goodmem 0.1.9

GoodMem's Convenient SDK for Python

GoodMem Python SDK

An OpenAI-style API for Goodmem with auto-inference of model parameters, streaming retrieval, async support, and auto-pagination. The SDK stays in sync with the server's OpenAPI spec — except for hand-written convenience methods (model registry auto-inference, flat post-processor kwargs, etc.) that wrap the generated layer. Please see ../docs/sdk_gen.md for the SDK generation details and ../docs/doc_gen.md for the doc generation details.

Installation

pip install goodmem

Usage

The programmatic way

from goodmem import Goodmem

client = Goodmem(
    base_url="http://localhost:8080",
    api_key="gm_..."
)

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}")

The Skill way

# One-time setup — copy the skill into your Claude Code skills directory
cp -r $(python -c "import goodmem; print(goodmem.__path__[0])")/skills ~/.claude/skills/goodmem

Once installed, Claude Code automatically loads the GoodMem SDK reference when you ask it to create embedders, store memories, run retrieval, etc.

Project structure

clients/v2/python/
├── goodmem/                        # Installable package
│   ├── client.py                   # Goodmem / AsyncGoodmem entry points
│   ├── errors.py                   # Typed exception hierarchy (APIError, NotFoundError, …)
│   ├── pagination.py               # Paginator / AsyncPaginator (auto next_token)
│   ├── streaming.py                # RetrieveMemoryStream (SSE → typed events)
│   ├── _base.py                    # CRUDNamespace / AsyncCRUDNamespace base classes
│   ├── _schema_bridge.py           # Generated constants (credential builders, enums)
│   ├── _registries.py              # Generated model-registry loader
│   ├── api/                        # Tier-3: hand-written convenience overrides
│   │   ├── embedders.py            #   5 override classes (embedders, llms, rerankers,
│   │   ├── llms.py                 #     spaces, memories) call apply_convenience_transforms
│   │   ├── rerankers.py            #     then super()
│   │   ├── spaces.py               #   5 thin pass-throughs (apikeys, users, ocr,
│   │   ├── memories.py             #     system, admin) — just `pass`
│   │   └── ...
│   ├── registries/                 # Declarative config (no code generation)
│   │   ├── convenience.py          #   Source of truth for transforms, param descriptions,
│   │   │                           #     defaults, maps_to/replaces semantics
│   │   ├── embedders.json          #   29 embedder models (provider, dims, modalities)
│   │   ├── llms.json               #   34 LLM models
│   │   └── rerankers.json          #   16 reranker models
│   └── _generated/                 # Auto-generated — do not edit
│       ├── models/                 #   Tier-1: pydantic v2 models (openapi-generator)
│       └── api/                    #   Tier-2: structural API bases (_gen/ output)
│           ├── embedders_base.py
│           ├── memories_base.py
│           └── ...
├── _gen/                       # Tier-2 code generator (reads OpenAPI spec → emits bases)
│   ├── emitters.py                 #   Method emitters (input×output shape composition)
│   ├── spec.py                     #   Route classification, streaming/merge configs
│   ├── bridge.py                   #   Generates _schema_bridge.py + _registries.py
│   ├── docstrings.py               #   Docstring builder from OpenAPI summaries/params
│   ├── metadata.py                 #   REST metadata extraction (paths, models, params)
│   └── validate.py                 #   9 structural validation checks
├── tests/                          # Unit + validation tests (run without server)
│   ├── test_convenience_registry.py
│   ├── test_validate.py            #   Wraps _gen/validate.py checks as pytest
│   ├── test_*.py
│   └── integration/                # Live server tests (auto-skip without env vars)
│       ├── conftest.py
│       ├── test_full_flow.py
│       └── ...
├── docs/                           # Internal dev docs
│   ├── sdk_gen.md                  #   Architecture + regeneration guide
│   ├── doc_gen.md                  #   Doc generation + docstring authoring
│   └── ...
├── _doc_gen/                       # Doc generation scripts (called via doc_gen.sh)
│   ├── generate_ref.py             #   SDK ref doc generator (griffe + convenience registry → MDX)
│   ├── generate_skill.py           #   Claude Code skill reference generator → goodmem/skills/
│   └── sync_rag_snippets.py        #   Sync examples/basic_rag.py snippets → goodmem-docs tutorial
├── examples/
│   └── basic_rag.py                # End-to-end RAG example (source for basic RAG tutorial in goodmem-docs)
├── vibe/                           # Claude Code non-interactive prompts
│   ├── audit_ref_doc.sh            #   Audit generated MDX docs for accuracy
│   ├── audit_ref_doc.md            #   Audit prompt + known issues list
│   ├── sdk2rest.sh                 #   Generate REST equivalents from SDK test snippets
│   └── sdk2rest.md                 #   SDK→REST conversion prompt
├── sdk_gen.sh                      # SDK generation: compile server → Tier-1 → Tier-2 → tests
├── doc_gen.sh                      # Doc generation: ref pages + tutorial sync + skill ref + tests
├── goodmem-reset.sh                # Delete all resources on a GoodMem server (test cleanup)
└── pyproject.toml

Development commands

# SDK generation (compile server → Tier-1 models → Tier-2 bases → unit & integration tests)
./sdk_gen.sh

# Documentation generation (ref pages + basic-rag tutorial sync + skill ref + tests)
./doc_gen.sh                   # ref pages + basic-rag tutorial sync + skill ref + tests
./doc_gen.sh --ref-only        # SDK reference pages only
./doc_gen.sh --sync-only       # sync only basic-rag tutorial code snippets

# Vibe auditing (Claude Code non-interactive)
./vibe/audit_ref_doc.sh        # audit generated MDX docs for accuracy
./vibe/sdk2rest.sh             # generate REST equivalents from SDK test snippets

In sdk_gen.sh, integration test is only activated when environment variables GOODMEM_BASE_URL and GOODMEM_API_KEY are set.

Warning: Before the integration tests, sdk_gen.sh, vibe/sdk2rest.sh, and CI all run goodmem-reset.sh to delete ALL resources on the target server. Never point GOODMEM_BASE_URL at a production server. We have a dedicated test server on Fly.io for this purpose. See ci/README.md for more details.

See docs/sdk_gen.md for the full regeneration workflow and docs/doc_gen.md for the doc and auditing pipelines.

Documentation

• SDK generation guide — architecture, regeneration, testing, tier details
• Doc generation guide — how ref docs are generated, docstring authoring
• CI pipeline — GitHub Actions workflow, secrets, Fly.io test server setup
• API reference — published SDK docs

TODO

1. Add gemini-embedding-001 to embedder registry once backend adds OPENAI_COMPATIBLE to ProviderType.
2. Add Anthropic, Google, Cohere, and Mistral LLMs to registry once backend adds matching LLMProviderType values.
3. Automate model registry updates to add new models as they are released.
4. Generate SDK to an intermediate representation, then map that to cURL, HTTPie, HTTPX, Go, JavaScript, etc.