💡 Examples

Initialize LightMem

import os
from datetime import datetime
from lightmem.memory.lightmem import LightMemory


LOGS_ROOT = "./logs"
RUN_TIMESTAMP = datetime.now().strftime("%Y%m%d_%H%M%S")
RUN_LOG_DIR = os.path.join(LOGS_ROOT, RUN_TIMESTAMP)
os.makedirs(RUN_LOG_DIR, exist_ok=True)

API_KEY='your_api_key'
API_BASE_URL='your_api_base_url'
LLM_MODEL='your_model_name' # such as 'gpt-4o-mini' (API) or 'gemma3:latest' (Local Ollama) ...
EMBEDDING_MODEL_PATH='/your/path/to/models/all-MiniLM-L6-v2'
LLMLINGUA_MODEL_PATH='/your/path/to/models/llmlingua-2-bert-base-multilingual-cased-meetingbank'

config_dict = {
    "pre_compress": True,
    "pre_compressor": {
        "model_name": "llmlingua-2",
        "configs": {
            "llmlingua_config": {
                "model_name": LLMLINGUA_MODEL_PATH,
                "device_map": "cuda",
                "use_llmlingua2": True,
            },
        }
    },
    "topic_segment": True,
    "precomp_topic_shared": True,
    "topic_segmenter": {
        "model_name": "llmlingua-2",
    },
    "messages_use": "user_only",
    "metadata_generate": True,
    "text_summary": True,
    "memory_manager": {
        "model_name": 'xxx', # such as 'openai' or 'ollama' ...
        "configs": {
            "model": LLM_MODEL,
            "api_key": API_KEY,
            "max_tokens": 16000,
            "xxx_base_url": API_BASE_URL # API model specific, such as 'openai_base_url' or 'deepseek_base_url' ...
        }
    },
    "extract_threshold": 0.1,
    "index_strategy": "embedding",
    "text_embedder": {
        "model_name": "huggingface",
        "configs": {
            "model": EMBEDDING_MODEL_PATH,
            "embedding_dims": 384,
            "model_kwargs": {"device": "cuda"},
        },
    },
    "retrieve_strategy": "embedding",
    "embedding_retriever": {
        "model_name": "qdrant",
        "configs": {
            "collection_name": "my_long_term_chat",
            "embedding_model_dims": 384,
            "path": "./my_long_term_chat", 
        }
    },
    "summary_retriever": {
        "model_name": "qdrant",
        "configs": {
            "collection_name": "my_chat_summaries",
            "embedding_model_dims": 384,
            "path": "./my_chat_summaries",
        }
    },
    "update": "offline",
    "logging": {
        "level": "DEBUG",
        "file_enabled": True,
        "log_dir": RUN_LOG_DIR,
    }
}

lightmem = LightMemory.from_config(config_dict)

Add Memory

session = {
"timestamp": "2025-01-10",
"turns": [
    [
        {"role": "user", "content": "My favorite ice cream flavor is pistachio, and my dog's name is Rex."}, 
        {"role": "assistant", "content": "Got it. Pistachio is a great choice."}], 
    ]
}


for turn_messages in session["turns"]:
    timestamp = session["timestamp"]
    for msg in turn_messages:
        msg["time_stamp"] = timestamp
        
    store_result = lightmem.add_memory(
        messages=turn_messages,
        force_segment=True,
        force_extract=True
    )

Offline Update

lightmem.construct_update_queue_all_entries()
lightmem.offline_update_all_entries(score_threshold=0.8)

Generate summaries

summary_result = lightmem.summarize()

Retrieve Memory

question = "What is the name of my dog?"
related_memories = lightmem.retrieve(question, limit=5)
print(related_memories)

MCP Server

LightMem also supports the Model Context Protocol (MCP) server:

# Running at Root Directory
cd LightMem

# Environment
pip install '.[mcp]'

# MCP Inspector [Optional]
npx @modelcontextprotocol/inspector python mcp/server.py

# Start API by HTTP (http://127.0.0.1:8000/mcp)
fastmcp run mcp/server.py:mcp --transport http --port 8000

The MCP config json file of your local client may looks like:

{
  "yourMcpServers": {
    "LightMem": {
      "url": "http://127.0.0.1:8000/mcp",
      "otherParameters": "..."
    }
  }
}

📁 Experimental Results

For transparency and reproducibility, we have shared the results of our experiments on Google Drive. This includes model outputs, evaluation logs, and predictions used in our study.

