Julep

Julep AI – Serverless AI Workflow Platform

Julep AI is a serverless platform designed for data science teams to build, iterate on, and deploy multi-step AI pipelines. It emphasizes production-readiness, scalability, and software-engineering discipline applied to AI development. Julep enables you to connect with any AI model, API, or data source, and to deploy complex workflows with state, retries, and long-running tasks in a secure, enterprise-friendly environment.

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What it does

• Create, deploy, and manage multi-step AI workflows using declarative YAML configuration.
• Integrate with diverse AI models, REST APIs, web tools, and data sources.
• Support for long-running tasks, automatic retries, fault tolerance, and scalable execution.
• Provides tooling to manage agents, tools, tasks, and executions with clear interfaces and observability.
• Aims to bring software-engineering practices to AI development: prompts as code, model independence, explicit context management, and structured reasoning.

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How to Use Julep

1. Create an Agent: Define an agent that can perform tasks, talk to users, or run AI workflows. Example setup includes selecting a model (e.g., GPT-4o-mini) and default settings.
2. Define Tools: Equip agents with tools such as web search, API calls, or custom integrations.
3. Define Tasks: Write multi-step processes in YAML with decision trees, loops, and parallel execution.
4. Deploy Executions: Kick off production-grade workflows with a single command, providing inputs for the task
5. Manage & Observe: Monitor executions, view traces, and iterate on workflows with observability built in.

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Core Capabilities

• Serverless, highly scalable AI workflows capable of handling thousands to millions of executions
• Long-running task support with robust fault tolerance and state management
• Tool and agent orchestration with explicit interfaces
• Model-agnostic architecture to avoid vendor lock-in and support easy model switching
• Structured, workflow-based AI development over traditional prompt chaining
• Production-ready deployment with observability and logging
• Private deployments and enterprise-ready security and compliance features
• Generous free tier with usage-based pricing for cloud, plus dedicated on-prem / enterprise options

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How It Differs From Other Solutions

• Unlike prompt-chain platforms (e.g., LangChain), Julep emphasizes persistent AI agents with complex workflows, state management, and long-running capabilities.
• It brings software engineering discipline to AI, including prompts-as-code, explicit tool interfaces, and full observability.
• It supports scalable, production-grade deployments with reliability features suited for enterprise needs.

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Use Cases

• Building multi-step AI pipelines for data processing, decision making, and automation
• RAG-based chatbots, web crawlers, and tool-augmented assistants
• Video processing hooks and external data integrations
• End-to-end automation with auditable execution traces

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Safety and Governance

• Enterprise-grade security, private deployments, and compliance features
• Clear separation of model providers and tools to reduce vendor lock-in
• Observability enables monitoring, validation, and continuous improvement

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Core Features

• Serverless AI workflows with auto-scaling across thousands to millions of executions
• YAML-based configuration to define multi-step, decision-driven processes
• Persistent AI agents with state management and long-running tasks
• Tools and integrations: web search, APIs, and custom connectors
• Model-agnostic architecture to swap providers without code changes
• Built-in observability: execution traces, monitoring, and debugging
• Private deployments and enterprise-grade security/compliance
• Flexible pricing: generous free tier, cloud usage-based plans, and on-prem/enterprise options