GoodMem Memory Connector API for agentic tasks and workflows

[MuaazWahid]

We built a memory connector for Semantic Kernel that can simplify the process of setting up memory management for AI agents in Semantic Kernel. As of now, Semantic Kernel developers who want to setup persistent memory for agentic tasks have to manage embedding services, models, and dimensions across collections.

GoodMem is a lightning-fast memory API for AI agents to easily read & write persistent memory for agents (embedding pipeline included).

GitHub feature request: #13626

Codebase: https://github.com/PAIR-Systems-Inc/goodmem-semantic-kernel

without GoodMem

# 1. Pick, configure, and maintain an embedding service
embedding_service = OpenAITextEmbedding(
    ai_model_id="text-embedding-3-small",
    api_key=OPENAI_API_KEY
)

# 2. Stand up a vector store and wire in the embedder
store = InMemoryVectorStore(embedding_service=embedding_service)
collection = store.get_collection("agent-memory", record_type=Memory)

# 3. Manually ensure embedding dimensions match your model
# 4. Begin to write agent logic

with GoodMem

async with GoodMemCollection(record_type=Memory, collection_name="agent-memory") as collection:
    await collection.upsert(memory_list)
    agent = ChatCompletionAgent(
        name="MemoryAgent",
        service=OpenAIChatCompletion(ai_model_id=openai_model),
        instructions=("Always search memory before answering factual questions."),
        function_choice_behavior=FunctionChoiceBehavior.Auto(),
        plugins=[collection.as_plugin(name="memory")],
    )

[aniruddhaadak80]

From my point of view, the value here is obvious because memory wiring is one of the first things that turns simple agent examples into infrastructure work.

The tradeoff I would want to understand is how much configuration is intentionally hidden. Abstracting embeddings and dimensions is great for adoption, but teams eventually need to reason about migration, re indexing, model changes, and debugging retrieval quality.

If the connector surfaces collection metadata and a clear story for re embedding over time, it feels much stronger as a long term integration instead of only an onboarding shortcut.

[MuaazWahid]

hi aniruddha, thanks for the feedback.
most of what you're asking about is already present in GoodMem. this plugin is a thin wrapper around our service; the full GoodMem API exposes more powerful features and configurations.

on metadata: GoodMem surfaces collection ("Space") metadata including labels for organization and filtering, space_embedders with default_retrieval_weight, standard audit fields (public_read, owner_id, created_at/updated_at), and default_chunking_config per space.

on re-embedding: memories are immutable once stored. dimensionality, model_identifier, and distribution_type cannot be changed while an embedder is referenced by a space. there is no automated re-embedding workflow today; that would be handled manually. we're planning to add a copy operation to move data between spaces as a cleaner path for re-indexing under a new embedder.

on retrieval debugging: this is manual at the moment and something we're looking to improve, it's on our radar as a future feature.

GoodMem is designed as a long-term memory infrastructure for AI agents, agnostic to embedders, models, and vendors. Happy to answer any further questions.