🔗 Access the data here: Google Drive - Experimental Results

Please feel free to download, explore, and use these resources for research or reference purposes.

LOCOMO:

Overview

backbone: gpt-4o-mini, judge model: gpt-4o-mini & qwen2.5-32b-instruct

Method	ACC(%) gpt-4o-mini	ACC(%) qwen2.5-32b-instruct	Memory-Con Tokens(k) Total	QA Tokens(k) total	Total(k)	Calls	Runtime(s) total
FullText	73.83	73.18	–	54,884.479	54,884.479	–	6,971
NaiveRAG	63.64	63.12	–	3,870.187	3,870.187	–	1,884
A-MEM	64.16	60.71	11,494.344	10,170.567	21,664.907	11,754	67,084
MemoryOS(eval)	58.25	61.04	2,870.036	7,649.343	10,519.379	5,534	26,129
MemoryOS(pypi)	54.87	55.91	5,264.801	6,126.111	11,390.004	10,160	37,912
Mem0	36.49	37.01	24,304.872	1,488.618	25,793.490	19,070	120,175
Mem0(api)	61.69	61.69	68,347.720	4,169.909	72,517.629	6,022	10,445
Mem0-g(api)	60.32	59.48	69,684.818	4,389.147	74,073.965	6,022	10,926

backbone: qwen3-30b-a3b-instruct-2507, judge model: gpt-4o-mini & qwen2.5-32b-instruct

Method	ACC(%) gpt-4o-mini	ACC(%) qwen2.5-32b-instruct	Memory-Con Tokens(k) Total	QA Tokens(k) total	Total(k)	Calls	Runtime(s) total
FullText	74.87	74.35	–	60,873.076	60,873.076	–	10,555
NaiveRAG	66.95	64.68	–	4,271.052	4,271.052	–	1,252
A-MEM	56.10	54.81	16,267.997	17,340.881	33,608.878	11,754	69,339
MemoryOS(eval)	61.04	59.81	3,615.087	9,703.169	11,946.442	4,147	13,710
MemoryOS(pypi)	51.30	51.95	6,663.527	7,764.991	14,428.518	10,046	20,830
Mem0	43.31	43.25	17,994.035	1,765.570	19,759.605	16,145	46,500

Details

backbone: gpt-4o-mini, judge model: gpt-4o-mini & qwen2.5-32b-instruct

Method	Summary Tokens(k) In	Summary Tokens(k) Out	Update Tokens(k) In	Update Tokens(k) Out	QA Tokens(k) In	QA Tokens(k) Out	Runtime(s) mem-con	Runtime(s) qa
FullText	–	–	–	–	54,858.770	25.709	–	6,971
NaiveRAG	–	–	–	–	3,851.029	19.158	–	1,884
A-MEM	1,827.373	492.883	7,298.878	1,875.210	10,113.252	57.315	60,607	6,477
MemoryOS(eval)	1,109.849	333.970	780.807	645.410	7,638.539	10.804	24,220	1,909
MemoryOS(pypi)	1,007.729	294.601	3,037.509	924.962	6,116.239	9.872	33,325	4,587
Mem0	8,127.398	253.187	12,722.011	3,202.276	1,478.830	9.788	118,268	1,907
Mem0(api)	\	\	\	\	4,156.850	13.059	4,328	6,117
Mem0-g(api)	\	\	\	\	4,375.900	13.247	5,381	5,545

backbone: qwen3-30b-a3b-instruct-2507, judge model: gpt-4o-mini & qwen2.5-32b-instruct

Method	Summary Tokens(k) In	Summary Tokens(k) Out	Update Tokens(k) In	Update Tokens(k) Out	QA Tokens(k) In	QA Tokens(k) Out	Runtime(s) mem-con	Runtime(s) qa
FullText	–	–	–	–	60,838.694	34.382	–	10,555
NaiveRAG	–	–	–	–	4,239.030	32.022	–	1,252
A-MEM	1,582.942	608.507	9,241.928	4,835.070	17,528.876	82.005	55,439	13,900
MemoryOS(eval)	1,222.139	531.157	1,044.307	817.484	9,679.996	23.173	12,697	1,012
MemoryOS(pypi)	2,288.533	516.024	2,422.693	1,436.277	7,743.391	21.600	19,822	1,007
Mem0	8,270.874	186.354	7,638.827	1,897.980	1,739.246	26.324	45,407	1,093

