Core Capabilities

1. Context Querying

Agents can query any available on-chain context using:

• Direct key-value access

• Graph-based traversal

• Schema-aware search

Example:

import requests

requests.get("https://api.dctx.ai/context/user.reputation.score", params={"address": "0xabc..."}).json()

2. Context Subscription

Agents can subscribe to updates for specific entities or schema types, enabling real-time reactions.

Example:

# Simulated polling or event listener
while True:
    signal = requests.get("https://api.dctx.ai/context/market.signal.volatility", params={"token": "ETH"}).json()
    if signal.get("alert") == True:
        print("⚠️ Volatility spike detected!")
    time.sleep(5)

3. Context Injection into AI Reasoning

Decontext allows context data to be injected directly into:

• LLM prompts

• Strategy modules

• Multi-agent communication channels

Example Prompt:

context = requests.get("https://api.dctx.ai/context/user.reputation.score", params={"address": "0xabc..."}).json()

prompt = f"User 0xABC has {context['score']} reputation and {context['votes']} governance votes in the last 30 days."

4. Behavioral Triggers

Agents can bind behaviors to context events using rule sets:

• “If wallet stake > 10 ETH AND behavior score > 80, initiate high-tier offer.”

• “If DAO proposal passes, trigger knowledge sync.”