Memsprout
Summary
Every engineer on your team has pasted the same auth convention into five different chat sessions this week — and next week, the new hire will start from scratch again. memsprout is a shared context store that pipes that knowledge directly into Cursor, Claude Code, Copilot, and any other MCP client, so the explanation happens once and every agent reads it from then on.
The core workflow is capture-once, retrieve-everywhere: a person or an agent writes a Memory through MCP tools, it lives in a Space scoped to the right team, and any connected MCP client pulls it on demand. The Space → Topic → Memory hierarchy keeps retrieval sharp as the store grows — 'Auth' and 'Onboarding' stay separate, so agents get the three results they need, not thirty. Attribution and version history on every Memory means you can see who wrote what and when it changed, which matters when a convention gets quietly updated mid-sprint. The ceiling appears when your context governance needs get more complex than owner/editor/viewer roles — teams running fine-grained per-environment or per-service access controls will find the permission model thin. No self-hosted option exists, so any team with a hard data-residency requirement is stopped before they start.
Bottom line: Pick this when your team is burning hours re-pasting the same decisions into Cursor and Claude Code; skip it when your compliance policy requires on-premise storage or when your access control requirements exceed three role tiers.
Pricing Plans
Subscription- Price
- $10 / month
Individual
Your own memory and Spaces. Collaborate with anyone else on memsprout.
- Unlimited personal Spaces
- Connect any MCP client
- Version history
- Share with other members
Team
One bill for the whole team. You add and pay for members.
- Everything in Individual
- Add teammates
- One invoice + admin controls
- Priority support
Enterprise
Custom pricing for larger teams needing compliance and dedicated support.
- SSO / SAML
- Audit logs
- Custom data retention
- 99.9% SLA
- Custom contracts
- Dedicated onboarding
- Priority support
- Unlimited seats
View full pricing on memsprout.com →
Pricing may have changed since last verified. Check the official site for current plans.
Community Performance Report Card
No community ratings yet. Be the first to rate this tool!
Community Benchmarks Community
Sign in to submit a benchmarkNo community benchmarks yet. Be the first to share a real-world data point.
Pros
Sign in to edit- Tool-agnostic MCP delivery, so the same captured context reaches Cursor, Claude Code, Copilot, and ChatGPT simultaneously — without maintaining a separate rules file for each tool.
- Agents write Memories back through MCP alongside humans, which means context accumulates during normal work rather than requiring a separate documentation step that never gets done.
- Space roles (owner, editor, viewer) apply identically to human teammates and to AI clients, so you control what each agent can read or write without a separate permission system.
- Per-member attribution and version history on every Memory, which means when a convention changes mid-sprint you can trace who updated it and what the previous value was — something a shared .cursorrules file cannot do.
- Space → Topic → Memory hierarchy keeps retrieval targeted as the store grows, so agents surface three relevant results rather than scanning an undifferentiated flat list.
Cons
Sign in to edit- No self-hosted deployment path exists: teams with data-residency requirements or policies against sending internal engineering conventions to a third-party cloud endpoint cannot use this tool at all, and the appropriate next step is a self-hosted MCP server backed by an internal vector store.
- The permission model tops out at three Space roles; teams that need per-environment, per-service, or attribute-based access controls will need to work around the structure by creating redundant Spaces — which defeats the 'one shared brain' premise and becomes a maintenance problem of its own.
- Teams that have standardized entirely on Claude will find Claude's native memory overlaps significantly with memsprout's value proposition; at that point the added MCP integration layer is overhead rather than benefit, and the native solution wins on simplicity.
Community Reviews
Sign in to write a reviewNo reviews yet. Be the first to share your experience.
About
- Platforms
- Web, MCP clients (Cursor, Claude Code, GitHub Copilot, ChatGPT)
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-07-23T02:23:24.314Z
Best For
Who it's for
- Engineering and product teams using multiple AI coding tools
- Teams replacing drifting per-repo CLAUDE.md or .cursorrules files
What it does well
- Team-wide AI agents sharing product decisions and engineering conventions
- Capturing onboarding, auth, and deployment knowledge once for all agents
- Maintaining versioned, attributable context across Cursor, Claude Code, and Copilot
Integrations
Discussion Community
Sign in to commentNo discussion yet. Sign in to start the conversation.
Compare Memsprout
Spotted incorrect or missing data? Join our community of contributors.
Sign Up to ContributeCommunity Notes & Tips Community
Sign in to contributeBe the first to contribute. General notes, observations, gotchas, and tips from people who use this tool day-to-day.
Recommended skills for this tool
Auto-curated by the AIDiveForge recommendation matrix. These skills are predicted to enhance this tool based on category, capability, and domain signals.
-
Meeting Summary Template transform 32%
Turn a raw transcript into a decision-focused recap: outcomes, owners, deadlines, open threads.
Why: category partial · caps 0/0 · domain ops
-
Standup Note Synthesizer transform 32%
Merge individual standup bullets from multiple people into a single team digest with blockers surfaced to the top.
Why: category partial · caps 0/0 · domain ops
-
Runbook Skeleton post 32%
Produce a first-draft runbook from a postmortem — detection, diagnosis, mitigation, rollback — so the next incident has a template to follow.
Why: category partial · caps 0/0 · domain ops
-
OKR Draft Critiquer post 32%
Score draft OKRs against SMART criteria and the outcome-not-output rule, with suggested rewrites for each failing key result.
Why: category partial · caps 0/0 · domain ops
Frequently Asked Questions
- Is Memsprout free?
- Memsprout has a permanent free tier alongside paid upgrades (paid plans from $10 / month). You can keep using a baseline version indefinitely without paying.
- Is Memsprout open source?
- No — Memsprout is a closed-source tool. Source code is not publicly available.
- Does Memsprout have an API?
- Yes. Memsprout exposes a developer API. See the official documentation at https://memsprout.com for details.
- What platforms does Memsprout support?
- Memsprout is available on: Web, MCP clients (Cursor, Claude Code, GitHub Copilot, ChatGPT).
Hours Saved & ROI Stories Community
Sign in to contributeBe the first to contribute. Concrete time/cost savings, with context. e.g. "Cut my code review backlog from 4h to 45m per week."
Curated lists that include this category
Per-repo CLAUDE.md and .cursorrules files solve the problem for one person in one repository, then drift the day after they are written. memsprout replaces that scattered approach with a single MCP endpoint at mcp.memsprout.com. Each piece of knowledge — a deployment gotcha, an auth convention, a product decision — is stored as a Memory inside a Topic inside a Space. Connect any MCP client once, and every agent on the team retrieves from the same store without copy-paste or re-explanation.
The differentiating layer is attribution and provenance. Each Memory records who captured it and when — whether that was a person typing it manually or an agent writing it back through the same MCP tools. Version history is kept on every Memory, so when the staging-database reset time changes, you can see the old value, the new value, and who made the call. That traceability is what separates memsprout from a shared wiki: the wiki is not agent-native, and the agent cannot write back to it.
The tool fits engineering and product teams running a mixed AI stack — people using Cursor alongside others on Claude Code or Copilot — where the absence of a shared context layer means each tool operates on one person’s private chat history. It breaks down when a team needs data residency guarantees, because no self-hosted deployment path exists. It also shows limits when access control needs exceed the three-tier Space role model; teams needing per-environment or per-service permission boundaries will find themselves working around the structure rather than inside it. A team that standardizes entirely on Claude will find Claude’s own native memory overlaps with this use case, which is the most likely reason to evaluate a competitor instead.