Performance metrics

backbone: gpt-4o-mini, judge model: gpt-4o-mini

Method	Overall ↑	Multi	Open	Single	Temp
FullText	73.83	68.79	56.25	86.56	50.16
NaiveRAG	63.64	55.32	47.92	70.99	56.39
A-MEM	64.16	56.03	31.25	72.06	60.44
MemoryOS(eval)	58.25	56.74	45.83	67.06	40.19
MemoryOS(pypi)	54.87	52.13	43.75	63.97	36.76
Mem0	36.49	30.85	34.38	38.41	37.07
Mem0(api)	61.69	56.38	43.75	66.47	59.19
Mem0-g(api)	60.32	54.26	39.58	65.99	57.01

backbone: gpt-4o-mini, judge model: qwen2.5-32b-instruct

Method	Overall ↑	Multi	Open	Single	Temp
FullText	73.18	68.09	54.17	86.21	49.22
NaiveRAG	63.12	53.55	50.00	71.34	53.89
A-MEM	60.71	53.55	32.29	69.08	53.58
MemoryOS(eval)	61.04	64.18	40.62	70.15	40.50
MemoryOS(pypi)	55.91	52.48	41.67	66.35	35.83
Mem0	37.01	31.91	37.50	38.53	37.38
Mem0(api)	61.69	54.26	46.88	67.66	57.01
Mem0-g(api)	59.48	55.32	42.71	65.04	53.58

backbone: qwen3-30b-a3b-instruct-2507, judge model: gpt-4o-mini

Method	Overall ↑	Multi	Open	Single	Temp
FullText	74.87	69.86	57.29	87.40	51.71
NaiveRAG	66.95	62.41	57.29	76.81	47.98
A-MEM	56.10	57.45	43.75	67.90	27.73
MemoryOS(eval)	61.04	62.77	51.04	72.29	33.02
MemoryOS(pypi)	51.30	52.48	40.62	61.59	26.48
Mem0	43.31	42.91	46.88	46.37	34.58
Mem0(api)	61.69	54.26	46.88	67.66	57.01
Mem0-g(api)	59.48	55.32	42.71	65.04	53.58

backbone: qwen3-30b-a3b-instruct-2507, judge model: qwen2.5-32b-instruct

Method	Overall ↑	Multi	Open	Single	Temp
FullText	74.35	68.09	63.54	86.33	51.71
NaiveRAG	64.68	60.28	52.08	75.62	43.61
A-MEM	54.81	56.74	39.58	67.42	24.61
MemoryOS(eval)	59.81	63.12	48.96	70.51	32.09
MemoryOS(pypi)	51.95	55.67	39.58	61.47	27.41
Mem0	43.25	45.04	46.88	45.78	33.96
Mem0(api)	61.69	54.26	46.88	67.66	57.01
Mem0-g(api)	59.48	55.32	42.71	65.04	53.58

⚙️ Configuration

All behaviors of LightMem are controlled via the BaseMemoryConfigs configuration class. Users can customize aspects like pre-processing, memory extraction, retrieval strategy, and update mechanisms by providing a custom configuration.

Key Configuration Options (Usage)

