Open Source
Github
★
2.4k
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.
Usecases
Company
Built by Engineers, for Engineers
© Julep AI Inc. 2024
Usecases
Company
Built by Engineers, for Engineers
© Julep AI Inc. 2024
Usecases
Company
Built by Engineers, for Engineers
© Julep AI Inc. 2024