Julep AI

Julep agents store what they learn about a user and pull it back into later conversations. The platform keeps this in a dedicated memory store rather than stuffing everything into the prompt. That matters once a conversation runs long. For a support bot, it remembers the last issue without being told twice.

You describe a workflow as a series of steps, and Julep handles the decision-making, loops, and parallel branches inside it. Tasks can be as short as two steps or as sprawling as a data pipeline. This is the part that separates it from a plain prompt chain. Most tools stop at one prompt. This one doesn't.

A session holds the conversation history and context for one agent-user pair, and it can be paused and resumed whenever you need, so a long gap between messages doesn't throw away everything the agent already knows. If a user comes back a week later, the agent picks up where it left off instead of starting cold.

Julep ships with a set of tools and lets you wire in external APIs, so an agent can look something up, act on it, and keep going in the same task without you hand-building the plumbing for each connection. That saves real work. Otherwise you'd write a new handler for every service you touch.

Failed steps retry on their own, and long-running tasks survive a crash without losing their place. Julep leans on a workflow engine under the hood to make this reliable. Reliability is everything here. For agents that run for hours, it separates a demo from something you'd trust in production. Without it, every long task becomes a babysitting job.

The platform includes a document store for RAG, the technique that lets an agent answer from your own material instead of guessing. You load your own documents, and the agent pulls relevant passages when it answers, so teams building knowledge assistants get this without bolting on a separate vector database.

The entire platform is open source, so you can run it on your own servers instead of trusting a vendor with your data. That appeals to teams with strict data rules or a preference for avoiding lock-in, since nothing about your agents leaves your network. The tradeoff? You own the setup. The maintenance can add up fast for a small team without dedicated ops help.