Browser-LLM Auto-Scaling Technology
Project description
A high-performance serving engine for web browsing AI.
❓ Use Cases
- I want to add web browsing AI to my app... BLAST serves web browsing AI with an OpenAI-compatible API and concurrency and streaming baked in.
- I need to automate workflows... BLAST will automatically cache and parallelize to keep costs down and enable interactive-level latencies.
- Just want to use this locally... BLAST makes sure you stay under budget and not hog your computer's memory.
🚀 Quick Start
pip install blastai && blastai serve
from openai import OpenAI
client = OpenAI(
api_key="not-needed",
base_url="http://127.0.0.1:8000"
)
# Stream real-time browser actions
stream = client.responses.create(
model="not-needed",
input="Compare fried chicken reviews for top 10 fast food restaurants",
stream=True
)
for event in stream:
if event.type == "response.output_text.delta":
print(event.delta if " " in event.delta else "<screenshot>", end="", flush=True)
✨ Features
- 🔄 OpenAI-Compatible API Drop-in replacement for OpenAI's API
- 🚄 High Performance Automatic parallelism and prefix caching
- 📡 Streaming Stream browser-augmented LLM output to users
- 📊 Concurrency Out-of-the-box support many users with efficient resource management
📚 Documentation
Visit documentation to learn more.
🤝 Contributing
Awesome! See our Contributing Guide for details.
📄 MIT License
As it should be!
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