AI Agents & LLMs

Make the Octav API easily discoverable and usable by AI agents, LLMs, and autonomous programs through standardized documentation formats.

llms.txt Standard - We support the llms.txt format for LLM-friendly documentation

Agent Skill (Recommended)

The fastest way to integrate Octav into any AI agent. Install the official skill and your agent instantly knows the full API:

npx skills add Octav-Labs/octav-api-skill

Works with Claude Code, Codex, ChatGPT, and any agent that supports the Agent Skills standard. The skill covers all endpoints, authentication, pagination patterns, TypeScript types, error handling, and credit optimization — no need to paste documentation manually.

What are AI Agents?

AI agents are autonomous programs designed to carry out specific tasks or simulations with a degree of independence. They can:

Process inputs and execute logic
Interact with APIs and external services
Make decisions based on data
Achieve predefined goals autonomously

Common Use Cases

Portfolio Assistants

AI agents that help users:

Track portfolio performance
Get balance updates
Analyze DeFi positions
Receive alerts on changes

Trading Bots

Automated trading systems that:

Monitor wallet activity
Track transaction patterns
Analyze on-chain behavior
Generate trading signals

Analytics Agents

Data analysis tools that:

Aggregate cross-chain data
Calculate metrics
Identify trends
Generate reports

Notification Systems

Alert systems that:

Watch for transactions
Monitor balances
Detect anomalies
Send notifications

LLMs.txt Integration

The llms.txt format makes web content easily parsable by Large Language Models. Access Octav’s LLM-friendly documentation:

https://api-docs.octav.fi/llms.txt

Point your AI agent or LLM to this URL to automatically understand the Octav API structure and capabilities

What’s Included

The llms.txt file contains:

API Overview - Introduction and key features
Authentication - How to access the API
Endpoints - All available endpoints with descriptions
Data Models - Response structures and types
Code Examples - Usage patterns
Best Practices - Integration guidelines

Quick Start for AI Agents

Load Documentation

Fetch and parse the llms.txt documentation:

import requests

# Load LLM-friendly docs
docs = requests.get('https://api-docs.octav.fi/llms.txt').text

# Feed to your LLM
prompt = f"""
Using this API documentation:
{docs}

How can I get the portfolio for address 0x123...?
"""

Get API Key

Obtain authentication credentials:

Sign up at data.octav.fi
Generate an API key
Purchase credits for API calls

Make API Calls

Use the API in your agent:

# Example: Portfolio checking agent
def check_portfolio(address, api_key):
    headers = {'Authorization': f'Bearer {api_key}'}
    response = requests.get(
        f'https://api.octav.fi/v1/portfolio?addresses={address}',
        headers=headers
    )
    return response.json()

# Agent logic
portfolio = check_portfolio('0x123...', API_KEY)
networth = portfolio[0]['networth']

print(f"Portfolio value: ${networth}")

Handle Responses

Process data intelligently:

def analyze_portfolio(portfolio_data):
    data = portfolio_data[0]

    analysis = {
        'total_value': float(data['networth']),
        'chains': list(data['chains'].keys()),
        'protocols': list(data['assetByProtocols'].keys()),
        'chain_count': len(data['chains'])
    }

    return analysis

Example AI Agent

Here’s a complete example of a portfolio monitoring agent:

import requests
import time
from datetime import datetime

class PortfolioAgent:
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = 'https://api.octav.fi'
        self.headers = {'Authorization': f'Bearer {api_key}'}

    def get_portfolio(self, address):
        """Fetch portfolio data for an address"""
        response = requests.get(
            f'{self.base_url}/v1/portfolio?addresses={address}',
            headers=self.headers
        )
        return response.json()[0]

    def analyze_holdings(self, portfolio):
        """Analyze portfolio composition"""
        analysis = {
            'timestamp': datetime.now().isoformat(),
            'total_value': float(portfolio['networth']),
            'chains': {},
            'protocols': {},
            'top_assets': []
        }

        # Analyze by chain
        for chain_key, chain in portfolio['chains'].items():
            analysis['chains'][chain['name']] = float(chain['value'])

        # Analyze by protocol
        for protocol_key, protocol in portfolio['assetByProtocols'].items():
            analysis['protocols'][protocol['name']] = float(protocol['value'])

        return analysis

    def monitor(self, address, check_interval=60):
        """Monitor portfolio and report changes"""
        last_value = None

        while True:
            portfolio = self.get_portfolio(address)
            current_value = float(portfolio['networth'])

            if last_value is not None:
                change = current_value - last_value
                change_pct = (change / last_value) * 100

                if abs(change_pct) > 1:  # Alert on 1% change
                    print(f"🚨 Portfolio changed by {change_pct:.2f}%")
                    print(f"   New value: ${current_value:,.2f}")

            last_value = current_value
            time.sleep(check_interval)

# Usage
agent = PortfolioAgent('YOUR_API_KEY')
analysis = agent.analyze_holdings(
    agent.get_portfolio('0x123...')
)
print(analysis)

Use Cases for AI Agents

Conversational Interfaces
Automated Alerts
Data Aggregation

Build chat-based portfolio assistants:

