blockrun
BlockRun works with Claude Code and Google Antigravity.
- risk
- unknown
- source
- community
- date added
- 2026-02-27
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"š° 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:
- Wallet auto-creates when
setup_agent_wallet()is called - 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")
- 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"
ā You forgot to enable Live Search. Add search=True:
response = client.chat("xai/grok-3", "What's trending?", search=True)
Module not found
ā Install the SDK: pip install blockrun-llm
Updates
pip install --upgrade blockrun-llm