Customize Automatic Memory Extraction

Automatic memory extraction can capture useful details without additional configuration. Custom instructions let an application add guidance to the extraction process when the default behavior needs to be tailored for a specific use case.

This article explains how to guide Oracle AI Agent Memory’s automated memory extraction with custom instructions.

Custom instructions provide additional, application-specific guidance for memory extraction. They are useful when your application needs extraction to reflect domain knowledge, product policy, or conversation-specific priorities.

Note: Use custom instructions when memory extraction should follow application-specific guidance. Default extraction is appropriate when no additional guidance is needed.

Hint: See Get Started with Agent Memory for how to install oracleagentmemory. If you need a local Oracle database for this example, follow Run Oracle AI Database Locally.

Configure Client-Level Extraction Instructions

Create the Oracle Agent Memory component with an Oracle DB connection or pool, an embedder, an LLM, and support-focused extraction instructions. These instructions apply to threads created or loaded through this client unless a thread provides its own extraction instructions.

import oracledb

from oracleagentmemory.core import MemoryExtractionConfig
from oracleagentmemory.core.embedders.embedder import Embedder
from oracleagentmemory.core.llms.llm import Llm
from oracleagentmemory.core.oracleagentmemory import OracleAgentMemory


embedder = Embedder(model="YOUR_EMBEDDING_MODEL")
memory_llm = Llm(model="YOUR_MEMORY_LLM")
db_pool = oracledb.SessionPool(
    user="YOUR DB USER",
    password="YOUR DB PASSWORD",
    dsn="YOUR DB CONNECT STRING",
)

support_extraction_instructions = """
Only extract customer support facts:
- order ids
- return requests
- delivery problems
Ignore greetings, small talk, and one-off troubleshooting text.
""".strip()

memory = OracleAgentMemory(
    connection=db_pool,
    embedder=embedder,
    llm=memory_llm,
    memory_extraction_config=MemoryExtractionConfig(
        memory_extraction_custom_instructions=support_extraction_instructions
    ),
)

API Reference: OracleAgentMemory

OracleThread

Extract Support Memories from Thread Messages

Create a support thread and add user and assistant messages. The example sets memory_extraction_config=MemoryExtractionConfig(memory_extraction_frequency=1) so add_messages() runs automated extraction immediately for the inserted turn. The custom instructions keep the extracted memory focused on order IDs, return requests, and delivery problems.

support_thread = memory.create_thread(
    thread_id="support_ticket_7421",
    user_id="customer_123",
    memory_extraction_config=MemoryExtractionConfig(memory_extraction_frequency=1),
)

#add_messages persists the turn and runs automated memory extraction because
#MemoryExtractionConfig(memory_extraction_frequency=1) is set for this thread.
support_thread.add_messages(
    [
        {
            "role": "user",
            "content": (
                "Hi, order 7421 arrived damaged. I need a return request "
                "and a replacement delivery."
            ),
        },
        {
            "role": "assistant",
            "content": "I can help start the return request for order 7421.",
        },
    ]
)

results = support_thread.search(
    "return request order 7421",
    max_results=5,
    record_types=["fact"],
)
for result in results:
    print(f"- [{result.record.record_type}] {result.content}")

Output:

- [fact] Customer reported damaged order 7421 and requested a return and replacement delivery.

The output shown above is illustrative. The exact memory text depends on the configured extraction LLM, but the extracted memories should follow the custom instructions supplied to the client.

The difference is easiest to see by comparing the kind of memory the extractor is asked to keep:

With custom instructions

The extractor keeps the durable customer-support fact and ignores the greeting and conversational wording.

- [fact] Customer reported damaged order 7421 and requested a return and replacement delivery.

Without custom instructions

The extractor may keep a broader conversation memory because the default extraction prompt is not scoped to support operations.

- [memory] The user contacted support about order 7421 and discussed a damaged delivery.

API Reference: OracleThread

OracleSearchResult

Override Instructions for One Thread

Pass memory_extraction_config=MemoryExtractionConfig(memory_extraction_custom_instructions=...) to create_thread() when one thread needs a narrower extraction policy than the client default. The thread-level value takes precedence for that thread and is persisted with the thread configuration.

billing_thread = memory.create_thread(
    thread_id="billing_ticket_9310",
    user_id="customer_123",
    memory_extraction_config=MemoryExtractionConfig(
        memory_extraction_frequency=1,
        memory_extraction_custom_instructions=(
            "Only extract billing facts, invoice identifiers, and payment issues."
        ),
    ),
)

billing_thread.add_messages(
    [
        {
            "role": "user",
            "content": "Invoice INV-9310 was paid twice and needs a refund.",
        }
    ]
)

