Mem0 pioneered the AI memory API category. It proved that AI agents need persistent memory and that developers will pay for a managed solution. But in 2026, the landscape has expanded. Whether you're looking for better pricing, different architecture, self-hosting options, or features Mem0 doesn't offer, there are strong alternatives.
| Smara | Zep | Letta | agentmemory | Custom PG | |
|---|---|---|---|---|---|
| Type | Managed + self-host | Managed + self-host | Open-source | Library | DIY |
| Pricing | Free-$199 | Free-$499 | Free | Free | Infra only |
| Decay scoring | Yes | No | No | No | DIY |
| Graph memory | Yes | No | Partial | No | DIY |
| Agent-scoped | Yes | Yes | Yes | No | DIY |
| Team/RBAC | Yes | Enterprise | No | No | DIY |
| MCP server | Native | Community | No | No | No |
Memory API with Ebbinghaus decay scoring, graph memory, and native MCP support. Stores memories as facts with vector embeddings in Postgres. Results ranked by a blend of semantic similarity (70%) and temporal decay (30%).
| Plan | Price | Memories | Agents | Teams |
|---|---|---|---|---|
| Free | $0/mo | 100 | 1 | 0 |
| Developer | $19/mo | 10,000 | 5 | 1 |
| Team | $79/mo | 100,000 | 25 | 5 |
| Business | $199/mo | Unlimited | Unlimited | Unlimited |
Long-term memory for AI assistants. Ingests full conversation transcripts and automatically extracts entities, summaries, and facts. Maintains a temporal graph of entities across conversations.
| Plan | Price | Sessions |
|---|---|---|
| Free | $0/mo | 100 sessions |
| Growth | $49/mo | 1,000 sessions |
| Scale | $499/mo | Unlimited |
Evolved from the MemGPT research paper. Gives the LLM itself tools to manage its own memory—reading, writing, searching, and archiving across three tiers: core memory, recall memory, and archival memory.
Minimal Python library. Stores memories in ChromaDB or SQLite. No API server, no authentication, no billing. Just a Python import.
from agentmemory import create_memory, search_memory
create_memory("preferences", "User prefers dark mode")
results = search_memory("preferences", "UI settings")Build your own memory system. Create a table with a vector column, store embeddings, search with cosine similarity. Add your own decay logic, deduplication, and organization.
CREATE EXTENSION vector;
CREATE TABLE memories (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id TEXT NOT NULL,
fact TEXT NOT NULL,
embedding VECTOR(1024),
importance FLOAT DEFAULT 0.5,
created_at TIMESTAMPTZ DEFAULT NOW()
);
SELECT id, fact, 1 - (embedding <=> $1::vector) AS similarity
FROM memories WHERE user_id = $2
ORDER BY embedding <=> $1::vector LIMIT 10;Choose Smara if you want the best retrieval quality (decay scoring), need graph memory or team features, and want managed hosting at a reasonable price.
Choose Zep if you're building enterprise AI assistants with conversation-level memory and need automatic entity extraction.
Choose Letta if you want the LLM to manage its own memory and you're comfortable self-hosting.
Choose agentmemory if you're prototyping locally and want the simplest possible integration.
Choose custom Postgres if you have specific requirements, database expertise, and development time to invest.
For most developers building AI agents with persistent memory in 2026, Smara offers the best combination of features and value—decay scoring alone is a meaningful improvement over flat retrieval, and you get graph memory, agents, teams, and MCP support at a fraction of Mem0's price.
Try Smara free. 100 memories, 1 agent, no credit card required.
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