Mem0

Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences. Use it with Respan to add memory to your AI product and route LLM calls through the Respan gateway for full observability over every memory add, search, and generation.

Set up Respan

Create an account at platform.respan.ai and grab an API key. For gateway, also add credits or a provider key.

Run npx @respan/cli setup to set up with your coding agent.

Gateway

Mem0 doesn’t expose its own SDK-level tracing instrumentor; route all calls through the Respan gateway to get full observability automatically.

Setup

Prerequisites

A Respan API key
A Mem0 API key

Install Mem0

$ pip install mem0ai

Set environment variables

$ export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
$ export MEM0_API_KEY="YOUR_MEM0_API_KEY"

Add memory with Mem0 SDK

Configure Mem0’s LLM provider to use the Respan gateway.

1 import os
2 from mem0 import Memory
3 
4 config = {
5     "llm": {
6         "provider": "openai",
7         "config": {
8             "model": "gpt-4.1-nano",
9             "temperature": 0.0,
10             "api_key": os.environ["RESPAN_API_KEY"],
11             "openai_base_url": "https://api.respan.ai/api/",
12         },
13     }
14 }
15 
16 m = Memory.from_config(config_dict=config)
17 
18 result = m.add(
19     "I like to take long walks on weekends.",
20     user_id="alice",
21     metadata={"category": "hobbies"},
22 )
23 print(result)

Add memory with the OpenAI SDK

Use the OpenAI SDK pointed at the Respan gateway and pass mem0_params via extra_body to add or search memories alongside the LLM call.

1 import os
2 from openai import OpenAI
3 
4 client = OpenAI(
5     api_key=os.environ["RESPAN_API_KEY"],
6     base_url="https://api.respan.ai/api/",
7 )
8 
9 messages = [
10     {"role": "system", "content": "You are a helpful assistant."},
11     {"role": "user", "content": "Context: I like eating carrots, and I like to play basketball."},
12 ]
13 
14 response = client.chat.completions.create(
15     model="gpt-4.1-nano",
16     messages=messages,
17     extra_body={
18         "mem0_params": {
19             "user_id": "user_1",
20             "org_id": "org_1",
21             "api_key": os.environ["MEM0_API_KEY"],
22             "add_memories": {"messages": messages},
23         }
24     },
25 )
26 print(response.choices[0].message.content)

Search memories

Pass search_memories to retrieve relevant memories before generation.

1 response = client.chat.completions.create(
2     model="gpt-4.1-nano",
3     messages=[
4         {"role": "system", "content": "You are a helpful assistant."},
5         {"role": "user", "content": "Based on {{mem0_search_memories_response}}, what is the user's favorite food?"},
6     ],
7     extra_body={
8         "mem0_params": {
9             "user_id": "user_1",
10             "org_id": "org_1",
11             "api_key": os.environ["MEM0_API_KEY"],
12             "search_memories": {
13                 "query": "What is the user's favorite food?",
14                 "top_k": 1,
15                 "fields": ["text"],
16                 "rerank": True,
17                 "keyword_search": True,
18                 "filter_memories": True,
19             },
20         }
21     },
22 )

Use the Respan SDK for type-safe params

$ pip install respan-sdk

1 import os
2 from openai import OpenAI
3 from respan_sdk.respan_types.services_types.mem0_types import (
4     Mem0Params, AddMemoriesParams, SearchMemoriesParams,
5 )
6 
7 client = OpenAI(
8     api_key=os.environ["RESPAN_API_KEY"],
9     base_url="https://api.respan.ai/api/",
10 )
11 
12 memory_msgs = [
13     {"role": "system", "content": "You are a helpful assistant."},
14     {"role": "user", "content": "Context: I like eating carrots."},
15 ]
16 
17 response = client.chat.completions.create(
18     model="gpt-4.1-nano",
19     messages=memory_msgs,
20     extra_body={
21         "mem0_params": Mem0Params(
22             user_id="user_1",
23             org_id="org_1",
24             api_key=os.environ["MEM0_API_KEY"],
25             add_memories=AddMemoriesParams(messages=memory_msgs),
26         ).model_dump(exclude_none=True),
27     },
28 )

Set up Respan

Create an account at platform.respan.ai and grab an API key. For gateway, also add credits or a provider key.

Run npx @respan/cli setup to set up with your coding agent.

Gateway

Mem0 doesn’t expose its own SDK-level tracing instrumentor; route all calls through the Respan gateway to get full observability automatically.

Setup

Prerequisites

A Respan API key
A Mem0 API key

Install Mem0

$ pip install mem0ai

Set environment variables

$ export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
$ export MEM0_API_KEY="YOUR_MEM0_API_KEY"

Add memory with Mem0 SDK

Configure Mem0’s LLM provider to use the Respan gateway.

1 import os
2 from mem0 import Memory
3 
4 config = {
5     "llm": {
6         "provider": "openai",
7         "config": {
8             "model": "gpt-4.1-nano",
9             "temperature": 0.0,
10             "api_key": os.environ["RESPAN_API_KEY"],
11             "openai_base_url": "https://api.respan.ai/api/",
12         },
13     }
14 }
15 
16 m = Memory.from_config(config_dict=config)
17 
18 result = m.add(
19     "I like to take long walks on weekends.",
20     user_id="alice",
21     metadata={"category": "hobbies"},
22 )
23 print(result)

Add memory with the OpenAI SDK

Use the OpenAI SDK pointed at the Respan gateway and pass mem0_params via extra_body to add or search memories alongside the LLM call.

