HoneyHive

Wrap the GoodMem REST API in a traced Python client so every GoodMem call surfaces as a HoneyHive span.

Stable· Python

Overview

GoodMem is a memory layer for AI agents that handles embedding, vector search, reranking, and LLM-powered answering server-side. The HoneyHive integration wraps the GoodMem REST API in a Python client whose every operation is decorated with HoneyHive's @trace, so each GoodMem call — creating spaces, storing memories, semantic retrieval — shows up as a span in the active HoneyHive session for tracing and observability.

Installation

pip install honeyhive-goodmem

The pip package is honeyhive-goodmem; the import name is honeyhive_goodmem.

Configuration

GoodMem connection settings are passed to the client through a GoodMemConfig object rather than environment variables:

Setting	Description
`base_url`	Base URL of your GoodMem server (no `/v1` suffix), e.g. `https://your-goodmem-server.example.com`.
`api_key`	GoodMem API key used to authenticate requests.
`verify_ssl`	Set to `false` to skip TLS verification for self-signed dev certs (default: `true`).

In addition, HoneyHive tracing is initialized separately via HoneyHiveTracer.init(), which requires your HoneyHive credentials:

Argument	Description
`api_key`	HoneyHive API key (e.g. `hh_...`).
`project`	HoneyHive project name that spans are reported under.

Quick start

from honeyhive import HoneyHiveTracer
from honeyhive_goodmem import GoodMemClient, GoodMemConfig

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

client = GoodMemClient(
    GoodMemConfig(
        base_url="https://your-goodmem-server.example.com",
        api_key="gm_xxxxxxxxxxxxxxxxxxxxxxxx",
    )
)

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)

Local development. The example above uses a TLS-verified production URL. If you are running GoodMem locally with a self-signed certificate, use https://localhost:8080 and disable TLS verification with the client's verify-SSL option (see the Configuration table above). Keep TLS verification enabled for any deployed server.

Retrieval post-processing

GoodMem's retrieval call accepts these optional post-processing parameters (each framework exposes them under its own naming convention):

Parameter	Range	Description
`reranker_id`	UUID	Reranker model that reorders matched chunks by relevance.
`llm_id`	UUID	LLM that generates a contextual answer (`abstractReply`) alongside the chunks.
`relevance_threshold`	0–1	Minimum relevance score for a result to be included.
`llm_temperature`	0–2	Sampling temperature for the LLM post-processor.
`max_results`	integer	Maximum number of results to return.
`chronological_resort`	boolean	Re-sort the final results by creation time instead of relevance.

Learn more

The full README, examples, and API reference are available on the PyPI package page.