agent-memory-layer 0.1.0

A reusable memory layer for SAP agentic workflows

agent-memory-layer

A reusable memory layer for agentic workflows built on LangGraph and SAP AI Core.

It gives your agents persistent memory across sessions, so facts learned in one conversation can be injected into the next.

How It Works

Session starts
  → inject_node fetches relevant facts from PostgreSQL
  → Facts added to agent state as working_memory

Session runs
  → Agent responds using injected context

Session ends
  → record_node saves the conversation as an EpisodeRecord

Background (periodic)
  → Consolidation pipeline processes unprocessed episodes
  → LLM extracts reusable facts via SAP AI Core
  → MemoryItems written to PostgreSQL with importance scores
  → Importance decaying pipeline runs every day once
  → Memories below threshold are automatically deleted

Installation

Install the core package:

pip install agent-memory-layer

If you are integrating with LangGraph, install the optional extra:

pip install "agent-memory-layer[langgraph]"

Requirements

• Python 3.11+
• PostgreSQL (local Docker or managed instance)
• SAP AI Core account with a deployed LLM model

Quick Start

1. Set Up PostgreSQL

The repo includes a docker-compose.yml for local development:

docker compose up -d

This starts PostgreSQL on localhost:5432 with:

• User: memory_user
• Password: memory_pass
• Database: agent_memory

2. Configure Credentials

Copy the example env file and fill in your SAP AI Core credentials:

cp .env.example .env

Edit .env:

AICORE_CLIENT_ID=your-client-id
AICORE_CLIENT_SECRET=your-client-secret
AICORE_AUTH_URL=https://your-tenant.authentication.eu10.hana.ondemand.com
AICORE_BASE_URL=https://api.ai.prod.eu-central-1.aws.ml.hana.ondemand.com/v2

3. Use MemoryManager (Recommended)

MemoryManager is the simplest way to use the library. It handles database setup, node creation, and the background scheduler in one object.

import asyncio
from agent_memory_layer import MemoryManager

async def main():
    # Create and start — connects to DB, runs migrations, starts scheduler
    manager = await MemoryManager.create(
        db_url="postgresql://memory_user:memory_pass@localhost:5432/agent_memory",
        aicore_client_id="...",
        aicore_client_secret="...",
        aicore_auth_url="...",
        aicore_base_url="...",
    )

    # Use manager.inject_node and manager.record_node in your LangGraph
    # (see "Wire Into LangGraph" below)

    # When your app shuts down
    await manager.close()

asyncio.run(main())

You can also create and start in two steps if you need to configure things between construction and startup:

manager = MemoryManager(
    db_url="postgresql://memory_user:memory_pass@localhost:5432/agent_memory",
    aicore_client_id="...",
    aicore_client_secret="...",
    aicore_auth_url="...",
    aicore_base_url="...",
)
await manager.start()

4. Wire Into LangGraph

from langgraph.graph import StateGraph, END
from langgraph.graph.message import add_messages
from langchain_core.messages import BaseMessage, HumanMessage
from typing import Annotated, Optional
from typing_extensions import TypedDict

# Define your state — must include these keys for memory to work
class AgentState(TypedDict, total=False):
    messages: Annotated[list[BaseMessage], add_messages]
    user_id: str
    agent_id: str
    workflow_id: Optional[str]
    working_memory: dict  # populated by inject_node

# Build the graph
graph = StateGraph(AgentState)

graph.add_node("inject_memory", manager.inject_node)
graph.add_node("agent", your_agent_node)
graph.add_node("record_memory", manager.record_node)

graph.set_entry_point("inject_memory")
graph.add_edge("inject_memory", "agent")
graph.add_edge("agent", "record_memory")
graph.add_edge("record_memory", END)

app = graph.compile()

# Invoke the graph
result = await app.ainvoke({
    "user_id": "user-123",
    "agent_id": "support-bot",
    "workflow_id": None,
    "messages": [HumanMessage(content="Our S4 system keeps timing out on month-end close")],
})

5. Access Injected Memories in Your Agent Node

After inject_node runs, your agent node can read the injected facts from state["working_memory"]:

async def your_agent_node(state: AgentState) -> dict:
    working_memory = state.get("working_memory", {})
    injected_facts = working_memory.get("injected_facts", [])

    # Build a system prompt that includes the memories
    system = "You are a helpful assistant."
    if injected_facts:
        system += "\n\nYou know these facts about the user:\n"
        system += "\n".join(f"- {fact}" for fact in injected_facts)

