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Smara vs Mem0: Which AI Memory API Should You Use in 2026?

If you're building AI agents that need to remember things between conversations, you've probably come across Mem0. It was one of the first dedicated memory APIs for AI, and for good reason—it solved a real problem. But the landscape has changed. Smara launched in early 2026 with a fundamentally different architecture, and developers are asking: which one should I actually use?

This is a fair, detailed comparison. I built Smara, so take my perspective with the appropriate grain of salt. But I'll present the facts and let you decide.

The Core Difference: Flat Memory vs. Decay-Ranked Memory

Mem0 stores memories as flat key-value pairs with vector embeddings. When you search, you get results ranked purely by semantic similarity. Every memory is treated equally regardless of when it was created or how important it is.

Smara uses Ebbinghaus decay curves to score memories. Each memory has an importance weight (0-1) and a time-based decay score. When you search, results are ranked by a blend of semantic similarity (70%) and decay score (30%). This means recent, important memories naturally float to the top, while stale, low-importance memories fade—just like human memory works.

# Mem0 retrieval: pure cosine similarity
results = mem0.search("user preferences", user_id="alice")
# Returns: everything ever stored, ranked only by embedding distance

# Smara retrieval: similarity x decay blend
results = smara.search(user_id="alice", q="user preferences")
# Returns: results ranked by recency + importance + similarity
# Each result includes: similarity, decay_score, blended score

This matters in practice. When an AI agent has thousands of memories for a user, flat retrieval returns outdated facts alongside current ones. Decay-ranked retrieval naturally prioritizes what's current and relevant.

Feature Comparison

Feature	Smara	Mem0
Vector search	Yes (pgvector, 1024d)	Yes (various backends)
Ebbinghaus decay scoring	Yes	No
Graph memory (edges between facts)	Yes	No
Agent-scoped memory	Yes (built-in)	Manual via metadata
Team/shared memory	Yes (RBAC)	No
Namespaces	Yes	Tags only
Duplicate detection	Automatic (cosine > 0.985)	Manual
Contradiction handling	Auto-replace (cosine 0.94-0.985)	Manual
MCP server	Native	Community
Self-hosting	Docker + Postgres	Docker + various
Context endpoint	Yes	No

Graph Memory

Smara lets you create typed, weighted edges between memories:

# Connect two memories with a relationship
curl -X POST https://api.smara.io/v1/graph/connect \
  -H "Authorization: Bearer smara_..." \
  -d '{
    "from_memory_id": "mem-1",
    "to_memory_id": "mem-2",
    "relationship_type": "contradicts",
    "weight": 0.9
  }'

# Traverse the graph up to 5 hops deep
curl "https://api.smara.io/v1/graph/traverse/mem-1?depth=3"

This enables knowledge graphs where your agent can follow connections between facts, not just search by similarity. Mem0 has no equivalent.

Agent-Scoped Memory

Smara has first-class agent entities. You create an agent, assign it skills, and its memories are automatically scoped:

# Store memory scoped to a specific agent
POST /v1/agents/{agentId}/memories
{ "user_id": "alice", "fact": "Prefers TypeScript over Python" }

# Search only this agent's memories
GET /v1/agents/{agentId}/memories?user_id=alice&q=language preference

Team Memory with RBAC

Smara supports team-based memory with role-based access control. You can share memories across team members with admin, member, and read_only roles. This is critical for multi-agent systems or team-based AI assistants. Mem0 has no team concept.

Pricing

Plan	Smara	Mem0
Free	100 memories, 1 agent	1,000 memories
Developer	$19/mo — 10K memories, 5 agents, 1 team	$99/mo — 10K memories
Team	$79/mo — 100K memories, 25 agents, 5 teams	$499/mo — 100K memories
Enterprise	$199/mo — unlimited	Custom ($1,000+/mo)

At the Developer tier, Smara is 80% cheaper than Mem0. At Team tier, 84% cheaper. Both offer self-hosting.

Developer Experience

Smara's API is RESTful with consistent patterns. Every endpoint follows /v1/{resource}:

from smara import Smara

client = Smara(api_key="smara_...")

# Store
client.store("alice", "Loves hiking on weekends", importance=0.7)

# Search with decay-ranked results
results = client.search("alice", "weekend activities")
for r in results:
    print(f"{r.fact} (score: {r.score}, decay: {r.decay_score})")

# Get LLM-ready context
context = client.context("alice", q="weekend plans", top_n=5)

Mem0's API is also clean and well-documented. It's been around longer and has more community examples. Both are straightforward. Mem0 wins on ecosystem maturity. Smara wins on built-in features.

Architecture

Smara: Single Fastify server + Postgres with pgvector. Embeddings via Voyage AI (1024d). One container + one database.

Mem0: Python service with pluggable vector stores (Pinecone, Qdrant, Weaviate, ChromaDB, pgvector). More flexible but more infrastructure.

For simplicity: Smara. For integration with existing vector databases: Mem0 might be easier.

MCP Native

Smara ships a first-party MCP server. Claude Code, Cursor, and any MCP-compatible tool can use Smara as a memory backend with zero custom code. Install it once and your AI assistant remembers everything across sessions.

Mem0 has community-built MCP integrations, but nothing officially maintained.

Migration Guide: Mem0 to Smara

If you're already on Mem0 and want to switch:

1. Export your memories from Mem0 using their API's list endpoint
2. Map the fields: Mem0's memory → Smara's fact
3. Set importance scores: Default to 0.5 or use recency as a proxy
4. Batch import: Use POST /v1/memories. Deduplication is automatic.
5. Update SDK calls: mem0.add() → smara.store()

# Migration script skeleton
import requests

SMARA_KEY = "smara_..."
MEM0_KEY = "..."

# 1. Export from Mem0
mem0_memories = requests.get(
    "https://api.mem0.ai/v1/memories/",
    headers={"Authorization": f"Token {MEM0_KEY}"},
    params={"user_id": "alice"}
).json()

# 2. Import to Smara
for mem in mem0_memories:
    requests.post(
        "https://api.smara.io/v1/memories",
        headers={"Authorization": f"Bearer {SMARA_KEY}"},
        json={
            "user_id": "alice",
            "fact": mem["memory"],
            "importance": 0.5,
            "source": "mem0-migration"
        }
    )

Verdict

Choose Mem0 if you need a proven, mature ecosystem with lots of community integrations and you're already invested in a specific vector database.

Choose Smara if you want decay-scored memory that behaves like human recall, built-in graph memory, agent and team support, MCP integration, and significantly lower pricing.

For most new projects in 2026, Smara gives you more for less. The Ebbinghaus decay model alone is a meaningful improvement over flat retrieval for any agent that accumulates memories over time.

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