AgentMemory vs Mem0

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

Tagline

AgentMemory

Open-source persistent memory runtime for AI coding agents, with hybrid retrieval and zero external dependencies.

Mem0

Persistent memory layer for AI agents and LLM apps

Pricing

AgentMemory

Free· Free, open-source (Apache 2.0); bring your own LLM API key

Mem0

Freemium· Free tier

Free trial

AgentMemory

Not listed

Mem0

Yes

API

AgentMemory

Yes

Mem0

Yes

Platforms

AgentMemory

cliapi

Mem0

—

Open source

AgentMemory

Yes · Apache-2.0

Mem0

Yes

GitHub stars

AgentMemory

29,984

checked 2026-10-29

Mem0

—

Last GitHub push

AgentMemory

2026-10-28

Mem0

—

First commit

AgentMemory

2026-02

Mem0

—

Package downloads (30 d)

AgentMemory

77,138 · npm @agentmemory/agentmemory

checked 2026-11-07

Mem0

—

Hacker News stories

AgentMemory

checked 2026-11-07

Mem0

checked 2026-10-16

Earliest Wayback capture

AgentMemory

2026-04-19

earliest archived capture of the site, not a launch date

Mem0

2024-08-01

earliest archived capture of the site, not a launch date

Company

AgentMemory

Mem0

—

Model used

AgentMemory

Multi-model (Claude, Gemini, MiniMax, OpenRouter)

Mem0

Model-agnostic — pluggable via OpenAI, Anthropic, Gemini, Ollama, LiteLLM (no in-house LLM)

Best for

AgentMemory

Pick AgentMemory if you want a drop-in, self-hosted memory layer for your coding agent stack without running a vector DB or graph store.

Mem0

Teams shipping stateful AI agents, copilots, or long-running chat apps where cross-session recall of user facts, preferences, and history is a product requirement rather than a nice-to-have.

Not for

AgentMemory

Skip it if you need a hosted, multi-tenant memory service with SLAs or a production-grade datastore behind it.

Mem0

One-off single-turn chatbots, stateless RAG-over-docs apps, or hobby projects where a short conversation buffer inside the prompt is already enough — the extra infra is not worth it.

Editorial score

AgentMemory

7.3 / 10

Mem0

—

Use cases

AgentMemory

agent-memorycoding-agentscontext-managementknowledge-graphmcp-server

Mem0

Long-lived customer support copilotsPersonal AI companions and journaling agentsHealthcare intake and follow-up assistantsSales and CRM enrichment agentsEducation tutors with per-student progressMulti-session coding agentsAutonomous research agentsE-commerce shopping assistants

Pros

AgentMemory

Zero external infrastructure: single Node process, JSON on disk
Triple-stream retrieval (BM25 + vectors + graph) with sub-20ms recall
Native plugins for Claude Code, Cursor, Cline, Windsurf, and 15+ agents
Apache 2.0, runs on your existing LLM subscription
Strong benchmark numbers (95.2% R@5 on LongMemEval-S)

Mem0

Open-source core with a permissive Apache-2.0 license and a large community — you can self-host end to end if you don't want a managed dependency
Model-agnostic and vector-store-agnostic: works with OpenAI, Anthropic, Gemini, Ollama, Qdrant, Pgvector, Chroma, Weaviate, Neo4j and more via drop-in providers
Genuine token savings — the hierarchical distillation writes compact facts instead of raw transcripts, which shrinks retrieval prompts and speeds up long-lived agents
Both hosted and self-hosted paths, so you can prototype on the free tier and later move to Kubernetes / air-gapped / on-prem without rewriting
Enterprise-grade governance on paid tiers: SOC 2 Type I, HIPAA, audit logs, SSO, project isolation — rare in the memory-layer space
Simple, well-documented Python and TypeScript SDKs with a small surface (add / search / update / delete) that drops into existing agent frameworks

Cons

AgentMemory

Pre-1.0 (v0.9.27), expect breaking changes
Single-process JSON storage may not scale to team-wide deployments
Self-hosted only; you operate the runtime
Effectiveness depends on the LLM provider you wire in

Mem0

Extraction quality is only as good as the LLM you point it at — cheap models produce shallow or noisy memories and can miss nuance
Managed pricing scales quickly if your agent writes memory aggressively; the $19 Starter's 50k add-cap is easy to blow past on chatty workloads
Adds an extra hop and its own vector/graph infra to reason about — for small single-session chatbots it is real overkill
Graph memory features are newer and less battle-tested than the flat vector-memory path; expect some rough edges on complex ontologies
Self-hosted setup with graph + vector + LLM providers has meaningful ops surface if you were hoping for a one-line install

Website

AgentMemory

agent-memory.dev

Mem0

mem0.ai

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.

Pick AgentMemory if

✅ Zero external infrastructure: single Node process, JSON on disk
✅ Triple-stream retrieval (BM25 + vectors + graph) with sub-20ms recall
✅ Native plugins for Claude Code, Cursor, Cline, Windsurf, and 15+ agents
✅ Apache 2.0, runs on your existing LLM subscription

Pick Mem0 if

✅ Open-source core with a permissive Apache-2.0 license and a large community — you can self-host end to end if you don't want a managed dependency
✅ Model-agnostic and vector-store-agnostic: works with OpenAI, Anthropic, Gemini, Ollama, Qdrant, Pgvector, Chroma, Weaviate, Neo4j and more via drop-in providers
✅ Genuine token savings — the hierarchical distillation writes compact facts instead of raw transcripts, which shrinks retrieval prompts and speeds up long-lived agents
✅ Both hosted and self-hosted paths, so you can prototype on the free tier and later move to Kubernetes / air-gapped / on-prem without rewriting

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