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## Summary This PR introduces comprehensive Supermemory integration for the Microsoft Agent Framework, providing three complementary approaches to add persistent memory capabilities to agents: middleware for automatic memory injection, context providers for session-based memory management, and tools for explicit memory operations. ## Key Changes - **SupermemoryChatMiddleware**: Automatic memory injection middleware that fetches relevant memories from Supermemory before LLM calls and optionally saves conversations. Supports three modes: - `"profile"`: Injects all static and dynamic profile memories - `"query"`: Searches for memories relevant to the current user message - `"full"`: Combines both profile and query modes - **SupermemoryContextProvider**: Idiomatic context provider following the Agent Framework pattern (similar to built-in Mem0 integration). Integrates with the session pipeline via `before_run()` and `after_run()` hooks for automatic memory retrieval and storage. - **SupermemoryTools**: FunctionTool-compatible tools that agents can use for explicit memory operations: - `search_memories()`: Search for specific memories - `add_memory()`: Add new memories - `get_profile()`: Retrieve user profile - **Utility Functions**: Helper functions for: - Memory deduplication across static, dynamic, and search result sources - Profile-to-markdown conversion for LLM consumption - Message extraction and conversation formatting - Logging with configurable verbosity - **Exception Hierarchy**: Custom exceptions for better error handling: - `SupermemoryConfigurationError`: Missing/invalid configuration - `SupermemoryAPIError`: API request failures - `SupermemoryNetworkError`: Network connectivity issues - `SupermemoryMemoryOperationError`: Memory operation failures - **Comprehensive Documentation**: README with quick start examples, configuration options, and API reference for all three integration approaches. - **Test Suite**: Unit tests covering middleware, context provider, tools, and utility functions with proper mocking and error scenarios. ## Implementation Details - Supports both async (aiohttp) and sync (requests) HTTP clients with automatic fallback - Handles multiple message formats (dict, objects with attributes, content arrays) - Configurable memory storage with optional conversation grouping via `conversation_id` - Environment variable fallback for API key configuration (`SUPERMEMORY_API_KEY`) - Background task management for non-blocking memory operations in middleware - Proper async/sync compatibility for the Supermemory SDK https://claude.ai/code/session_012idB5y6UGK3zmeFULgTc4z
113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
"""Tests for Supermemory middleware."""
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import pytest
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from supermemory_agent_framework import (
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AgentSupermemory,
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SupermemoryChatMiddleware,
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SupermemoryMiddlewareOptions,
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)
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from supermemory_agent_framework.middleware import (
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_get_last_user_message,
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_get_conversation_content,
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)
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def _make_conn(**kwargs):
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kwargs.setdefault("api_key", "test-key")
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kwargs.setdefault("container_tag", "user-123")
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return AgentSupermemory(**kwargs)
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class TestGetLastUserMessage:
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def test_dict_messages(self) -> None:
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messages = [
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{"role": "system", "content": "You are helpful."},
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{"role": "user", "content": "Hello!"},
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{"role": "assistant", "content": "Hi there!"},
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{"role": "user", "content": "How are you?"},
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]
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assert _get_last_user_message(messages) == "How are you?"
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def test_no_user_message(self) -> None:
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messages = [
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{"role": "system", "content": "You are helpful."},
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{"role": "assistant", "content": "Hi!"},
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]
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assert _get_last_user_message(messages) == ""
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def test_empty_messages(self) -> None:
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assert _get_last_user_message([]) == ""
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assert _get_last_user_message(None) == ""
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def test_content_parts(self) -> None:
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Hello"},
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{"type": "text", "text": "world"},
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],
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}
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]
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assert _get_last_user_message(messages) == "Hello world"
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class TestGetConversationContent:
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def test_basic_conversation(self) -> None:
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messages = [
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{"role": "user", "content": "Hello!"},
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{"role": "assistant", "content": "Hi there!"},
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{"role": "user", "content": "How are you?"},
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]
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result = _get_conversation_content(messages)
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assert "User: Hello!" in result
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assert "Assistant: Hi there!" in result
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assert "User: How are you?" in result
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class TestMiddlewareOptions:
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def test_defaults(self) -> None:
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options = SupermemoryMiddlewareOptions()
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assert options.verbose is False
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assert options.mode == "profile"
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assert options.add_memory == "never"
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def test_custom_options(self) -> None:
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options = SupermemoryMiddlewareOptions(
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verbose=True,
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mode="full",
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add_memory="always",
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)
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assert options.verbose is True
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assert options.mode == "full"
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assert options.add_memory == "always"
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class TestMiddlewareConfiguration:
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def test_accepts_connection(self) -> None:
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conn = _make_conn()
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middleware = SupermemoryChatMiddleware(conn)
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assert middleware._container_tag == "user-123"
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def test_uses_connection_client(self) -> None:
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conn = _make_conn()
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middleware = SupermemoryChatMiddleware(conn)
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assert middleware._supermemory_client is conn.client
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def test_conversation_id_from_connection(self) -> None:
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conn = _make_conn(conversation_id="conv-abc")
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middleware = SupermemoryChatMiddleware(conn)
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assert middleware._connection.conversation_id == "conv-abc"
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assert middleware._connection.custom_id == "conversation_conv-abc"
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def test_auto_generated_conversation_id(self) -> None:
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conn = _make_conn()
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middleware = SupermemoryChatMiddleware(conn)
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assert middleware._connection.conversation_id is not None
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assert len(middleware._connection.conversation_id) > 0
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def test_entity_context_from_connection(self) -> None:
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conn = _make_conn(entity_context="User is a Python developer")
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middleware = SupermemoryChatMiddleware(conn)
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assert middleware._connection.entity_context == "User is a Python developer"
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