Mem0

What it does

Mem0 describes itself as the memory layer for AI agents, providing drop-in memory infrastructure so context persists across sessions. The GitHub README introduction says it enhances assistants and agents with an intelligent memory layer that remembers user preferences, adapts to individual needs, and learns over time, with use cases in AI assistants, customer support, healthcare, productivity, and gaming.

Core capabilities listed include multi-level memory retaining user, session, and agent state, a developer-friendly API with cross-platform SDKs, and a fully managed service option. The README 2026 algorithm notes describe single-pass extraction, agent-generated facts as first-class memories, entity linking across memories, multi-signal retrieval combining semantic, keyword, and entity matching, and temporal reasoning for dated queries.

Delivery options cover a Python and npm library installed as mem0ai, a self-hosted server run with Docker Compose with dashboard and API key auth, and a hosted cloud platform at app.mem0.ai. The library requires an LLM for extraction, with OpenAI models as defaults for chat and embeddings, and supports other LLMs and embedding providers through configuration.

Verified capabilities

Quick start

Add and search memory with the Python library

Install mem0ai from the README library quickstart, instantiate Memory, and use search and add in a chat loop with an OpenAI client as shown in the basic usage example.

pip install mem0ai

from mem0 import Memory

memory = Memory()
memory.add("Prefers dark mode", user_id="alice")
results = memory.search(query="What does Alice prefer?", filters={"user_id": "alice"}, top_k=3)
print(results)

Source: https://github.com/mem0ai/mem0. Examples use placeholders only. Never paste a real key into a profile, config file you share, or a ticket.

MCP support: Mem0 integration references describe an MCP server option and editor plugins with MCP server connection, exposing memory profile, search, and conclude tools in compatible agents.

Works with

Only sourced support is listed. A missing framework means AgentsUse has not verified it yet, not that it cannot work.

Health and maintenance

Pricing and license

Freemium model with open source self-hosting at no license cost, a free Hobby cloud tier with monthly add and retrieval request limits, and paid Starter and Pro cloud tiers priced per month with larger request allowances, unlimited projects on Pro, graph memory, and advanced analytics. Enterprise is custom with on-prem deployment, audit logs, SSO, and SLA support, and usage-based pricing is also offered.

Official pricing or docs →

Limitations and safety

• The README states managed platform benchmark scores include proprietary optimizations not available in the open source SDK, so open source users should expect directionally similar but not identical results.
• Mem0 requires an LLM to function for extraction, with OpenAI as the default, so self-hosted use still depends on a model provider or local model configuration.
• Advanced cloud features such as graph memory, Dream consolidation, advanced analytics, and private support are limited to Pro and Enterprise tiers on the pricing page.

Alternatives to Mem0

Related tools

Common questions