agent-memory

Add memory capabilities to your agent. Use when: (1) User asks about 'memory', 'state', 'remember', 'conversation history', (2) Want to persist conversations or user preferences, (3) Adding checkpointing or long-term storage.

Quality

65%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./agent-langgraph-advanced/.claude/skills/agent-memory/SKILL.md

Adding Memory to Your Agent

Name: agent-memory
Rating: 57.599999999999994 (1 reviews)
Author: databricks

Note: This template does not include memory by default. Use this skill to add memory capabilities. For a pre-configured memory template, see:

agent-langgraph-advanced - Short-term and long-term memory with long-running background tasks

Memory Types

Type	Use Case	Storage	Identifier
Short-term	Conversation history within a session	`AsyncCheckpointSaver`	`thread_id`
Long-term	User facts that persist across sessions	`AsyncDatabricksStore`	`user_id`

Prerequisites

Add memory dependency to pyproject.toml:

dependencies = [
    "databricks-langchain[memory]",
]

Then run uv sync

Configure Lakebase - See lakebase-setup skill for:
- Creating/configuring Lakebase instance
- Initializing tables (CRITICAL first-time step)

Quick Setup Summary

Adding memory requires changes to 4 files:

File	What to Add
`pyproject.toml`	Memory dependency
`.env`	Lakebase env vars (for local dev)
`databricks.yml`	Lakebase database resource + env vars in config block
`agent_server/agent.py`	Memory tools and AsyncDatabricksStore

Key Principles

Before implementing memory, understand these patterns from the production implementation.

1. Factory Function Pattern

Memory tools should be returned from a factory function, not defined as standalone functions:

def memory_tools():
    @tool
    async def get_user_memory(query: str, config: RunnableConfig) -> str:
        ...
    @tool
    async def save_user_memory(memory_key: str, memory_data_json: str, config: RunnableConfig) -> str:
        ...
    @tool
    async def delete_user_memory(memory_key: str, config: RunnableConfig) -> str:
        ...
    return [get_user_memory, save_user_memory, delete_user_memory]

2. User ID Extraction

Extract user_id from the request, checking custom_inputs first. Return None (not a default) to let the caller decide:

def get_user_id(request: ResponsesAgentRequest) -> Optional[str]:
    custom_inputs = dict(request.custom_inputs or {})
    if "user_id" in custom_inputs:
        return custom_inputs["user_id"]
    if request.context and getattr(request.context, "user_id", None):
        return request.context.user_id
    return None

3. Separate Error Handling

Check user_id and store separately with distinct error messages:

user_id = config.get("configurable", {}).get("user_id")
if not user_id:
    return "Memory not available - no user_id provided."

store: Optional[BaseStore] = config.get("configurable", {}).get("store")
if not store:
    return "Memory not available - store not configured."

4. JSON Validation for Save

Validate JSON input before storing - the LLM may pass invalid JSON:

try:
    memory_data = json.loads(memory_data_json)
    if not isinstance(memory_data, dict):
        return f"Failed: memory_data must be a JSON object, not {type(memory_data).__name__}"
    await store.aput(namespace, memory_key, memory_data)
except json.JSONDecodeError as e:
    return f"Failed to save memory: Invalid JSON - {e}"

5. Pass Store via RunnableConfig

Pass the store through config, not as a function parameter:

config = {"configurable": {"user_id": user_id, "store": store}}
# Tools access via: config.get("configurable", {}).get("store")

Complete Example

A full implementation is available in this skill's examples folder:

# Copy to your project
cp .claude/skills/agent-memory/examples/memory_tools.py agent_server/

See examples/memory_tools.py for production-ready code including all helper functions.

Production Reference

For implementations in the pre-built templates:

File	Description
`agent-langgraph-advanced/agent_server/utils_memory.py`	Memory tools factory, helpers, error handling
`agent-langgraph-advanced/agent_server/agent.py`	Integration with agent, store initialization

Key functions:

memory_tools() - Factory returning get/save/delete tools
get_user_id() - Extract user_id from request
resolve_lakebase_instance_name() - Handle hostname vs instance name
get_lakebase_access_error_message() - Helpful error messages

Configuration Files

Step 1: databricks.yml (Lakebase Resource)

Add the Lakebase database resource to your app:

resources:
  apps:
    agent_langgraph:
      name: "your-app-name"
      source_code_path: ./

      resources:
        # ... other resources (experiment, UC functions, etc.) ...

        # Lakebase instance for long-term memory
        - name: 'database'
          database:
            instance_name: '<your-lakebase-instance-name>'
            database_name: 'databricks_postgres'
            permission: 'CAN_CONNECT_AND_CREATE'

Important: The name: 'database' must match the value_from reference in the databricks.yml config.env block.

Step 2: databricks.yml config block (Environment Variables)

Add the Lakebase environment variables to your app's config.env in databricks.yml:

config:
        command: ["uv", "run", "start-app"]
        env:
          # ... other env vars ...

          # Lakebase instance name (resolved from database resource)
          - name: LAKEBASE_INSTANCE_NAME
            value_from: "database"

          # Embedding configuration
          - name: EMBEDDING_ENDPOINT
            value: "databricks-gte-large-en"
          - name: EMBEDDING_DIMS
            value: "1024"

Important: LAKEBASE_INSTANCE_NAME uses value_from: "database" to resolve from the database resource at deploy time.

