How to Give Your AI Agent Memory

AI agents can reason, use tools, and complete complex tasks. But there is one problem: most of them forget everything once the session ends.

That means every new conversation can feel like starting from scratch.

Agent memory gives an AI system a way to remember useful information across sessions, such as your preferences, previous decisions, goals, or important context.

In this tutorial, we will build an AI agent with long-term memory using the OpenAI Agents SDK and Mem0. We will then connect the same memory system to OpenCode so your coding agent can remember information across sessions too.

A simple agent memory system follows a straightforward loop: the user sends a message, the agent searches its memory for relevant context, uses that information while reasoning, and then saves anything useful for future conversations.

There are two main types of memory:

• Short-term memory: Information from the current conversation or task, such as recent messages and temporary state.
• Long-term memory: Information that persists across sessions, such as preferences, decisions, goals, and previous experiences.

The key part is retrieval. Instead of adding every previous conversation to the prompt, the agent searches its memory and brings back only the information that is relevant to the current request.

This keeps the context smaller while still allowing the agent to behave as if it remembers previous interactions.

Building an Agent with Long-Term Memory

In this section, we will build a simple agent that can search, save, and reuse information across conversations.

Start by installing the OpenAI Agents SDK and Mem0:

pip install openai-agents mem0ai

Next, set the API keys:

export OPENAI_API_KEY="your-openai-key"
export MEM0_API_KEY="your-mem0-key"

Then import the required classes and create a Mem0 client:

from agents import Agent, Runner, function_tool
from mem0 import MemoryClient

memory = MemoryClient()
USER_ID = "abid"