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julep

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Basic Information

Julep is an open-source platform for building and running agent-based AI workflows. It provides a serverless backend to orchestrate complex, multi-step processes that combine Large Language Models and external tools without requiring users to manage infrastructure. The repo supplies SDKs for Python and Node.js, a CLI (beta for Python), REST API endpoints, and YAML or code-first workflow definitions so developers can declare agents, memory, tool access, and step logic. Julep is aimed at creating agents that maintain context and long-term memory, handle branching logic, loops, parallel execution, and call third-party APIs. The project includes documentation, cookbooks with example workflows, and community resources to help teams prototype and deploy intelligent workflows and agent logic at scale.

Links

Stars
5675
App URL
https://github.com/julep-ai/julep
Github Repository
https://github.com/julep-ai/julep/blob/main/README.md

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App Details

Features
Persistent memory and session management for agents so they can retain context over time. A modular workflow engine that supports YAML or code task definitions with conditional logic, loops, and error handling. Built-in tool orchestration to integrate web search, databases, and external APIs to enable retrieval-augmented behaviors. Serverless, parallel execution model that manages scaling and concurrency. Reliability features such as retries, self-healing steps, robust error handling, and real-time monitoring and logging. Multi-language SDKs (Python and Node.js), a REST API, and a command-line interface to run and manage workflows. A cookbook of example agents and tutorials, plus community and contribution guides to extend the platform.
Use Cases
Julep reduces operational overhead by handling infrastructure, scaling, and concurrency so developers can focus on AI logic and agent behavior. It enables creation of agents that remember past interactions and perform multi-step, long-running tasks with branching logic and external tool calls. Teams can prototype quickly using SDKs or YAML, run workflows in the cloud, monitor executions on the dashboard, and iterate with provided cookbooks and tutorials. Built-in reliability and observability help maintain production workflows. The repository and docs support integration into applications via REST, SDKs, or CLI, and the community and contribution guides make it practical for organizations to adopt, extend, and collaborate on intelligent automation and agent-based workflows.

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