supermemory/apps/docs/integrations/agent-framework.mdx

---
title: "Microsoft Agent Framework"
sidebarTitle: "MS Agent Framework"
description: "Add persistent memory to Microsoft Agent Framework agents with Supermemory"
icon: "microsoft"
---

Microsoft's [Agent Framework](https://github.com/microsoft/agent-framework) is a Python framework for building AI agents with tools, handoffs, and context providers. Supermemory integrates natively as a context provider, tool set, or middleware — so your agents remember users across sessions.

## What you can do

- Automatically inject user memories before every agent run (context provider)
- Give agents tools to search and store memories on their own
- Intercept chat requests to add memory context via middleware
- Combine all three for maximum flexibility

## Setup

Install the package:

```bash
pip install --pre supermemory-agent-framework
```

Or with uv:

```bash
uv add --prerelease=allow supermemory-agent-framework
```

<Warning>The `--pre` / `--prerelease=allow` flag is required because `agent-framework-core` depends on pre-release versions of Azure packages.</Warning>

Set up your environment:

```bash
# .env
SUPERMEMORY_API_KEY=your-supermemory-api-key
OPENAI_API_KEY=your-openai-api-key
```

<Note>Get your Supermemory API key from [console.supermemory.ai](https://console.supermemory.ai).</Note>

---

## Connection

All integration points share a single `AgentSupermemory` connection. This ensures the same API client, container tag, and conversation ID are used across middleware, tools, and context providers.

```python
from supermemory_agent_framework import AgentSupermemory

conn = AgentSupermemory(
    api_key="your-supermemory-api-key",  # or set SUPERMEMORY_API_KEY env var
    container_tag="user-123",            # memory scope (e.g., user ID)
    conversation_id="session-abc",       # optional, auto-generated if omitted
    entity_context="The user is a Python developer.",  # optional
)
```

### Connection options

| Parameter | Type | Default | Description |
|---|---|---|---|
| `api_key` | `str` | env var | Supermemory API key. Falls back to `SUPERMEMORY_API_KEY` |
| `container_tag` | `str` | `"msft_agent_chat"` | Memory scope (e.g., user ID) |
| `conversation_id` | `str` | auto-generated | Groups messages into a conversation |
| `entity_context` | `str` | `None` | Custom context about the user, prepended to memories |

Pass this connection to any integration:

```python
middleware = SupermemoryChatMiddleware(conn, options=...)
tools = SupermemoryTools(conn)
provider = SupermemoryContextProvider(conn, mode="full")
```

---

## Context provider (recommended)

The most idiomatic integration. Follows the same pattern as Agent Framework's built-in Mem0 provider — memories are automatically fetched before the LLM runs and conversations can be stored afterward.

```python
import asyncio
from agent_framework import AgentSession
from agent_framework.openai import OpenAIResponsesClient
from supermemory_agent_framework import AgentSupermemory, SupermemoryContextProvider

async def main():
    conn = AgentSupermemory(container_tag="user-123")

    provider = SupermemoryContextProvider(conn, mode="full")

    agent = OpenAIResponsesClient().as_agent(
        name="MemoryAgent",
        instructions="You are a helpful assistant with memory.",
        context_providers=[provider],
    )

    session = AgentSession()
    response = await agent.run(
        "What's my favorite programming language?",
        session=session,
    )
    print(response.text)

asyncio.run(main())
```

### How it works

1. **`before_run()`** — Searches Supermemory for the user's profile and relevant memories, then injects them into the session context as additional instructions
2. **`after_run()`** — If `store_conversations=True`, saves the conversation to Supermemory so future sessions have more context

### Configuration options

| Parameter | Type | Default | Description |
|---|---|---|---|
| `connection` | `AgentSupermemory` | required | Shared connection |
| `mode` | `str` | `"full"` | `"profile"`, `"query"`, or `"full"` |
| `store_conversations` | `bool` | `False` | Save conversations after each run |
| `context_prompt` | `str` | built-in | Custom prompt describing the memories |
| `verbose` | `bool` | `False` | Enable detailed logging |

---

## Memory tools

Give agents explicit control over memory operations. The agent decides when to search or store information.

```python
import asyncio
from agent_framework.openai import OpenAIResponsesClient
from supermemory_agent_framework import AgentSupermemory, SupermemoryTools

async def main():
    conn = AgentSupermemory(container_tag="user-123")
    tools = SupermemoryTools(conn)

    agent = OpenAIResponsesClient().as_agent(
        name="MemoryAgent",
        instructions="""You are a helpful assistant with memory.
When users share preferences, save them. When they ask questions, search memories first.""",
    )

    response = await agent.run(
        "Remember that I prefer Python over JavaScript",
        tools=tools.get_tools(),
    )
    print(response.text)

asyncio.run(main())
```

