I'm only somewhat familiar with Koog, but these these are major differences according to my understanding:
- Execution model: Koog is a library for defining agents that run within a single process. AOR agents execute across a distributed cluster, whether one node or thousands.
- Deployment and scaling: Koog provides no deployment or scaling mechanisms. That's something you need to figure out on your own. AOR includes built-in deployment, updating, and scaling.
- Integration complexity: Koog must be combined with other tools (monitoring tool, databases, deployment tools, etc.) to approximate a complete agent platform. AOR is fully integrated, including built-in high-performance, durable storage for any data model.
- Experimentation and evaluation: Koog has no features for experimentation or online evaluation. AOR includes extensive support for both.
- Scalability: AOR scales horizontally for both computation and storage. With Koog, you'd need to design and operate that infrastructure yourself.
- Observability: Koog's observability is limited to traces and basic telemetry exposed via OpenTelemetry. AOR provides a much broader set of telemetry, including "time to first token" and online evaluation charts. You can also split all time-series charts automatically by any metadata you attach to your runs (e.g. see how agent latency differs by the choice of model used). Plus, it's all built-in and automatic.
Please correct me if I'm wrong on any aspect of Koog.
I understand this is meant as a demo of what Rama can do. As a potential user I am not keen on running a distributed system that is a black box and contains all of its data – how do I access it? How do I back it up?
It's not meant as just a demo of what Rama can do. It's a fully featured tool that supports the end-to-end workflow of building and maintaining robust LLM agents. It has an easy-to-learn API and you don't need to learn how to program Rama itself.
Rama isn't open source, but it's far from a black box. All data structures and computation are fully visible in the UI. You can inspect depots, topologies, and PStates, and see exactly what's stored and how it changes over time. Everything is also accessible through the Rama client API for direct querying. The PState schemas used by Agent-o-rama are defined here: https://github.com/redplanetlabs/agent-o-rama/blob/master/sr...
Backups are easy: you configure a “backup provider” (we provide one for S3) and a schedule for incremental backups. The free version can also be backed up with a short maintenance window. Full details are here: https://redplanetlabs.com/docs/~/backups.html
Hey, project lead here. I'm happy to answer any questions you have about Agent-o-rama or its technical internals.
Can you compare it with JetBrains' Koog, if you are familiar with it? https://www.jetbrains.com/koog/
I see you're still using Clojure since writing Apache Storm!
I'm only somewhat familiar with Koog, but these these are major differences according to my understanding:
- Execution model: Koog is a library for defining agents that run within a single process. AOR agents execute across a distributed cluster, whether one node or thousands.
- Deployment and scaling: Koog provides no deployment or scaling mechanisms. That's something you need to figure out on your own. AOR includes built-in deployment, updating, and scaling.
- Integration complexity: Koog must be combined with other tools (monitoring tool, databases, deployment tools, etc.) to approximate a complete agent platform. AOR is fully integrated, including built-in high-performance, durable storage for any data model.
- Experimentation and evaluation: Koog has no features for experimentation or online evaluation. AOR includes extensive support for both.
- Scalability: AOR scales horizontally for both computation and storage. With Koog, you'd need to design and operate that infrastructure yourself.
- Observability: Koog's observability is limited to traces and basic telemetry exposed via OpenTelemetry. AOR provides a much broader set of telemetry, including "time to first token" and online evaluation charts. You can also split all time-series charts automatically by any metadata you attach to your runs (e.g. see how agent latency differs by the choice of model used). Plus, it's all built-in and automatic.
Please correct me if I'm wrong on any aspect of Koog.
I understand this is meant as a demo of what Rama can do. As a potential user I am not keen on running a distributed system that is a black box and contains all of its data – how do I access it? How do I back it up?
It's not meant as just a demo of what Rama can do. It's a fully featured tool that supports the end-to-end workflow of building and maintaining robust LLM agents. It has an easy-to-learn API and you don't need to learn how to program Rama itself.
Rama isn't open source, but it's far from a black box. All data structures and computation are fully visible in the UI. You can inspect depots, topologies, and PStates, and see exactly what's stored and how it changes over time. Everything is also accessible through the Rama client API for direct querying. The PState schemas used by Agent-o-rama are defined here: https://github.com/redplanetlabs/agent-o-rama/blob/master/sr...
Backups are easy: you configure a “backup provider” (we provide one for S3) and a schedule for incremental backups. The free version can also be backed up with a short maintenance window. Full details are here: https://redplanetlabs.com/docs/~/backups.html