Master Julep AI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install the Julep Python or Node.js SDK via pip or npm Clone the repository from github.com/julep
ai/julep and follow the self
hosting guide to deploy Julep on your infrastructure Define an agent with memory configuration and tool access using the SDK or YAML Create a multi
step task workflow with your agent logic Execute the workflow through the SDK and monitor progress via built
💡 Quick Start: Follow these 4 steps in order to get up and running with Julep AI quickly.
Explore the key features that make Julep AI powerful for agent workflows.
Rich, structured memory system that goes beyond conversation history to maintain context, relationships, learned behaviors, and domain-specific knowledge. Supports semantic search across stored memories and knowledge graph traversal for connecting related concepts.
A customer service agent that remembers a returning customer's preferences, past issues, communication style, and product history — providing increasingly personalized service without requiring the customer to repeat themselves.
YAML or code-defined task workflows with conditional branching, loops, parallel execution, error handling with automatic retries, and self-healing steps. Workflows can run for hours, days, or weeks with pause and resume capabilities.
An onboarding workflow that collects documents from a new customer over several days, runs background verification checks in parallel, provisions their account, and sends scheduled follow-up messages — all as a single managed workflow.
Structured toolkit integration allowing agents to invoke web search, databases, third-party APIs, and custom tools within their workflows. Handles authentication, rate limiting, and error recovery for external tool calls automatically.
A research agent that combines web search results with database queries and internal document analysis, orchestrating multiple tool calls within a single research workflow and synthesizing findings into a comprehensive report.
Built-in support for serving multiple users or organizations from shared infrastructure with strict data boundaries, authentication, and granular access controls between agent instances.
A SaaS platform where each enterprise customer gets dedicated AI agents with isolated memories and data, sharing underlying compute resources while maintaining complete data separation.
Native support for spawning concurrent workflow branches, executing multiple operations simultaneously, and aggregating results. Julep manages concurrency, scheduling, and result coordination automatically.
A market analysis agent that simultaneously queries five different data sources, processes results in parallel branches, and merges findings into a unified competitive analysis — completing in minutes rather than sequentially running for hours.
Automatic retry mechanisms, error recovery, and robust task management that keeps long-running workflows operational. Includes real-time monitoring, logging, and progress tracking for full observability.
A financial monitoring agent running 24/7 that automatically recovers from API timeouts, retries failed data fetches, and alerts operators only when issues exceed automatic recovery capabilities.
No. The Julep hosted backend and dashboard were shut down on December 31, 2025. The founding team has stated that the julep.ai domain redirects to memory.store, though users should verify current redirect behavior independently. The platform is available only as an open-source, self-hosted solution via the GitHub repository at github.com/julep-ai/julep. The founding team has pivoted to building memory.store, an MCP-compatible memory service.
Julep maintains structured, searchable memory that captures relationships, context, learned patterns, and domain-specific knowledge — not just message logs. Agents can perform semantic search across stored memories and build knowledge graphs that connect related concepts, entities, and events. This enables agents to recall relevant context from weeks or months ago, recognize patterns across interactions, and build increasingly rich domain understanding over time.
Julep uses a container-based architecture and can be deployed on any platform that supports Docker, including AWS, GCP, Azure, on-premise Kubernetes clusters, or a single VM for development. Refer to the self-hosting documentation in the GitHub repository for current resource requirements, configuration, and scaling recommendations.
Compared to the other agent platforms in our directory, Julep is more opinionated and infrastructure-focused than LangChain, providing a full stateful backend rather than a library of building blocks. Unlike CrewAI, which centers on multi-agent collaboration patterns, Julep specializes in long-running workflows with durable state. Relative to Letta (formerly MemGPT), Julep emphasizes workflow orchestration alongside memory, while Letta focuses more narrowly on memory-centric agent design.
Memory.store is the new product from the Julep founding team, launched as part of the late-2025 strategic shift. Julep remains open-source and focused on full agent workflow infrastructure for developers who self-host, while memory.store is reported to be a consumer-facing, MCP-compatible service that provides shared persistent memory across AI tools. The two products serve different audiences: Julep targets developers building custom agent backends, while memory.store targets end users who want memory across existing AI assistants.
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Tutorial updated March 2026