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Alma
Pricing
- Model
- Free
Summary
Every AI agent you talk to starts from zero — your name, your role, your working style, typed again into a fresh chat window — because there's no memory layer that belongs to you rather than to the vendor. Alma is a local-first MCP server that holds that context on your machine and exposes it to agents only as far as you allow.
Alma stores facts and preferences — name, role, working style, answer preferences, current context, principles — as a self-model any MCP-compatible agent can read at session start. The data stays on your machine; no hosted account, no vendor lock-in. Access is scoped, so an agent can read the slice it needs without touching the full store. Every durable write goes through an event log, which means changes are auditable and can be reversed. The project is explicitly labeled experimental by the maintainer, so APIs are unstable and behavior can change between commits.
Bottom line: Pick Alma when you're wiring together several local MCP agents and are tired of re-seeding context every session — skip it when you need a production-stable memory layer with versioned APIs and active support.
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Pros
Sign in to edit- Data stays on your machine with no hosted dependency, so you are not handing a vendor a copy of your personal context as the price of cross-session memory.
- Scoped reads let an agent access only the slice of your self-model it needs, so a compromised or poorly-written agent cannot pull your full personal store in a single call.
- Every durable write is recorded in an event log, so you can see exactly what an agent changed and reverse it — without that, silent context corruption is invisible until it surfaces in agent behavior.
- Apache-2.0 license with full source available, so you can fork, audit, or extend the schema without waiting on a maintainer or negotiating a license.
- MCP-native design means any agent runtime that already speaks MCP can connect without a custom integration layer, which keeps the wiring minimal for teams already in that ecosystem.
Cons
Sign in to edit- The maintainer explicitly labels this an experimental hobby project with unstable APIs — if you build an agent pipeline against Alma today, a schema or behavior change in the next commit can break your integration with no migration path or changelog guarantee.
- There is no hosted or managed option, which means every team member who wants to use it runs their own instance; shared or multi-user memory setups require infrastructure work the project does not address.
- Non-MCP agent runtimes get no native support — teams using agents that don't speak MCP natively must write and maintain their own adapter, at which point they are owning a second codebase.
- Zero community infrastructure (no issues filed, no pull requests, two stars at the time of curation) means bugs you find are bugs you fix yourself; teams that need a responsive maintainer or community workarounds will switch to a memory layer with an active user base before the first production incident.
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About
- Platforms
- Local (Rust)
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-06-24T14:16:32.141Z
Best For
Who it's for
- Users wanting portable, local memory for multiple AI agents
- Developers building agent tools that respect user consent
- Experimental local-first AI memory setups
What it does well
- Providing agents with user facts and preferences across chats
- Scoped reads of personal context without full store access
- Auditable and reversible memory changes via event log
- Least-privilege integration for AI agents via MCP
Integrations
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Frequently Asked Questions
- Is Alma free?
- Yes — Alma is fully free to use. There is no paid tier.
- Is Alma open source?
- Yes. Alma is open source.
- Does Alma have an API?
- Yes. Alma exposes a developer API. See the official documentation at https://github.com/almakit/alma for details.
- Can I self-host Alma?
- Yes. Alma supports self-hosting on your own infrastructure.
- What platforms does Alma support?
- Alma is available on: Local (Rust).
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Curated lists that include this category
Alma sits between you and your AI agents as a local MCP server: it holds the facts and preferences you keep restating across chat sessions and hands them to agents on demand. The core workflow is straightforward — you populate your self-model once (name, role, working style, answer preferences, context, values), and any MCP-compatible agent can query the relevant slice at session start rather than asking you to re-introduce yourself. The server runs on your machine; nothing is transmitted to an external service.
The key differentiating design is least-privilege access combined with an event log. Rather than granting an agent permanent write access to your full context, Alma exposes only the scopes you approve per connection, and every change that persists is recorded so you can audit what was written and roll it back. For users who have watched agents silently mutate shared memory stores, that audit trail is the feature that changes the threat model.
Alma fits a specific setup: developers experimenting with local-first agent toolchains who want a consent-respecting memory layer without spinning up a hosted service. It breaks down quickly outside that envelope. The project README explicitly describes it as an experimental hobby project with no stability guarantees — APIs and behavior can change at any commit. Teams building production agent pipelines, or anyone who needs SLA-backed uptime and versioned API contracts, will hit that ceiling and need to evaluate a more mature alternative. There is no managed hosting option and no commercial support path.
The implementation is Rust-based and Apache-2.0 licensed. MCP schemas are versioned under a 2026-06 namespace in the repository. Integration requires an MCP-compatible agent runtime; the docs describe the server surface rather than a plugin ecosystem, so teams connecting non-MCP agents will need to write their own adapter layer.