AgentBay vs Mem0
An honest, feature-by-feature comparison. We highlight where AgentBay leads, where Mem0 leads, and where they differ in approach.
TL;DR
AgentBay uses 4-strategy search fusion (alias + tag + FTS + vector), includes confidence decay, poison detection, and runs fully offline. Mem0 has graph memory for entity relationships and publishes LOCOMO benchmark results. Choose AgentBay if you need multi-strategy search, local mode, or security features. Choose Mem0 if graph-first entity tracking is your primary need.
| Feature | AgentBay | Mem0 |
|---|---|---|
| Search Strategy | 4-strategy RRF fusion | Vector search |
| Cross-Encoder Reranking | Yes | No |
| Query Expansion | Heuristic (zero latency) | LLM-based |
| Graph Memory | Auto entity extraction (9 types) | Native graph memory |
| Confidence Decay | 4-tier half-life (7d-365d) | No |
| Memory Tiers | working / episodic / semantic / procedural | Single tier |
| Poison Detection | 20+ patterns (prompt injection, exfil) | No |
| Local Mode | SQLite + FastEmbed (zero cloud) | Partial |
| Self-Hosted | Docker + standalone server | Docker |
| Dreaming (consolidation) | Nightly: promote, synthesize, heal | No |
| Brain Time Machine | Snapshots + rollback | No |
| Multi-Agent Teams | 5-role permissions, SSE events | Basic sharing |
| Python SDK | v1.1.0 (20 LLM providers) | v0.1.x |
| TypeScript SDK | v1.0.0 (brain.chat, local mode) | v0.1.x |
| MCP Server | 117 tools (HTTP + npm) | No |
| Framework Integrations | 15 (LangChain, CrewAI, etc.) | LangChain, CrewAI |
| Published Benchmarks | Yes | LOCOMO benchmark |
| Pricing (free tier) | 1,000 entries, 5,000 calls | 1,000 memories |
| Field Encryption | AES-256-GCM | No |
| Adaptive Learning | Per-project search weight tuning | No |
Graph Memory: Mem0 has native graph memory that tracks entity relationships ("Thomas works at AgentBay", "AgentBay uses PostgreSQL"). AgentBay extracts entities automatically and links entries sharing 2+ entities, but Mem0's graph is more purpose-built for entity-centric queries.
LLM-Based Query Expansion: Mem0 uses an LLM to expand queries with semantically related terms. AgentBay uses a heuristic dictionary (zero latency, zero cost) which covers common programming terms but may miss domain-specific expansions.
LOCOMO Benchmark: Mem0 publishes results on the LOCOMO benchmark, claiming 26% higher accuracy than OpenAI Memory. We have not yet run LOCOMO against AgentBay. When we do, we will publish results on our benchmarks page regardless of outcome.
Search Quality: 4-strategy RRF fusion (alias + tag + FTS + vector) with optional cross-encoder reranking. Mem0 uses vector search only, which struggles with exact name lookups and tag-based queries.
Security: Poison detection (20+ patterns for prompt injection, data exfiltration, destructive commands), AES-256-GCM field encryption, confidence-based trust levels. Mem0 has no memory-layer security.
Local Mode: Full offline operation with SQLite + FastEmbed. No API key, no cloud dependency, no data leaving your machine. Sync to cloud when ready. Mem0's local mode is more limited.
Memory Lifecycle: Confidence decay (memories fade unless accessed), memory tiers (working → procedural), dreaming consolidation (nightly synthesis), brain time machine (snapshot + rollback). These features maintain memory quality automatically over time.
AgentBay (Python)
from agentbay import AgentBay
# Local mode (no signup)
ab = AgentBay()
# Store
ab.memory.store(
title="Auth pattern",
content="JWT with refresh tokens...",
type="PATTERN",
tags=["auth", "jwt"]
)
# Recall
results = ab.memory.recall("auth")
# Auto-memory chat
reply = ab.brain.chat(
"How does auth work?",
provider="openai",
model="gpt-4o"
)Mem0 (Python)
from mem0 import Memory
m = Memory()
# Store
m.add(
"JWT with refresh tokens...",
user_id="user1",
metadata={"category": "auth"}
)
# Recall
results = m.search(
"auth",
user_id="user1"
)
# No built-in chat wrapper
# Must integrate manually