Cadenya
Paid
Developers building agentic workflows
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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Paid
Developers building agentic workflows
Free · Open source
Long-term continuity in multi-session coding projects
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.
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