Mem0 alternatives: who should own the memory store?

The options at a glance

MongrelDB: the engine you own

MongrelDB is not a memory framework with a storage backend hidden underneath. It is the storage engine. Dense embeddings, sparse vectors, lexical content, set fingerprints, metadata, and timestamps each get a specialized index while resolving through one shared RowId space. Scored hybrid search applies hard filters first, unions named retrievers, and fuses ranks with deterministic RRF.

For agent memory, that means one query can mean "similar to this thought, containing this exact fragment, in this project, for this user, after this date, not a duplicate" without stitching separate systems together. WAL, MVCC snapshots, constraints, SQL, CDC, replication, and page-level AES-256-GCM encryption come with the engine. The Hermes Agent memory plugin is a working example: MongrelDB-backed memory in native FFI or daemon mode, dense ANN with all-MiniLM-L6-v2 at 384 dimensions by default, and a model-free sparse path when no embedding model is configured.

Mem0: the incumbent, and why teams look around

Mem0's public repository describes a universal memory layer for AI agents, Apache-2.0 licensing, Python and JavaScript SDKs, multi-level memory, and a managed service option. Its 2026 README describes a memory algorithm with single-pass extraction, agent-generated facts, entity linking, multi-signal retrieval, and temporal reasoning.

Two details push teams toward alternatives. First, the open-source path does not own storage: you configure an external vector store such as Qdrant, Chroma, PGVector, Pinecone, Milvus, or Redis behind it. Second, Mem0's published benchmark table discloses that the managed platform includes proprietary optimizations not available in the open-source SDK, which matters if you are evaluating self-hosting claims. None of this makes Mem0 a bad choice; it makes the boundary explicit. If the framework abstraction fits, use it. If the storage boundary is the requirement, look down the stack.

Zep Cloud and Graphiti: temporal memory as a service or framework

Zep's current product is Zep Cloud, a managed agent-memory platform built around temporal context graphs: entities, relationships, episodes, fact validity windows, and prompt-ready context blocks. The old Community Edition is deprecated, and the public getzep/zep repository now carries examples and integrations rather than the product.

Graphiti is the open-source counterpart, Apache-2.0, a Python framework for temporal knowledge graphs. It is not self-contained storage: its README lists an external graph database requirement and an LLM API key. If you want Zep's worldview without Zep Cloud, Graphiti plus that infrastructure is the path to evaluate.

Letta: an agent runtime with memory, not only a memory store

Letta, formerly MemGPT, is an Apache-2.0 framework for building stateful agents with memory that can learn over time. The current source lives in the letta-code repository, which includes the agent harness, an interactive terminal UI, an App Server for local or self-hosted agents, channels for Slack, Telegram, and Discord, a desktop app for macOS, Windows, and Linux, a TypeScript Agent SDK, and Letta Cloud for syncing memory across machines.

Letta is the right comparison when the question is "which agent runtime should own memory" rather than "which database should store it." If you are already building on a different agent stack, adopting a second runtime for memory alone is a heavy trade.

The DIY path: pgvector or sqlite-vec

Plenty of teams answer "mem0 alternative" with infrastructure they already run. pgvector adds exact and approximate vector search to Postgres, including half-precision, binary, and sparse vectors, and inherits ACID transactions, point-in-time recovery, and JOINs from Postgres itself. sqlite-vec is a tiny pure-C SQLite extension for exact KNN via brute-force scan, still pre-v1 by its own README, paired with FTS5 for text when you need a do-it-yourself hybrid.

The DIY path gives you full ownership of storage with mature operational tooling, at the cost of designing the memory layer yourself: extraction policy, ranking, deduplication, retention, and the forget path are all yours to build. That is the same work every framework option is trying to sell you back.

How to choose

Stay with a framework or service when

• you want memory behavior, extraction, and entity handling decided for you;
• the framework's retrieval model already matches your agent design;
• external LLM, embedding, and vector store dependencies are acceptable;
• a managed memory platform fits your privacy and availability requirements.

Choose MongrelDB when

• memory should remain local, embedded, or inside your own service;
• you need SQL and native transactions around the memory store;
• retrieval needs exact text and metadata filters as first-class signals beside vectors;
• encryption at rest, credentials, backup, and recovery are part of the requirement;
• you want to build your own memory policy without renting the category abstraction.

Evaluation checklist

1. Write down the memory types you store: user facts, session state, agent actions, project state, and tool results.
2. Test queries that mix paraphrase, exact phrase, project filters, and recency. A vector-only test will flatter the wrong system.
3. Measure the full path: extraction, embedding, write, commit, retrieval, rerank, and deletion. Do not compare one ANN number against a complete memory API.
4. Count external dependencies on the write path and the search path. Each LLM, vector store, or hosted service is another availability and privacy boundary.
5. Run a delete and correction test. Agent memory that cannot forget cleanly will become a liability.

Mem0 alternatives FAQ

What is the best self-hosted Mem0 alternative?

It depends on the layer you want to own. MongrelDB fits when you want the embedded storage and retrieval engine itself. Letta fits when you want an agent framework with memory built in. Graphiti fits when you want a temporal knowledge graph and accept its external graph database and LLM requirements.

Is MongrelDB a drop-in Mem0 replacement?

No. Mem0 is a memory framework with extraction behavior and a managed platform. MongrelDB is the embedded database engine underneath a memory system, with hybrid retrieval indexes, SQL, transactions, and encryption. You keep your own memory policy on top.

Which Mem0 alternatives are open source?

MongrelDB is MIT or Apache-2.0, Letta is Apache-2.0, Graphiti is Apache-2.0, pgvector uses the PostgreSQL license, and sqlite-vec is MIT or Apache-2.0. Mem0's own SDK is Apache-2.0; its managed platform includes proprietary optimizations that its published benchmark table says are not in the open-source SDK.

Can I run agent memory without an external vector database?

Yes. MongrelDB runs embedded or as a local daemon with dense ANN and sparse retrieval built in. sqlite-vec runs inside SQLite with exact KNN. pgvector keeps vectors inside Postgres. Each removes the separate vector store that Mem0's open-source path asks you to configure.

Why look for a Mem0 alternative at all?

The common reasons are ownership and dependencies. Mem0's open-source path relies on configured LLM and embedding components plus an external vector store such as Qdrant, Chroma, PGVector, Pinecone, Milvus, or Redis. Teams that need memory to obey the same durability, encryption, and recovery rules as the rest of the product often move down the stack to an engine.