Julep is a backend-as-a-service to build agents and workflows. Main features:
1. Experimentation Velocity:
With most agent frameworks, agents are code that need to be deployed. To iterate on the prompts or update the agent, you need to make changes to the code and deploy it to your server. This makes agents hard to iterate on quickly. But with agents and tasks declared as data, all that happens in the background is make an api call to update or make a new entry in the database. This increases the experimentation velocity.
2. Scalability and long-running background tasks:
Scaling is a hard software engineering problem and the solution is to define large scale systems to be distributed, stateful and scalable. Agents as data is a very helpful paradigm because you have to solve for this problem only once at the orchestrator level. Julep can handle 100s of thousands of executions paralely.
3. Updating an agent / User Personalization:
Tools such as create agent, update agent etc. can update the agent recursively. Let’s assume an agent is chatting with a user and mentions that they only like to communicate in a professional tone. The agent can call a tool to 'update the agent' and modify/update the instructions to include that the user only likes to communicate in a professional tone.
This was the reason to create a backend when the market was flooded with agent frameworks. In Julep, you can declare a workflow in YAML, and it can automatically scale Millions of users.
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Show HN: BaaS to build agents as data, not code · HackerTrans
Julep is a backend-as-a-service to build agents and workflows. Main features:
1. Experimentation Velocity:
With most agent frameworks, agents are code that need to be deployed. To iterate on the prompts or update the agent, you need to make changes to the code and deploy it to your server. This makes agents hard to iterate on quickly. But with agents and tasks declared as data, all that happens in the background is make an api call to update or make a new entry in the database. This increases the experimentation velocity.
2. Scalability and long-running background tasks:
Scaling is a hard software engineering problem and the solution is to define large scale systems to be distributed, stateful and scalable. Agents as data is a very helpful paradigm because you have to solve for this problem only once at the orchestrator level. Julep can handle 100s of thousands of executions paralely.
3. Updating an agent / User Personalization:
Tools such as create agent, update agent etc. can update the agent recursively. Let’s assume an agent is chatting with a user and mentions that they only like to communicate in a professional tone. The agent can call a tool to 'update the agent' and modify/update the instructions to include that the user only likes to communicate in a professional tone.
This was the reason to create a backend when the market was flooded with agent frameworks. In Julep, you can declare a workflow in YAML, and it can automatically scale Millions of users.