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Build infinitely scalable AI pipelines in minutes

API to build multi-step agent workflows

CUSTOMER STORIES

Vidyo built personalised marketing intelligence for thousands of users using posting trends and current top trends.

Iterate and deploy AI features super fast by bringing data from different sources in one place.

CUSTOMER STORIES

Vidyo built personalised marketing intelligence for thousands of users using posting trends and current top trends.

Iterate and deploy AI features super fast by bringing data from different sources in one place.

CUSTOMER STORIES

Vidyo built personalised marketing intelligence for thousands of users using posting trends and current top trends.

Iterate and deploy AI features super fast by bringing data from different sources in one place.

Build with Julep

Create an agent

Define agents that can talk to users inside of a session

Add tools

Equip agents with tools - web search, API calls, or custom integrations

Define your tasks

Define multi-step processes in YAML with decision trees, loops, and parallel execution

Deploy

Execute production-grade workflows with one command

agent = julep.agents.create(

   name="Spiderman",

   about="AI that can crawl the web and extract data",

   model="gpt-4o-mini",

   default_settings={

       "temperature": 0.75,

       "max_tokens": 10000,

       "top_p": 1.0,

   }

)

Create an agent

Define agents that can talk to users inside of a session

Add tools

Equip agents with tools - web search, API calls, or custom integrations

Define your tasks

Define multi-step processes in YAML with decision trees, loops, and parallel execution

Deploy

Execute production-grade workflows with one command

agent = julep.agents.create(

   name="Spiderman",

   about="AI that can crawl the web and extract data",

   model="gpt-4o-mini",

   default_settings={

       "temperature": 0.75,

       "max_tokens": 10000,

       "top_p": 1.0,

   }

)

Create an agent

Define agents that can talk to users inside of a session

Add tools

Equip agents with tools - web search, API calls, or custom integrations

julep.agents.tools.create(

   agent_id=agent.id,

   name="my_weather_tool",

   integration={

       "provider": "weather",

       "setup": {

           "openweathermap_api_key": "OPENWEATHERMAP_API_KEY"

       }

   }

)

Define your tasks

Define multi-step processes in YAML with decision trees, loops, and parallel execution

iimport yaml

task = julep.task.create("""

name: Weather Reporting Task

main:

- tool: spider_crawler

  arguments:

   url: _.url

- prompt: |

   Here’s a crawl of {{inputs[0].url}}:

   {{_.documents}}

   Create a short summary (~100 words) of the website.

""")       - city

Deploy

Execute production-grade workflows with one command

execution = julep.executions.create(

   task_id=task.id,

   input={

       "url": "https://spider.cloud"

   }

)

agent = julep.agents.create(

   name="Cristopher",

   about="An agent specialized in weather reporting",

   model="gpt-4o-mini",

)

Connect with any AI model,
API or data source

Seamlessly integrate with your existing stack and favorite AI models.

LLM Platforms

Languages

Integrations

ANY API accessible using REST API

Build AI pipelines
in minutes.
Scale to millions.

Rapid Prototyping

"From idea to demo in minutes" using built-in RAG and state management

Production Ready

"Go live instantly with managed infrastructure" and features such as long-running tasks, automatic retries and error handling

Modular Design

"Build features like Lego blocks" by connecting to any external API, switch between LLMs and add custom tools

Infinite Scale

Handle millions of concurrent users with automatic scaling and load balancing

Future Proof

"Add new AI models anytime" "Integrate with any new tool or API"

Complete Control

"Full visibility into AI operations" "Manage costs and performance easily"

Real people, Real result

  • Julep to me sits in the intersection of what zapier did for simplifying workflows, and what vercel did for simplifying shipping.

    Suryansh Tibarewal

    CEO, EssentiallySports

  • For Vidyo, we shipped 6 months worth of product development in 6 hours. GAME CHANGER!!

    Vedant Maheshwari

    CEO, Vidyo.ai

  • "Future-proof. It has unlocked so many of our items on the roadmap."

    Madhavan

    CEO, Reclaim Protocol

  • "Same experience as reviewing designs on Figma with my designer. Sat with my developer once, and we kept brainstorming, testing, prototyping and shipping at once. I got control back in my hands as a non-developer."

    Akshay Pruthi

    CEO, Calm Sleep

  • Julep to me sits in the intersection of what zapier did for simplifying workflows, and what vercel did for simplifying shipping.

    Suryansh Tibarewal

    CEO, EssentiallySports

Questions?
Answers.

What is Julep?

Julep is a platform for creating AI agents that remember past interactions and can perform complex tasks. It provides the complete infrastructure layer between LLMs and your software, with built-in support for long-term memory and multi-step process management.

What is Julep?

