entroplain 0.2.1

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

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🚀 Works With Any Agent (Proxy Method)

The proxy is the easiest way to use Entroplain with OpenClaw, Claude Code, or any other agent framework:

How It Works

Your Agent → Proxy (localhost:8765) → Real API
               │
               ▼
         Entropy Monitor
               │
               ▼
         Early Exit Check

The proxy intercepts all LLM API calls, monitors entropy, and terminates streams when reasoning converges.

Setup (One-Time)

# Install with proxy support
pip install entroplain[proxy]

# Start the proxy
entroplain-proxy --port 8765 --log-entropy

# Point your agent to the proxy
export OPENAI_BASE_URL=http://localhost:8765/v1

# or for NVIDIA:
export NVIDIA_BASE_URL=http://localhost:8765/v1

# or for Anthropic:
export ANTHROPIC_BASE_URL=http://localhost:8765/v1

That's it! Now run your agent normally and entropy monitoring is automatic.

Proxy Options

# Monitor only, don't exit early
entroplain-proxy --port 8765 --no-early-exit

# Custom thresholds
entroplain-proxy --port 8765 --entropy-threshold 0.2 --min-valleys 3

# Enable cost tracking
entroplain-proxy --port 8765 --model gpt-4o --log-entropy

# Launch dashboard
entroplain-dashboard --port 8050

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🎯 Dashboard

Real-time entropy visualization:

# Start the dashboard
entroplain-dashboard --port 8050

# Open in browser
open http://localhost:8050

The dashboard shows:

• Live entropy curve with valley markers
• Token count and valleys detected
• Cost savings in real-time
• Status badges (active/idle/exited)

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💰 Cost Tracking

Track actual savings from early exit:

from entroplain import CostTracker

tracker = CostTracker(model="gpt-4o")
tracker.track_input(100)   # 100 input tokens
tracker.track_output(50)   # 50 output tokens
tracker.set_full_estimate(150)  # Would have been 150

estimate = tracker.get_estimate()
print(f"Saved ${estimate.cost_saved_usd:.4f} ({estimate.savings_percent:.1f}%)")

Supported pricing: GPT-4o, GPT-4-turbo, Claude 4, Llama 3.1 (NVIDIA), or custom rates.

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Direct Usage (Python)

If you want more control, use Entroplain directly:

from entroplain import EntropyMonitor, NVIDIAProvider

monitor = EntropyMonitor()
provider = NVIDIAProvider()

for token in provider.stream_with_entropy(
    model="meta/llama-3.1-70b-instruct",
    messages=[{"role": "user", "content": "Solve: x^2 = 16"}]
):
    monitor.track(token.token, token.entropy)
    print(token.token, end="")

    if monitor.should_exit():
        print("\n[Early exit - reasoning converged]")
        break

print(f"\nStats: {monitor.get_stats()}")

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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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Exit Strategies

Choose how Entroplain detects convergence:

Strategy	Description
combined	Entropy low OR valleys plateau, AND velocity stable (default)
valleys_plateau	Exit when reasoning milestones stabilize
entropy_drop	Exit when model confidence is high
velocity_zero	Exit when entropy stops changing
repetition	Exit when model starts repeating itself
confidence	Exit when top token prob > 95% for N tokens

monitor = EntropyMonitor(
    exit_condition="repetition",  # or "confidence", "combined", etc.
    repetition_threshold=0.3,      # Exit when 30% of recent tokens repeat
)

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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()

response = ollama.generate(
    model="llama3.1",
    prompt="Think through this carefully...",
    options={"num_ctx": 4096}
)

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)

---

CLI

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

# Stream with early exit
entroplain stream "Explain quantum computing" --exit-on-converge

# Run the proxy (works with any agent)
entroplain-proxy --port 8765 --log-entropy --model gpt-4o

# Launch the dashboard
entroplain-dashboard --port 8050

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

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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,
        exit_condition: str = "combined"
    ):
        ...

    def track(self, token: str, entropy: float, confidence: float = 0.0) -> EntropyPoint:
        """Track a token and its entropy value."""

    def should_exit(self) -> bool:
        """Determine if reasoning has converged."""

    def get_valleys(self) -> List[Tuple[int, float]]:
        """Get all entropy valleys (local minima)."""

    def get_stats(self) -> Dict:
        """Get current statistics."""

    def reset(self) -> None:
        """Clear all tracked data."""

CostTracker

class CostTracker:
    def __init__(self, model: str = "default"):
        ...

    def track_input(self, tokens: int):
        """Track input tokens."""

    def track_output(self, tokens: int):
        """Track output tokens."""

    def set_full_estimate(self, tokens: int):
        """Set estimated output if no early exit."""

    def get_estimate(self) -> CostEstimate:
        """Get cost estimate with savings."""

EntropyProxy

# Run the proxy
entroplain-proxy --port 8765 --log-entropy --model gpt-4o

# Options
--entropy-threshold 0.15    # Exit threshold
--min-valleys 2             # Minimum valleys
--no-early-exit             # Monitor only, don't exit
--log-entropy               # Log entropy values
--model gpt-4o              # Model for cost tracking
--no-cost-tracking          # Disable cost tracking

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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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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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Links

• PyPI: https://pypi.org/project/entroplain/
• npm: https://www.npmjs.com/package/entroplain
• GitHub: https://github.com/entroplain/entroplain
• Issues: https://github.com/entroplain/entroplain/issues

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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