honeyhive-goodmem 0.1.0

GoodMem integration for HoneyHive.

honeyhive-goodmem

GoodMem integration for HoneyHive.

This package wraps the GoodMem REST API with a Python client whose every operation is decorated with HoneyHive's @trace, so calls show up as spans in the active HoneyHive session. The operation shapes mirror the reference GoodMem integration so behavior is identical across frameworks.

Installation

pip install honeyhive-goodmem

For local development:

pip install -e .

Quickstart

from honeyhive import HoneyHiveTracer
from honeyhive_goodmem import GoodMemClient, GoodMemConfig

HoneyHiveTracer.init(api_key="hh_...", project="my-project")

client = GoodMemClient(
    GoodMemConfig(
        base_url="https://localhost:8080",
        api_key="gm_xxxxxxxxxxxxxxxxxxxxxxxx",
        verify_ssl=False,  # self-signed local server
    )
)

embedders = client.list_embedders()
embedder_id = embedders["embedders"][0]["embedder_id"]

space = client.create_space(name="quickstart", embedder_id=embedder_id)
space_id = space["space_id"]

client.create_memory(
    space_id=space_id,
    text_content="The capital of France is Paris.",
)

results = client.retrieve_memories(
    query="What is the capital of France?",
    space_ids=[space_id],
    max_results=3,
)
print(results)

Available operations

GoodMemClient exposes the following 11 traced methods, matching the reference GoodMem integration:

Method	Description
list_embedders	List embedder models available on the server
list_spaces	List all spaces accessible to the API key
get_space	Fetch a space by ID
create_space	Create a space (idempotent by name)
update_space	Update a space's name / labels / public-read flag
delete_space	Delete a space
create_memory	Store text or a file as a memory
list_memories	List memories in a space
retrieve_memories	Semantic retrieval, with optional reranker / LLM
get_memory	Fetch a memory by ID (with original content)
delete_memory	Delete a memory

Retrieval options

GoodMemClient.retrieve_memories accepts the GoodMem post-processor parameters:

Parameter	Type	Description
reranker_id	UUID	Reranker model to improve result ordering
llm_id	UUID	LLM used to generate a contextual abstract reply
relevance_threshold	0–1	Minimum score for including a result
llm_temperature	0–2	Creativity for the LLM post-processor
max_results	int	Cap on returned chunks
chronological_resort	bool	Reorder results by memory creation time

License

MIT