Julep is a platform for creating AI agents that remember past interactions and can perform complex tasks. It provides the complete infrastructure layer between LLMs and your software, with built-in support for long-term memory and multi-step process management.

What is Julep?

Julep is a platform for creating AI agents that remember past interactions and can perform complex tasks. It provides the complete infrastructure layer between LLMs and your software, with built-in support for long-term memory and multi-step process management.

How is Julep different from LangChain?

While LangChain is great for creating sequences of prompts and managing model interactions, Julep is built for creating persistent AI agents with advanced task capabilities. Think of LangChain as a tool for prompt chains, while Julep is a complete platform for building production-ready AI systems with complex workflows, state management, and long-running tasks.

How is Julep different from LangChain?

While LangChain is great for creating sequences of prompts and managing model interactions, Julep is built for creating persistent AI agents with advanced task capabilities. Think of LangChain as a tool for prompt chains, while Julep is a complete platform for building production-ready AI systems with complex workflows, state management, and long-running tasks.

How is Julep different from LangChain?

While LangChain is great for creating sequences of prompts and managing model interactions, Julep is built for creating persistent AI agents with advanced task capabilities. Think of LangChain as a tool for prompt chains, while Julep is a complete platform for building production-ready AI systems with complex workflows, state management, and long-running tasks.

How does Julep handle errors and reliability?

Julep includes built-in self-healing capabilities: - Automatic retries for failed steps - Message resending - Task recovery - Error handling - Real-time monitoring.

How does Julep handle errors and reliability?

Julep includes built-in self-healing capabilities: - Automatic retries for failed steps - Message resending - Task recovery - Error handling - Real-time monitoring.

How does Julep handle errors and reliability?

Julep includes built-in self-healing capabilities: - Automatic retries for failed steps - Message resending - Task recovery - Error handling - Real-time monitoring.

How does Julep's approach differ from typical AI development?

While most platforms focus on prompt engineering and chaining LLM calls, Julep brings software engineering discipline to AI development. Through our 8-Factor Agent methodology, we treat AI components as proper system elements: - Prompts as Code: Track prompts separately from application code, enabling systematic improvements and proper versioning. - Clear Tool Interfaces: Define explicit interfaces for all tool interactions, making capabilities modular and maintainable. -Model Independence: Treat model providers as external, replaceable resources to avoid vendor lock-in and enable easy switching. -Context Management: Explicitly define how application and user state is managed and reduced. -Ground Truth Examples: Maintain clear examples of expected prompt results for validation and testing. -Structured Reasoning: Separate processes into deliberative (planned, multi-step) and impromptu (quick response) reasoning. -Workflow-Based Model: complex processes as clear workflows rather than chains of prompts. -Full Observability: Save execution traces for debugging, monitoring, and continuous improvement.

How does Julep's approach differ from typical AI development?

While most platforms focus on prompt engineering and chaining LLM calls, Julep brings software engineering discipline to AI development. Through our 8-Factor Agent methodology, we treat AI components as proper system elements: - Prompts as Code: Track prompts separately from application code, enabling systematic improvements and proper versioning. - Clear Tool Interfaces: Define explicit interfaces for all tool interactions, making capabilities modular and maintainable. -Model Independence: Treat model providers as external, replaceable resources to avoid vendor lock-in and enable easy switching. -Context Management: Explicitly define how application and user state is managed and reduced. -Ground Truth Examples: Maintain clear examples of expected prompt results for validation and testing. -Structured Reasoning: Separate processes into deliberative (planned, multi-step) and impromptu (quick response) reasoning. -Workflow-Based Model: complex processes as clear workflows rather than chains of prompts. -Full Observability: Save execution traces for debugging, monitoring, and continuous improvement.

How does Julep's approach differ from typical AI development?

While most platforms focus on prompt engineering and chaining LLM calls, Julep brings software engineering discipline to AI development. Through our 8-Factor Agent methodology, we treat AI components as proper system elements: - Prompts as Code: Track prompts separately from application code, enabling systematic improvements and proper versioning. - Clear Tool Interfaces: Define explicit interfaces for all tool interactions, making capabilities modular and maintainable. -Model Independence: Treat model providers as external, replaceable resources to avoid vendor lock-in and enable easy switching. -Context Management: Explicitly define how application and user state is managed and reduced. -Ground Truth Examples: Maintain clear examples of expected prompt results for validation and testing. -Structured Reasoning: Separate processes into deliberative (planned, multi-step) and impromptu (quick response) reasoning. -Workflow-Based Model: complex processes as clear workflows rather than chains of prompts. -Full Observability: Save execution traces for debugging, monitoring, and continuous improvement.

Build faster using Julep API

Deploy multi-step workflows easily with

built-in tools and

State management that is

production ready from day one.

Build faster using Julep API

Deploy multi-step workflows easily with built-in tools and State management that is production ready from day one.