davila7/blockrun

BlockRun

BlockRun works with Claude Code and Google Antigravity.

You can't generate images. You have no real-time X/Twitter access. BlockRun gives you these capabilities.

Philosophy

You have a wallet. When you need a capability you lack, you pay for it autonomously.

| You Need | You Call | Pricing | |----------|----------|---------| | Image generation | DALL-E | $0.04/image | | Real-time X data | Grok + Live Search | $0.025/source | | Second opinion | GPT-5.2 | $1.75/M input, $14/M output | | Cheaper processing | DeepSeek | $0.14/M input, $0.28/M output |

How it works: BlockRun uses x402 micropayments to route your requests to OpenAI, xAI, Google, and other providers. No API keys needed - your wallet pays per token.

Budget Control (Optional)

If the user specifies a budget (e.g., "use at most $1"), track spending and stop when budget is reached:

from blockrun_llm import setup_agent_wallet

client = setup_agent_wallet()
budget = 1.0  # User's budget

# Before each call, check if within budget
spending = client.get_spending()
if spending['total_usd'] >= budget:
    print(f"Budget reached: ${spending['total_usd']:.4f} spent")
    # Stop making calls
else:
    response = client.chat("openai/gpt-5.2", "Hello!")

# At the end, report spending
spending = client.get_spending()
print(f"💰 Total spent: ${spending['total_usd']:.4f} across {spending['calls']} calls")

When to Use

| Trigger | Your Action | |---------|-------------| | User explicitly requests ("blockrun second opinion with GPT on...", "use grok to check...", "generate image with dall-e") | Execute via BlockRun | | User needs something you can't do (images, live X data) | Suggest BlockRun, wait for confirmation | | You can handle the task fine | Do it yourself, don't mention BlockRun |

Example User Prompts

Users will say things like:

| User Says | What You Do | |-----------|-------------| | "blockrun generate an image of a sunset" | Call DALL-E via ImageClient | | "use grok to check what's trending on X" | Call Grok with search=True | | "blockrun GPT review this code" | Call GPT-5.2 via LLMClient | | "what's the latest news about AI agents?" | Suggest Grok (you lack real-time data) | | "generate a logo for my startup" | Suggest DALL-E (you can't generate images) | | "blockrun check my balance" | Show wallet balance via get_balance() | | "blockrun deepseek summarize this file" | Call DeepSeek for cost savings |

Wallet & Balance

Use setup_agent_wallet() to auto-create a wallet and get a client. This shows the QR code and welcome message on first use.

Initialize client (always start with this):

from blockrun_llm import setup_agent_wallet

client = setup_agent_wallet()  # Auto-creates wallet, shows QR if new

Check balance (when user asks "show balance", "check wallet", etc.):

balance = client.get_balance()  # On-chain USDC balance
print(f"Balance: ${balance:.2f} USDC")
print(f"Wallet: {client.get_wallet_address()}")

Show QR code for funding:

from blockrun_llm import generate_wallet_qr_ascii, get_wallet_address

# ASCII QR for terminal display
print(generate_wallet_qr_ascii(get_wallet_address()))

SDK Usage

Prerequisite: Install the SDK with pip install blockrun-llm

Basic Chat

from blockrun_llm import setup_agent_wallet

client = setup_agent_wallet()  # Auto-creates wallet if needed
response = client.chat("openai/gpt-5.2", "What is 2+2?")
print(response)

# Check spending
spending = client.get_spending()
print(f"Spent ${spending['total_usd']:.4f}")

Real-time X/Twitter Search (xAI Live Search)

IMPORTANT: For real-time X/Twitter data, you MUST enable Live Search with search=True or search_parameters.

from blockrun_llm import setup_agent_wallet

client = setup_agent_wallet()

# Simple: Enable live search with search=True
response = client.chat(
    "xai/grok-3",
    "What are the latest posts from @blockrunai on X?",
    search=True  # Enables real-time X/Twitter search
)
print(response)

Advanced X Search with Filters

from blockrun_llm import setup_agent_wallet

client = setup_agent_wallet()

response = client.chat(
    "xai/grok-3",
    "Analyze @blockrunai's recent content and engagement",
    search_parameters={
        "mode": "on",
        "sources": [
            {
                "type": "x",
                "included_x_handles": ["blockrunai"],
                "post_favorite_count": 5
            }
        ],
        "max_search_results": 20,
        "return_citations": True
    }
)
print(response)

Image Generation

from blockrun_llm import ImageClient

client = ImageClient()
result = client.generate("A cute cat wearing a space helmet")
print(result.data[0].url)

xAI Live Search Reference

Live Search is xAI's real-time data API. Cost: $0.025 per source (default 10 sources = ~$0.26).

