agent:779c70f5-55ff-d18d-0502-ecf7ed827954",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "messages",
+        "step": (
+            AIMessageChunk(
+                content="",
+                additional_kwargs={},
+                response_metadata={
+                    "finish_reason": "stop",
+                    "model_name": "gpt-4o-2024-08-06",
+                    "system_fingerprint": "fp_90122d973c",
+                },
+                id="run-56b61190-e66d-49d8-bc7e-f0d939f78cc0",
+            ),
+            {
+                "langgraph_step": 3,
+                "langgraph_node": "agent",
+                "langgraph_triggers": ("branch:to:agent",),
+                "langgraph_path": ("__pregel_pull", "agent"),
+                "langgraph_checkpoint_ns": "agent:779c70f5-55ff-d18d-0502-ecf7ed827954",
+                "checkpoint_ns": "agent:779c70f5-55ff-d18d-0502-ecf7ed827954",
+                "ls_provider": "openai",
+                "ls_model_name": "gpt-4o",
+                "ls_model_type": "chat",
+                "ls_temperature": None,
+            },
+        ),
+    },
+    {
+        "mode": "updates",
+        "step": {
+            "agent": {
+                "messages": [
+                    AIMessage(
+                        content=(
+                            "The weather in Tokyo is currently sunny. "
+                            "If you need more details or have other questions, "
+                            "feel free to ask!"
+                        ),
+                        additional_kwargs={},
+                        response_metadata={
+                            "finish_reason": "stop",
+                            "model_name": "gpt-4o-2024-08-06",
+                            "system_fingerprint": "fp_90122d973c",
+                        },
+                        id="run-56b61190-e66d-49d8-bc7e-f0d939f78cc0",
+                    )
+                ]
+            }
+        },
+    },
+]
+
+_MOCK_DATA_STREAM = [
+    DataStreamStartStep("b13fde6b6ae9420891bfad3e6126a92d"),
+    DataStreamText("Got"),
+    DataStreamText(" it"),
+    DataStreamText("!"),
+    DataStreamText(" Before"),
+    DataStreamText(" I"),
+    DataStreamText(" fetch"),
+    DataStreamText(" the"),
+    DataStreamText(" current"),
+    DataStreamText(" weather"),
+    DataStreamText(" in"),
+    DataStreamText(" Tokyo"),
+    DataStreamText(" for"),
+    DataStreamText(" you"),
+    DataStreamText(","),
+    DataStreamText(" I'm"),
+    DataStreamText(" going"),
+    DataStreamText(" to"),
+    DataStreamText(" use"),
+    DataStreamText(" a"),
+    DataStreamText(" tool"),
+    DataStreamText(" call"),
+    DataStreamText(" to"),
+    DataStreamText(" obtain"),
+    DataStreamText(" the"),
+    DataStreamText(" latest"),
+    DataStreamText(" data"),
+    DataStreamText("."),
+    DataStreamText(" This"),
+    DataStreamText(" involves"),
+    DataStreamText(" connecting"),
+    DataStreamText(" to"),
+    DataStreamText(" a"),
+    DataStreamText(" service"),
+    DataStreamText(" that"),
+    DataStreamText(" provides"),
+    DataStreamText(" weather"),
+    DataStreamText(" information"),
+    DataStreamText(","),
+    DataStreamText(" specifically"),
+    DataStreamText(" for"),
+    DataStreamText(" the"),
+    DataStreamText(" city"),
+    DataStreamText(" you're"),
+    DataStreamText(" interested"),
+    DataStreamText(" in"),
+    DataStreamText("."),
+    DataStreamText(" Now"),
+    DataStreamText(","),
+    DataStreamText(" I'll"),
+    DataStreamText(" perform"),
+    DataStreamText(" the"),
+    DataStreamText(" tool"),
+    DataStreamText(" call"),
+    DataStreamText(" to"),
+    DataStreamText(" get"),
