AG2 + Memanto

AG2 (the AutoGen-style multi-agent stack) agents normally forget context when a chat ends. The memanto-ag2 package registers Memanto memory tools on your AssistantAgent and UserProxyAgent so the LLM can store and retrieve durable memory through Moorcheh — the same remember → recall → answer pattern as other Memanto framework adapters.

Drop-in tools

register_memanto_tools() wires memanto_remember, memanto_recall, and memanto_answer in one call.

AG2-native

Plain Python functions with Annotated hints for register_for_llm / register_for_execution.

Shared namespace

One Memanto agent_id per team or app (like a memory bank); GroupChat agents can share it.

Selective tools

Enable or disable remember, recall, or answer with include_* flags.

How It Works

User message → UserProxyAgent → AssistantAgent (LLM)
                      ↑                    │
                      │         tool_call: memanto_remember | recall | answer
                      └──── execute tool ──┘
                                │
                                ▼
                         SdkClient → Moorcheh (persistent agent namespace)

Memanto does not replace AG2 or your LLM. The model chooses when to call memory tools (unlike AgentCore, which recalls automatically before each handler turn).

AG2 tool	Memanto SDK	Role
`memanto_remember`	`remember()`	Store typed memories (preference, fact, event, …)
`memanto_recall`	`recall()`	Semantic search over stored memories
`memanto_answer`	`answer()`	RAG-style synthesis (similar to “reflect” tools in other integrations)

Memory is scoped by agent_id (e.g. my-ag2-team). Use the same id across chats and processes to share memory; use different ids to isolate tenants or teams.

Prerequisites

Python 3.10+
Memanto and a Moorcheh API key
AG2 0.9.x for runtime chat: pip install "ag2>=0.9,<1" (provides from autogen import AssistantAgent)
An LLM API key your AG2 config uses (e.g. OPENAI_API_KEY for OpenAI)

Install ag2 (AI agents), not a2g — an unrelated PyPI package. Do not use ag2 1.x with this adapter yet; it uses a different API. Pin ag2>=0.9,<1.

Install

Install the adapter

pip install memanto-ag2
pip install "ag2>=0.9,<1"   # AG2 runtime only — not required for unit/smoke tests

memanto-ag2 installs memanto for you via its package dependencies. Use pip install memanto memanto-ag2 only if you want to pin or upgrade memanto explicitly.

Configure Moorcheh

export MOORCHEH_API_KEY=your_key_xxxxxxxxxxxxxxxxxx

Or run memanto once to write ~/.memanto/.env.

Register tools on your agents

import os
from autogen import AssistantAgent, UserProxyAgent
from memanto.cli.client.sdk_client import SdkClient
from memanto_ag2 import openai_llm_config, register_memanto_tools

llm_config = openai_llm_config("gpt-4o-mini")

assistant = AssistantAgent(
    name="assistant",
    llm_config=llm_config,
    system_message=(
        "You have memanto_remember, memanto_recall, and memanto_answer. "
        "Use memanto_remember when the user asks you to store something."
    ),
)
user_proxy = UserProxyAgent(name="user", human_input_mode="NEVER")

client = SdkClient(api_key=os.environ["MOORCHEH_API_KEY"])
register_memanto_tools(
    assistant,
    executor=user_proxy,
    client=client,
    agent_id="my-ag2-team",
)

user_proxy.initiate_chat(
    assistant,
    message="Remember that I prefer Python over JavaScript.",
    max_turns=2,
)

GroupChat with shared memory

Register the same agent_id on every assistant that should read or write shared memory; use one executor (UserProxyAgent) to run tool calls:

executor = UserProxyAgent(name="executor", human_input_mode="NEVER")
for agent in [researcher, writer]:
    register_memanto_tools(agent, executor=executor, client=client, agent_id="team-memory")

Global defaults

from memanto_ag2 import configure, register_memanto_tools

configure(source="ag2-prod", recall_limit=15)
register_memanto_tools(assistant, executor=user_proxy, agent_id="my-bank")

Verify persistence

Run a chat that calls memanto_remember (check AG2 logs for EXECUTING FUNCTION memanto_remember and a Memanto memory ID).
Start a new Python process with the same agent_id.
Ask the assistant to memanto_recall or memanto_answer — prior facts should appear even though AG2 chat history is empty.

From the Memanto repo:

export MOORCHEH_API_KEY=your_key
python integrations/ag2/scripts/smoke_test.py

Steps 1–5 use the API only. Set OPENAI_API_KEY and install ag2 for optional Step 6 (live AG2 chat). Unit tests (no API key):

cd integrations/ag2
PYTHONPATH=memanto_ag2:.. python -m pytest tests/test_tools.py -q

AgentCore vs AG2

	AgentCore	AG2 (this page)
Trigger	Automatic recall/retain each handler turn	LLM invokes tools when needed
Identity	`tenant` + `user_id` + `agent_name`	You choose `agent_id`
Best for	Bedrock AgentCore Runtime handlers	AutoGen/AG2 chats, GroupChat

Shared memory across integrations

All Memanto integration packages use the same Moorcheh-backed agents when they share an agent_id:

Integration	Package	What it does
AG2	`memanto-ag2`	AG2 tool registration for remember / recall / answer.
`integrations/agentcore`	`memanto-agentcore`	Recall/retain wrapper for Bedrock AgentCore Runtime.
`integrations/crewai`	`crewai-memanto`	CrewAI tools for multi-agent memory.
`integrations/langgraph`	`langgraph-memanto`	LangGraph `BaseStore`, nodes, and tools.
`integrations/mcp`	`memanto-mcp`	MCP server for Claude Desktop, Cursor, etc.

Integrations

AG2 (AutoGen)

AG2 + Memanto

Drop-in tools

AG2-native

Shared namespace

Selective tools

How It Works

Prerequisites

Install

Register tools on your agents

GroupChat with shared memory

Global defaults

Verify persistence

AgentCore vs AG2

Shared memory across integrations

Next Steps

​AG2 + Memanto

Drop-in tools

AG2-native

Shared namespace

Selective tools

​How It Works

​Prerequisites

​Install

​Register tools on your agents

​GroupChat with shared memory

​Global defaults

​Verify persistence

​AgentCore vs AG2

​Shared memory across integrations

​Next Steps

AG2 + Memanto

How It Works

Prerequisites

Install

Register tools on your agents

GroupChat with shared memory

Global defaults

Verify persistence

AgentCore vs AG2

Shared memory across integrations

Next Steps