Config

Reference for shared and provider-specific reranker configuration options in Mem0, including top_k and API key settings.

Common Configuration Parameters

All rerankers share these common configuration parameters:

Parameter	Description	Type	Default
provider	Reranker provider name	str	Required
top_k	Maximum number of results to return after reranking	int	None
api_key	API key for the reranker service	str	None

Provider-Specific Configuration

Zero Entropy

Parameter	Description	Type	Default
model	Model to use: zerank-1 or zerank-1-small	str	"zerank-1"
api_key	Zero Entropy API key	str	None

Cohere

Parameter	Description	Type	Default
model	Cohere rerank model	str	"rerank-v3.5"
api_key	Cohere API key	str	None
return_documents	Whether to return document texts in response	bool	False
max_chunks_per_doc	Maximum chunks per document	int	None

Sentence Transformer

Parameter	Description	Type	Default
model	HuggingFace cross-encoder model name	str	"cross-encoder/ms-marco-MiniLM-L-6-v2"
device	Device to run model on (cpu, cuda, etc.)	str	None
batch_size	Batch size for processing	int	32
show_progress_bar	Show progress during processing	bool	False

Hugging Face

Parameter	Description	Type	Default
model	HuggingFace reranker model name	str	"BAAI/bge-reranker-large"
api_key	HuggingFace API token	str	None
device	Device to run model on (cpu, cuda, etc.)	str	None

LLM-based

Parameter	Description	Type	Default
model	LLM model to use for scoring	str	"gpt-4o-mini"
provider	LLM provider (openai, anthropic, etc.)	str	"openai"
api_key	API key for LLM provider	str	None
temperature	Temperature for LLM generation	float	0.0
max_tokens	Maximum tokens for LLM response	int	100
scoring_prompt	Custom prompt template for scoring	str	Default scoring prompt

LLM Reranker

Parameter	Description	Type	Default
llm.provider	LLM provider for reranking	str	Required
llm.config	LLM configuration object	dict	Required
top_n	Number of results to return	int	None

Environment Variables

You can set API keys using environment variables:

• ZERO_ENTROPY_API_KEY - Zero Entropy API key
• COHERE_API_KEY - Cohere API key
• HUGGINGFACE_API_KEY - HuggingFace API token
• OPENAI_API_KEY - OpenAI API key (for LLM-based reranker)
• ANTHROPIC_API_KEY - Anthropic API key (for LLM-based reranker)

Basic Configuration Example

Python

config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "my_memories",
            "path": "./chroma_db"
        }
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-5-mini"
        }
    },
    "reranker": {
        "provider": "zero_entropy",
        "config": {
            "model": "zerank-1",
            "top_k": 5
        }
    }
}

TypeScript SDK

The self-hosted TypeScript SDK (mem0ai/oss) supports the same five providers. Config keys are camelCase (apiKey, topK, maxLength) and each provider’s SDK is a peer dependency you install per reranker.

Provider	Install	Default model	Key config fields
cohere	pnpm add cohere-ai	rerank-v3.5	apiKey, model, topK
zero_entropy	pnpm add zeroentropy	zerank-1	apiKey, model, topK
sentence_transformer	pnpm add @huggingface/transformers	Xenova/ms-marco-MiniLM-L-6-v2	model, device, maxLength, normalize, topK
huggingface	pnpm add @huggingface/transformers	Xenova/bge-reranker-base	model, device, maxLength, normalize, topK
llm_reranker	None (uses your LLM provider’s own SDK)	openai / gpt-4o-mini	provider, model, apiKey, llm (nested override), topK

import { Memory } from "mem0ai/oss";

const memory = new Memory({
  reranker: {
    provider: "zero_entropy",
    config: { apiKey: process.env.ZERO_ENTROPY_API_KEY, topK: 5 },
  },
});

The local cross-encoder providers (sentence_transformer, huggingface) run on Transformers.js and default to ONNX (Xenova/*) model mirrors, so Python default model strings must be swapped for their ONNX equivalents. batchSize and showProgressBar are accepted for parity with Python but are no-ops in the TypeScript runtime. See the reranker feature guide for full examples.