Comparison · Updated May 2, 2026

Sprintra vs claude-mem vs Mem0 vs Letta: Which AI Memory Tool Should You Pick?

Quick answer

Pick Sprintra if you use Claude Code or Cursor and want memory plus a project workspace (sprints, decisions, KB) at zero per-tool-call cost. Pick claude-mem for memory-only Claude Code use if AGPL-3.0 is fine. Pick Mem0 as an SDK when you're building your own AI app. Pick Letta for stateful agent frameworks (formerly MemGPT). Most developers settle on one.

The AI memory category has fractured in 2026. What looked like a single problem in 2024 — "agents forget things" — has split into four distinct shapes, each with a leader. This post walks the four leaders, what they actually do, and how to pick.

The decision framework (in 30 seconds)

Answer two questions:

Are you using an AI coding agent (Claude Code, Cursor, Codex), or are you building your own AI app? If using → claude-mem or Sprintra. If building → Mem0 or Letta.
Do you also need a project workspace (sprints, decisions, knowledge base)? If yes → Sprintra. If no → claude-mem.

That's it. The rest is detail.

Side-by-side scorecard

	Sprintra	claude-mem	Mem0	Letta
Best for	Claude Code / Cursor users who want PM + memory	Solo Claude Code memory only	Building your own AI app (SDK)	Stateful agent frameworks
Pricing	$0 OSS / $5 Team / Contact Business	Free OSS	$0 / $19 / $249 / Enterprise	OSS + paid hosted
License	MIT	AGPL-3.0	Apache 2.0	Apache 2.0
Capture cost	Zero LLM cost	LLM call per observation	Per-API-call	Per-API-call
GitHub stars	Growing	71k+	41k	16k+
Funding	Solo, no public	No public	$24M Series A	Public funding
Team mode	Yes (RBAC, multi-org)	No	Yes (paid)	Yes
Hosted SaaS	Yes	No	Yes	Yes
Multi-IDE	Claude Code, Cursor, Codex, Antigravity, Gemini	Claude Code primary	SDK — any agent	Framework — any client
PM features	Yes (full)	No	No	No

How they actually work

Sprintra

MCP server. Captures user prompts to a private timeline, agent-written session digests on every Stop hook, and indexes Claude Code's existing JSONL transcripts in place. Plus a full project workspace: features, sprints, stories, decisions (with conflict detection), knowledge base, dependency graph, releases. Zero per-tool-call LLM cost — the agent that's already in your context writes the digest. Free OSS local mode (SQLite); hosted SaaS at $5/seat for teams. Read the Memory Layer docs.

claude-mem

Claude Code plugin. Captures every tool-call observation, AI-compresses each into a vector store (ChromaDB) plus SQLite. Semantic + keyword search via its own MCP tools. The category mindshare leader at 71k+ GitHub stars. AGPL-3.0 (procurement blocker for many enterprises) plus a PolyForm Noncommercial subdirectory. Single-user local. Full Sprintra vs claude-mem comparison →

Mem0

Memory layer SDK for any agent application. Hybrid graph + vector + key-value memory. Apache 2.0. 41k GitHub stars, 14M+ PyPI downloads. $24M Series A from Basis Set, GitHub Fund, YC. Pricing has a sharp $19→$249 cliff that the community has criticized publicly. Mem0 is best when you're building your own AI product and need a memory primitive — not a drop-in for Claude Code.

Letta (formerly MemGPT)

Stateful agents framework based on the 2023 MemGPT paper by UC Berkeley researchers. Treats agents as long-lived stateful processes with memory hierarchies (working / archival). Apache 2.0. Strong technical reputation. Best if you're building agents from scratch with a stateful design pattern, not adding memory to an existing IDE workflow.

Our honest take

These tools solve different layers of the same problem

Mem0 and Letta operate at the SDK / framework layer. claude-mem and Sprintra operate at the user-facing tool layer. Within the user-facing layer, claude-mem is memory-only and Sprintra is memory + project workspace. None of them are wrong — they're solving different problems.

We built Sprintra because we kept hitting the limits of memory-only tools. Memory tells you what was said; project context tells you what was decided, what's in flight, what's blocked, what depends on what. The latter is what AI coding sessions actually need to ship products instead of prototypes.

Frequently asked questions

Which AI memory tool should I pick — Sprintra, claude-mem, Mem0, or Letta?

Pick by use case. Sprintra is best for Claude Code/Cursor users who want memory plus a project workspace (sprints, decisions, KB). claude-mem is best for memory-only Claude Code use, especially solo. Mem0 is best as an SDK if you're building your own AI app and need a memory layer. Letta is best for stateful agent frameworks (formerly MemGPT). Each solves a different layer of the AI memory problem.

What's the cheapest option?

All four have a free tier. Sprintra OSS MCP is free forever (MIT license). claude-mem is free OSS (AGPL-3.0). Mem0 is free up to 10K memories (Apache 2.0). Letta is free OSS. The hidden cost is per-tool-call LLM compression: claude-mem charges this on every observation, Sprintra captures at zero LLM cost. For heavy users, that compute cost difference is the largest financial variable.

Do these tools work together or are they competitors?

They overlap on the memory layer. claude-mem and Mem0's memory primitives are similar — vector store + semantic search. Sprintra includes a memory layer plus a full project workspace, so installing Sprintra alongside claude-mem is redundant for the memory part. Letta operates at a different layer (stateful agent framework), so it can compose with the others. In practice, most users settle on one.

What about Anthropic's Auto Memory and Auto Dream?

Anthropic shipped Auto Memory + Auto Dream for Claude Code in early 2026, providing first-party chat-history summarization. For solo Claude-Code-only users with simple memory needs, this may be sufficient. Third-party tools win on (a) cross-IDE support — Auto Memory is Claude-only, (b) project-workspace features Anthropic doesn't ship, (c) team mode and multi-user privacy, (d) data portability and self-hosting.

Sources

Try Sprintra free

The project brain for AI coding agents. Memory + sprints + decisions + KB. Free OSS. 30-second install.

Install in 30 seconds

Last updated May 2, 2026.