BIG NEWS: Julep AI selected for Meta Llama Startup Program

Build business focused RAG chatbots

Transform any knowledge base into an intelligent AI assistant that delivers accurate, context-aware answers grounded in your data.

Trusted by teams building
production-grade AI agents

The proof is in production.

Real-world performance of orchestrated workflows.

10M+

steps executed per month

5X

faster iteration speed

99.99%

execution success rate

“We migrated to Julep’s workflow engine to power our agents. The clarity, debuggability, and reliability it brought reduced our development overhead by 40%.”

Lead ML Engineer

AI-native SaaS

How it works

How it works

Step 1

Processes documents, breaking them into semantic chunks for optimal retrieval while ensuring efficient vector search.

Define what information you need and set up your AI agent with Spider integration

Define what information you need and set up your AI agent with Spider integration



Step 2

Transform data into high-dimensional vector embeddings, which are stored in Julep's built-in vector database

Your agent crawls the target website, following relevant links and understanding page context

Step 3

Perform semantic search to retrieve the most relevant information, then LLMs synthesize accurate grounded in your data.

Tools Used

Document Store API

Document Store API

Document Store API

Vector Database

Vector Database

Vector Database

Web Search Integration

Web Search Integration

Web Search Integration

Session Management

Session Management

Session Management

Why builders choose Julep

Why builders choose Julep

Julep empowers you to build and deploy robust and performant AI applications quickly.

Persistent Memory Across Crawling Sessions

Remember entire conversation histories and user preferences across sessions, for true personalization

Isometric illustration of layered memory panels representing AI agent memory storage and contextual data persistence across sessions

Multi-Step Task Workflows

Built-in support for conditional logic, parallel processing, and automatic retry mechanisms



Built-in support for conditional logic, parallel processing, and automatic retry mechanisms

Network diagram illustration showing connected workflow nodes for multi-step AI task automation and process management

Unified Tool Orchestration

Integrate multiple data sources and APIs - from internal documents to web searches



Integrate multiple data sources and APIs - from internal documents to web searches

Isometric illustration of interconnected data blocks representing AI agent architecture and modular workflow components
Julep AI agent YAML workflow configuration showing API integrations for weather, news, and database tools with conditional logic programming
Julep AI agent YAML workflow configuration showing API integrations for weather, news, and database tools with conditional logic programming

Expand this Workflow

Add custom embedding models

Implement hybrid search

Enable multi-modal RAG

Configure reranking models

Set up continuous learning

Spin up agents in minutes.

Scale insights, not infra.

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SOC 2 Type II Compliant

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Dedicated AI Expert Support

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Configurable Data Retention

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Role-Based Access Controls

Spin up agents in minutes.

Scale insights, not infra.

Small check mark in grey color

SOC 2 Type II Compliant

Small check mark in grey color

Dedicated AI Expert Support

Small check mark in grey color

Configurable Data Retention

Small check mark in grey color

Role-Based Access Controls

Spin up agents in minutes.

Scale insights, not infra.

Small check mark in grey color

SOC 2 Type II Compliant

Small check mark in grey color

Dedicated AI Expert Support

Small check mark in grey color

Configurable Data Retention

Small check mark in grey color

Role-Based Access Controls