Five ways to start. Each takes under 5 minutes. Pick the one that matches what you want to build.
Give Claude Desktop or Cursor access to Strue's research, data, and Arloop library via MCP.
Sign up for free at strue.com/signup. Or get a trial key instantly:
curl https://api.strue.com/v1/trial
Open Claude Desktop settings and add this to your MCP config:
{
"mcpServers": {
"strue": {
"command": "npx",
"args": ["-y", "pentos-bridge"],
"env": {
"STRUE_API_KEY": "sk_your_key_here"
}
}
}
}.cursor/mcp.json in your project.Claude now has access to these tools. Try saying:
"Search Arloop for research on decentralized training" "Research the current state of Bittensor subnets" "Pull recent X posts about TAO price" "Store this analysis permanently on Arweave"
Install the Strue plugin for Hermes Agent. Deep lifecycle integration — memory syncs with TEE, research checks Arloop automatically.
curl https://api.strue.com/v1/trial # or sign up: strue.com/signup
Copy the plugin file into your Hermes plugins directory:
cp strue_plugin.py ~/.hermes/plugins/
hermes config set STRUE_API_KEY sk_your_key_here
Hermes now has 12 decentralized tools + lifecycle hooks. Try:
"Research the current state of decentralized AI" "Store this analysis permanently on Arweave" "Check open research bounties" "What's the current ETH price?"
Add Strue's decentralized infrastructure to any ElizaOS agent with a single plugin install.
npm install @strue/eliza-plugin
Add the plugin and your API key to your Eliza character JSON:
{
"plugins": ["@strue/eliza-plugin"],
"settings": {
"STRUE_API_KEY": "sk_your_key_here"
}
}Your Eliza agent now has 10 action handlers for decentralized AI:
STRUE_RESEARCH — deep research with Arloop cache STRUE_INFERENCE — decentralized LLM inference STRUE_MEMORY_STORE / STRUE_MEMORY_RECALL — TEE memory STRUE_SOCIAL_DATA — real-time X/Twitter data STRUE_STORE — permanent Arweave/Hippius storage STRUE_PUBLISH — publish to Arloop library STRUE_BOUNTIES — open research bounties STRUE_PRICE — on-chain Chainlink prices
Run closed-loop experiments using Bridge. Modify, run, evaluate, keep or discard, repeat.
pip install pentos-bridge
export STRUE_API_KEY=sk_your_key_here
mkdir my-experiment && cd my-experiment pentos-bridge autoresearch init \ --metric "val_loss" \ --direction "lower_is_better" \ --baseline 0.85
This creates a program.md with your experiment config and a run.py scaffold.
pentos-bridge autoresearch run --experiments 20
Bridge will iterate: propose a change, run your code, measure the metric, keep or discard, repeat. Each experiment is logged with full provenance.
pentos-bridge autoresearch publish
Your experiment log is permanently stored on Arweave with a verified autoresearch badge. Anyone can build on your results.
Use the Strue API directly. OpenAI-compatible inference, research, data, storage, and more.
curl https://api.strue.com/v1/trial # or sign up: strue.com/signup
pip install requests # or: pip install openai (OpenAI-compatible)
import requests
API = "https://api.strue.com"
KEY = "sk_your_key"
H = {"Authorization": f"Bearer {KEY}", "Content-Type": "application/json"}
# Inference (OpenAI-compatible)
r = requests.post(f"{API}/v1/chat/completions", headers=H, json={
"model": "deepseek-ai/DeepSeek-V3",
"messages": [{"role": "user", "content": "What is Bittensor?"}]
})
print(r.json()["choices"][0]["message"]["content"])
# Research
r = requests.post(f"{API}/v1/research", headers=H, json={
"query": "State of decentralized AI", "depth": "standard"
})
print(r.json()["report"])A complete 50-line Python agent with inference, Arloop search, and conversation:
curl -O https://strue.com/agent-template.py export STRUE_API_KEY=sk_your_key python agent-template.py