Framework Integrations

Native Mubit memory adapters for popular agent frameworks. This page is a reference index; full walkthroughs live on the per-framework pages.

Mubit ships native adapters for popular agent frameworks. Each adapter plugs into the framework's own memory or store interface, so agents pick up persistent semantic memory and cross-session learning without restructuring how they're built. Pick a framework, copy the minimal snippet, then jump to the full walkthrough on the framework's page.

At a glance

All adapters use the canonical Mubit SDK transport internally. Every Python adapter exposes the full MAS surface — see Feature parity matrix.

CrewAI

Routes CrewAI's unified Memory system through Mubit. Agent observations persist across runs and are queryable across crews.

pip install mubit-crewai[crewai]

from mubit_crewai import MubitCrewMemory
from crewai import Crew, Process
 
memory = MubitCrewMemory(api_key="mbt_...", session_id="crew-run-1")
 
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    memory=memory.as_crew_memory(),
)
crew.kickoff(inputs={"topic": "AI safety"})

Full walkthrough → /sdk/integrations/crewai

LangGraph

Use Mubit as a persistent store for LangGraph StateGraph nodes. Each node reads or writes through PutOp / SearchOp.

pip install mubit-langgraph[langgraph]

from mubit_langgraph import MubitStore
from langgraph.store.base import PutOp, SearchOp
 
store = MubitStore(api_key="mbt_...")
NS = ("memories", "user-1", "session-1")
 
store.batch([PutOp(namespace=NS, key="finding-1", value={"text": "...", "intent": "lesson"})])
results = store.batch([SearchOp(namespace_prefix=NS, query="security issues", limit=5)])
 
graph.compile(store=store)

Full walkthrough → /sdk/integrations/langgraph

LangChain

Drop-in BaseMemory for any LangChain chain. Loads context before each call and saves interactions after; cross-session memory is automatic via Mubit's semantic retrieval.

pip install mubit-langchain[langchain]

from mubit_langchain import MubitChatMemory
from langchain_openai import ChatOpenAI
 
memory = MubitChatMemory(api_key="mbt_...", session_id="chat-1")
llm = ChatOpenAI(model="gpt-4o-mini")
 
context = memory.load_memory_variables({"input": "What happened yesterday?"})
# build messages with context["history"], call LLM
memory.save_context({"input": question}, {"output": response})

Full walkthrough → /sdk/integrations/langchain

LlamaIndex

Exposes MubitChatMemoryBuffer, a BaseMemory-style adapter that backs LlamaIndex chat memory with Mubit's persistent semantic retrieval across sessions.

pip install mubit-llama-index[llama-index]

from llama_index.core import Settings
from mubit_llama_index import MubitChatMemoryBuffer
 
Settings.memory = MubitChatMemoryBuffer(api_key="mbt_...", session_id="session-1")

Full walkthrough → /sdk/integrations/llamaindex

Google ADK

Plug Mubit into ADK's Runner as a BaseMemoryService. Session events are auto-ingested; memory search enriches agent context on the next turn.

pip install mubit-adk[adk]

from mubit_adk import MubitMemoryService
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
 
memory = MubitMemoryService(api_key="mbt_...")
runner = Runner(
    agent=root_agent, app_name="travel",
    session_service=InMemorySessionService(),
    memory_service=memory,
)

Full walkthrough → /sdk/integrations/google-adk

Agno

Two integration surfaces: MemoryDb for Agno's built-in memory system, and a Toolkit with LLM-callable memory tools.

pip install mubit-agno[agno]

from agno.agent import Agent
from agno.memory.v2.memory import Memory
from mubit_agno import MubitAgnoMemory
 
mubit = MubitAgnoMemory(api_key="mbt_...", session_id="run-1")
 
agent = Agent(
    name="Assistant",
    memory=Memory(db=mubit.as_memory_db()),
    tools=[mubit.as_toolkit()],
    enable_agentic_memory=True,
)
agent.run("What do we know about the production database?")

Full walkthrough → /sdk/integrations/agno

AutoGen

MubitMemory implements autogen_core.memory.Memory: attach it to an AssistantAgent and recall is injected as a SystemMessage before every model turn, while new content persists via add(). MubitGroupChatHook forwards multi-agent handoff/feedback signals. Targets autogen-core / autogen-agentchat 0.7.x (not the legacy pyautogen 0.2 line).

pip install "mubit-autogen[autogen]"

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from mubit_autogen import MubitMemory
 
memory = MubitMemory(api_key="mbt_...", session_id="support-1", agent_id="support")
 
agent = AssistantAgent(
    name="support",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    memory=[memory],  # Mubit recall injected before every model turn
)

Full walkthrough → /sdk/integrations/autogen

Agent Lightning

Bridges Agent Lightning rollout rewards into Mubit's attribution loop. MubitHook (a subclass of agentlightning.Hook) recalls lessons at on_rollout_start and credits the recalled entry_ids via record_outcome at on_rollout_end; MubitRewardEmitter gives custom training loops explicit emit_reward / emit_event / emit_step_outcome calls.

pip install "mubit-agent-lightning[agent-lightning]"

import agentlightning as agl
from mubit_agent_lightning import MubitHook
 
hook = MubitHook(api_key="mbt_...", session_id="my-rl-run")
trainer = agl.Trainer(..., hooks=[hook])  # recall at rollout start, credit at rollout end
trainer.fit(my_lit_agent, ...)

Full walkthrough → /sdk/integrations/agent-lightning

Vercel AI SDK

Middleware that wraps any AI SDK model with automatic memory injection and interaction capture.

npm install @mubit-ai/ai-sdk

import { wrapLanguageModel, generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { mubitMemoryMiddleware } from "@mubit-ai/ai-sdk";
 
const model = wrapLanguageModel({
  model: openai("gpt-4o"),
  middleware: mubitMemoryMiddleware({
    apiKey: process.env.MUBIT_API_KEY,
    sessionId: "session-1",
    agentId: "support-agent",
  }),
});
 
await generateText({ model, prompt: "How do I reset my password?" });

Full walkthrough → /sdk/integrations/vercel-ai-sdk

MCP

Exposes Mubit as 21 tools over MCP stdio transport. Usable by any MCP-compatible agent (Claude Desktop, Cursor, custom agents).

npm install @mubit-ai/mcp

MUBIT_API_KEY=$MUBIT_API_KEY MUBIT_ENDPOINT=https://api.mubit.ai npx @mubit-ai/mcp

Tools available: mubit_remember, mubit_recall, mubit_context, mubit_forget, mubit_archive, mubit_dereference, mubit_reflect, mubit_learned, mubit_lessons, mubit_checkpoint, mubit_outcome, mubit_step_outcome, mubit_strategies, mubit_register_agent, mubit_list_agents, mubit_handoff, mubit_feedback, mubit_diagnose, mubit_memory_health, mubit_ingest_status, mubit_status.

Full walkthrough → /sdk/integrations/mcp

Common MAS extensions

Every adapter exposes these Mubit-specific methods beyond the base framework interface:

Feature parity matrix

All Python adapters expose the full Mubit MAS surface through the shared mubit-integration-base client. JS/TS adapters expose the same surface in camelCase.

"via client" means the adapter forwards the method to its underlying MubitControlClient (e.g. MubitChatMemoryBuffer(...).record_outcome(...) on LlamaIndex, mubit.client.diagnose(...) on Vercel AI SDK). The framework-facing surface stays focused on the memory contract the framework expects, while the full MAS coordination remains available in the same process.