### Available tools

The agent gets three tools:

- **`search_memories`** — Search for relevant memories by query
- **`add_memory`** — Store new information for later recall
- **`get_profile`** — Fetch the user's full profile (static + dynamic facts)

---

## Chat middleware

Intercept chat requests to automatically inject memory context. Useful when you want memory injection without the session-based context provider pattern.

```python
import asyncio
from agent_framework.openai import OpenAIResponsesClient
from supermemory_agent_framework import (
    AgentSupermemory,
    SupermemoryChatMiddleware,
    SupermemoryMiddlewareOptions,
)

async def main():
    conn = AgentSupermemory(container_tag="user-123")

    middleware = SupermemoryChatMiddleware(
        conn,
        options=SupermemoryMiddlewareOptions(
            mode="full",
            add_memory="always",
        ),
    )

    agent = OpenAIResponsesClient().as_agent(
        name="MemoryAgent",
        instructions="You are a helpful assistant.",
        middleware=[middleware],
    )

    response = await agent.run("What's my favorite programming language?")
    print(response.text)

asyncio.run(main())
```

---

## Memory modes

```python
SupermemoryContextProvider(conn, mode="full")  # or "profile" / "query"
```

| Mode | What it fetches | Best for |
|---|---|---|
| `"profile"` | User profile (static + dynamic facts) only | Personalization without query overhead |
| `"query"` | Memories relevant to the current message only | Targeted recall, no profile data |
| `"full"` (default) | Profile + query search combined | Maximum context |

---

## Example: support agent with memory

A support agent that remembers customers across sessions:

```python
import asyncio
from agent_framework import AgentSession
from agent_framework.openai import OpenAIResponsesClient
from supermemory_agent_framework import (
    AgentSupermemory,
    SupermemoryChatMiddleware,
    SupermemoryMiddlewareOptions,
    SupermemoryContextProvider,
    SupermemoryTools,
)

async def main():
    conn = AgentSupermemory(
        container_tag="customer-456",
        conversation_id="support-session-789",
        entity_context="Enterprise customer on the Pro plan.",
    )

    provider = SupermemoryContextProvider(
        conn,
        mode="full",
        store_conversations=True,
    )

    middleware = SupermemoryChatMiddleware(
        conn,
        options=SupermemoryMiddlewareOptions(
            mode="full",
            add_memory="always",
        ),
    )

    tools = SupermemoryTools(conn)

    agent = OpenAIResponsesClient().as_agent(
        name="SupportAgent",
        instructions="""You are a customer support agent.

Use the user context provided to personalize your responses.
Reference past interactions when relevant.
Save important new information about the customer.""",
        context_providers=[provider],
        middleware=[middleware],
    )

    session = AgentSession()

    # First interaction
    response = await agent.run(
        "My order hasn't arrived yet. Order ID is ORD-789.",
        session=session,
        tools=tools.get_tools(),
    )
    print(response.text)

    # Follow-up — agent automatically has context from first message
    response = await agent.run(
        "Actually, can you also check my previous order?",
        session=session,
        tools=tools.get_tools(),
    )
    print(response.text)

asyncio.run(main())
```

---

## Error handling

The package provides specific exception types:

```python
from supermemory_agent_framework import (
    AgentSupermemory,
    SupermemoryConfigurationError,
    SupermemoryAPIError,
    SupermemoryNetworkError,
)

try:
    conn = AgentSupermemory()  # no API key set
except SupermemoryConfigurationError as e:
    print(f"Missing API key: {e}")
```

| Exception | When |
|---|---|
| `SupermemoryConfigurationError` | Missing API key or invalid config |
| `SupermemoryAPIError` | API returned an error response |
| `SupermemoryNetworkError` | Connection failure |
| `SupermemoryTimeoutError` | Request timed out |
| `SupermemoryMemoryOperationError` | Memory add/search failed |

---

## Related docs

<CardGroup cols={2}>
  <Card title="User profiles" icon="user" href="/user-profiles">
    How automatic profiling works
  </Card>
  <Card title="Search" icon="search" href="/search">
    Filtering and search modes
  </Card>
  <Card title="OpenAI Agents SDK" icon="message-bot" href="/integrations/openai-agents-sdk">
    Memory for OpenAI Agents SDK
  </Card>
  <Card title="LangChain" icon="link" href="/integrations/langchain">
    Memory for LangChain apps
  </Card>
</CardGroup>