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Scoped memory for LLM agents. Install and go.

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Most agent frameworks treat memory as an afterthought — a global dict, a hacked-together file, or nothing at all. MemBus fixes that.

It gives your agents a clean, scoped memory layer backed by a real server — so memories survive restarts, agents can share context, and you can swap the storage backend without touching a line of agent code.

---

Install

pip install membus

After installing, type membus in your terminal to open the interactive quick-start guide.

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How it works

MemBus has two parts:

Part	What it does	Where it runs
membus-server	Stores memories, handles scopes, auth, and adapters	Your server (Render, Railway, self-hosted)
membus (this package)	Python client — read, write, manage memories	Your agent code

The client talks to the server over HTTP. Swap the server's adapter in one env var — no client code changes ever.

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Quick Start

from membus import MemBus, Scope

bus = MemBus()  # uses your deployed server by default

# Store something
bus.write("user_name", "Alice", scope=Scope.USER, scope_id="user_123")

# Get it back — anywhere, any agent
name = bus.read("user_name", scope=Scope.USER, scope_id="user_123")
# → "Alice"

# Clean up a session when it ends
bus.manage(scope=Scope.SESSION, operation="prune", scope_id="sess_456")

---

Memory Scopes

The four scopes are the heart of MemBus. Each one answers the question: who should remember this, and for how long?

Scope	Lifetime	Use it for
user	Forever (until deleted)	Preferences, name, history — anything tied to a person
session	One conversation	Scratchpad state, context window overflow
agent	Long-lived	An agent's private notes, persona, learned behaviours
org	Shared across the team	Research findings, shared context, team knowledge

# Each scope is fully isolated — same key, different scope = different memory
bus.write("status", "active",   scope=Scope.USER,    scope_id="u1")
bus.write("status", "thinking", scope=Scope.SESSION,  scope_id="s1")
bus.write("status", "idle",     scope=Scope.AGENT,    scope_id="researcher")

---

Storage Adapters

Your server decides where memories actually live. The client doesn't care — it just calls the API.

Adapter	Backed by	Survives restarts	Best for
memory	RAM	❌	Dev & testing — zero infra
redis	Redis	✅	Fast, session-friendly production
supabase	Postgres	✅	Persistent, Postgres-backed
chroma	ChromaDB	✅	Semantic / vector memory

Switch adapters in one line on your server:

# In your server's .env
MEMBUS_ADAPTER=supabase

No client code changes. Ever.

You can also declare which adapter you expect in your client code — MemBus will warn you if there's a mismatch:

bus = MemBus(expected_adapter="supabase")
bus.health()
# ⚠ UserWarning: expected 'supabase' but server is running 'memory'

---

Operations

write — store a memory

bus.write(
    key="model_preference",
    value="gpt-4o",
    scope=Scope.USER,
    scope_id="user_123",
    ttl=3600,  # optional: expire in 1 hour
)

read — retrieve a memory

model = bus.read(
    key="model_preference",
    scope=Scope.USER,
    scope_id="user_123",
    default="gpt-4o-mini",  # fallback if not found
)

manage — keep memory clean

# Remove null/empty memories
bus.manage(scope=Scope.SESSION, operation="prune", scope_id="s1")

# Remove exact duplicates
bus.manage(scope=Scope.SESSION, operation="deduplicate", scope_id="s1")

# LLM-powered compression (requires OpenAI key on server)
bus.manage(scope=Scope.SESSION, operation="compress", scope_id="s1")

flush — wipe a scope

deleted = bus.flush(scope=Scope.SESSION, scope_id="sess_456")
# → 12

stats — see what's stored

bus.stats()
# → {"total_keys": 42, "by_scope": {"user": 10, "session": 5, "agent": 20, "org": 7}, "adapter": "supabase"}

---

MCP Interface

MemBus is MCP-native. Expose all memory operations as tools that any MCP-compatible agent can call directly.

Standalone MCP server:

from membus import MemBus
from membus.mcp import MemBusMCPServer

bus = MemBus(
    default_scope_ids={"user": "u1", "session": "s1", "agent": "my_agent", "org": "team"},
)

server = MemBusMCPServer(bus, name="membus")
server.run()  # stdio — plug into Claude, AutoGen, CrewAI, or any MCP host

Register into an existing FastMCP server:

from membus.mcp import register_membus_tools

register_membus_tools(my_existing_mcp_server, bus)
# Adds: memory_read, memory_write, memory_manage, memory_flush, memory_stats

---

Multi-Agent Example (AutoGen)

from membus import MemBus, Scope
from membus.mcp import register_membus_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_core.tools import FunctionTool

bus = MemBus()

# Researcher stores findings
bus.write("findings", "LLMs excel at reasoning tasks", scope=Scope.ORG, scope_id="team")

# Analyst reads them — even in a separate process or server
findings = bus.read("findings", scope=Scope.ORG, scope_id="team")

Memories flow between agents through MemBus — not through chat history. That means they survive context resets, cross-process boundaries, and outlive any single conversation.

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CLI

# Open the interactive guide
membus

# Check server health
membus health --server https://your-server.onrender.com

# View memory stats
membus stats --server https://your-server.onrender.com

# Flush a scope
membus flush --server https://your-server.onrender.com --scope session --scope-id sess_456

# Show the guide again any time
membus guide

Skip the flags by setting env vars:

export MEMBUS_SERVER_URL=https://your-server.onrender.com
export MEMBUS_API_KEY=your-secret-key

membus stats   # just works
membus health

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Deploy Your Own Server

MemBus client connects to a membus-server instance you control. Deploy one in minutes:

1. Clone membus-server
2. Push to GitHub
3. Deploy on Render — render.yaml is included, everything is pre-configured
4. Set your env vars in the Render dashboard
5. Point the client at your URL

bus = MemBus(server_url="https://your-membus.onrender.com", api_key="your-key")

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Links

• GitHub
• membus-server
• PyPI