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Katra
Pricing
- Model
- Free
Summary
AI agents that run long sessions lose context the moment the process restarts — every conversation starts cold, every fact must be re-established. Katra exists to fix that by giving agents a memory layer that survives restarts, scales across multiple agents, and speaks the MCP protocol they already use.
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.
Bottom line: Pick Katra when you need persistent, self-hosted memory for MCP agents and can absorb the operational overhead of running your own memory service — avoid it when you need a mature, battle-tested memory backend with documented production case studies.
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Pros
Sign in to edit- 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.
Cons
Sign in to edit- 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.
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About
- Platforms
- Docker
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-07-01T02:29:20.172Z
Best For
Who it's for
- Developers building MCP-compatible AI agents
- Teams needing self-hosted memory infrastructure
- Experimentation with cognitive memory architectures
What it does well
- Providing persistent memory for long-running AI agents
- Enabling shared memory across multiple agents
- Supporting semantic search and knowledge graphs in agent workflows
Integrations
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Frequently Asked Questions
- Is Katra free?
- Yes — Katra is fully free to use. There is no paid tier.
- Is Katra open source?
- Yes. Katra is open source.
- Does Katra have an API?
- Yes. Katra exposes a developer API. See the official documentation at https://github.com/kolegadev/katra-agentic-memory for details.
- Can I self-host Katra?
- Yes. Katra supports self-hosting on your own infrastructure.
- What platforms does Katra support?
- Katra is available on: Docker.
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Curated lists that include this category
Most agent frameworks treat memory as an afterthought — a dictionary in RAM that evaporates when the process dies. Katra is purpose-built infrastructure that sits outside your agent process, stores memories persistently, and exposes them over the MCP protocol so any compatible agent can read and write without custom integration code. The core workflow: deploy via Docker Compose or Helm, configure your agent to point at the Katra MCP endpoint, and your agent gains episodic recall (what happened in prior sessions), semantic search (find memories by meaning, not exact string), and a knowledge graph (relationships between entities across sessions).
The differentiating design choice is the ‘memory appliance’ model. Rather than a library you embed in your agent code, Katra runs as a standalone service — which means multiple agents on the same team or in the same pipeline share a single memory store without any coordination code on your side. The vendor describes support for OpenClaw, Claude Code, OpenCode, Codex CLI, and Kolega Code out of the box, so teams already using those clients skip the integration layer entirely.
Katra fits teams building MCP-compatible agents who want self-hosted control over where memories live — useful when data residency or privacy requirements rule out third-party memory APIs. It also fits experimentation with cognitive memory architectures where you want episodic, semantic, and graph-based recall in one place. Where it does not fit: teams that need a managed service with an SLA, teams whose agents do not speak MCP and cannot be adapted to it, and teams that need production reliability guarantees the project cannot yet provide given its early community footprint.
The repo ships with a Dockerfile, Docker Compose configuration, Helm charts for AWS deployment, an SDK directory, a dashboard, and a watcher component — the vendor states these are available under the Apache-2.0 license. Terraform configuration for AWS infrastructure is also included, which means teams that already run IaC pipelines have a starting point rather than a blank canvas.