Recall vs Mem0
Both give an agent long-term memory about a user. Mem0 extracts free-text memories into a vector store and, since 2.0, adds them without overwriting; Recall extracts typed statements into append-only records where a newer fact replaces an older one by rule and the old one stays readable. The same eight rows as every comparison on this site.
Recall and Mem0
| Recall | Mem0 | |
|---|---|---|
| Storage ownership | Your own database: polign_db in your S3, GCS or Azure bucket, or PostgreSQL, MongoDB or Qdrant you already run. No vendor control plane. | Open source in your own vector store (Qdrant by default, 20+ supported) with a SQLite history table; or the Mem0 Platform, a hosted service with on-prem on the Custom plan. |
| Typed records | Yes. Each fact is a subject, a predicate from a registry (or coined on first use) and a typed value; text is extracted into those by a model you name. | No. Memories are free-text sentences the extraction model writes; metadata and user, agent and run ids as filters. Platform graph memory links entities for ranking, not typed relations. |
| Corrections and supersession | By rule at read time: a single-valued predicate's newer statement replaces the older one; a text statement can name what it makes untrue. Reads carry what was replaced. | Since mem0ai 2.0.0 (April 2026) add() is add-only: "memories accumulate; nothing is overwritten." The reader is handed every version and picks. Explicit update() and delete() exist. |
| Resume after restart | Yes. Working state, turns, outputs, pointers and in-flight tool calls per agent id, fenced by a lease; a new process resumes from a briefing. | No. Mem0 stores memories, not agent state; checkpointing is left to the framework. |
| Shared across agents | Yes. One database, keys per namespace through recall serve, Kubernetes workload identity, packages for LangGraph, OpenAI Agents SDK, Pydantic AI, LiveKit, Vapi and MCP hosts. | Yes, by user, agent and run ids on one store; the Platform adds per-user API keys and a dashboard. |
| Audit and history | Append-only records with time and source; history, as_of reads, diff, and audit bundles replayable offline. | Open source keeps a history table per memory, written on explicit update and delete. No as-of read. Platform documents expiration_date rather than a changelog. |
| Self-hosted path | pip install polign-recall runs a local database; recall serve or the Helm chart for a shared one. Apache-2.0. | Library mode in any Python process, or a Docker stack with dashboard, keys and audit log (make bootstrap). Apache-2.0. |
| Pricing | Free to self-host, no limits. Paid: design partner seat (60 days free, then $12k for 12 months) or a Production Agreement at $24k a year, flat. | Platform free tier: 10,000 adds and 1,000 retrievals a month for 30 days, then 50,000 adds and 10,000 retrievals with your own model key; usage-based past that; Custom plan for SSO, audit and on-prem. Open source is free. |
When to pick the other tool
Pick Mem0 when you want a hosted memory service with a dashboard and per-user keys and would rather not run a database; when your memories are naturally free text and the reader model can be trusted to pick the current one from several; or when you need one of the twenty-odd vector stores Mem0 supports that Recall does not (Recall runs on polign_db, PostgreSQL, MongoDB and Qdrant). Mem0 has the larger community and more framework examples. On the changing-facts scenario, mem0 answered 18 of 18 with a strong reader model, the same as Recall, at about four times the tokens per saved message; where it fell short was forgetting one fact from a sentence that held two.
Where to go next
- Recall compared with mem0 and Zep: the measured scenario
- Corrective memory, with the record it writes
- Recall and Mem0 fill the same OpenClaw memory slot
Sources, read on November 3, 2026: Mem0 memory operations, mem0ai 2.0.0 release notes, timestamps, self-hosting, pricing; mem0ai 2.2.1 on PyPI. Recall rows from the architecture page and pricing.
Recall is typed, durable memory and state for AI agents. It stores corrections, decisions and task state as append-only records in your own object store (S3 or GCS), so an agent can be corrected without retraining and resumed after a crash, restart or pod replacement without snapshotting the process.
Try it
One pip install brings the client, the recall binary and the database. The snippet on
this page runs as written.