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⚡ Building applications with LLMs through composability ⚡
ed5e589191
Here we accept ToolMessages of the form
```python
ToolMessage(
content=<representation of screenshot> (see below),
tool_call_id="abc123",
additional_kwargs={"type": "computer_call_output"},
)
```
and translate them to `computer_call_output` items for the Responses
API.
We also propagate `reasoning_content` items from AIMessages.
## Example
### Load screenshots
```python
import base64
def load_png_as_base64(file_path):
with open(file_path, "rb") as image_file:
encoded_string = base64.b64encode(image_file.read())
return encoded_string.decode('utf-8')
screenshot_1_base64 = load_png_as_base64("/path/to/screenshot/of/application.png")
screenshot_2_base64 = load_png_as_base64("/path/to/screenshot/of/desktop.png")
```
### Initial message and response
```python
from langchain_core.messages import HumanMessage, ToolMessage
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model="computer-use-preview",
model_kwargs={"truncation": "auto"},
)
tool = {
"type": "computer_use_preview",
"display_width": 1024,
"display_height": 768,
"environment": "browser"
}
llm_with_tools = llm.bind_tools([tool])
input_message = HumanMessage(
content=[
{
"type": "text",
"text": (
"Click the red X to close and reveal my Desktop. "
"Proceed, no confirmation needed."
)
},
{
"type": "input_image",
"image_url": f"data:image/png;base64,{screenshot_1_base64}",
}
]
)
response = llm_with_tools.invoke(
[input_message],
reasoning={
"generate_summary": "concise",
},
)
response.additional_kwargs["tool_outputs"]
```
### Construct ToolMessage
```python
tool_call_id = response.additional_kwargs["tool_outputs"][0]["call_id"]
tool_message = ToolMessage(
content=[
{
"type": "input_image",
"image_url": f"data:image/png;base64,{screenshot_2_base64}"
}
],
# content=f"data:image/png;base64,{screenshot_2_base64}", # <-- also acceptable
tool_call_id=tool_call_id,
additional_kwargs={"type": "computer_call_output"},
)
```
### Invoke again
```python
messages = [
input_message,
response,
tool_message,
]
response_2 = llm_with_tools.invoke(
messages,
reasoning={
"generate_summary": "concise",
},
)
```
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| libs | ||
| scripts | ||
| .gitattributes | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| .readthedocs.yaml | ||
| CITATION.cff | ||
| LICENSE | ||
| Makefile | ||
| MIGRATE.md | ||
| poetry.toml | ||
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| README.md | ||
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Note
Looking for the JS/TS library? Check out LangChain.js.
LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
pip install -U langchain
To learn more about LangChain, check out the docs. If you’re looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
Use LangChain for:
- Real-time data augmentation. Easily connect LLMs to diverse data sources and external / internal systems, drawing from LangChain’s vast library of integrations with model providers, tools, vector stores, retrievers, and more.
- Model interoperability. Swap models in and out as your engineering team experiments to find the best choice for your application’s needs. As the industry frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without losing momentum.
LangChain’s ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
To improve your LLM application development, pair LangChain with:
- LangSmith - Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangGraph - Build agents that can reliably handle complex tasks with LangGraph, our low-level agent orchestration framework. LangGraph offers customizable architecture, long-term memory, and human-in-the-loop workflows — and is trusted in production by companies like LinkedIn, Uber, Klarna, and GitLab.
- LangGraph Platform - Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in LangGraph Studio.
Additional resources
- Tutorials: Simple walkthroughs with guided examples on getting started with LangChain.
- How-to Guides: Quick, actionable code snippets for topics such as tool calling, RAG use cases, and more.
- Conceptual Guides: Explanations of key concepts behind the LangChain framework.
- API Reference: Detailed reference on navigating base packages and integrations for LangChain.