1 import os
2 from openai import OpenAI
3 
4 client = OpenAI(
5     api_key=os.environ["RESPAN_API_KEY"],
6     base_url="https://api.respan.ai/api/",
7 )
8 
9 messages = [
10     {"role": "system", "content": "You are a helpful assistant."},
11     {"role": "user", "content": "Context: I like eating carrots, and I like to play basketball."},
12 ]
13 
14 response = client.chat.completions.create(
15     model="gpt-4.1-nano",
16     messages=messages,
17     extra_body={
18         "mem0_params": {
19             "user_id": "user_1",
20             "org_id": "org_1",
21             "api_key": os.environ["MEM0_API_KEY"],
22             "add_memories": {"messages": messages},
23         }
24     },
25 )
26 print(response.choices[0].message.content)

Search memories

Pass search_memories to retrieve relevant memories before generation.

1 response = client.chat.completions.create(
2     model="gpt-4.1-nano",
3     messages=[
4         {"role": "system", "content": "You are a helpful assistant."},
5         {"role": "user", "content": "Based on {{mem0_search_memories_response}}, what is the user's favorite food?"},
6     ],
7     extra_body={
8         "mem0_params": {
9             "user_id": "user_1",
10             "org_id": "org_1",
11             "api_key": os.environ["MEM0_API_KEY"],
12             "search_memories": {
13                 "query": "What is the user's favorite food?",
14                 "top_k": 1,
15                 "fields": ["text"],
16                 "rerank": True,
17                 "keyword_search": True,
18                 "filter_memories": True,
19             },
20         }
21     },
22 )

Use the Respan SDK for type-safe params

$ pip install respan-sdk

1 import os
2 from openai import OpenAI
3 from respan_sdk.respan_types.services_types.mem0_types import (
4     Mem0Params, AddMemoriesParams, SearchMemoriesParams,
5 )
6 
7 client = OpenAI(
8     api_key=os.environ["RESPAN_API_KEY"],
9     base_url="https://api.respan.ai/api/",
10 )
11 
12 memory_msgs = [
13     {"role": "system", "content": "You are a helpful assistant."},
14     {"role": "user", "content": "Context: I like eating carrots."},
15 ]
16 
17 response = client.chat.completions.create(
18     model="gpt-4.1-nano",
19     messages=memory_msgs,
20     extra_body={
21         "mem0_params": Mem0Params(
22             user_id="user_1",
23             org_id="org_1",
24             api_key=os.environ["MEM0_API_KEY"],
25             add_memories=AddMemoriesParams(messages=memory_msgs),
26         ).model_dump(exclude_none=True),
27     },
28 )

$	export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
$	export MEM0_API_KEY="YOUR_MEM0_API_KEY"

1	import os
2	from mem0 import Memory
3
4	config = {
5	"llm": {
6	"provider": "openai",
7	"config": {
8	"model": "gpt-4.1-nano",
9	"temperature": 0.0,
10	"api_key": os.environ["RESPAN_API_KEY"],
11	"openai_base_url": "https://api.respan.ai/api/",
12	},
13	}
14	}
15
16	m = Memory.from_config(config_dict=config)
17
18	result = m.add(
19	"I like to take long walks on weekends.",
20	user_id="alice",
21	metadata={"category": "hobbies"},
22	)
23	print(result)

1	import os
2	from openai import OpenAI
3
4	client = OpenAI(
5	api_key=os.environ["RESPAN_API_KEY"],
6	base_url="https://api.respan.ai/api/",
7	)
8
9	messages = [
10	{"role": "system", "content": "You are a helpful assistant."},
11	{"role": "user", "content": "Context: I like eating carrots, and I like to play basketball."},
12	]
13
14	response = client.chat.completions.create(
15	model="gpt-4.1-nano",
16	messages=messages,
17	extra_body={
18	"mem0_params": {
19	"user_id": "user_1",
20	"org_id": "org_1",
21	"api_key": os.environ["MEM0_API_KEY"],
22	"add_memories": {"messages": messages},
23	}
24	},
25	)
26	print(response.choices[0].message.content)

1	response = client.chat.completions.create(
2	model="gpt-4.1-nano",
3	messages=[
4	{"role": "system", "content": "You are a helpful assistant."},
5	{"role": "user", "content": "Based on {{mem0_search_memories_response}}, what is the user's favorite food?"},
6	],
7	extra_body={
8	"mem0_params": {
9	"user_id": "user_1",
10	"org_id": "org_1",
11	"api_key": os.environ["MEM0_API_KEY"],
12	"search_memories": {
13	"query": "What is the user's favorite food?",
14	"top_k": 1,
15	"fields": ["text"],
16	"rerank": True,
17	"keyword_search": True,
18	"filter_memories": True,
19	},
20	}
21	},
22	)

1	import os
2	from openai import OpenAI
3	from respan_sdk.respan_types.services_types.mem0_types import (
4	Mem0Params, AddMemoriesParams, SearchMemoriesParams,
5	)
6
7	client = OpenAI(
8	api_key=os.environ["RESPAN_API_KEY"],
9	base_url="https://api.respan.ai/api/",
10	)
11
12	memory_msgs = [
13	{"role": "system", "content": "You are a helpful assistant."},
14	{"role": "user", "content": "Context: I like eating carrots."},
15	]
16
17	response = client.chat.completions.create(
18	model="gpt-4.1-nano",
19	messages=memory_msgs,
20	extra_body={
21	"mem0_params": Mem0Params(
22	user_id="user_1",
23	org_id="org_1",
24	api_key=os.environ["MEM0_API_KEY"],
25	add_memories=AddMemoriesParams(messages=memory_msgs),
26	).model_dump(exclude_none=True),
27	},
28	)