Alma vs Provena

Alma and Provena are both agent frameworks tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Alma

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.

Provena

Provena wraps around retrieval steps, tools, and context assembly logic to log where every chunk of data came from, hash it for tamper detection, and surface that audit trail when something breaks or an auditor asks. The vendor describes six framework adapters, an MCP server, PostgreSQL storage, and a policy engine — covering most standard Python-based pipelines without requiring a hosted service. Installation is self-hosted and free. The ceiling appears when your compliance requirement goes beyond audit trails: Provena is a passive tracking library, not an enforcement layer, so it records what happened but does not block a bad retrieval from reaching the model. Teams with hard EU AI Act enforcement obligations pair it with a separate policy gate.

Attribute	Alma	Provena
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	Yes
Self-hosted option	Yes	Yes
Platforms	Local (Rust)	Python
Pros	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.	Cryptographic hashing of context chunks at retrieval time, so you can prove after a bad decision whether the data was modified between ingestion and inference — without this, you are reconstructing events from logs that were never designed for forensics. Six framework adapters described in the docs, which means most Python-based RAG or agent stacks get instrumentation without a custom integration layer. PostgreSQL-backed audit storage, so the provenance trail is queryable and retainable for the duration a compliance regime requires — not just written to a flat log that gets rotated. Policy engine that can flag staleness and provenance violations against configurable rules, which means a single misconfigured retriever shows up as an anomaly rather than silently degrading answer quality for weeks. Fully self-hosted and open-source, so the audit data never leaves your infrastructure — a hard requirement for teams in regulated industries where sending context logs to a third-party SaaS is not an option.
Cons	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.	Provena is a passive observer: it records what entered the context pipeline but does not block a stale or untrusted source from reaching the model. Teams whose compliance requirement is active enforcement — reject this retrieval, do not just log it — must build a blocking layer on top, effectively maintaining two systems where they expected one. With 23 open issues and 2 stars on GitHub at the time of scrape, the project is early-stage and community support is thin. When an adapter breaks against a framework update, the fix timeline depends on a single maintainer; teams with production SLAs are on their own until a patch lands. PostgreSQL is the only described storage backend. Pipelines already standardised on a different data store — a managed cloud warehouse, an observability platform — face a schema translation step or run a second database exclusively for provenance records, which most teams will not accept at scale.

Bottom line

Alma and Provena are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between Alma and Provena?

Alma is Free and open source, while Provena is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Alma better than Provena?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.

Alma vs Provena: which should I pick?

Pick Alma if its pricing model, openness, or platform fit matches your constraints; pick Provena otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.