Keywords AI - Mem0 — ContextQMD

Build AI applications with persistent memory and comprehensive LLM observability by integrating Mem0 with Keywords AI.

Overview

Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. Keywords AI provides complete LLM observability.

Combining Mem0 with Keywords AI allows you to:

Add persistent memory to your AI applications
Track interactions across sessions
Monitor memory usage and retrieval with Keywords AI observability
Optimize token usage and reduce costs

<Note> You can get your Mem0 API key from the <a href="https://app.mem0.ai/?utm_source=oss&utm_medium=integration-keywords" rel="nofollow">Mem0 dashboard</a>. </Note>

Setup and Configuration

Install the necessary libraries:

bash

pip install mem0ai keywordsai-sdk

Set up your environment variables:

python

import os

# Set your API keys
os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
os.environ["KEYWORDSAI_API_KEY"] = "your-keywords-api-key"
os.environ["KEYWORDSAI_BASE_URL"] = "https://api.keywordsai.co/api/"

Basic Integration Example

Here's a simple example of using Mem0 with Keywords AI:

python

from mem0 import Memory
import os

# Configuration
api_key = os.getenv("MEM0_API_KEY")
keywordsai_api_key = os.getenv("KEYWORDSAI_API_KEY")
base_url = os.getenv("KEYWORDSAI_BASE_URL") # "https://api.keywordsai.co/api/"

# Set up Mem0 with Keywords AI as the LLM provider
config = {
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-5-mini",
            "temperature": 0.0,
            "api_key": keywordsai_api_key,
            "openai_base_url": base_url,
        },
    }
}

# Initialize Memory
memory = Memory.from_config(config)

# Add a memory
result = memory.add(
    "I like to take long walks on weekends.",
    user_id="alice",
    metadata={"category": "hobbies"},
)

print(result)

Advanced Integration with OpenAI SDK

For more advanced use cases, you can integrate Keywords AI with Mem0 through the OpenAI SDK:

python

from openai import OpenAI
import os
import json

# Initialize client
client = OpenAI(
    api_key=os.environ.get("KEYWORDSAI_API_KEY"),
    base_url=os.environ.get("KEYWORDSAI_BASE_URL"),
)

# Sample conversation messages
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]

# Add memory and generate a response
response = client.chat.completions.create(
    model="openai/gpt-4.1-nano",
    messages=messages,
    extra_body={
        "mem0_params": {
            "user_id": "test_user",
            "api_key": os.environ.get("MEM0_API_KEY"),
            "add_memories": {
                "messages": messages,
            },
        }
    },
)

print(json.dumps(response.model_dump(), indent=4))

For detailed information on this integration, refer to the official Keywords AI Mem0 integration documentation.

Key Features

Memory Integration: Store and retrieve relevant information from past interactions
LLM Observability: Track memory usage and retrieval patterns with Keywords AI
Session Persistence: Maintain context across multiple user sessions
Cost Optimization: Reduce token usage through efficient memory retrieval

Conclusion

Integrating Mem0 with Keywords AI provides a powerful combination for building AI applications with persistent memory and comprehensive observability. This integration enables more personalized user experiences while providing insights into your application's memory usage.

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