Expert on Odoo's external JSON-RPC and XML-RPC APIs. Covers authentication, model calls, record CRUD, and real-world integration examples in Python, JavaScript, and curl.
✓Works with OpenClaudeOverview
Odoo exposes a powerful external API via JSON-RPC and XML-RPC, allowing any external application to read, create, update, and delete records. This skill guides you through authenticating, calling models, and building robust integrations.
When to Use This Skill
- Connecting an external app (e.g., Django, Node.js, a mobile app) to Odoo.
- Running automated scripts to import/export data from Odoo.
- Building a middleware layer between Odoo and a third-party platform.
- Debugging API authentication or permission errors.
How It Works
- Activate: Mention
@odoo-rpc-apiand describe the integration you need. - Generate: Get copy-paste ready RPC call code in Python, JavaScript, or curl.
- Debug: Paste an error and get a diagnosis with a corrected call.
Examples
Example 1: Authenticate and Read Records (Python)
import xmlrpc.client
url = 'https://myodoo.example.com'
db = 'my_database'
username = 'admin'
password = 'my_api_key' # Use API keys, not passwords, in production
# Step 1: Authenticate
common = xmlrpc.client.ServerProxy(f'{url}/xmlrpc/2/common')
uid = common.authenticate(db, username, password, {})
print(f"Authenticated as UID: {uid}")
# Step 2: Call models
models = xmlrpc.client.ServerProxy(f'{url}/xmlrpc/2/object')
# Search confirmed sale orders
orders = models.execute_kw(db, uid, password,
'sale.order', 'search_read',
[[['state', '=', 'sale']]],
{'fields': ['name', 'partner_id', 'amount_total'], 'limit': 10}
)
for order in orders:
print(order)
Example 2: Create a Record (Python)
new_partner_id = models.execute_kw(db, uid, password,
'res.partner', 'create',
[{'name': 'Acme Corp', 'email': 'info@acme.com', 'is_company': True}]
)
print(f"Created partner ID: {new_partner_id}")
Example 3: JSON-RPC via curl
curl -X POST https://myodoo.example.com/web/dataset/call_kw \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "call",
"id": 1,
"params": {
"model": "res.partner",
"method": "search_read",
"args": [[["is_company", "=", true]]],
"kwargs": {"fields": ["name", "email"], "limit": 5}
}
}'
# Note: "id" is required by the JSON-RPC 2.0 spec to correlate responses.
# Odoo 16+ also supports the /web/dataset/call_kw endpoint but
# prefer /web/dataset/call_kw for model method calls.
Best Practices
- ✅ Do: Use API Keys (Settings → Technical → API Keys) instead of passwords — available from Odoo 14+.
- ✅ Do: Use
search_readinstead ofsearch+readto reduce network round trips. - ✅ Do: Always handle connection errors and implement retry logic with exponential backoff in production.
- ✅ Do: Store credentials in environment variables or a secrets manager (e.g., AWS Secrets Manager,
.envfile). - ❌ Don't: Hardcode passwords or API keys directly in scripts — rotate them and use env vars.
- ❌ Don't: Call the API in a tight loop without batching — bulk operations reduce server load significantly.
- ❌ Don't: Use the master admin password for API integrations — create a dedicated integration user with minimum required permissions.
Limitations
- Does not cover OAuth2 or session-cookie-based authentication — the examples use API key (token) auth only.
- Rate limiting is not built into the Odoo XMLRPC layer; you must implement throttling client-side.
- The XML-RPC endpoint (
/xmlrpc/2/) does not support file uploads — use the REST-basedir.attachmentmodel via JSON-RPC for binary data. - Odoo.sh (SaaS) may block some API calls depending on plan; verify your subscription supports external API access.
Related AI/ML Integration Skills
Other Claude Code skills in the same category — free to download.
OpenAI Integration
Integrate OpenAI API with best practices
Claude API Setup
Set up Claude/Anthropic API integration
Embedding Search
Implement vector embedding search
RAG Pipeline
Build Retrieval-Augmented Generation pipeline
Prompt Template
Create reusable prompt templates with variables
AI Streaming
Implement streaming AI responses
LangChain Setup
Set up LangChain for AI workflows
Model Comparison
Compare responses from multiple AI models
Want a AI/ML Integration skill personalized to YOUR project?
This is a generic skill that works for everyone. Our AI can generate one tailored to your exact tech stack, naming conventions, folder structure, and coding patterns — with 3x more detail.