LangGraph

Expose GoodMem memory operations as LangGraph tools to give graphs and agents long-term semantic memory.

Stable· Python (3.10+)

Overview

GoodMem is a memory layer for AI agents that handles embedding, vector search, reranking, and LLM-powered answering server-side. The LangGraph integration exposes those operations — creating spaces, storing memories, and semantic retrieval — as LangGraph tools you can bind to any prebuilt agent, ToolNode, or custom graph to give it long-term semantic memory.

Installation

pip install langgraph-goodmem

The pip package is langgraph-goodmem; the import name is langgraph_goodmem. Requires Python 3.10+.

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 used to authenticate requests.
`GOODMEM_VERIFY_SSL`	Set to `false` to skip TLS verification for self-signed dev certs (default: `true`).

Each tool also accepts goodmem_base_url and goodmem_api_key constructor arguments if you prefer to configure them in code instead of the environment.

Quick start

from langgraph_goodmem import (
    GoodMemCreateSpace,
    GoodMemCreateMemory,
    GoodMemRetrieveMemories,
)
from langgraph.prebuilt import create_react_agent

goodmem_kwargs = {
    "goodmem_base_url": "https://your-goodmem-server.example.com",
    "goodmem_api_key": "your-api-key",
}

tools = [
    GoodMemCreateSpace(**goodmem_kwargs),
    GoodMemCreateMemory(**goodmem_kwargs),
    GoodMemRetrieveMemories(**goodmem_kwargs),
]

agent = create_react_agent(model="gpt-4o", tools=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.