Supermemory Alternatives: 7 Agent Memory Tools Compared

Supermemory alternatives are other memory layers for AI agents: Hindsight, Mem0, Zep, Graphiti, Letta, Cognee and Honcho. People usually look for one when they need fully open-source, self-run infrastructure (Supermemory’s local server is a closed-source binary), want memory without bundled RAG and connectors, or need a different memory model such as a temporal graph or an agent that manages its own memory.

Below: what Supermemory does, a comparison table, a short section on each alternative, and how to pick. Facts come from each project’s README, docs and pricing page, checked on 8 October 2026.

What Supermemory does, and why people look for alternatives

Supermemory is a hosted memory and context API. It extracts facts from conversations, maintains a static and dynamic profile per user, runs hybrid search over memories and document chunks, and syncs data from Google Drive, Gmail, Notion, OneDrive and GitHub. It also ships plugins for Claude Code, Cursor, Codex and other tools. Our Supermemory explainer covers how it works.

The common reasons to compare it with others:

1. Open infrastructure. The GitHub repo is MIT, but the docs say the local server’s “source is not in the public repository.” It’s a free binary under a “lite license.” Self-hosting beyond that is on the $399/month Scale plan or Enterprise.
2. You only need memory. If you already have a RAG stack, Supermemory’s bundled document search and connectors are extra surface.
3. A different memory model. Temporal fact validity, agent-edited memory, or reflection over memories are things other systems lead on.
4. Benchmarks you can compare. Supermemory’s headline LongMemEval number is Recall@15, a retrieval metric, while most vendors report answer accuracy. For answer accuracy on changed facts and preferences by system, see the knowledge-update table in semantic memory in AI agents.

Supermemory alternatives at a glance

Prices are the vendors’ published list prices and change often.

Where comparable benchmark numbers exist, they come from Mem0’s comparison pages (answer accuracy, vendor-run):

Sources: Mem0 vs Supermemory and Mem0 vs Hindsight. Zep, Graphiti, Letta, Cognee and Honcho aren’t on those pages, so they’re left out rather than guessed.

Hindsight

Hindsight is the strongest pick here if you’re leaving Supermemory for open infrastructure or better long-history recall. It’s an MIT-licensed memory server with the full server in the open-source release: graph, temporal recall, consolidation, reflect and mental models are all free to self-host. Supermemory’s server source isn’t public, and Mem0 keeps graph memory and temporal reasoning on its hosted Platform (graph memory only on custom paid plans).

It’s built around three operations. retain extracts facts, entities, time data and relationships with an LLM. recall runs semantic, BM25, graph and time-range searches in parallel, merges them with rank fusion and a cross-encoder reranker, and trims the result to a max_tokens budget. reflect reasons over stored memories to answer questions that need thinking, not lookup. In the background it consolidates facts into observations that keep quotes from their evidence, and keeps mental models (standing answers) up to date.

On the numbers Mem0 publishes on its own comparison pages, Hindsight scores 94.6 on LongMemEval, against 85.2 for Supermemory and 94.4 for Mem0. On the long-history BEAM test it leads Mem0 by 9.8 points at 1M tokens (73.9 vs 64.1) and 15.5 at 10M (64.1 vs 48.6). All of these are vendor-run. It stores data in PostgreSQL with pgvector (or an embedded Postgres), works with 25+ LLM providers including local ones, and has 60+ integrations, including Claude Code, Codex, Cursor, LangGraph, CrewAI and n8n.

• Pick Hindsight if you want fully open, self-hosted memory with time-aware recall, consolidation and reasoning, for agents with long histories, and no separate graph database.
• Look elsewhere if you want connectors and document RAG bundled in, as Supermemory provides. Every retain and consolidation pass makes LLM calls, recall returns more context than Mem0 by default (24K-44K tokens by Mem0’s measurement, so set max_tokens), and the README says it “may be overkill” for simple automation workflows.

More in Hindsight explained and Mem0 vs Hindsight.

Mem0

Mem0 is the closest functional match for user memory. An LLM extracts facts from each add call in a single pass, and search fuses semantic, BM25 and entity signals. Memories are scoped by user_id, agent_id and run_id. It runs as a library, a self-hosted Docker server with dashboard and API keys, or the hosted Mem0 Platform.

Two caveats from Mem0’s own docs: graph memory was removed from the open-source SDK in April 2026 and is now on the Platform, on Custom plans only, and its benchmark scores reflect the managed platform, not the open-source SDK.

• Pick Mem0 if you want Supermemory-style user memory that you can run entirely yourself.
• Look elsewhere if you want document RAG and connectors in the same API.

More in Mem0 vs Supermemory, what Mem0 is and Mem0 alternatives.

Zep and Graphiti

Zep builds a temporal context graph per user or account from chat and business data. Each fact carries a validity window, so when a user changes cities, the old fact is invalidated with a date rather than lost. It’s a managed service; Community Edition was deprecated in April 2025.

Graphiti is the Apache 2.0 engine under Zep. You run the graph database and build users and sessions yourself.

• Pick Zep for managed memory where “what’s true now vs before” matters, such as customer and account state.
• Pick Graphiti for the same model on your own infrastructure.
• Look elsewhere if you don’t want a graph database or need document RAG.

See what is Zep memory and Zep alternatives.

Letta

Letta (formerly MemGPT) is an agent runtime, not a memory API. Letta Code agents keep memory as Markdown files in a git-backed store, search their own message history, and consolidate lessons in background “dreaming.” It’s Apache 2.0.

• Pick Letta if you’re building the agent itself and want it to own its memory.
• Look elsewhere if you need memory behind an existing app. The Agent SDK is TypeScript only, and the Python client is deprecated.

Details, including the October 2026 memory changes in Letta Code, in the Letta guide; if you’re choosing between Letta and LangChain’s memory, see Letta vs LangChain memory.

Cognee

Cognee turns documents, code and conversations into a knowledge graph plus vector index, with remember, recall, improve and forget operations. Since v1.6 it can build and search text memory with local models and no LLM key. It has SDKs for Python, TypeScript and Rust.

• Pick Cognee if you used Supermemory mostly for document memory and want it open source with graph retrieval.
• Look elsewhere for simple per-user chat facts; the graph pipeline is more than that needs.

See Cognee alternatives.

Honcho

Honcho from Plastic Labs models users and agents as peers. You store messages on sessions; a background worker reasons over them and builds a representation of each peer, which you query in natural language or pull as prompt-ready context. It’s AGPL-3.0, runs on Postgres with pgvector, and has a managed service and plugins for Claude Code, Codex, Cursor, OpenClaw and Hermes.

• Pick Honcho if you want deep per-user modeling (“what does this user respond to?”), including what one agent knows about another.
• Look elsewhere if AGPL is a problem for your distribution model, or you need document RAG.

More in Honcho LLM memory.

How to choose a Supermemory alternative

Start from why you’re leaving.

Two checks before you switch:

1. Do you rely on Supermemory’s RAG and connectors? Most alternatives are memory-only. You’d pair them with your own retrieval stack; see RAG vs agent memory for where the line falls.
2. Who pays for extraction? Self-hosting removes per-token platform fees, but Mem0, Graphiti, Honcho and Hindsight all call an LLM on writes. Estimate tokens per message before comparing bills.

Vectorize publishes its own Supermemory alternatives roundup; like any comparison from a memory vendor, read it as such. For a neutral fact sheet across eleven systems, see the LLM memory comparison.