pura

# Pura — cut your agent's LLM costs 40-60% Your OpenClaw agent probably sends every request to GPT-4o. Most of those requests are simple enough for a model that costs 10x less. Pura sits between your agent and the LLM providers, scores each request's complexity, and routes it to the cheapest model that can handle it. Cascade routing: tries Groq first ($0.00059/1K tokens). If the response looks weak (too short, hedging language, refusal), it escalates to Gemini, then OpenAI, then Anthropic. Your agent gets a good answer. You pay the minimum. Drop-in OpenAI-compatible. One env var change. ## What you get - 4 providers (Groq, Gemini, OpenAI, Anthropic) behind one endpoint - Automatic complexity scoring — no manual model selection - Cascade routing — cheapest sufficient tier per request - Per-request cost headers so your agent tracks spend - Daily cost reports and income statements - Free tier: 5,000 requests, no credit card ## Setup Get an API key: ```bash curl -s -X POST https://api.pura.xyz/api/keys \ -H "Content-Type: application/json" \ -d '{"label":"my-agent"}' | python3 -m json.tool ``` Save the returned key (starts with `pura_`): ```bash export PURA_API_KEY="pura_your_key_here" ``` ## Sending requests Pura is OpenAI SDK-compatible. Change your base URL and you're done: ```python from openai import OpenAI import os client = OpenAI( base_url="https://api.pura.xyz/v1", api_key=os.environ["PURA_API_KEY"] ) response = client.chat.completions.create( model="auto", # Pura picks the cheapest capable model messages=[{"role": "user", "content": "Hello"}] ) ``` Or with curl: ```bash curl -s -X POST https://api.pura.xyz/v1/chat/completions \ -H "Authorization: Bearer $PURA_API_KEY" \ -H "Content-Type: application/json" \ -d '{"messages": [{"role": "user", "content": "Hello"}], "stream": false}' ``` ## Response headers Every response includes routing metadata: | Header | Value | |--------|-------| | `X-Pura-Provider` | Which provider handled it (openai, anthropic, groq, gemini) | | `X-Pura-Model` | Specific model used | | `X-Pura-Cost` | Estimated cost in USD | | `X-Pura-Budget-Remaining` | Remaining daily budget in USD | | `X-Pura-Tier` | Complexity tier (cheap, mid, premium) | | `X-Pura-Quality` | Provider quality score (0-1) | ## Cost reports ```bash # 24h spend breakdown curl -s https://api.pura.xyz/api/report \ -H "Authorization: Bearer $PURA_API_KEY" | python3 -m json.tool # Formatted income statement curl -s https://api.pura.xyz/api/income \ -H "Authorization: Bearer $PURA_API_KEY" | python3 -m json.tool ``` ## Lightning wallet 5,000 free requests included. After that, fund a Lightning wallet: ```bash # Get a funding invoice curl -s -X POST https://api.pura.xyz/api/wallet/fund \ -H "Authorization: Bearer $PURA_API_KEY" \ -H "Content-Type: application/json" \ -d '{"amount": 10000}' | python3 -m json.tool # Check balance curl -s https://api.pura.xyz/api/wallet/balance \ -H "Authorization: Bearer $PURA_API_KEY" | python3 -m json.tool ``` ## Model behavior Set `model="auto"` or omit the model field entirely — both trigger cascade routing, where Pura scores your request's complexity and picks the cheapest provider that can handle it. If you specify a model name directly (e.g. `gpt-4o`, `claude-sonnet-4-20250514`), Pura skips the cascade and routes straight to that provider. ## Explicit model routing Override auto-routing by specifying a model: ```bash # Force GPT-4o curl -s -X POST https://api.pura.xyz/v1/chat/completions \ -H "Authorization: Bearer $PURA_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model": "gpt-4o", "messages": [{"role":"user","content":"Hello"}], "stream": false}' ``` Supported models: `gpt-4o`, `claude-sonnet-4-20250514`, `llama-3.3-70b-versatile`, `gemini-2.0-flash`. ## Routing hints Influence provider selection without forcing a specific model: ```json { "messages": [{"role": "user", "content": "..."}], "routing": { "quality": "high", "prefer": "anthropic", "maxCost": 0.01, "maxLatency": 5000 } } ``` ## How routing works Pura scores each request's complexity based on message length, code blocks, reasoning triggers, and conversation depth. Simple tasks go to Groq or Gemini. Complex reasoning goes to Anthropic or OpenAI. Quality scores (derived from recent success rate and latency) weight the selection so underperforming providers get fewer requests until they recover. Cascade routing adds a second layer: if the cheapest provider's response looks bad (too short, hedging, refusal, incomplete), Pura automatically retries at the next tier up. You only pay premium prices when the cheap answer was genuinely insufficient. ## Typical cost savings | Request type | Direct GPT-4o cost | Pura cascade cost | Savings | |---|---|---|---| | Simple Q&A ("What is X?") | $0.005 | $0.00059 (Groq) | 88% | | Code explanation | $0.005 | $0.00125 (Gemini) | 75% | | Complex reasoning | $0.005 | $0.005 (GPT-4o) | 0% | | Long-context analysis | $0.005 | $0.003 (Claude) | 40% | In practice, 70-80% of agent requests are simple enough for the cheapest tier. ## Security note Your API keys for OpenAI, Anthropic, Groq, and Gemini stay on the Pura server. They never touch your agent or the OpenClaw runtime. If you are running an OpenClaw agent with plugins from untrusted sources, routing through Pura means those plugins cannot access your provider API keys. ## Links - Gateway: <https://api.pura.xyz> - Website: <https://pura.xyz> - Compare cascade routing: <https://pura.xyz/compare> - Docs: <https://pura.xyz/docs> - GitHub: <https://github.com/puraxyz/puraxyz>