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. Pass user_id at tool-call time; the same memory is 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']}"}

# Stable per-run user_id — in production this is your auth-resolved tenant
USER_ID = "alex"

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.get(
            f"{RICORD}/memories/recall",
            params={"user_id": USER_ID, "query": query, "k": 5},
            headers=HEADERS,
        )
    hits = r.json().get("hits", [])
    return "\n".join(h["content"] for h in hits) 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",
            json={"user_id": USER_ID, "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. Per-user scoping is a parameter. Pass user_id in your tool functions; the layer handles isolation. Multi-tenant by design.
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