| name | sdk-py |
| description | Help writing Python code using the fiberai package. Use when the user wants to build a Python application, script, or automation that calls Fiber AI APIs programmatically. |
| user-invocable | true |
| argument-hint | <what you want to build> |
Fiber AI Python SDK
Help the user write Python code using the fiberai package.
When to Use
- User wants to BUILD something with Fiber AI in Python
- User says "python", "script", "automation", "data pipeline", "backend"
- User wants a custom integration, ETL pipeline, or automation
Do Not Use When
- User just wants to search or enrich via chat — use
/fiber:search or /fiber:enrich instead
- User wants to write TypeScript code — use
/fiber:sdk-ts instead
Quick Start
import os
from fiberai import Client
from fiberai.api.search.company_search import sync as company_search_sync
from fiberai.models.company_search_body import CompanySearchBody
client = Client(base_url="https://api.fiber.ai")
result = company_search_sync(
client=client,
body=CompanySearchBody(
api_key=os.environ["FIBER_API_KEY"],
search_params={},
page_size=25,
),
)
Key Concepts
- The SDK provides typed functions for every API endpoint
- API functions are standalone module functions, not methods on the client — import them from
fiberai.api.{domain}.{operation}
- Each module exports 4 variants:
sync, sync_detailed, asyncio, asyncio_detailed
- Request bodies are typed model classes (not raw dicts) — import from
fiberai.models.{model_name}
api_key is a field on every body model (for POST) or query parameter (for GET) — not a header- Search filters go inside
search_params (not filters)
- Pagination uses
cursor (not page)
- The
Client and AuthenticatedClient are the two exported client classes
Schema & Type Discovery
Fiber's request/response schemas are large and evolve across versions. The SDK is the best source of truth:
- From the installed package: inspect
fiberai.models.<ModelName> for every field. Use help(fiberai.models.CompanySearchBody) or IDE autocomplete to discover available fields for search_params, enrichment_type, etc. Model classes enforce correct field names at construction time.
- From online docs: per-operation pages at
https://api.fiber.ai/ai-docs/<operationId>.md describe every field with examples. Start with https://api.fiber.ai/llms.txt for routing.
- From MCP at runtime: call
get_endpoint_details_full("<operationId>") on the Core MCP to get the full current schema.
- Open-source examples:
https://github.com/fiber-ai/open-fiber has working code samples.
Async Support
The same Client instance supports async via the asyncio function variant:
import os
from fiberai import Client
from fiberai.api.search.company_search import asyncio as company_search_async
from fiberai.models.company_search_body import CompanySearchBody
client = Client(base_url="https://api.fiber.ai")
result = await company_search_async(
client=client,
body=CompanySearchBody(
api_key=os.environ["FIBER_API_KEY"],
search_params={},
page_size=25,
),
)
There is no separate AsyncClient class. Use the asyncio or asyncio_detailed function variants with the same Client.
Important Rules
- Always use environment variables for API keys — never hardcode secrets
api_key goes in the body model (POST) or query params (GET), not as a header- Body parameters must be constructed as typed model instances, not raw dicts
- The
Client supports both sync and async via context managers
- Error responses are typed unions — check the response type or use
raise_on_unexpected_status=True on the Client for automatic error raising (raises errors.UnexpectedStatus)
- Check https://api.fiber.ai/docs/ for current API schemas — parameters and response shapes may change between versions
Detailed Examples
See the references/ folder for:
- Full client setup with error handling
- Search, enrichment, and async patterns
For AI agents: machine-readable docs
