Katra vs Provena

Katra 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.

Katra

Katra is self-hosted memory infrastructure: drop it on any Docker-capable machine, point your MCP-compatible agent at it, and you get episodic recall, semantic search, knowledge graphs, and temporal analysis without rebuilding your agent. The architecture is a single deployable unit — the vendor describes it as a 'memory appliance' — which means setup friction is low for teams that already run Docker or Helm on AWS. Where it breaks: Katra is memory infrastructure, not an agent runner, so teams expecting built-in task planning or tool execution will need to wire those themselves. The project is early-stage with five stars on GitHub and no reported production deployments in public community channels, which means you are taking on the role of early adopter rather than stepping into a proven stack.

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	Katra	Provena
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	Yes	Yes
Self-hosted option	Yes	Yes
Platforms	Docker	Python
Pros	MCP-native protocol support, so agents that already speak MCP connect without writing a custom memory adapter — which means teams skip the integration sprint that usually delays memory features. Self-hosted deployment via Docker Compose or Helm, so memory data stays inside your own infrastructure — which means teams with data residency or privacy requirements can use persistent agent memory without routing sensitive context through a third-party API. Shared memory store across multiple agents, so agents running in parallel read from the same knowledge base — which means you avoid the state-sync problem where two agents contradict each other because they each only remember their own session. Episodic recall, semantic search, and knowledge graphs available in a single service, so you do not need to stitch together three separate systems — which means teams experimenting with cognitive memory architectures start from a single deployable unit rather than an integration exercise. Apache-2.0 open-source license with Terraform, Helm, and SDK artifacts included, so teams can audit the full stack and adapt it — which means there is no vendor lock-in risk if the project direction diverges from your needs.	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	Katra does not run agents or execute tools — it is only a memory layer. Teams that expected a full agent runtime will need to run a separate agent framework alongside it, which means maintaining two systems from day one rather than one. The project has a small public footprint (five GitHub stars at time of writing, no issues or pull requests filed publicly), which means there is no community-sourced troubleshooting record to draw on when the memory service behaves unexpectedly in production. Teams hitting edge cases file the first bug report themselves. Agents that do not support MCP cannot use Katra without a custom adapter layer. Teams whose agent stack is locked to a non-MCP framework — LangGraph with a native memory backend, for example — face a non-trivial porting effort and at that point are likely to evaluate mem0 or a purpose-built LangGraph memory extension instead of adapting Katra.	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

Katra 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 Katra and Provena?

Katra 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 Katra 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.

Katra vs Provena: which should I pick?

Pick Katra 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.