---
title: "Highlights"
description: "Major product launches, headline features, and milestones for Mem0."
mode: "wide"
---

<Update label="2026-05-13" description="Temporal Reasoning for Mem0 Platform v3">

**Temporal Reasoning — Time-Aware Retrieval for Platform v3**

Mem0 Platform v3 can now interpret time-aware memories and queries so assistants retrieve the right information for questions about the past, upcoming plans, and current state.

- **Time-aware search intent** — Queries like `last week`, `upcoming`, `right now`, and `as of March 2025` return contextually appropriate results automatically
- **Enabled by default** — No per-request toggle required for v3 writes or searches
- **Anchored relative queries** — `reference_date` anchors relative search phrases for tests, backfills, and reproducible demos
- **Normal response shape** — Temporal reasoning affects ranking while preserving existing client response patterns

See [Temporal Reasoning](/platform/features/temporal-reasoning) for usage details.

</Update>

<Update label="2026-05-08" description="Memory Decay">

**Memory Decay — Recently-Used Memories Surface Higher, Automatically**

Per-project search-time ranking bias that boosts recently-touched memories and gently dampens stale ones. Off by default; opt in per project via the `decay` field on the project endpoint, or via `client.project.update(decay=True)` in the SDKs (Python `v2.0.2` / TypeScript `v3.0.3`).

- **Soft bias, never a filter.** The scaling factor stays in `0.3×–1.5×`. Decay can reorder candidates but never zeros them out — anything that surfaced before decay can still surface after.
- **Reinforcement loop.** Every memory returned in a search has its access history updated, so frequently-used facts naturally float to the top over time.
- **Public score still clamped to `[0, 1]`.** Existing API contract preserved; no client-side changes needed.
- **v3 search only**, fully reversible. See [Memory Decay docs](/platform/features/memory-decay).

</Update>

<Update label="2026-04-14" description="Mem0 SDK v2.0.0 / v3.0.0">

**New Memory Algorithm — State-of-the-Art Accuracy at ~3-4x Lower Cost**

Ground-up rewrite of the memory pipeline with 20+ point benchmark improvements:

- **LoCoMo:** 71.4 → **91.6** (+20) — multi-turn conversation recall
- **LongMemEval:** 67.8 → **93.4** (+26) — long-term memory across sessions
- **BEAM (1M tokens):** **64.1** — production-scale memory evaluation
- **Agent memories are first-class** — Previous algorithm: 46% on assistant recall. New: **100%**
- **Temporal reasoning works** — "Where did I live before SF?" Previous: 51%. New: **93%**
- **~3-4x fewer tokens** — Under 7K tokens per retrieval vs 25K+ for full-context approaches
- **ADD-only extraction** — Memories accumulate; nothing is overwritten or deleted
- **Hybrid retrieval** — Semantic + BM25 keyword + entity boost, scored in parallel
- **Graph memory (built-in)**: entities extracted, embedded, and linked across memories, with no external graph store required

Breaking changes: external graph stores removed from OSS (replaced by built-in graph memory), `search()` defaults changed, deprecated params removed. See [migration guide](/migration/oss-v2-to-v3).

</Update>

<Update label="2026-04-06" description="Mem0 Skill Graph">

**Mem0 Skill Graph — In-Context Documentation for AI Agents**

AI coding agents in Claude Code, Cursor, and Codex can now access Mem0 knowledge directly in their workflow — no doc searching required. Three interconnected skills launched:

- **mem0 Core Skill** — Complete Python and TypeScript SDK reference, REST API patterns, and integration guides for LangChain, CrewAI, Autogen, and more
- **mem0-cli Skill** — Terminal command reference, configuration walkthroughs, and CI/CD recipes
- **mem0-vercel-ai-sdk Skill** — Vercel AI SDK provider API, memory-augmented generation patterns, and multi-provider setup

</Update>

<Update label="2026-04-06" description="Mem0 CLI v0.2.2">

**Official Mem0 CLI — Now on PyPI and npm**

A full-featured command-line interface for Mem0, available in both Python and Node.js:

- **Install:** `pip install mem0-cli` or `npm install -g @mem0/cli`
- **Full command suite** — `add`, `search`, `list`, `get`, `update`, `delete`, `import`, `config`, `init`, `status`, `entity`, `event`
- **Interactive setup** — `mem0 init` with email verification or direct API key entry
- **Works everywhere** — Platform (Mem0 Cloud) and self-hosted OSS modes
- **Scriptable** — `--json` flag for CI/CD pipelines and automation
- **Dual SDK** — Same commands, same experience across Python and Node.js

</Update>

<Update label="2026-04-06" description="OpenClaw v1.0.4">

**OpenClaw Plugin — Production-Ready**

The OpenClaw Mem0 plugin went from initial release to production-ready in one week (v1.0.0 → v1.0.4):

- **Skills-based memory architecture** — New extraction pipeline with skill-loader, batched extraction, and domain-aware memory triage
- **Dream gate** — Automatic memory consolidation during idle periods for higher-quality long-term recall
- **Interactive CLI** — `openclaw mem0 init`, `status`, `config`, `import`, and `event` commands
- **Unified tool naming** — `memory_add` and `memory_delete` replace 4 legacy tools, matching the platform API
- **Security hardened** — Path traversal protection, pinned dependencies, 329 tests across 10 files

</Update>

<Update label="2026-04-02" description="Mem0 Plugin for AI Editors">

**Mem0 Plugin for Claude Code, Cursor, and Codex**

Launched a unified Mem0 plugin across three major AI development environments — Claude Code and Cursor first (March 25), then Codex (April 2):

- **9 MCP memory tools** — add, search, get, update, delete, bulk delete, entity management via `mcp.mem0.ai`
- **Lifecycle hooks** — Automatic memory capture at session start, context compaction, task completion, and session end
- **Cloud MCP server** — Managed endpoint replaces local MCP and Smithery setup
- **Streamable HTTP transport** — New MCP transport protocol for real-time streaming
- **Codex-specific skill** — Dedicated skill in `mem0-plugin/skills/mem0-codex` for Codex workflows

</Update>

<Update label="2026-03-21" description="New Providers">

**Apache AGE, Turbopuffer, MiniMax, and pgvector for Node.js**

Major expansion of the provider ecosystem:

- **Apache AGE** — New graph store support, bringing the total to 4 graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE). **Note:** All external graph store backends (Neo4j, Memgraph, Kuzu, Apache AGE) were subsequently removed in v2.0.0 (2026-04-14). Graph memory is now built-in entity linking with no external graph store required; see the [v2.0.0 entry above](#mem0-sdk-v2-0-0-v3-0-0).
- **Turbopuffer** — New vector database provider for Python SDK
- **MiniMax** — New LLM provider with dedicated AWS Bedrock support
- **pgvector for Node.js** — PostgreSQL vector support added to the TypeScript OSS SDK
- **Reasoning models** — `reasoning_effort` parameter for OpenAI o1/o3-style models

</Update>

<Update label="2026-03-14" description="Mem0 Platform Skill">

**Mem0 Platform Skill on skills.sh**

First skill launch — a dedicated Mem0 skill providing platform API reference, quickstart patterns, and integration examples directly inside agent sessions. Available on [skills.sh](https://skills.sh) for any compatible AI coding agent.

</Update>
