odyn 0.5.1

Odyn is a python client for Business Central Web Services API.

Odyn

Async-first Python client for Microsoft Dynamics 365 Business Central on-premises OData Web Services. Returns native Polars DataFrames with built-in caching, rate limiting, retry logic, and a fluent query builder.

Scope: Odyn targets Business Central OData Web Services only. It is not designed for the standard Business Central API v2.0 endpoints.

Why Odyn

Integrating with Business Central Web Services means dealing with NTLM/Basic auth, manual OData query strings, pagination loops, and converting JSON blobs into something useful. Odyn handles all of that behind a single async call that returns a Polars DataFrame.

• Async + sync — built on httpx; sync wrapper included for scripts and notebooks
• Polars DataFrames — columnar, fast, zero-copy where possible
• Fluent query builder — type-safe filters via the F singleton, method chaining, raw escape hatch
• Parquet caching — local file cache with TTL, SHA256 keys, hit/miss stats
• Resilience — exponential backoff with jitter, rate limiting via token bucket, concurrency control
• Batch operations — concurrent chunked lookups with progress callbacks
• Delta sync — get_since() / get_before() for incremental loads
• Streaming — page-by-page async iteration for large datasets
• Hooks — plug in request/response observers for logging, metrics, or tracing

Requirements

• Python 3.12+
• httpx >= 0.28
• polars >= 1.36
• aiolimiter >= 1.2

Installation

pip install odyn

Or with uv:

uv add odyn

Quick Start

Async

import asyncio
from odyn import BCWebServiceClient, BasicAuth
from odyn.query import ODataQuery, F

async def main():
    async with BCWebServiceClient.create(
        server="https://bc-server:7048",
        instance="BC210",
        auth=BasicAuth("DOMAIN\\user", "password"),
        company="CRONUS International Ltd.",
    ) as client:
        # All customers as a Polars DataFrame
        customers = await client.get("customers")

        # Filtered query
        query = (
            ODataQuery()
            .select("No", "Name", "Balance_LCY")
            .filter(F.Balance_LCY > 1000)
            .filter(F.Blocked == False)
            .order_by("Balance_LCY desc")
            .top(50)
        )
        top_customers = await client.get("customers", query=query)

        # Single record by key
        customer = await client.get_by_key("customers", "C00010")

asyncio.run(main())

Sync

from odyn import BCWebServiceClientSync, BasicAuth

with BCWebServiceClientSync.create(
    server="https://bc-server:7048",
    instance="BC210",
    auth=BasicAuth("user", "password"),
    company="CRONUS",
) as client:
    df = client.get("customers")
    print(df)

API Key Authentication

from odyn import BCWebServiceClient, APIKeyAuth

auth = APIKeyAuth("my-secret-api-key")

# Or with a Bearer token in the Authorization header
auth = APIKeyAuth("my-key", header_name="Authorization", prefix="Bearer")

Query Builder

from odyn.query import ODataQuery, F

query = (
    ODataQuery()
    .select("No", "Name", "Balance_LCY")
    .filter(F.Status == "Active")          # eq
    .filter(F.Balance_LCY > 1000)          # gt
    .filter(F.Type.is_in(["Customer", "Vendor"]))  # IN via OR chain
    .expand("SalesLines")
    .order_by("Name asc")
    .top(100)
    .skip(50)
)

# Raw OData for functions not covered by the DSL
query = ODataQuery().filter_raw("contains(Name, 'Corp')")

# Combine expressions with & and |
expr = (F.Status == "Active") & (F.Balance_LCY > 0)
expr = (F.City == "London") | (F.City == "Berlin")

Caching

async with BCWebServiceClient.create(
    server="https://bc-server:7048",
    instance="BC210",
    auth=BasicAuth("user", "pass"),
    cache_dir="~/.cache/odyn",
    cache_ttl=3600,  # 1 hour
) as client:
    df = await client.get("customers")                   # cache miss — fetches from API
    df = await client.get("customers")                   # cache hit — reads Parquet
    df = await client.get("customers", use_cache=False)  # force refresh

    client.cleanup_cache()  # remove expired entries

Batch Operations

customer_ids = ["C001", "C002", ..., "C500"]

df = await client.get_batch(
    "customers",
    field="No",
    values=customer_ids,
    batch_size=50,
    select=["No", "Name", "Balance_LCY"],
)

Delta Sync

from datetime import datetime, timedelta, timezone

since = (datetime.now(timezone.utc) - timedelta(hours=1)).isoformat()
updated = await client.get_since("customers", since)

Streaming

async for page in client.get_stream("largeDataset"):
    process(page)  # each page is a Polars DataFrame

Documentation

Full documentation lives in docs/:

Guide	Description
Getting Started	Installation, prerequisites, first connection
Client	Creating and configuring the client
Authentication	BasicAuth, APIKeyAuth, custom headers
Query Builder	Filters, select, expand, order, the F singleton
Caching	ParquetCache, TTL, cache management
Sync Client	Synchronous wrapper for non-async contexts
Advanced	Hooks, streaming, batch ops, delta sync, concurrency
Exceptions	Exception hierarchy and error handling
API Reference	Every class, method, parameter, and type
Troubleshooting	Common issues and solutions
LLM Context	Single-file complete reference for AI assistants

License

MIT