langchain-lightningprox 0.1.0

LangChain integration for LightningProx - Pay-per-use AI via Lightning Network micropayments

langchain-lightningprox

LangChain integration for LightningProx — pay-per-use AI access via Bitcoin Lightning Network micropayments.

No API keys. No accounts. No subscriptions. Just Lightning payments.

Installation

pip install langchain-lightningprox

Quick Start

from langchain_lightningprox import LightningProxLLM

# Initialize with your LNBits wallet
llm = LightningProxLLM(
    lnbits_url="https://demo.lnbits.com",
    lnbits_admin_key="your_admin_key_here"
)

# Use it like any LangChain LLM
response = llm.invoke("Explain quantum computing in one sentence.")
print(response)

How It Works

┌──────────────┐      ┌───────────────┐      ┌─────────────┐
│  Your Code   │ ──▶  │ LightningProx │ ──▶  │  Claude/GPT │
│              │      │               │      │             │
│  (LNBits)    │ ─$─▶ │   (verify)    │      │  (respond)  │
│              │ ◀──  │               │ ◀──  │             │
└──────────────┘      └───────────────┘      └─────────────┘

1. Your code sends a prompt via the LangChain interface
2. LightningProx returns a Lightning invoice (~5-50 sats)
3. The library automatically pays via your LNBits wallet
4. LightningProx verifies payment and forwards to Claude/GPT
5. Response returned to your code

All automatic. No manual intervention.

Setup

1. Get an LNBits Wallet

The easiest way to start is with demo.lnbits.com:

1. Go to https://demo.lnbits.com
2. Create a new wallet
3. Click your wallet → API info
4. Copy the Admin key

2. Fund Your Wallet

Send a small amount of sats (~500) to your wallet's Lightning address. You can use any Lightning wallet (Phoenix, Muun, Cash App, etc.) to send.

3. Use It

from langchain_lightningprox import LightningProxLLM

llm = LightningProxLLM(
    lnbits_url="https://demo.lnbits.com",
    lnbits_admin_key="your_admin_key_here"
)

# Single query
response = llm.invoke("What is Bitcoin?")
print(response)

Configuration

llm = LightningProxLLM(
    # Required
    lnbits_admin_key="your_admin_key",
    
    # Optional (defaults shown)
    lnbits_url="https://demo.lnbits.com",
    model="claude-sonnet-4-20250514",  # or "gpt-4-turbo"
    max_tokens=256,
    api_url="https://lightningprox.com/v1/messages",
)

Available Models

Model	Provider	Best For
claude-sonnet-4-20250514	Anthropic	General use (default)
claude-3-5-sonnet-20241022	Anthropic	General use
gpt-4-turbo	OpenAI	General use

Examples

Basic Usage

from langchain_lightningprox import LightningProxLLM

llm = LightningProxLLM(
    lnbits_url="https://demo.lnbits.com",
    lnbits_admin_key="your_key"
)

# Simple question
answer = llm.invoke("What causes rainbows?")
print(answer)

With Environment Variables

import os
from langchain_lightningprox import LightningProxLLM

llm = LightningProxLLM(
    lnbits_url=os.getenv("LNBITS_URL", "https://demo.lnbits.com"),
    lnbits_admin_key=os.getenv("LNBITS_ADMIN_KEY")
)

Multiple Queries

questions = [
    "What is photosynthesis?",
    "How do airplanes fly?",
    "Why is the sky blue?"
]

for q in questions:
    print(f"Q: {q}")
    print(f"A: {llm.invoke(q)}\n")

Using Different Models

# Use GPT-4 Turbo instead
llm = LightningProxLLM(
    lnbits_admin_key="your_key",
    model="gpt-4-turbo"
)

Pricing

• ~5-50 sats per request (depending on response length)
• 50% discount on cached/repeated queries
• No minimums, no subscriptions

At current rates, 1000 sats ≈ $1 USD gets you roughly 20-200 queries.

Use Cases

Autonomous Agents

Perfect for AI agents that need to pay for their own intelligence:

class ResearchAgent:
    def __init__(self):
        self.llm = LightningProxLLM(
            lnbits_admin_key=os.getenv("AGENT_WALLET_KEY")
        )
    
    def research(self, topic):
        return self.llm.invoke(f"Summarize recent developments in {topic}")

Pay-Per-Use Applications

Build apps where users pay per query without managing API keys:

def answer_question(user_question, user_wallet_key):
    llm = LightningProxLLM(lnbits_admin_key=user_wallet_key)
    return llm.invoke(user_question)

Cost-Controlled Experiments

Test prompts without committing to monthly subscriptions:

# Each query costs ~5-50 sats
# Perfect for experimentation
for prompt_variation in prompt_variations:
    result = llm.invoke(prompt_variation)
    evaluate(result)

Alternative Wallet Providers

While this library uses LNBits by default, you can integrate other Lightning wallets by subclassing:

class StrikeLightningProx(LightningProxLLM):
    def _pay_invoice(self, payment_request: str) -> bool:
        # Implement Strike API payment
        pass

Supported wallet APIs:

• LNBits (built-in)
• Strike (subclass)
• Voltage (subclass)
• LND (subclass)
• Any wallet with a payment API

Error Handling

from langchain_lightningprox import LightningProxLLM

llm = LightningProxLLM(lnbits_admin_key="your_key")

try:
    response = llm.invoke("Hello!")
except RuntimeError as e:
    if "Payment failed" in str(e):
        print("Insufficient balance in wallet")
    else:
        print(f"Error: {e}")

Why Lightning Payments?

• Micropayments — Pay fractions of a cent per request
• No accounts — Payment IS authentication
• Instant — Settlements in milliseconds
• Global — Works anywhere, no banking required
• Autonomous — Agents can pay without human intervention

Links

• Website: lightningprox.com
• Docs: lightningprox.com/docs
• API Capabilities: lightningprox.com/api/capabilities
• GitHub: github.com/unixlamadev-spec/langchain-lightningprox

License

MIT