To reduce costs, set max_search_results to a lower value:

# Only use 5 sources (~$0.13)
response = client.chat("xai/grok-3", "What's trending?",
    search_parameters={"mode": "on", "max_search_results": 5})

Search Parameters

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | mode | string | "auto" | "off", "auto", or "on" | | sources | array | web,news,x | Data sources to query | | return_citations | bool | true | Include source URLs | | from_date | string | - | Start date (YYYY-MM-DD) | | to_date | string | - | End date (YYYY-MM-DD) | | max_search_results | int | 10 | Max sources to return (customize to control cost) |

Source Types

X/Twitter Source:

{
    "type": "x",
    "included_x_handles": ["handle1", "handle2"],  # Max 10
    "excluded_x_handles": ["spam_account"],        # Max 10
    "post_favorite_count": 100,  # Min likes threshold
    "post_view_count": 1000      # Min views threshold
}

Web Source:

{
    "type": "web",
    "country": "US",  # ISO alpha-2 code
    "allowed_websites": ["example.com"],  # Max 5
    "safe_search": True
}

News Source:

{
    "type": "news",
    "country": "US",
    "excluded_websites": ["tabloid.com"]  # Max 5
}

Available Models

| Model | Best For | Pricing | |-------|----------|---------| | openai/gpt-5.2 | Second opinions, code review, general | $1.75/M in, $14/M out | | openai/gpt-5-mini | Cost-optimized reasoning | $0.30/M in, $1.20/M out | | openai/o4-mini | Latest efficient reasoning | $1.10/M in, $4.40/M out | | openai/o3 | Advanced reasoning, complex problems | $10/M in, $40/M out | | xai/grok-3 | Real-time X/Twitter data | $3/M + $0.025/source | | deepseek/deepseek-chat | Simple tasks, bulk processing | $0.14/M in, $0.28/M out | | google/gemini-2.5-flash | Very long documents, fast | $0.15/M in, $0.60/M out | | openai/dall-e-3 | Photorealistic images | $0.04/image | | google/nano-banana | Fast, artistic images | $0.01/image |

M = million tokens. Actual cost depends on your prompt and response length.

Cost Reference

All LLM costs are per million tokens (M = 1,000,000 tokens).

| Model | Input | Output | |-------|-------|--------| | GPT-5.2 | $1.75/M | $14.00/M | | GPT-5-mini | $0.30/M | $1.20/M | | Grok-3 (no search) | $3.00/M | $15.00/M | | DeepSeek | $0.14/M | $0.28/M |

| Fixed Cost Actions | | |-------|--------| | Grok Live Search | $0.025/source (default 10 = $0.25) | | DALL-E image | $0.04/image | | Nano Banana image | $0.01/image |

Typical costs: A 500-word prompt (~750 tokens) to GPT-5.2 costs ~$0.001 input. A 1000-word response (~1500 tokens) costs ~$0.02 output.

Setup & Funding

Wallet location: $HOME/.blockrun/.session (e.g., /Users/username/.blockrun/.session)

First-time setup:

1. Wallet auto-creates when setup_agent_wallet() is called
2. Check wallet and balance:

from blockrun_llm import setup_agent_wallet
client = setup_agent_wallet()
print(f"Wallet: {client.get_wallet_address()}")
print(f"Balance: ${client.get_balance():.2f} USDC")

3. Fund wallet with $1-5 USDC on Base network

Show QR code for funding (ASCII for terminal):

from blockrun_llm import generate_wallet_qr_ascii, get_wallet_address
print(generate_wallet_qr_ascii(get_wallet_address()))

Troubleshooting

"Grok says it has no real-time access" → You forgot to enable Live Search. Add search=True:

response = client.chat("xai/grok-3", "What's trending?", search=True)

Module not found → Install the SDK: pip install blockrun-llm

Updates

pip install --upgrade blockrun-llm