Store and Search Memory

In this article, you will create an Agent Memory component, store both thread messages and durable memories, and then search them back.

Tip: For package setup, see Get Started with Agent Memory. If you need a local Oracle AI Database for this example, see Run Oracle AI Database Locally.

Store Messages and Memories

Create the Agent Memory component with an Oracle DB connection or pool, add a short thread, and persist one durable user memory alongside the messages.

Note: By default, OracleAgentMemory expects an LLM so OracleThread can periodically extract durable memories from recent thread messages added through add_messages(). If you do not want automatic memory extraction, set extract_memories=False either when constructing the client or when calling create_thread().

from oracleagentmemory.core.oracleagentmemory import OracleAgentMemory
from oracleagentmemory.apis.searchscope import SearchScope
from oracleagentmemory.core.embedders.embedder import Embedder
from oracleagentmemory.core.llms.llm import Llm

embedder = Embedder(model="YOUR_EMBEDDING_MODEL")
llm = Llm(model="YOUR_LLM")
db_pool = ...  #an oracledb connection or connection pool
memory = OracleAgentMemory(connection=db_pool, embedder=embedder, llm=llm)

messages = [
    {
        "role": "user",
        "content": (
            "Orange juice has become my favorite breakfast drink lately, "
            "what can I pair it with?"
        ),
    },
    {
        "role": "assistant",
        "content": (
            "Nice! Orange juice goes great with something savory. "
            "Try eggs and toast, avocado toast, or a breakfast sandwich."
        ),
    },
]

thread = memory.create_thread(user_id="user_123")
#add_messages will add messages to the DB and extract memories automatically
thread.add_messages(messages)
#add_memory adds memory to the DB
thread.add_memory("The user likes orange juice with breakfast.")

Search the Agent Memory

Search the stored memories and messages, scoped to that user.

results = memory.search(query="orange juice", scope=SearchScope(user_id="user_123"))
for result in results:
    print(f"- [{result.record.record_type}] {result.content}")

Output:

-[memory] The user likes orange juice with breakfast.
-[message] Orange juice has become my favorite breakfast drink lately, what can I pair it with?
-[message] Nice! Orange juice goes great with something savory.
        Try eggs and toast, avocado toast, or a breakfast sandwich.

Conclusion

In this article we learned how to create an Agent Memory client for a single user, store thread messages alongside durable memories, and search those records back with user-scoped retrieval.

Tip: Having learned the core single-user memory workflow, you may also be interested in Use Agent Memory Short-Term APIs with LangGraph.

Full Code

#Copyright © 2026 Oracle and/or its affiliates.
#isort:skip_file
#fmt: off
#Agent Memory Code Example - Quickstart
#_--------------------------------------

#How to use:
#Create a new Python virtual environment and install the latest oracleagentmemory version.

#You can now run the script
#1. As a Python file:
#```bash
#python quickstart.py
#```
#2. As a Notebook (in VSCode):
#When viewing the file,
#- press the keys Ctrl + Enter to run the selected cell
#- or Shift + Enter to run the selected cell and move to the cell below


##Add memories

#%%
from oracleagentmemory.core.oracleagentmemory import OracleAgentMemory
from oracleagentmemory.apis.searchscope import SearchScope
from oracleagentmemory.core.embedders.embedder import Embedder
from oracleagentmemory.core.llms.llm import Llm

embedder = Embedder(model="YOUR_EMBEDDING_MODEL")
llm = Llm(model="YOUR_LLM")
db_pool = ...  #an oracledb connection or connection pool
memory = OracleAgentMemory(connection=db_pool, embedder=embedder, llm=llm)

messages = [
    {
        "role": "user",
        "content": (
            "Orange juice has become my favorite breakfast drink lately, "
            "what can I pair it with?"
        ),
    },
    {
        "role": "assistant",
        "content": (
            "Nice! Orange juice goes great with something savory. "
            "Try eggs and toast, avocado toast, or a breakfast sandwich."
        ),
    },
]

thread = memory.create_thread(user_id="user_123")
thread.add_messages(messages)
thread.add_memory("The user likes orange juice with breakfast.")


##Search memories

#%%
results = memory.search(query="orange juice", scope=SearchScope(user_id="user_123"))
for result in results:
    print(f"- [{result.record.record_type}] {result.content}")
#- [memory] The user likes orange juice with breakfast.
#- [message] Orange juice has become my favorite breakfast drink lately, what can I pair it with?
#- [message] Nice! Orange juice goes great with something savory.
#Try eggs and toast, avocado toast, or a breakfast sandwich.