    # Use the system prompt with your LLM call
    # ...

    return {"messages": [AIMessage(content=response)]}

6. Important: Message Format for record_node

record_node expects messages to be a list[dict] (e.g., [{"role": "user", "content": "..."}]). If your LangGraph uses the add_messages reducer (which stores BaseMessage objects), you need to convert them before they reach record_node. Wrap it like this:

from langchain_core.messages import BaseMessage

def build_record_wrapper(record_node):
    async def wrapper(state):
        messages = state.get("messages", [])
        serialized = []
        for m in messages:
            if isinstance(m, BaseMessage):
                serialized.append({"role": m.type, "content": m.content})
            elif isinstance(m, dict):
                serialized.append(m)
        return await record_node({**state, "messages": serialized})
    return wrapper

# Use in your graph
graph.add_node("record_memory", build_record_wrapper(manager.record_node))

Advanced Usage: Manual Setup (Without MemoryManager)

If you need more control over individual components:

from agent_memory_layer import (
    MemoryConfig,
    apply_migrations,
    build_inject_node,
    build_record_node,
    build_scheduler,
    get_pool,
)

async def main():
    config = MemoryConfig()  # reads from env vars / .env

    async with get_pool(config) as pool:
        await apply_migrations(pool, config)

        inject = build_inject_node(pool, config)
        record = build_record_node(pool, config)

        # Build your graph with inject and record nodes...

        # Start background consolidation and decay
        scheduler = build_scheduler(
            pool,
            config,
            scopes=[{"user_id": "u1", "agent_id": "support_agent", "workflow_id": None}],
        )
        scheduler.start()

        # ... run your app ...

        scheduler.shutdown()

Triggering Consolidation Manually

Instead of waiting for the scheduler, you can trigger consolidation directly:

from agent_memory_layer import (
    run_consolidation,
    EpisodeRepository,
    MemoryItemRepository,
    ConsolidationJobRepository,
)

episode_repo = EpisodeRepository(pool, schema="sap_agent_memory")
memory_repo = MemoryItemRepository(pool, schema="sap_agent_memory")
job_repo = ConsolidationJobRepository(pool, schema="sap_agent_memory")

job = await run_consolidation(
    episode_repo=episode_repo,
    memory_repo=memory_repo,
    job_repo=job_repo,
    config=config,
    user_id="user-123",
    agent_id="support-bot",
    workflow_id=None,
)

if job:
    print(f"Status: {job.status}, Facts created: {job.facts_created}")

Running the E2E Test

The repo includes a self-contained end-to-end test script that builds a mini LangGraph, runs two conversation turns, consolidates memories in between, and verifies that facts carry across turns.

# 1. Start PostgreSQL
docker compose up -d

# 2. Set credentials (or put them in .env)
export AICORE_CLIENT_ID="..."
export AICORE_CLIENT_SECRET="..."
export AICORE_AUTH_URL="..."
export AICORE_BASE_URL="..."

# 3. Install the library with LangGraph support
pip install -e ".[langgraph]"

# 4. Run
python scripts/e2e_test.py

Expected output:

=== agent-memory-layer E2E Test ===

[1/8] Starting MemoryManager...                    ✓ OK
[2/8] Building mini LangGraph...                   ✓ OK
[3/8] Turn 1: "I'm a Python developer..."          ✓ OK
[4/8] Consolidating episodes...                    ✓ OK  — 5 facts created
[5/8] Turn 2: "What language for my project?"      ✓ OK
[6/8] Verifying memory injection...                ✓ OK  — memories carried across turns
[7/8] Testing decay...                             ✓ OK
[8/8] Closing MemoryManager...                     ✓ OK

LangGraph State Keys

Your state dictionary must include these keys for memory to work:

Key	Type	Description
user_id	str | None	Identifies the user
agent_id	str | None	Identifies the agent
workflow_id	str | None	Identifies the workflow
messages	list[dict]	Conversation turns (role + content)

After inject_node runs, the state will also contain:

Key	Type	Description
working_memory	dict	Contains injected_facts (list of strings), session_id, scratch, etc.

Scopes

A scope is a combination of user_id, agent_id, and workflow_id. Memory is isolated per scope — different users and agents do not share facts.

All three fields are optional. Use only what makes sense for your use case:

Use Case	Scope
Memory per user	user_id only
Memory per agent	agent_id only
Memory per user and agent	user_id + agent_id
Fully isolated per run	All three fields set

Configuration Reference

All settings can be provided via environment variables, a .env file, or passed directly to MemoryManager/MemoryConfig.

