CAMEL-AI

Expose GoodMem's RAG memory as a CAMEL BaseToolkit so a ChatAgent can store and recall documents.

Stable· Python

Overview

GoodMem is a memory layer for AI agents that handles embedding, vector search, reranking, and LLM-powered answering server-side. This integration packages the GoodMem API as a CAMEL BaseToolkit: drop GoodMemToolkit into a CAMEL ChatAgent and the agent can store, list, and semantically retrieve documents from a GoodMem space alongside its other tools.

Installation

pip install camel-goodmem

The pip package is camel-goodmem; the import name is camel_goodmem. The optional example runner needs OpenAI, installable with pip install camel-goodmem[examples].

Configuration

Variable	Description
`GOODMEM_BASE_URL`	Base URL of your GoodMem server (no `/v1` suffix), e.g. `https://your-goodmem-server.example.com`.
`GOODMEM_API_KEY`	API key sent as `X-API-Key` to authenticate requests.
`GOODMEM_VERIFY_SSL`	Set to `false` to skip TLS verification for self-signed dev certs (default: `true`).

When all three are set, GoodMemToolkit() can be constructed with no arguments.

Quick start

import os

os.environ["GOODMEM_BASE_URL"] = "https://your-goodmem-server.example.com"
os.environ["GOODMEM_API_KEY"] = "gm_xxxxxxxxxxxxxxxxxxxxxxxx"
os.environ["GOODMEM_VERIFY_SSL"] = "false"  # self-signed local server

from camel.agents import ChatAgent
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType

from camel_goodmem import GoodMemToolkit

toolkit = GoodMemToolkit()

embedder_id = toolkit.goodmem_list_embedders()[0]["embedderId"]
space_id = toolkit.goodmem_create_space(
    name="quickstart", embedder_id=embedder_id
)["spaceId"]

agent = ChatAgent(
    system_message=(
        f"You are an assistant whose long-term memory lives in GoodMem "
        f"space '{space_id}'. Store facts the user shares, and answer "
        "their questions from that space."
    ),
    model=ModelFactory.create(
        model_platform=ModelPlatformType.DEFAULT,
        model_type=ModelType.DEFAULT,
    ),
    tools=toolkit.get_tools(),
)

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.