Contact Home

Integrate Agent Ledger in minutes

Simple code examples for Python, Node.js, curl, and Go. Compatible with OpenAI, Anthropic, and all major AI models.

Quick Connect โ€” OpenAI & Azure Integration

Get started in seconds with copy-paste code snippets for Python and Node.js.

Python + OpenAI

import openai
import requests

AGENT_LEDGER_API_KEY = "your_api_key_here"
AGENT_LEDGER_AGENT_ID = "your_agent_id_here"

def log_decision(prompt, response, risk_score=None):
    requests.post(
        f"https://agentledgerhq.com/api/v1/agents/{AGENT_LEDGER_AGENT_ID}/decisions",
        headers={"Authorization": f"Bearer {AGENT_LEDGER_API_KEY}"},
        json={
            "decision_type": "llm_completion",
            "input_data": {"prompt": prompt},
            "output_data": {"response": response},
            "risk_score": risk_score or 50
        }
    )

# Usage:
client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "your prompt here"}]
)
log_decision("your prompt here", response.choices[0].message.content)

Node.js + OpenAI

const OpenAI = require('openai');
const fetch = require('node-fetch');

const AGENT_LEDGER_API_KEY = 'your_api_key_here';
const AGENT_LEDGER_AGENT_ID = 'your_agent_id_here';

async function logDecision(prompt, response, riskScore = 50) {
  await fetch(`https://agentledgerhq.com/api/v1/agents/${AGENT_LEDGER_AGENT_ID}/decisions`, {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${AGENT_LEDGER_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      decision_type: 'llm_completion',
      input_data: { prompt },
      output_data: { response },
      risk_score: riskScore
    })
  });
}

// Usage:
const client = new OpenAI();
const response = await client.chat.completions.create({
  model: 'gpt-4',
  messages: [{ role: 'user', content: 'your prompt here' }]
});
await logDecision('your prompt here', response.choices[0].message.content);

Azure OpenAI

from openai import AzureOpenAI
import requests

AGENT_LEDGER_API_KEY = "your_api_key_here"
AGENT_LEDGER_AGENT_ID = "your_agent_id_here"

# Azure configuration
AZURE_API_KEY = "your_azure_api_key"
AZURE_ENDPOINT = "https://your-instance.openai.azure.com/"
AZURE_DEPLOYMENT = "gpt-4-deployment"

def log_decision(prompt, response, risk_score=None):
    requests.post(
        f"https://agentledgerhq.com/api/v1/agents/{AGENT_LEDGER_AGENT_ID}/decisions",
        headers={"Authorization": f"Bearer {AGENT_LEDGER_API_KEY}"},
        json={
            "decision_type": "llm_completion",
            "input_data": {"prompt": prompt},
            "output_data": {"response": response},
            "risk_score": risk_score or 50
        }
    )

# Usage with Azure OpenAI:
client = AzureOpenAI(
    api_key=AZURE_API_KEY,
    api_version="2024-02-15-preview",
    azure_endpoint=AZURE_ENDPOINT
)

response = client.chat.completions.create(
    deployment_id=AZURE_DEPLOYMENT,
    messages=[{"role": "user", "content": "your prompt here"}]
)
log_decision("your prompt here", response.choices[0].message.content)

Python Integration

Log AI agent decisions directly from your Python application.

import requests
import json
from datetime import datetime

# Initialize Agent Ledger client
api_key = "your_api_key_here"
base_url = "https://api.agentledgerhq.com"

def log_agent_decision(agent_id, action, output, risk_level="medium"):
    """Log an AI agent decision to Agent Ledger."""
    payload = {
        "agent_id": agent_id,
        "action": action,
        "output": output,
        "risk_level": risk_level,
        "timestamp": datetime.utcnow().isoformat(),
        "model": "gpt-4",
        "tokens_used": 1250
    }
    
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    
    response = requests.post(
        f"{base_url}/api/v1/decisions",
        json=payload,
        headers=headers
    )
    
    return response.json()