Option	Default	Usage (allowed values and behavior)
pre_compress	False	True / False. If True, input messages are pre-compressed using the pre_compressor configuration before being stored. This reduces storage and indexing cost but may remove fine-grained details. If False, messages are stored without pre-compression.
pre_compressor	None	dict / object. Configuration for the pre-compression component (PreCompressorConfig) with fields like model_name (e.g., llmlingua-2, entropy_compress) and configs (model-specific parameters). Effective only when pre_compress=True.
topic_segment	False	True / False. Enables topic-based segmentation of long conversations. When True, long conversations are split into topic segments and each segment can be indexed/stored independently (requires topic_segmenter). When False, messages are stored sequentially.
precomp_topic_shared	False	True / False. If True, pre-compression and topic segmentation can share intermediate results to avoid redundant processing. May improve performance but requires careful configuration to avoid cross-topic leakage.
topic_segmenter	None	dict / object. Configuration for topic segmentation (TopicSegmenterConfig), including model_name and configs (segment length, overlap, etc.). Used when topic_segment=True.
messages_use	'user_only'	'user_only' / 'assistant_only' / 'hybrid'. Controls which messages are used to generate metadata and summaries: user_only uses user inputs, assistant_only uses assistant responses, hybrid uses both. Choosing hybrid increases processing but yields richer context.
metadata_generate	True	True / False. If True, metadata such as keywords and entities are extracted and stored to support attribute-based and filtered retrieval. If False, no metadata extraction occurs.
text_summary	True	True / False. If True, a text summary is generated and stored alongside the original text (reduces retrieval cost and speeds review). If False, only the original text is stored. Summary quality depends on memory_manager.
memory_manager	MemoryManagerConfig()	dict / object. Controls the model used to generate summaries and metadata (MemoryManagerConfig), e.g., model_name (openai, ollama, etc.) and configs. Changing this affects summary style, length, and cost.
extract_threshold	0.5	float (0.0 - 1.0). Threshold used to decide whether content is important enough to be extracted as metadata or highlight. Higher values (e.g., 0.8) mean more conservative extraction; lower values (e.g., 0.2) extract more items (may increase noise).
index_strategy	None	'embedding' / 'context' / 'hybrid' / None. Determines how memories are indexed: 'embedding' uses vector-based indexing (requires embedders/retriever) for semantic search; 'context' uses text-based/contextual retrieval (requires context_retriever) for keyword/document similarity; and 'hybrid' combines context filtering and vector reranking for robustness and higher accuracy.
text_embedder	None	dict / object. Configuration for text embedding model (TextEmbedderConfig) with model_name (e.g., huggingface) and configs (batch size, device, embedding dim). Required when index_strategy or retrieve_strategy includes 'embedding'.
multimodal_embedder	None	dict / object. Configuration for multimodal/image embedder (MMEmbedderConfig). Used for non-text modalities.
history_db_path	os.path.join(lightmem_dir, "history.db")	str. Path to persist conversation history and lightweight state. Useful to restore state across restarts.
retrieve_strategy	'embedding'	'embedding' / 'context' / 'hybrid'. Strategy used at query time to fetch relevant memories. Pick based on data and query type: semantic queries -> 'embedding'; keyword/structured queries -> 'context'; mixed -> 'hybrid'.
context_retriever	None	dict / object. Configuration for context-based retriever (ContextRetrieverConfig), e.g., model_name='BM25' and configs like top_k. Used when retrieve_strategy includes 'context'.
embedding_retriever	None	dict / object. Vector store configuration (EmbeddingRetrieverConfig), e.g., model_name='qdrant' and connection/index params. Used when retrieve_strategy includes 'embedding'.
summary_retriever	None	dict / object. Configuration for summary-specific vector store (EmbeddingRetrieverConfig). When configured, summaries are stored in a separate collection for hierarchical retrieval. Used in StructMem mode to store and retrieve session/topic summaries independently from detailed memories.
update	'offline'	'online' / 'offline'. 'offline': batch or scheduled updates to save cost and aggregate changes — this is the fully supported mode with complete functionality. 'online': reserved for future development (currently a no-op placeholder; memory will not be persisted when this mode is set).
kv_cache	False	True / False. If True, attempt to precompute and persist model KV caches to accelerate repeated LLM calls (requires support from the LLM runtime and may increase storage). Uses kv_cache_path to store cache.
kv_cache_path	os.path.join(lightmem_dir, "kv_cache.db")	str. File path for KV cache storage when kv_cache=True.
graph_mem	False	True / False. When True, some memories will be organized as a graph (nodes and relationships) to support complex relation queries and reasoning. Requires additional graph processing/storage.
extraction_mode	'flat'	'flat' / 'event'. Memory extraction mode: 'flat' extracts factual entries as independent units suitable for general knowledge retention; 'event' extracts event-level structures with both factual and relational components, preserving temporal bindings and causal relationships. Use 'event' for narrative-heavy or time-sensitive scenarios.
version	'v1.1'	str. Configuration/API version. Only change if you know compatibility implications.
logging	'None'	dict / object. Configuration for logging enabled.

🏆 Contributors

JizhanFang

Xinle-Deng

Xubqpanda

HaomingX

453251

James-TYQ

evy568

Norah-Feathertail

TongjiCst

We welcome contributions from the community! If you'd like to contribute, please fork the repository and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

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