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Make any LLM to think like OpenAI o1 and deepseek R1
| Date | Stars |
|---|---|
| 2026-07-31 | 488 |
| 2026-08-05 | 488 |
| 2026-08-06 | 488 |
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# 🤔 LLM-Reasoner: Make any LLM to think deeper like OpenAI o1 and deepseek R1!
Make any LLM to think deeper like OpenAI o1 and deepseek R1!
## ✨ What's Cool About It?
- 🧠 **Step-by-Step Reasoning**: No more black-box answers! See exactly how your LLM thinks, similar to O1's methodical approach
- 🔄 **Real-time Progress**: Watch the reasoning unfold with smooth animations
- 🎯 **Multi-Provider Support**: Works with all provider supported by LiteLLM
- 🎮 **Sweet UI**: A slick Streamlit interface to play with
- 🛠️ **Power-User CLI**: For when you want to get nerdy with it
- 📊 **Confidence Tracking**: Know how sure your LLM is about each step
## 🚀 Quick Start
Pop this in your terminal:
```bash
pip install llm-reasoner
```
Got API keys? Drop 'em in:
```bash
# Pick your flavor:
export OPENAI_API_KEY="sk-your-key" # OpenAI fan?
export ANTHROPIC_API_KEY="your-key" # Team Claude?
export VERTEX_PROJECT="your-project" # Google enthusiast?
```
## 🎮 Jump Right In!
```bash
# List your available models
llm-reasoner models
# Generate a reasoning chain
llm-reasoner reason "How do planes fly?" --min-steps 5
# Launch the UI
llm-reasoner ui
```
### Interactive UI
### Let's see it in action as SDK
```python
from llm_reasoner import ReasonChain
import asyncio
async def main():
# Create a chain with your preferred settings
chain = ReasonChain(
model="gpt-4", # Choose your model
min_steps=3, # Minimum reasoning steps
temperature=0.2, # Control creativity
timeout=30.0 # Set your timeout
)
# Watch it think step by step!
async for step in chain.generate_with_metadata("Why is the sky blue?"):
print(f"\nStep {step.number}: {step.title}")
print(f"Thinking Time: {step.thinking_time:.2f}s")
print(f"Confidence: {step.confidence:.2f}")
print(step.content)
asyncio.run(main())
```
## 🌟 Cool Features You'll Love
### Rich Metadata for Each Step
```python
async for step in chain.generate_with_metadata(query):
print(f"Title: {step.title}") # What's this step about?
print(f"Content: {step.content}") # The actual thinking
print(f"Confidence: {step.confidence}") # How sure is it?
print(f"Time: {step.thinking_time}s") # How long did it take?
```
### Custom Model Registration
Want to use your own models? We've got you covered! You can register custom models through both Python and CLI:
```python
from llm_reasoner import model_registry
# Add your own models
model_registry.register_model(
name="my-cool-model",
provider="custom-provider",
context_window=8192
)
```
Using the CLI:
```bash
# Register a new model
llm-reasoner register-model my-custom-model azure --context-window 16384
# List all available models (including your custom ones)
llm-reasoner models
# Set your custom model as default
llm-reasoner set-model my-custom-model
# Use your custom model
llm-reasoner reason "What is quantum computing?" --model my-custom-model
```
## 🎛️ Power User Settings
Fine-tune your chains:
```python
chain = ReasonChain(
model="claude-2", # Pick your model
max_tokens=750, # Control response length
temperature=0.2, # Adjust randomness
timeout=30.0, # Set API timeout
min_steps=5 # Minimum reasoning steps
)
# Clear history if needed
chain.clear_history()
```
## 🔧 Model Support
Out of the box, we support:
- OpenAI: GPT-4, GPT-3.5-Turbo
- Anthropic: Claude 2
- Google: Gemini Pro
- Azure OpenAI models
- Custom models through our flexible provider system LiteLLM
Need to use a different model? Just register it with our CLI or Python API!
## 🎨 UI Walkthrough
1. LaExcerpt of 4,667 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3da1ce22e2459f89, llm:Repository name and description: 'LLM-Reasoner' / 'Make any LLM to think like OpenAI o1 and deepseek R1' — implies prompting/chain-of-thought or reasoning techniques for LLMs.