# Example: Log a compliance decision
result = log_agent_decision(
    agent_id="sales-agent-01",
    action="Generated contract proposal",
    output="Contract for customer XYZ Inc.",
    risk_level="medium"
)

print(f"Decision logged: {result['id']}")

Node.js Integration

Log AI agent decisions from your Node.js/Express application.

const axios = require('axios');

// Initialize Agent Ledger client
const apiKey = process.env.AGENT_LEDGER_API_KEY;
const baseUrl = 'https://api.agentledgerhq.com';

async function logAgentDecision(agentId, action, output, riskLevel = 'medium') {
  try {
    const payload = {
      agent_id: agentId,
      action: action,
      output: output,
      risk_level: riskLevel,
      timestamp: new Date().toISOString(),
      model: 'gpt-4',
      tokens_used: 1250
    };

    const response = await axios.post(
      `${baseUrl}/api/v1/decisions`,
      payload,
      {
        headers: {
          'Authorization': `Bearer ${apiKey}`,
          'Content-Type': 'application/json'
        }
      }
    );

    return response.data;
  } catch (error) {
    console.error('Failed to log decision:', error.message);
    throw error;
  }
}

// Example: Log a credit decision
const result = await logAgentDecision(
  'finance-agent-01',
  'Evaluated credit application',
  'Credit approved for $50,000',
  'high'
);

console.log(`Decision logged: ${result.id}`);

cURL Integration

Log decisions directly via HTTP requests.

curl -X POST https://api.agentledgerhq.com/api/v1/decisions \
  -H "Authorization: Bearer your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "agent_id": "hiring-agent-01",
    "action": "Screened job candidate",
    "output": "Candidate approved for technical interview",
    "risk_level": "high",
    "timestamp": "'$(date -u +%Y-%m-%dT%H:%M:%SZ)'",
    "model": "gpt-4",
    "tokens_used": 980
  }'

# Response:
# {
#   "id": "dec_1a2b3c4d",
#   "status": "logged",
#   "risk_score": 67,
#   "eu_ai_act_category": "high-risk"
# }

Go Integration

Log AI decisions from your Go microservices.

package main

import (
  "bytes"
  "encoding/json"
  "net/http"
  "time"
)

type Decision struct {
  AgentID   string `json:"agent_id"`
  Action    string `json:"action"`
  Output    string `json:"output"`
  RiskLevel string `json:"risk_level"`
  Timestamp string `json:"timestamp"`
  Model     string `json:"model"`
  TokensUsed int    `json:"tokens_used"`
}

func logAgentDecision(apiKey string, decision Decision) error {
  payload, _ := json.Marshal(decision)
  
  req, _ := http.NewRequest(
    "POST",
    "https://api.agentledgerhq.com/api/v1/decisions",
    bytes.NewBuffer(payload),
  )
  
  req.Header.Set("Authorization", "Bearer "+apiKey)
  req.Header.Set("Content-Type", "application/json")
  
  client := &http.Client{}
  resp, err := client.Do(req)
  defer resp.Body.Close()
  
  return err
}

// Example: Log a compliance decision
decision := Decision{
  AgentID:    "compliance-agent-01",
  Action:     "Reviewed document for GDPR compliance",
  Output:     "Document approved with 2 minor updates",
  RiskLevel:  "medium",
  Timestamp:  time.Now().UTC().Format(time.RFC3339),
  Model:      "gpt-4",
  TokensUsed: 1200,
}

logAgentDecision("your_api_key", decision)

Compatible with all AI models

Agent Ledger works seamlessly with all major AI models and frameworks. No proprietary lock-in.

OpenAI (GPT-4)
โœ… Tested
Anthropic (Claude)
โœ… Tested
Mistral AI
โœ… Tested
Llama (Meta)
โœ… Tested
Azure OpenAI
โœ… Tested
Google Gemini
โœ… Tested
Bekijk volledige API documentatie โ†’

Ready to log your first decision?

Start with our free plan. No credit card required. Integrate in minutes.

Start free โ†’