goodmem 0.1.3

GoodMem's Convenient SDK for Python

GoodMem Python SDK II

A Python SDK designed to be easy to use and easy to maintain. OpenAI-style API with auto-inference of model parameters, streaming retrieval, async support, and auto-pagination.

To see how this SDK compares to the previous goodmem-client SDK, see Then vs Now.

Installation

pip install goodmem

Usage

from goodmem import GoodMemClient

client = GoodMemClient(
    base_url="http://localhost:8080",
    token="your-api-token"
)

embedder = client.embedders.create(
    display_name="OpenAI Embedder",
    model_identifier="text-embedding-3-large",
    api_key="sk-your-openai-key",
)

print(f"Created: {embedder.embedder_id}")

How It Works

The SDK has two layers:

• Generated layer (goodmem/_generated/models/) — Pydantic v2 models auto-generated from the server's OpenAPI spec via openapi-generator. These define request/response schemas.
• Convenience layer (goodmem/api/, goodmem/client.py, etc.) — Hand-written wrapper that adds model registry auto-inference, parameter forwarding, streaming, pagination, and a clean client interface. Users never directly import from _generated/.

The convenience layer transforms the raw generated SDK into something pleasant to use: client.embedders.create(model_identifier="...", api_key="...") instead of manually constructing nested request objects.

Documentation

• Build a basic RAG agent
• API reference
• Old vs new SDK
• Design and maintenance guide — for developers understanding the architecture and re-generation workflows

TODO

1. Add CI/CD pipeline to publish to PyPI. Integrate into ../build_all.sh and build_client.sh.
2. Add gemini-embedding-001 to embedder registry once backend adds OPENAI_COMPATIBLE to ProviderType.
3. Add Anthropic, Google, Cohere, and Mistral LLMs to registry once backend adds matching LLMProviderType values.