Step 3: .env (Local Development)

# Lakebase configuration for long-term memory
LAKEBASE_INSTANCE_NAME=<your-instance-name>
EMBEDDING_ENDPOINT=databricks-gte-large-en
EMBEDDING_DIMS=1024

Integration Example

Minimal example showing how to integrate memory into your streaming function:

from agent_server.utils_memory import memory_tools, get_user_id

@stream()
async def streaming(request: ResponsesAgentRequest):
    user_id = get_user_id(request)

    async with AsyncDatabricksStore(
        instance_name=LAKEBASE_INSTANCE_NAME,
        embedding_endpoint=EMBEDDING_ENDPOINT,
        embedding_dims=EMBEDDING_DIMS,
    ) as store:
        await store.setup()  # Creates tables if needed

        tools = await mcp_client.get_tools() + memory_tools()
        config = {"configurable": {"user_id": user_id, "store": store}}

        agent = create_react_agent(model=model, tools=tools)
        async for event in agent.astream(messages, config):
            yield event

Initialize Tables and Deploy

Initialize Lakebase Tables (First Time Only)

Before deploying, initialize the tables locally:

uv run python -c "$(cat <<'EOF'
import asyncio
from databricks_langchain import AsyncDatabricksStore

async def setup():
    async with AsyncDatabricksStore(
        instance_name="<your-instance-name>",
        embedding_endpoint="databricks-gte-large-en",
        embedding_dims=1024,
    ) as store:
        await store.setup()
        print("Tables created!")

asyncio.run(setup())
EOF
)"

Deploy

After initializing tables, deploy your agent. See deploy skill for full instructions.

Short-Term Memory

For conversation history within a session, use AsyncCheckpointSaver:

from databricks_langchain import AsyncCheckpointSaver

async with AsyncCheckpointSaver(instance_name=LAKEBASE_INSTANCE_NAME) as checkpointer:
    agent = create_react_agent(
        model=model,
        tools=tools,
        checkpointer=checkpointer,
    )

    config = {"configurable": {"thread_id": thread_id}}
    async for event in agent.astream(messages, config):
        yield event

See the agent-langgraph-advanced template for a complete implementation.

Testing Memory

Test Locally

# Start the server
uv run start-app

# Save a memory
curl -X POST http://localhost:8000/invocations \
  -H "Content-Type: application/json" \
  -d '{
      "input": [{"role": "user", "content": "Remember that I am on the shipping team"}],
      "custom_inputs": {"user_id": "alice@example.com"}
  }'

# Recall the memory
curl -X POST http://localhost:8000/invocations \
  -H "Content-Type: application/json" \
  -d '{
      "input": [{"role": "user", "content": "What team am I on?"}],
      "custom_inputs": {"user_id": "alice@example.com"}
  }'

# Delete a memory
curl -X POST http://localhost:8000/invocations \
  -H "Content-Type: application/json" \
  -d '{
      "input": [{"role": "user", "content": "Forget what team I am on"}],
      "custom_inputs": {"user_id": "alice@example.com"}
  }'

Test Deployed App

# Get OAuth token (PATs don't work for apps)
TOKEN=$(databricks auth token --host <workspace-url> | jq -r '.access_token')

# Test memory save
curl -X POST https://<app-url>/invocations \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
      "input": [{"role": "user", "content": "Remember I prefer detailed explanations"}],
      "custom_inputs": {"user_id": "alice@example.com"}
  }'

First-Time Setup Checklist

Added databricks-langchain[memory] to pyproject.toml
Run uv sync to install dependencies
Created or identified Lakebase instance
Added Lakebase env vars to .env (for local dev)
Added database resource to databricks.yml
Added LAKEBASE_INSTANCE_NAME to databricks.yml config.env
Initialized tables locally by running await store.setup()
Deployed with databricks bundle deploy && databricks bundle run

Troubleshooting

Issue	Cause	Solution
"embedding_dims is required"	Missing parameter	Add `embedding_dims=1024` to AsyncDatabricksStore
"relation 'store' does not exist"	Tables not created	Run `await store.setup()` locally first
"Unable to resolve Lakebase instance 'None'"	Missing env var	Check `LAKEBASE_INSTANCE_NAME` in databricks.yml `config.env`
"permission denied for table store"	Missing grants	Add `database` resource to databricks.yml
"Memory not available - no user_id"	Missing user_id	Pass `custom_inputs.user_id` in request
Memory not persisting	Different user_ids	Use consistent user_id across requests
App not updated after deploy	Forgot to run bundle	Run `databricks bundle run agent_langgraph` after deploy

Pre-Built Memory Templates

For fully configured implementations without manual setup:

Template	Memory Type	Key Features
agent-langgraph-advanced	Short-term + Long-term	AsyncCheckpointSaver, AsyncDatabricksStore, memory tools

Next Steps

Configure Lakebase: see lakebase-setup skill
Test locally: see run-locally skill
Deploy: see deploy skill

Repository: databricks/app-templates
Path: agent-langgraph-advanced/.claude/skills/agent-memory/SKILL.md
Commit: 2a4c792

Last updated: 3 days ago
First committed: 4 months ago

Also appears in

databricks/app-templates

In sync

since May 6, 2026

databricks/app-templates

In sync

since May 6, 2026

databricks/app-templates

In sync

since May 6, 2026

databricks/app-templates

In sync

since May 6, 2026

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.