COGNITIVEX · MIGRATION GUIDE

How to migrate from Mem0 to CognitiveX

Export your Mem0 memories to JSON, map them to the LCM's four-tier memory, and import them over MCP. No rewrite. The LLM stays swappable.

The migration is three steps: export your Mem0 memories to JSON, map each record onto one of CognitiveX’s four memory tiers, then write them in through the same Model Context Protocol tool call your agent already uses. Because both systems live behind a memory tool, you change where memory is stored, not how your agent calls it.

The reason to switch is the category difference. Mem0 is a memory store: it extracts the facts your users state and recalls them later. CognitiveX is the Large Cognition Model (the LCM): the memory is the model. It keeps your stated facts, but it also learns the patterns users repeat, decays stale memories by salience, and consolidates episodes into durable preferences overnight. If you only need stated-fact recall, Mem0 is enough. If you want memory that gets smarter, that is the line you cross here.

WHAT MAPS TO WHAT

How Mem0 records land in the LCM

Mem0 stores extracted facts (optionally with a graph of entity relationships). CognitiveX sorts memory into four tiers, so the only real prep work is deciding which tier each export row belongs to. In practice most Mem0 records are semantic facts, and you can let the cognition engines re-sort the rest over time.

Mem0 concept	CognitiveX tier	What lands there
Stated fact / preference	semantic	“Prefers dark mode”, “Works in healthcare”
Conversation / event memory	episodic	“Asked about pricing on Jun 3”
How-to / procedure note	procedural	“Deploy via the staging branch first”
Identity / core trait	foundational	Stable identity anchors and values
Graph relationships	built by consolidation	Relationships emerge from what recurs

Not sure? Default to semantic. Storing memories is free, and dream consolidation re-weights and promotes them later.

STEP 1

Export your Mem0 memories to JSON

Mem0’s client lets you page through all memories for a user. Pull them and write a flat JSON array, one object per memory. If you self-host Mem0, the same client call works against your own instance.

from mem0 import MemoryClient
import json

client = MemoryClient()  # or Memory() for self-hosted

records = []
for user_id in ["alice", "bob"]:
    for m in client.get_all(user_id=user_id):
        records.append({
            "user_id": user_id,
            "text": m["memory"],
            "created_at": m.get("created_at"),
            "metadata": m.get("metadata", {}),
        })

with open("mem0_export.json", "w") as f:
    json.dump(records, f, indent=2)

You now have a portable mem0_export.json. This file is provider-neutral; it is the same artifact you would keep as a backup regardless of where memory lives next.

STEP 2

Map each record to a memory tier

Walk the export and assign a memory_type using the table above. A small heuristic gets you most of the way; everything you are unsure about defaults to semantic.

import json

with open("mem0_export.json") as f:
    records = json.load(f)

def classify(text: str) -> str:
    t = text.lower()
    if any(k in t for k in ("i am", "my name", "i value")):
        return "foundational"
    if any(k in t for k in ("how to", "always", "step", "deploy")):
        return "procedural"
    if any(k in t for k in ("on ", "asked", "yesterday", "last week")):
        return "episodic"
    return "semantic"  # default: timeless fact / preference

for r in records:
    r["memory_type"] = classify(r["text"])

Do not over-engineer this. The LCM’s salience and pattern-detection engines re-rank and re-tier memories as they get used; a perfect first-pass classification is not the point. Getting the data in is.

STEP 3

Import over MCP, no rewrite

CognitiveX is MCP-native, so importing is just calling the remember tool once per record. If your agent already calls a memory tool, this is the same call surface you point at the new endpoint. Storing memories is free, so you can replay the whole export in one pass.

# Pseudocode against the CognitiveX MCP "remember" tool.
# Wire it through your MCP client of choice (Claude, your SDK, etc.).

for r in records:
    remember(
        content=r["text"],
        memory_type=r["memory_type"],   # semantic | episodic | procedural | foundational
        # optional: attribute to the same user namespace you exported from
    )

That is the whole migration. Recall now works over MCP the same way storage did: point any client at CognitiveX and it consolidates server-side. From here, the parts Mem0 did not do start happening on their own: repeated episodes get promoted into semantic preferences, stale memories decay by salience, and overnight dream consolidation synthesizes relationships across what you imported.

For the full tool schema, auth, and SDK setup, see the CognitiveX platform docs and SDK.

THE PAYOFF

What changes after the import

Mem0 stores the facts your users state. CognitiveX keeps those and adds the cognition layer on top, which is the reason to go through the migration at all.

Pattern learning. Recurring behavior with no sentence to extract still gets captured as episodes and promoted into preferences.
Salience-weighted decay. Stale memories lose weight instead of accumulating forever and diluting recall.
Overnight consolidation. A dream pass compresses memory and synthesizes relationships across what you imported.
LLM stays infrastructure. The model is swappable; the intelligence lives in the memory architecture, not in any one provider.

For the honest side-by-side of when Mem0 is enough and when the consolidation layer earns the switch, read the CognitiveX vs Mem0 breakdown or the full comparison.

QUESTIONS

Migration FAQ

Will migrating from Mem0 require a rewrite of my agent?

No. CognitiveX is exposed over the Model Context Protocol, so it drops in wherever you already call a memory tool. You change the endpoint your client points at, not your agent's control flow. The LLM stays swappable.

What happens to memories that Mem0 stored as plain facts?

They import as semantic memories, the LCM's tier for timeless facts and preferences. You do not need to pre-classify everything; storing is free, and the cognition engines re-weight salience and promote recurring patterns over time.

Do I lose anything by leaving Mem0's graph memory behind?

Mem0's graph layer models explicit entity relationships you write. CognitiveX builds relationships during overnight dream consolidation from what actually recurs, rather than requiring you to author the graph up front. Different mechanism, overlapping outcome.

Can I run both during the transition?

Yes. Because both sit behind a tool call, you can dual-write for a sprint (keep Mem0 as the read path while CognitiveX builds up its memory) then cut the read path over once recall quality matches. There is no hard cutover.

Ready to move your memory?

Plug CognitiveX in over MCP, replay your export, and let the memory start learning. The LLM stays swappable.

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