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

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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:
  1. Add persistent memory to your AI applications
  2. Track interactions across sessions
  3. Monitor memory usage and retrieval with Keywords AI observability
  4. Optimize token usage and reduce costs
You can get your Mem0 API key, user_id, and org_id from the Mem0 dashboard. These are required for proper integration.

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Setup and Configuration

Install the necessary libraries: 
pip install mem0 keywordsai-sdk
Set up your environment variables: 
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/"

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Basic Integration Example

Here’s a simple example of using Mem0 with Keywords AI: 
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-4.1-nano-2025-04-14",
            "temperature": 0.0,
            "api_key": keywordsai_api_key,
            "openai_base_url": base_url,
        },
    }
}

# Initialize Memory
memory = Memory.from_config(config_dict=config)

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

print(result)

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Advanced Integration with OpenAI SDK

For more advanced use cases, you can integrate Keywords AI with Mem0 through the OpenAI SDK: 
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",
            "org_id": "org_1",
            "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.

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Key Features

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

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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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