One Spraay gateway URL for batch payments, DeFi quotes, AI inference, and on-chain data.
Memory
Bittensor AI
Try itDecentralized AI inference via Bittensor SN64 (43+ chat models) and SN19 (image gen) plus embeddings — OpenAI-compatible, paid in USDC on Base/Solana via Spraay x402. No Bittensor wallet needed. Censorship-resistant, keyless.
What it does
Decentralized AI inference via Bittensor SN64 (43+ chat models) and SN19 (image gen) plus embeddings — OpenAI-compatible, paid in USDC on Base/Solana via Spraay x402. No Bittensor wallet needed. Censorship-resistant, keyless.
The skill document
Bittensor Decentralized AI 🧬
Decentralized AI inference routed through the Bittensor network — no Bittensor wallet, no TAO, no subnet registration required. Your agent pays USDC per call via x402 and the Spraay gateway handles the Bittensor routing.
- SN64 (Chutes) — 43+ chat models, fully OpenAI
/v1/chat/completionscompatible - SN19 (Nineteen AI) — image generation, OpenAI
/v1/images/generationscompatible - Embeddings — text embeddings, OpenAI
/v1/embeddingscompatible
Every response comes from decentralized miners competing on quality. Same API shape as OpenAI, different infrastructure underneath.
⚠️ Before you install
This skill sends your prompts and data to the Bittensor decentralized network via the Spraay x402 gateway (
gateway.spraay.app).
- External transmission: Prompts, text, and image requests leave your local environment and are routed to Bittensor subnet miners through the gateway. The gateway operator and Bittensor miners process your inputs.
- Real money: Each call costs USDC via x402 micropayments. Your agent's wallet is debited per request ($0.001–$0.05 per call).
- Decentralized routing: Responses come from competing miners on the Bittensor network. Response quality and latency may vary more than centralized providers. Model availability depends on active miners.
- Privacy: The gateway operator and Bittensor validators/miners may see your prompts and inputs. Do not send sensitive or proprietary content unless you accept this.
Use a dedicated wallet with limited funds for testing.
How to call endpoints
bash {baseDir}/scripts/bittensor.sh METHOD ENDPOINT '{"key":"value"}'
The script requires bash and curl. All calls go to https://gateway.spraay.app.
Why Bittensor over centralized inference?
- Censorship-resistant — no single provider can block or filter your requests
- Competitive quality — miners compete for rewards, driving model quality up
- No vendor lock-in — OpenAI-compatible API shape means easy swap-in
- Different aesthetic — image generation via SN19 produces a distinct visual style from FLUX/SDXL
- Decentralization signal — relevant for agents operating in Web3/crypto contexts where decentralized infrastructure matters
When to use this skill
Use this skill when the user explicitly wants decentralized or Bittensor-specific inference:
- "Use Bittensor", "decentralized AI", "SN64", "SN19", "Chutes", "Nineteen AI"
- "Run this through a decentralized model"
- "I want censorship-resistant inference"
- "Generate an image on Bittensor"
For centralized compute with more models and predictable latency, use the Spraay Compute & Futures skill instead.
Available endpoints (4 tools)
List Models — $0.001
List all available decentralized models on Bittensor. Returns model IDs, capabilities, and pricing. Call this first to see what's available.
bash {baseDir}/scripts/bittensor.sh GET /bittensor/v1/models '{}'
OpenAI /v1/models compatible response format.
Chat Completions — $0.03
Chat completions via Bittensor SN64 (Chutes). 43+ models including Llama, Mistral, DeepSeek, Qwen, and more. Fully OpenAI-compatible — supports system prompts, temperature, max_tokens, and multi-turn conversation.
bash {baseDir}/scripts/bittensor.sh POST /bittensor/v1/chat/completions '{
"model": "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4",
"messages": [
{"role": "system", "content": "You are a helpful DeFi analyst."},
{"role": "user", "content": "Explain impermanent loss in two sentences."}
],
"max_tokens": 200,
"temperature": 0.7
}'
Tips:
- Use
modelfrom the/bittensor/v1/modelslist — model availability depends on active miners - Multi-turn works — pass the full conversation history in
messages - Response format matches OpenAI:
choices[0].message.content
Image Generation — $0.05
Image generation via Bittensor SN19 (Nineteen AI). OpenAI /v1/images/generations compatible. Produces a distinct visual style compared to centralized FLUX/SDXL.
bash {baseDir}/scripts/bittensor.sh POST /bittensor/v1/images/generations '{
"prompt": "a neon-lit cyberpunk street market at night, rain-slicked pavement, volumetric fog",
"n": 1,
"size": "1024x1024"
}'
Tips:
- Returns a URL to the generated image
- Good for: artistic/stylized images, concept art, social media visuals
- Different aesthetic from FLUX — try both and compare for your use case
Embeddings — $0.005
Text embeddings via Bittensor. OpenAI /v1/embeddings compatible. Use for semantic search, RAG pipelines, document clustering, and similarity matching.
bash {baseDir}/scripts/bittensor.sh POST /bittensor/v1/embeddings '{
"input": "Decentralized AI inference via Bittensor subnet miners"
}'
Returns a vector array compatible with any vector database (Pinecone, Weaviate, ChromaDB, pgvector).
Cost reference
| Endpoint | Method | Price | Bittensor Subnet |
|---|---|---|---|
| Models | GET | $0.001 | — |
| Chat Completions | POST | $0.03 | SN64 (Chutes) |
| Image Generation | POST | $0.05 | SN19 (Nineteen) |
| Embeddings | POST | $0.005 | Bittensor |
Total cost for a typical workflow (list models + chat + image): ~$0.08 USDC
Workflows
Decentralized content creation — use chat completions for copy, image generation for visuals. Fully decentralized pipeline with no centralized AI provider in the loop.
RAG with decentralized embeddings — embed documents via Bittensor, store in a vector DB, retrieve and answer with Bittensor chat completions. End-to-end decentralized knowledge pipeline.
A/B testing against centralized models — run the same prompt through Bittensor chat and through the centralized Spraay Compute text-inference endpoint, compare quality and latency.
Web3-native agents — agents operating in crypto/DeFi contexts where using decentralized infrastructure is a feature, not just a choice. Useful for trust signaling and alignment with decentralization values.
Changelog
v1.0.0
- Initial release with 4 Bittensor endpoints: models, chat completions (SN64), image generation (SN19), embeddings.
Related terms
Bittensor, SN64, SN19, Chutes, Nineteen AI, decentralized AI, decentralized inference, censorship-resistant AI, TAO, OpenAI compatible, agent inference, x402 payments, USDC micropayments, keyless AI, Web3 AI, decentralized embeddings, subnet mining
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