API Reference: OracleAgentMemory

OracleThread

Update or Clear Thread Instructions

Use update_thread() to change persisted extraction instructions for an existing thread. Pass None to clear the thread-level instructions so future thread handles use the client-level instructions or, if none are configured, the SDK’s normal extraction behavior.

memory.update_thread(
    "support_ticket_7421",
    memory_extraction_config=MemoryExtractionConfig(
        memory_extraction_custom_instructions=(
            "Only extract product defects and replacement requests."
        )
    ),
)

memory.update_thread(
    "support_ticket_7421",
    memory_extraction_config=MemoryExtractionConfig(
        memory_extraction_custom_instructions=None
    ),
)

Note: get_thread(..., memory_extraction_config=MemoryExtractionConfig(memory_extraction_custom_instructions=...)) can apply instructions to the returned live thread handle without updating the persisted thread configuration.

API Reference: OracleAgentMemory

OracleThread

Conclusion

In this guide we learned how to configure client-level custom extraction instructions, override them for a specific thread, and update or clear thread-level extraction instructions.

Having learned how to customize automated extraction, you may now proceed to Use Agent Memory Short-Term APIs with LangGraph.

Full Code

#Copyright © 2026 Oracle and/or its affiliates.
#This software is under the Apache License 2.0
#(LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0) or Universal Permissive License
#(UPL) 1.0 (LICENSE-UPL or https://oss.oracle.com/licenses/upl), at your option.

#Oracle Agent Memory Code Example - Customize Automatic Memory Extraction
#------------------------------------------------------------------------

##Configure custom memory extraction instructions

import oracledb

from oracleagentmemory.core import MemoryExtractionConfig
from oracleagentmemory.core.embedders.embedder import Embedder
from oracleagentmemory.core.llms.llm import Llm
from oracleagentmemory.core.oracleagentmemory import OracleAgentMemory


embedder = Embedder(model="YOUR_EMBEDDING_MODEL")
memory_llm = Llm(model="YOUR_MEMORY_LLM")
db_pool = oracledb.SessionPool(
    user="YOUR DB USER",
    password="YOUR DB PASSWORD",
    dsn="YOUR DB CONNECT STRING",
)

support_extraction_instructions = """
Only extract customer support facts:
- order ids
- return requests
- delivery problems
Ignore greetings, small talk, and one-off troubleshooting text.
""".strip()

memory = OracleAgentMemory(
    connection=db_pool,
    embedder=embedder,
    llm=memory_llm,
    memory_extraction_config=MemoryExtractionConfig(
        memory_extraction_custom_instructions=support_extraction_instructions
    ),
)



##Extract support memories from thread messages

support_thread = memory.create_thread(
    thread_id="support_ticket_7421",
    user_id="customer_123",
    memory_extraction_config=MemoryExtractionConfig(memory_extraction_frequency=1),
)

#add_messages persists the turn and runs automated memory extraction because
#MemoryExtractionConfig(memory_extraction_frequency=1) is set for this thread.
support_thread.add_messages(
    [
        {
            "role": "user",
            "content": (
                "Hi, order 7421 arrived damaged. I need a return request "
                "and a replacement delivery."
            ),
        },
        {
            "role": "assistant",
            "content": "I can help start the return request for order 7421.",
        },
    ]
)

results = support_thread.search(
    "return request order 7421",
    max_results=5,
    record_types=["fact"],
)
for result in results:
    print(f"- [{result.record.record_type}] {result.content}")
#- [fact] Customer reported damaged order 7421 and requested a return and replacement delivery.



##Override custom instructions for one thread

billing_thread = memory.create_thread(
    thread_id="billing_ticket_9310",
    user_id="customer_123",
    memory_extraction_config=MemoryExtractionConfig(
        memory_extraction_frequency=1,
        memory_extraction_custom_instructions=(
            "Only extract billing facts, invoice identifiers, and payment issues."
        ),
    ),
)

billing_thread.add_messages(
    [
        {
            "role": "user",
            "content": "Invoice INV-9310 was paid twice and needs a refund.",
        }
    ]
)



##Update or clear thread custom instructions

memory.update_thread(
    "support_ticket_7421",
    memory_extraction_config=MemoryExtractionConfig(
        memory_extraction_custom_instructions=(
            "Only extract product defects and replacement requests."
        )
    ),
)

memory.update_thread(
    "support_ticket_7421",
    memory_extraction_config=MemoryExtractionConfig(
        memory_extraction_custom_instructions=None
    ),
)