A memory backend. Or a portable artifact.

Different layers, often complementary

Mem0 answers "what should this agent remember about this user across sessions?" The job is online: the agent is running, a query came in, recall the relevant facts, ship them in the prompt. Latency budget is single-digit milliseconds, and the data is small but high-stakes (preferences, recent decisions, names).

kolm answers "given enough captured exchanges, can a smaller model do this without the frontier?" The job is offline: enough pairs accumulated, train a LoRA, sign the artifact, ship it. The data is bulky and the model is the deliverable - the recall happens inside the weights.

You can run both. Mem0 is right for the "remember this user prefers metric units" turn-by-turn substrate; kolm is right for the "compile this customer-service bot to a 3 GB file" exit. Both consume the same captured stream.

Where Mem0 wins

Honest concession. Mem0 is the better online-memory product. The vector store is mature, the SDK ergonomics are clean, the hosted plan is the fastest way to add long-term memory to an agent that's already shipped. If your bottleneck is "this agent forgets the user between sessions and that's the user complaint," Mem0 is the right answer.

kolm has nothing in that lane. Our /v1/recall exists but is task-cache shaped, not user-memory shaped. We don't try to be a memory backend; we treat memory as a means to a compileable corpus, not as the product.

Side-by-side

When to use Mem0

Use Mem0 when the question is about retrieval at turn-time - "what does this agent need to recall to give a good answer right now?" Anything where the value is recalling a fact in time to put it in the next prompt.

# add a fact at session end:
client.add("User prefers concise replies", user_id="alice")

# recall in the next session:
ctx = client.search(query, user_id="alice", limit=5)

When to use kolm

Use kolm when the question is about the model itself - "given enough of these exchanges, can a smaller, signed, portable model do the task?" The captured pairs become labelled training data; the verifier becomes the quality gate; the artifact becomes a deliverable that runs anywhere.

# point traffic at the kolm capture proxy:
ANTHROPIC_BASE_URL=https://kolm.ai/v1/capture/anthropic

# once enough pairs accumulate, compile:
kolm compile "summarize support tickets" \
  --namespace support \
  --base qwen2.5-7b

ok wrote support-summarize.kolm  k_score=0.89  signature=hmac-sha256

Can I use both?

Yes. Most production agent stacks will run something like Mem0 for online user-memory recall and kolm for the offline compile path. Mem0 keeps the agent personable across sessions; kolm replaces the expensive frontier hop with a cheap signed model once the namespace has enough volume. They consume the same captured pairs and produce non-overlapping deliverables.