AI Core (Required)

Variable	Description
AICORE_CLIENT_ID	OAuth client ID
AICORE_CLIENT_SECRET	OAuth client secret
AICORE_AUTH_URL	OAuth token endpoint
AICORE_BASE_URL	AI Core API base URL
AICORE_RESOURCE_GROUP	Resource group (default: default)
AICORE_LLM_MODEL	LLM model name (default: gpt-4o)

Database

Variable	Default	Description
SAP_MEMORY_DB_URL	—	Full connection string (overrides individual fields below)
SAP_MEMORY_DB_HOST	localhost	PostgreSQL host
SAP_MEMORY_DB_PORT	5432	PostgreSQL port
SAP_MEMORY_DB_NAME	sap_memory	Database name
SAP_MEMORY_DB_USER	postgres	Database user
SAP_MEMORY_DB_PASSWORD	postgres	Database password
SAP_MEMORY_DB_SCHEMA	sap_agent_memory	PostgreSQL schema

Memory Tuning

Variable	Default	Description
SAP_MEMORY_INJECT_MIN_IMPORTANCE	0.1	Minimum importance score to inject a fact
SAP_MEMORY_INJECT_LIMIT	20	Max facts injected per session
SAP_MEMORY_CONSOLIDATION_INTERVAL_HOURS	6	How often consolidation runs
SAP_MEMORY_CONSOLIDATION_CLEANUP_INTERVAL_HOURS	1	Cleanup job interval
SAP_MEMORY_CONSOLIDATION_DECAY_DAILY_HOUR	2	Daily decay hour (UTC)
SAP_MEMORY_CONSOLIDATION_DECAY_FACTOR	0.95	Importance multiplier per decay cycle
SAP_MEMORY_CONSOLIDATION_DECAY_THRESHOLD	0.05	Delete facts below this score

Architecture

┌─────────────────────────────────────────────────┐
│                  LangGraph Flow                  │
│                                                  │
│  inject_node ──→ your_agent_node ──→ record_node │
│       │                                   │      │
│       ▼                                   ▼      │
│  Reads facts                      Saves episode  │
│  from DB                          to DB           │
└─────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────┐
│            Background Scheduler                  │
│                                                  │
│  Consolidation (every N hours):                  │
│    Load episodes → Call LLM → Extract facts      │
│    → Write MemoryItems → Mark episodes done      │
│                                                  │
│  Decay (daily):                                  │
│    Multiply importance by decay_factor            │
│    → Delete items below threshold                 │
└─────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────┐
│               PostgreSQL Tables                  │
│                                                  │
│  episodes           Raw conversation records     │
│  memory_items       Consolidated facts + scores  │
│  consolidation_jobs Job tracking / idempotency   │
└─────────────────────────────────────────────────┘

API Reference

MemoryManager

The recommended entry point. Handles DB setup, node creation, and the scheduler.

Method	Description
MemoryManager(*, db_url, aicore_*, ...)	Constructor — saves config, nothing is running yet
await MemoryManager.create(*, ...)	Shortcut: creates instance + calls start()
await manager.start()	Connects to DB, runs migrations, builds nodes, starts scheduler
manager.inject_node	LangGraph node function — fetches memories into state
manager.record_node	LangGraph node function — saves conversation as episode
await manager.close()	Stops scheduler, closes DB pool

Models

Class	Description
WorkingMemoryFrame	Ephemeral per-session frame with injected_facts, scratch, session_id
EpisodeRecord	Raw conversation saved by record_node (id, messages, scope, timestamps)
MemoryItem	Consolidated fact with content, importance, and scope
ConsolidationJob	Tracks a consolidation run (status, facts_created, checksum)

Storage Repositories

Class	Key Methods
EpisodeRepository	save(), get_unconsolidated(), mark_consolidated()
MemoryItemRepository	upsert(), fetch_by_scope(), delete(), decay_importance()
ConsolidationJobRepository	create(), update_status(), get_last_for_scope()

Functions

Function	Description
build_inject_node(pool, config)	Returns an async LangGraph node that injects memories
build_record_node(pool, config)	Returns an async LangGraph node that saves episodes
build_scheduler(pool, config, scopes=)	Returns a configured APScheduler with consolidation + decay jobs
run_consolidation(...)	Manually triggers one consolidation pass for a scope
get_pool(config)	Async context manager for DB connection pool
apply_migrations(pool, config)	Runs SQL migrations to set up the schema

Cloud Foundry / SAP BTP

If deploying to Cloud Foundry, use MemoryConfig.from_vcap_services() to automatically read credentials from service bindings:

import json, os
from agent_memory_layer import MemoryConfig

vcap = json.loads(os.environ["VCAP_SERVICES"])
config = MemoryConfig.from_vcap_services(vcap)

AI Core and PostgreSQL credentials are extracted from the aicore and postgresql bindings respectively.

Inspecting Stored Data

Connect to the database to see what's stored:

# If using Docker
docker exec -it agent-memory-layer-db psql -U memory_user -d agent_memory

# Query examples
SELECT * FROM sap_agent_memory.memory_items ORDER BY importance DESC;
SELECT * FROM sap_agent_memory.episodes ORDER BY created_at DESC;
SELECT * FROM sap_agent_memory.consolidation_jobs ORDER BY started_at DESC;

License

Apache 2.0 — see LICENSE for details.