+    DataStreamText(" the"),
+    DataStreamText(" weather"),
+    DataStreamText(" in"),
+    DataStreamText(" Tokyo"),
+    DataStreamText("."),
+    DataStreamToolCall(
+        tool_call_id="call_TeWMRmlCYAjYehoHxliW1jPS",
+        tool_name="get_weather",
+        args={"city": "Tokyo"},
+    ),
+    DataStreamToolResult(
+        tool_call_id="call_TeWMRmlCYAjYehoHxliW1jPS",
+        result="The weather in Tokyo is sunny",
+    ),
+    DataStreamFinishStep(
+        finish_reason=DataStreamFinishStepReason.TOOL_CALLS,
+    ),
+    DataStreamStartStep("ac4e84aa6d474e11b60b19a002c4854f"),
+    DataStreamText("The"),
+    DataStreamText(" weather"),
+    DataStreamText(" in"),
+    DataStreamText(" Tokyo"),
+    DataStreamText(" is"),
+    DataStreamText(" currently"),
+    DataStreamText(" sunny"),
+    DataStreamText("."),
+    DataStreamText(" If"),
+    DataStreamText(" you"),
+    DataStreamText(" need"),
+    DataStreamText(" more"),
+    DataStreamText(" details"),
+    DataStreamText(" or"),
+    DataStreamText(" have"),
+    DataStreamText(" other"),
+    DataStreamText(" questions"),
+    DataStreamText(","),
+    DataStreamText(" feel"),
+    DataStreamText(" free"),
+    DataStreamText(" to"),
+    DataStreamText(" ask"),
+    DataStreamText("!"),
+    DataStreamFinishStep(
+        finish_reason=DataStreamFinishStepReason.STOP,
+    ),
+    DataStreamFinishRun(),
+]
+
+
+class MockAgent:
+    def stream(
+        self, *args: Any, **kwargs: Any
+    ) -> Generator[tuple[str, Any], None, None]:
+        for stream_part in _MOCK_STREAM:
+            mode = stream_part["mode"]
+            step = stream_part["step"]
+            yield mode, step
+
+    async def astream(
+        self, *args: Any, **kwargs: Any
+    ) -> AsyncGenerator[tuple[str, Any], None]:
+        for mode, step in self.stream(*args, **kwargs):
+            yield mode, step
+
+    def get_state(self, *args: Any, **kwargs: Any) -> MagicMock:
+        state = MagicMock()
+        state.next = False
+        return state
+
+
+@pytest.fixture
+def streamer() -> LanggraphStreamer:
+    return LanggraphStreamer(MockAgent())  # type: ignore[arg-type]
+
+
+def test_stream(streamer: LanggraphStreamer) -> None:
+    data_stream = list(streamer.stream("You are a helpful assistant", []))
+    assert len(data_stream) == len(_MOCK_DATA_STREAM)
+    for a, b in zip(data_stream, _MOCK_DATA_STREAM):
+        assert compare_stream_parts(a, b)
+
+
+@pytest.mark.asyncio
+async def test_async_stream(streamer: LanggraphStreamer) -> None:
+    parts = []
+    async for part in streamer.async_stream("You are a helpful assistant", []):
+        parts.append(part)
+    assert len(parts) == len(_MOCK_DATA_STREAM)
+    for a, b in zip(parts, _MOCK_DATA_STREAM):
+        assert compare_stream_parts(a, b)
diff --git a/tests/mock_streamer.py b/tests/mock_streamer.py
new file mode 100644
index 0000000..19338ae
--- /dev/null
+++ b/tests/mock_streamer.py
@@ -0,0 +1,22 @@
+from typing import AsyncGenerator, Generator, Sequence
+
+from ai_datastream.messages import ChatMessage
+from ai_datastream.stream_parts import DataStreamPart
+from ai_datastream.streamer import AsyncStreamer, Streamer
+
+
+class MockStreamer(Streamer, AsyncStreamer):
+    def __init__(self, stream_parts: Sequence[DataStreamPart]):
+        self.stream_parts = stream_parts
+
+    def stream(
+        self, prompt: str, messages: Sequence[ChatMessage]
+    ) -> Generator[DataStreamPart, None, None]:
+        for part in self.stream_parts:
+            yield part
+
+    async def async_stream(
+        self, prompt: str, messages: Sequence[ChatMessage]
+    ) -> AsyncGenerator[DataStreamPart, None]:
+        for part in self.stream_parts:
+            yield part