Best AI Memory for AutoGen (2026)

What AutoGen ships (and what it doesn't)

AutoGen v0.4 is a rewrite. It went from the conversational v0.2 API to an event-driven async runtime split across three packages (autogen-core, autogen-agentchat, autogen-ext). The orchestration story is strong: GroupChats, RoundRobinGroupChat, SelectorGroupChat, Swarm patterns, code-execution agents, all production-grade.

What it ships for memory:

• BufferedChatCompletionContext — keeps the last N messages of the agent's context. Solves "don't overflow the context window" for long runs.
• HeadAndTailChatCompletionContext — keeps the first M and last N messages. Useful when the system message + initial setup matter.
• Tool-result caching — function-call outputs get persisted within the run.

What it doesn't ship: cross-run memory, semantic recall, entity extraction, conflict resolution, GDPR delete, multi-tenant scoping, or a UI for what the team has learned. Those gaps are why people end up here.

The quick answer

If you want a hosted memory layer that drops into AutoGen as a Tool without breaking the v0.4 async story: Ricord. If you want OSS and have engineers: Mem0 OSSwrapped as a FunctionTool. If your agents are single-task and don't need cross-run memory: BufferedChatCompletionContext alone is fine. The matrix is below.

The decision matrix

Ten criteria, six options. AutoGen's built-in BufferedChatCompletionContextis included as the "do nothing" baseline so the cost of adding real memory is honest.

Slot-by-slot — which fits your AutoGen build

If your team runs one task at a time and starts fresh

BufferedChatCompletionContext alone is enough. Your agents share context within the run; nothing outlives team.run(). Right call for evaluation harnesses, one-shot tasks, single-purpose agents.

If your team needs cross-run memory at all

Ricord as a FunctionTool on every agent that needs to remember. One API key, one shared memory — reachable by every agent in the GroupChat. Drop-in below.

If you want OSS and have Python engineers

Mem0 OSS wraps cleanly as an AutoGen FunctionTool. Permissive Apache license; production-grade pieces (conflict resolution, multi-tenant, hard delete) are your responsibility to wire up.

If you're building a custom agent runtime

Lettabundles runtime + memory. Most AutoGen builds won't switch runtimes for this — the event-driven v0.4 design is the framework's real value-add — but if Letta's memory-first stance fits your roadmap, the swap is real.

If extraction-pipeline depth is the goal

Cognee (AGPL-3) is the right pick if your team needs configurable extraction stages. Cognee details →

Ricord as an AutoGen FunctionTool — the drop-in

AutoGen v0.4 expects async tools. Define two async functions, register them as FunctionTools, attach to every AssistantAgent that should remember:

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMentionTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.tools import FunctionTool
import httpx, os

RICORD = "https://api.ricord.ai/v1"
HEADERS = {"Authorization": f"Bearer {os.environ['RICORD_API_KEY']}"}

async def ricord_recall(query: str) -> str:
    """Recall what we know about a topic from persistent memory."""
    async with httpx.AsyncClient() as client:
        r = await client.post(
            f"{RICORD}/memories/search",
            json={"query": query, "limit": 5},
            headers=HEADERS,
        )
    return r.json().get("context") or "No memory found."

async def ricord_save(content: str) -> str:
    """Save a fact for future recall across runs."""
    async with httpx.AsyncClient() as client:
        await client.post(
            f"{RICORD}/memories/fact",
            json={"content": content},
            headers=HEADERS,
        )
    return "Saved."

# Wrap as FunctionTools
recall_tool = FunctionTool(ricord_recall, description="Recall facts from persistent memory.")
save_tool = FunctionTool(ricord_save, description="Save a fact for future recall.")

# Attach to agents — all agents in the team see the same memory
model = OpenAIChatCompletionClient(model="gpt-4o")
researcher = AssistantAgent(
    name="researcher",
    model_client=model,
    tools=[recall_tool, save_tool],
    system_message="Use ricord_recall before answering. Use ricord_save after learning anything new.",
)
critic = AssistantAgent(
    name="critic",
    model_client=model,
    tools=[recall_tool],
    system_message="Critique the researcher's answer using prior context from ricord_recall.",
)

team = RoundRobinGroupChat(
    [researcher, critic],
    termination_condition=TextMentionTermination("TERMINATE"),
)

# Run it — multiple agents, one shared memory
async def main():
    result = await team.run(task="What deploy command should I use for the new project?")
    print(result.messages[-1].content)

Why Ricord wins for production AutoGen teams

1. Shared memory across the GroupChat by default. Every agent that has the tools reads + writes the same memory. No syncing between agents, no namespace juggling. BufferedChatCompletionContext is per-agent; Ricord is per-team.
2. Survives across team.run() calls. AutoGen's built-in primitives reset between runs. Ricord persists what was learned, so the next run starts with prior context auto-fetched.
3. Conflict resolution at ingest. When the team contradicts itself across runs (it will), Ricord supersedes the older fact at write time. Recalls return the current truth.
4. Tenant isolation built in. Each API key is its own isolated memory space, and Ricord Teams give you shared, access-controlled spaces — multi-tenant without running any infra.
5. Browsable wiki view. Your team can inspect what the AutoGen team has learned at ricord.ai/dashboard — organized by entity, with backlinks and a 3D graph view.
6. Cross-client.The same memory the AutoGen backend writes is reachable from Claude Desktop, Cursor, Codex, Zed, Gemini CLI, Windsurf, and Cline via Ricord's MCP server — useful when the devs running AutoGen also debug from an IDE.

When to stick with the built-in

BufferedChatCompletionContext is the right call when:

• The team is single-task per run (eval harness, classifier, one-shot extractor)
• You don't need anything to survive past team.run()
• You don't need a UI for the team to inspect what was learned
• You're still validating the agent topology — adding memory layer is premature

The day any of those flips, attach the Ricord FunctionTools to your agents. AutoGen's tools boundary is the right seam.

Getting started

pip install autogen-agentchat autogen-ext httpx
# Get an API key at https://ricord.ai/login?signup=true
export RICORD_API_KEY=rc_live_...
# Drop the FunctionTools above into your AutoGen agents