entroplain 0.1.1

Entropy-based early exit for efficient agent reasoning

Entroplain

Entropy-based early exit for efficient agent reasoning.

Stop burning tokens. Know when your agent has finished thinking.

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What It Does

Entroplain monitors your LLM's predictive entropy — the uncertainty in its output distribution — to detect when reasoning has converged.

High entropy → Model is searching, exploring, uncertain
Low entropy  → Model is confident, converged, ready to output

Key insight: Reasoning follows a multi-modal entropy trajectory. Local minima ("valleys") mark reasoning milestones. Exit at the right valley, save 40-60% compute with minimal accuracy loss.

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Quick Start

Install

# Python (pip)
pip install entroplain

# Node.js (npm)
npm install entroplain

Requirements

Python: 3.8+

Node.js: 18+

For cloud providers: Set API keys via environment variables:

export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...
export NVIDIA_API_KEY=nvapi-...

For local models: Install Ollama or llama.cpp

Use with Any Agent

from entroplain import EntropyMonitor

monitor = EntropyMonitor()

# Stream tokens with entropy tracking
async for token, entropy in monitor.stream(agent.generate()):
    print(f"{token} (entropy: {entropy:.3f})")
    
    # Detect reasoning convergence
    if monitor.is_converged():
        break  # Early exit — reasoning complete

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How It Works

1. Track Entropy Per Token

Every token has an entropy value derived from the model's output distribution:

entropy = -sum(p * log2(p) for p in probabilities if p > 0)

2. Detect Valleys

Local minima in the entropy trajectory indicate reasoning milestones:

Entropy: 0.8 → 0.6 → 0.3* → 0.5 → 0.2* → 0.1*
                       ↑           ↑
                    Valley 1    Valley 2

3. Exit at the Right Moment

When valley count plateaus and velocity stabilizes, reasoning is complete.

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Experimental Evidence

Tested on Llama-3.1-70b via NVIDIA API:

Difficulty	Avg Valleys	Avg Entropy	Avg Velocity
Easy	61.3	0.3758	0.4852
Medium	53.0	0.3267	0.4394
Hard	70.2	0.2947	0.4095

Finding: Hard problems have more entropy valleys (70.2 vs 61.3) — valleys correlate with reasoning complexity.

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Platform Support

Platform	Support	How to Enable
Local (llama.cpp, Ollama)	✅ Full	Built-in, no config
OpenAI	✅ Yes	logprobs: true
Anthropic Claude	✅ Yes (Claude 4)	logprobs: True
Google Gemini	✅ Yes	response_logprobs=True
NVIDIA NIM	✅ Yes	logprobs: true
OpenRouter	⚠️ Partial	~23% of models support it

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Integration Examples

OpenAI / NVIDIA / OpenRouter

from openai import OpenAI
from entroplain import EntropyMonitor

client = OpenAI()
monitor = EntropyMonitor()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Solve this step by step..."}],
    logprobs=True,
    top_logprobs=5,
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content:
        token = chunk.choices[0].delta.content
        entropy = monitor.calculate_entropy(chunk.choices[0].logprobs)
        
        if monitor.should_exit():
            print("\n[Early exit — reasoning converged]")
            break
        
        print(token, end="")

Ollama (Local)

import ollama
from entroplain import EntropyMonitor

monitor = EntropyMonitor()

# Ollama exposes logits for local models
response = ollama.generate(
    model="llama3.1",
    prompt="Think through this carefully...",
    options={"num_ctx": 4096}
)

# Direct access to token probabilities
for token_data in response.get("token_probs", []):
    entropy = monitor.calculate_from_logits(token_data["logits"])
    monitor.track(token_data["token"], entropy)

Anthropic Claude

from anthropic import Anthropic
from entroplain import EntropyMonitor

client = Anthropic()
monitor = EntropyMonitor()

with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Analyze this..."}],
) as stream:
    for text in stream.text_stream:
        entropy = monitor.get_entropy()
        if monitor.should_exit():
            break
        print(text, end="", flush=True)

Agent Frameworks

OpenClaw:

# In your agent config
entropy_monitor:
  enabled: true
  exit_threshold: 0.15  # Exit when entropy drops below this
  min_valleys: 3        # Require at least N reasoning milestones

Claude Code:

{
  "hooks": {
    "on_token": "entroplain.hooks.track_entropy",
    "on_converge": "entroplain.hooks.early_exit"
  }
}

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Configuration

Environment Variables

# For cloud providers
ENTROPPLAIN_OPENAI_API_KEY=sk-...
ENTROPPLAIN_ANTHROPIC_API_KEY=sk-ant-...
ENTROPPLAIN_NVIDIA_API_KEY=nvapi-...

