Mem0 runs an LLM over every message your code sends it and stores the facts in a vector database. recall indexes the chat files Claude Code, Codex and Cursor already write, with no model calls, and writes notes overnight with a model you choose.
Mem0 is a memory layer: your application sends it messages, a model distils them into facts, and queries get the nearest facts back. recall is a reader: it opens the transcripts Claude Code, Codex and Cursor already keep, indexes them without a model, and writes a few markdown notes a night from them.
Both give an agent long-term memory. One is a service you integrate; the other is a tool you install.
| recall | Mem0 | |
|---|---|---|
| Runs | On your machine | Your code calls it: library, Docker server or Mem0's cloud |
| Licence | MIT | Apache 2.0; graph, consolidation and dashboard are platform-only |
| Model calls to store a chat | 0 | 1 LLM call per add(), plus an embedding |
| A search takes | 11 ms local, 47 with embeddings | ~1 s p50 on its platform |
| Price | Free | Free tier; $19 to $249 a month; Enterprise |
| Chat text leaves your machine | No, with a local model | Yes: to the extraction LLM and the vector store, or Mem0's cloud |
| Claude Code, Codex, Cursor | MCP, skill, session hook | Hosted MCP server, Claude Code plugin |
On SWE-ContextBench, the one benchmark both have run, the authors asked Mem0 for the past fix behind a new GitHub issue across 300 Claude Code sessions. It found it in its top three 39.4% of the time, after $0.24 of LLM calls to take the sessions in. recall 0.5.1 finds it 76.8% of the time with none. The paper does not define its metric, so read the gap loosely; it is not small.
On LongMemEval Mem0 is ahead, 94.4% to 88.4%, with a bigger model as both answerer and judge, from a platform run with optimisations its open-source SDK lacks. recall's retrieval alone, which no judge can move, finds an answering session in the top five 98.7% of the time. The cost of recall's way is context: it hands the model whole chats, ~44k tokens for ten, against Mem0's ~7k of facts.
Mem0 pays per message: an LLM call and an embedding on every add(), and metered adds and retrievals on the platform, from 10,000 free adds a month to $249 for Pro. A coding agent produces a lot of messages.
recall pays nothing to index or search. Its one model cost is the nightly notes on the day's new chats, with the model you chose: nothing through a local one, covered by a Claude or ChatGPT subscription, or batch prices on an API key.
| recall | Mem0 | |
|---|---|---|
| Model calls to store a chat | 0 | 1 LLM call per add(), plus an embedding |
| Price | Free | Free tier; $19 to $249 a month; Enterprise |
| To take in 300 sessions | $0, no model calls | $0.24, 21 min of LLM calls |
| Cost of memory | One nightly run, your model | Metered adds and retrievals |
Mem0 reports a p50 of 0.88 to 1.09 seconds for a search on its platform, and each add() waits on an LLM unless you turn inference off.
recall's search is a local index with no model in the path: 11 ms on ~2,800 chats, 47 ms with the embedding model, the query's embedding included. Nothing waits at write time, because recall writes nothing: it reads chats the agents already saved.
| recall | Mem0 | |
|---|---|---|
| A search takes | 11 ms local, 47 with embeddings | ~1 s p50 on its platform |
| Wait at write time | None: reads saved chats | An LLM call, unless inference is off |
With Mem0 your messages go to the extraction LLM, the embedding model and the vector store, and on the platform to Mem0's cloud, which claims SOC 2 Type I, HIPAA readiness and GDPR, with private-cloud and air-gapped options on Enterprise. The hosted MCP server, the route for Claude Code and Cursor, needs a platform account.
recall reads your chats read-only on your machine, redacts secrets before indexing, sends no telemetry, and never pushes its git repository. Pick a local model and no chat text leaves at all; pick a hosted one and it receives redacted excerpts once a night.
| recall | Mem0 | |
|---|---|---|
| Runs | On your machine | Your code calls it: library, Docker server or Mem0's cloud |
| Licence | MIT | Apache 2.0; graph, consolidation and dashboard are platform-only |
| Chat text leaves your machine | No, with a local model | Yes: to the extraction LLM and the vector store, or Mem0's cloud |
| Compliance | Nothing to certify: no service | SOC 2 Type I, HIPAA-ready, GDPR |
| Telemetry | None | A platform account for the MCP server |
Mem0 keeps facts: on each add() a model reads the new messages with the existing memories in its prompt and emits new ones. Since April 2026 nothing is rewritten or deleted; a time-aware ranking prefers the newer fact at read time.
recall keeps every chat in a full-text index and a few notes beside it: preferences, project facts, decisions, lessons and open threads, each with a quote from its chat, in markdown under git. A nightly run folds new chats in rule by rule, superseding what changed. Edit a note and your edit wins. Notes written by Claude Haiku 5.5 hold 91% of the conventions a mentor set on MemoryCode, and loaded at session start they lift an agent from 32% to 75% on long histories.
| recall | Mem0 | |
|---|---|---|
| Captured by | Reading the agents' own chat files | Your app sending messages |
| Memory is | Markdown notes in git, plus an index of every chat | Extracted facts as vectors, add-only |
| Retrieval | Full-text index, five rankings fused; optional local embeddings | Semantic, re-scored by BM25 and entities |
| You can edit it | Yes, and the edit wins | Through the API or dashboard |
git revert.For one job, yes: giving coding agents memory across sessions. Mem0 is a general memory API for applications you build; recall is for the chats you already have with Claude Code, Codex and Cursor, with no code of yours.
On the coding benchmark both have run, recall finds the past fix in its top three 76.8% of the time against Mem0's 39.4%. On LongMemEval, a chat benchmark, Mem0 reports 94.4% with GPT-5 and recall measures 88.4% with Claude Haiku 5.5, different answer models and judges, so that ranking is loose.
No. The index is SQLite full-text search, ranked five ways and fused. An embedding model is optional, local through Ollama or any OpenAI-compatible endpoint. Mem0 needs a vector store and an embedding model to run at all.
Partly. Mem0's library and Docker server run on your machine, but extraction still needs an LLM and an embedding model, and graph memory, consolidation and the dashboard are platform-only. recall's index and search never call a model, and the nightly notes can use a local one.