# Example: ChatGPT-style portfolio assistant
def portfolio_chat_handler(user_message, address, api_key):
    agent = PortfolioAgent(api_key)
    portfolio = agent.get_portfolio(address)

    if "balance" in user_message.lower():
        return f"Your portfolio is worth ${portfolio['networth']}"

    elif "chains" in user_message.lower():
        chains = ", ".join(portfolio['chains'].keys())
        return f"You have assets on: {chains}"

    elif "top holding" in user_message.lower():
        # Find largest position
        max_protocol = max(
            portfolio['assetByProtocols'].items(),
            key=lambda x: float(x[1]['value'])
        )
        return f"Your top holding is {max_protocol[1]['name']}: ${max_protocol[1]['value']}"

# User: "What's my balance?"
response = portfolio_chat_handler(
    "What's my balance?",
    "0x123...",
    API_KEY
)
print(response)  # "Your portfolio is worth $12,345.67"

Send notifications on portfolio changes:

class AlertAgent(PortfolioAgent):
    def __init__(self, api_key, webhook_url):
        super().__init__(api_key)
        self.webhook_url = webhook_url

    def send_alert(self, message):
        requests.post(
            self.webhook_url,
            json={'text': message}
        )

    def check_thresholds(self, address, thresholds):
        portfolio = self.get_portfolio(address)
        value = float(portfolio['networth'])

        if value < thresholds['min']:
            self.send_alert(
                f"⚠️  Portfolio below ${thresholds['min']:,}"
            )

        if value > thresholds['max']:
            self.send_alert(
                f"🎉 Portfolio above ${thresholds['max']:,}"
            )

# Usage
agent = AlertAgent(API_KEY, SLACK_WEBHOOK)
agent.check_thresholds('0x123...', {
    'min': 10000,
    'max': 100000
})

Aggregate data across multiple addresses:

class AggregatorAgent(PortfolioAgent):
    def aggregate_portfolios(self, addresses):
        total_value = 0
        all_chains = set()
        all_protocols = set()

        for address in addresses:
            portfolio = self.get_portfolio(address)
            total_value += float(portfolio['networth'])
            all_chains.update(portfolio['chains'].keys())
            all_protocols.update(portfolio['assetByProtocols'].keys())

        return {
            'total_value': total_value,
            'unique_chains': len(all_chains),
            'unique_protocols': len(all_protocols),
            'address_count': len(addresses)
        }

# Usage
agent = AggregatorAgent(API_KEY)
summary = agent.aggregate_portfolios([
    '0x123...',
    '0x456...',
    '0x789...'
])
print(f"Combined portfolio: ${summary['total_value']:,.2f}")

Best Practices

Rate Limiting

Respect API rate limits (360 requests/minute):

import time
from collections import deque

class RateLimitedAgent(PortfolioAgent):
    def __init__(self, api_key, max_rpm=360):
        super().__init__(api_key)
        self.max_rpm = max_rpm
        self.requests = deque()

    def _check_rate_limit(self):
        now = time.time()
        # Remove requests older than 1 minute
        while self.requests and self.requests[0] < now - 60:
            self.requests.popleft()

        if len(self.requests) >= self.max_rpm:
            sleep_time = 60 - (now - self.requests[0])
            time.sleep(sleep_time)

    def get_portfolio(self, address):
        self._check_rate_limit()
        self.requests.append(time.time())
        return super().get_portfolio(address)

Error Handling

Handle API errors gracefully:

def safe_api_call(self, func, *args, max_retries=3):
    for attempt in range(max_retries):
        try:
            return func(*args)
        except requests.exceptions.RequestException as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)  # Exponential backoff

Credit Management

Monitor and manage API credits:

def check_credits(self):
    response = requests.get(
        f'{self.base_url}/v1/credits',
        headers=self.headers
    )
    credits = response.json()

    if credits < 100:
        print(f"⚠️  Low credits: {credits}")

    return credits

Authentication

Get your API key

Pricing

Understand credit costs

Portfolio Endpoint

Main data endpoint

llms.txt Spec

Learn about the standard

Getting Started

Endpoints

Playground

AI Development

Reference

Agent Skill (Recommended)

What are AI Agents?

Common Use Cases

Portfolio Assistants

Trading Bots

Analytics Agents

Notification Systems

LLMs.txt Integration

What’s Included

Quick Start for AI Agents

Example AI Agent

Use Cases for AI Agents

Best Practices

Authentication

Pricing

Portfolio Endpoint

llms.txt Spec

Getting Started

Endpoints

Playground

AI Development

Reference

​Agent Skill (Recommended)

​What are AI Agents?

​Common Use Cases

Portfolio Assistants

Trading Bots

Analytics Agents

Notification Systems

​LLMs.txt Integration

​What’s Included

​Quick Start for AI Agents

​Example AI Agent

​Use Cases for AI Agents

​Best Practices

​Related Resources

Authentication

Pricing

Portfolio Endpoint

llms.txt Spec

Agent Skill (Recommended)

What are AI Agents?

Common Use Cases

LLMs.txt Integration

What’s Included

Quick Start for AI Agents

Example AI Agent

Use Cases for AI Agents

Best Practices

Related Resources