Framework Adapters

July 23, 2026 · View on GitHub

SuperLocalMemory V3 Documentation https://superlocalmemory.com | Part of Qualixar

Nine Python packages back their framework's native memory interface with the local SLM data root. Install alongside the target framework; all data stays in the configured data root unless an optional SLM provider, connector, or backup feature is explicitly enabled.

Prerequisites: Python 3.11+, SuperLocalMemory V3 installed in the same environment.

LangGraph

Install: pip install langgraph-superlocalmemory
Requires: langgraph >= 1.0.0
Implements: BaseStore
Class: SuperLocalMemoryStore

from langgraph_superlocalmemory import SuperLocalMemoryStore

store = SuperLocalMemoryStore()
store.put(("users", "1"), "profile", {"name": "Ada", "role": "engineer"})
item = store.get(("users", "1"), "profile")
results = list(store.search(("users",), filter={"role": "engineer"}))
store.delete(("users", "1"), "profile")

Drop in as the store= argument to create_react_agent(...) or StateGraph(...).compile(store=store).

Semantic Kernel

Install: pip install semantic-kernel-superlocalmemory
Requires: semantic-kernel >= 1.34.0
Implements: VectorStore; get_collection returns a VectorStoreCollection
Class: SuperLocalMemoryVectorStore

from semantic_kernel_superlocalmemory import SuperLocalMemoryVectorStore

store = SuperLocalMemoryVectorStore()
collection = store.get_collection(Doc, collection_name="docs")
await collection.ensure_collection_exists()
await collection.upsert(Doc(id="d1", text="hello world"))
doc = await collection.get("d1")

Written against Semantic Kernel 1.44. Uses the post-1.34 semantic_kernel.data.vector API; the deprecated MemoryStoreBase is not used.

Microsoft Agent Framework

Install: pip install agent-framework-superlocalmemory
Requires: agent-framework-core >= 1.5.0
Implements: ContextProvider and HistoryProvider
Classes: SuperLocalMemoryContextProvider, SuperLocalMemoryHistoryProvider

from agent_framework_superlocalmemory import (
    SuperLocalMemoryContextProvider,
    SuperLocalMemoryHistoryProvider,
)

history = SuperLocalMemoryHistoryProvider()
memory = SuperLocalMemoryContextProvider(max_recall=10)
# agent = ChatAgent(..., context_providers=[memory], history_provider=history)

HistoryProvider implements get_messages / save_messages per session.
ContextProvider overrides before_run (inject context) and after_run (persist turn).

LangChain

Install: pip install langchain-superlocalmemory
Requires: langchain-core >= 1.0.0
Implements: BaseChatMessageHistory
Class: SuperLocalMemoryChatMessageHistory

from langchain_core.messages import AIMessage, HumanMessage
from langchain_superlocalmemory import SuperLocalMemoryChatMessageHistory

history = SuperLocalMemoryChatMessageHistory(session_id="my-chat-session")
history.add_messages([
    HumanMessage(content="What is SuperLocalMemory?"),
    AIMessage(content="A local-first memory system for AI assistants."),
])
messages = history.messages   # chronological list
history.clear()

LlamaIndex

Install: pip install llama-index-storage-chat-store-superlocalmemory
Requires: SuperLocalMemory V3 installed in the same virtual environment
Implements: BaseChatStore
Class: SuperLocalMemoryChatStore

from llama_index.storage.chat_store.superlocalmemory import SuperLocalMemoryChatStore
from llama_index.core.memory import ChatMemoryBuffer

chat_store = SuperLocalMemoryChatStore()
memory = ChatMemoryBuffer.from_defaults(
    chat_store=chat_store, chat_store_key="user-123", token_limit=3000)
# Or directly:
chat_store.add_message("session-1", ChatMessage(role=MessageRole.USER, content="Hi"))

CrewAI

Install: pip install crewai-superlocalmemory
Requires: crewai >= 1.14.6
Implements: StorageBackend
Class: SuperLocalMemoryBackend

from crewai_superlocalmemory import SuperLocalMemoryBackend

backend = SuperLocalMemoryBackend()
# Pass as storage_backend= in your CrewAI memory configuration.

Each MemoryRecord is stored as one SLM memory with session_id = "crewai-rec:<id>". The backend supports scoped search and cosine-ranked retrieval over stored embeddings.

AutoGen

Install: pip install autogen-superlocalmemory
Requires: autogen-agentchat >= 0.7.5
Implements: Memory (autogen-core)
Class: SuperLocalMemoryMemory

from autogen_superlocalmemory import SuperLocalMemoryMemory
from autogen_core.memory import MemoryContent, MemoryMimeType

mem = SuperLocalMemoryMemory()
await mem.add(MemoryContent(content="Ada prefers dark mode.", mime_type=MemoryMimeType.TEXT))
result = await mem.query("Ada preferences")
# agent = AssistantAgent("assistant", memory=[mem], model_client=...)

For projects using Microsoft Agent Framework (agent-framework-core), prefer the agent-framework-superlocalmemory adapter instead.

Google ADK

Install: pip install google-adk-superlocalmemory
Requires: google-adk >= 2.5.0
Implements: BaseMemoryService
Class: SuperLocalMemoryService

from google.adk.agents import Agent
from google.adk.runners import Runner
from google_adk_superlocalmemory import SuperLocalMemoryService

service = SuperLocalMemoryService()
runner = Runner(agent=agent, app_name="my-app", memory_service=service)
# After a session runs, events are stored and retrievable via SLM CLI, MCP, and dashboard.

Implements add_session_to_memory (idempotent upsert per session) and search_memory (semantic recall). Custom database path: SuperLocalMemoryService(db_path="/path/to/memory.db").

OpenAI Agents

Install: pip install openai-agents-superlocalmemory
Requires: openai-agents >= 0.18.3
Implements: SessionABC
Class: SLMSession

from agents import Runner
from openai_agents_superlocalmemory import SLMSession

session = SLMSession(session_id="user-42-conv-7")
runner = Runner(session=session)
result = await runner.run("What is the capital of France?")

Manages the ordered TResponseInputItem conversation history for one session_id. Custom path: SLMSession(session_id="s1", db_path="/path/to/memory.db").

All Adapters at a Glance

Framework	Install name	Interface	Class
LangGraph	`langgraph-superlocalmemory`	`BaseStore`	`SuperLocalMemoryStore`
Semantic Kernel	`semantic-kernel-superlocalmemory`	`VectorStore`	`SuperLocalMemoryVectorStore`
Microsoft Agent Framework	`agent-framework-superlocalmemory`	`ContextProvider` / `HistoryProvider`	`SuperLocalMemoryContextProvider`, `SuperLocalMemoryHistoryProvider`
LangChain	`langchain-superlocalmemory`	`BaseChatMessageHistory`	`SuperLocalMemoryChatMessageHistory`
LlamaIndex	`llama-index-storage-chat-store-superlocalmemory`	`BaseChatStore`	`SuperLocalMemoryChatStore`
CrewAI	`crewai-superlocalmemory`	`StorageBackend`	`SuperLocalMemoryBackend`
AutoGen	`autogen-superlocalmemory`	`Memory`	`SuperLocalMemoryMemory`
Google ADK	`google-adk-superlocalmemory`	`BaseMemoryService`	`SuperLocalMemoryService`
OpenAI Agents	`openai-agents-superlocalmemory`	`SessionABC`	`SLMSession`

Source is under ide/integrations/<framework>/ in the repository. Each adapter has its own README with extended usage examples, prerequisites, and scope notes.