# For local models
ENTROPPLAIN_LOCAL_PROVIDER=ollama  # or llama.cpp
ENTROPPLAIN_LOCAL_MODEL=llama3.1

Exit Conditions

monitor = EntropyMonitor(
    # Exit when entropy drops below threshold
    entropy_threshold=0.15,
    
    # Require minimum valleys before exit
    min_valleys=2,
    
    # Exit when velocity stabilizes (change < this)
    velocity_threshold=0.05,
    
    # Don't exit before N tokens
    min_tokens=50,
    
    # Custom exit condition
    exit_condition="valleys_plateau"  # or "entropy_drop", "velocity_zero"
)

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CLI Usage

# Analyze a prompt's entropy trajectory
entroplain analyze "What is 2+2?" --model gpt-4o

# Stream with early exit
entroplain stream "Solve this step by step: x^2 = 16" --exit-on-converge

# Benchmark entropy patterns
entroplain benchmark --problems gsm8k --output results.json

# Visualize entropy trajectory
entroplain visualize results.json --output entropy_plot.png

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API Reference

EntropyMonitor

class EntropyMonitor:
    def __init__(
        self,
        entropy_threshold: float = 0.15,
        min_valleys: int = 2,
        velocity_threshold: float = 0.05,
        min_tokens: int = 50
    ): ...
    
    def calculate_entropy(self, logprobs: List[float]) -> float:
        """Calculate Shannon entropy from log probabilities."""
    
    def track(self, token: str, entropy: float) -> None:
        """Track a token and its entropy value."""
    
    def get_valleys(self) -> List[Tuple[int, float]]:
        """Get all entropy valleys (local minima)."""
    
    def get_velocity(self) -> float:
        """Get current entropy velocity (rate of change)."""
    
    def should_exit(self) -> bool:
        """Determine if reasoning has converged."""
    
    def is_converged(self) -> bool:
        """Alias for should_exit()."""
    
    def get_trajectory(self) -> List[float]:
        """Get full entropy trajectory."""
    
    def reset(self) -> None:
        """Clear all tracked data."""

calculate_entropy(logprobs)

from entroplain import calculate_entropy

# From log probabilities
entropy = calculate_entropy([-0.5, -2.1, -0.1, -5.2])
# Returns: 0.847

# From probabilities
entropy = calculate_entropy([0.6, 0.125, 0.9, 0.005], from_probs=True)

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Research

Paper

See paper.md for the full research proposal: "Entropy-Based Early Exit for Efficient Agent Reasoning"

Key Findings

1. H1 Supported: Entropy valleys correlate with reasoning complexity (70.2 valleys for hard problems vs 61.3 for easy)
2. H2 Supported: Entropy velocity differs by difficulty (0.4852 easy vs 0.4095 hard)
3. Potential: 40-60% compute reduction with 95%+ accuracy retention

Citation

@software{entroplain2026,
  title = {Entroplain: Entropy-Based Early Exit for Efficient Agent Reasoning},
  author = {Entroplain Contributors},
  year = {2026},
  url = {https://github.com/entroplain/entroplain}
}

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Roadmap

• v0.1.0 — Core entropy tracking (Python)
• v0.2.0 — Multi-provider support (OpenAI, Anthropic, Gemini, NVIDIA)
• v0.3.0 — Local model support (llama.cpp, Ollama)
• v0.4.0 — Agent framework integrations (OpenClaw, Claude Code)
• v0.5.0 — JavaScript/Node.js SDK
• v1.0.0 — Production release with benchmarks

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Development Setup

git clone https://github.com/entroplain/entroplain.git
cd entroplain
pip install -e ".[dev]"
pytest

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License

MIT License — see LICENSE for details.

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Acknowledgments

• Research inspired by early exit architectures in transformers
• Experimental validation using NVIDIA NIM API
• Built for the agent-first future of AI