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Julep AI

Open-source platform for building stateful AI agents with persistent memory, multi-step workflow orchestration, and tool integration — now self-hosted only after the managed backend sunset in late 2025.

Starting atFree (Open Source)
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In Plain English

An open-source backend platform for AI agents that maintains persistent memory and orchestrates complex multi-step workflows — self-hosted after the managed service sunset in 2025.

OverviewFeaturesPricingGetting StartedUse CasesLimitationsFAQAlternatives

Overview

Julep AI is a free, open-source agent platform for building stateful AI agents with persistent memory, multi-step workflow orchestration, and integrated tooling, available exclusively as a self-hosted deployment after its managed cloud service was sunset in late 2025; it is best suited for developer teams with DevOps capacity who need production-grade agent infrastructure at no licensing cost. Unlike the majority of agent frameworks that treat each interaction as a standalone event, Julep provides the backend plumbing necessary for agents to maintain rich, structured memory across sessions, execute complex multi-step workflows defined in YAML or code, and coordinate parallel tasks that can run for hours, days, or weeks with automatic retries and self-healing. The project is hosted on GitHub at github.com/julep-ai/julep under an open-source license, where contributors can inspect the full codebase and submit pull requests. Julep's workflow engine supports conditional branching, loops, parallel execution, and pause/resume semantics — capabilities that go well beyond what lighter agent libraries offer out of the box. Its persistent memory system stores not just conversation history but structured knowledge with semantic search and knowledge-graph traversal, enabling agents to recall context, recognize patterns, and build on prior interactions in meaningful ways. For teams operating in regulated industries such as healthcare or finance, self-hosting Julep provides complete data sovereignty with built-in multi-tenant isolation. The platform ships with Python and Node.js SDKs plus a REST API and CLI, giving developers flexibility in how they define and manage agent workflows. Following the December 2025 sunset of the hosted cloud backend, the founding team shifted focus to a new product called memory.store, an MCP-compatible memory layer. The Julep open-source project continues to be available on GitHub, though prospective adopters should evaluate community activity and commit frequency to gauge ongoing maintenance momentum before committing to a production deployment.

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Editorial Review

Julep AI is a capable open-source agent platform for building stateful AI agents with persistent memory and complex workflow orchestration. Following the sunset of its managed cloud service in late 2025, it is now exclusively self-hosted. Best suited for developer teams with DevOps resources who need production-grade agent infrastructure with multi-tenant isolation, long-running workflow support, and complete data sovereignty at no licensing cost. Prospective adopters should check the GitHub repository's commit activity and community engagement to assess ongoing maintenance before committing to production use.

Key Features

Persistent Agent Memory+

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.

Use Case:

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.

Multi-Step Workflow Engine+

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.

Use Case:

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.

Tool Orchestration System+

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.

Use Case:

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.

Multi-Tenant Data Isolation+

Built-in support for serving multiple users or organizations from shared infrastructure with strict data boundaries, authentication, and granular access controls between agent instances.

Use Case:

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.

Parallel Execution Engine+

Native support for spawning concurrent workflow branches, executing multiple operations simultaneously, and aggregating results. Julep manages concurrency, scheduling, and result coordination automatically.

Use Case:

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.

Self-Healing and Reliability+

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.

Use Case:

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.

Pricing Plans

Self-Hosted Open Source

$0

  • ✓Full Julep platform source code on GitHub
  • ✓Persistent memory, workflows, and tool orchestration
  • ✓Multi-tenant data isolation
  • ✓Python, Node.js, and YAML SDKs
  • ✓No per-seat or per-API-call licensing fees
  • ✓You provide and operate your own infrastructure
See Full Pricing →Free vs Paid →Is it worth it? →

Ready to get started with Julep AI?

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Getting Started with Julep AI

  1. 1Install the Julep Python or Node.js SDK via pip or npm
  2. 2Clone the repository from github.com/julep-ai/julep and follow the self-hosting guide to deploy Julep on your infrastructure
  3. 3Define an agent with memory configuration and tool access using the SDK or YAML
  4. 4Create a multi-step task workflow with your agent logic
  5. 5Execute the workflow through the SDK and monitor progress via built-in logging
Ready to start? Try Julep AI →

Best Use Cases

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Long-Running Customer Onboarding Workflows: Orchestrate multi-day onboarding processes that collect documents, run verification checks, provision accounts, and send follow-up communications — all managed as a single stateful workflow with automatic error recovery and pause/resume.

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Persistent Customer Service Agents: Build support agents that remember each customer's history, preferences, past issues, and communication style across every interaction, delivering increasingly personalized service without requiring customers to repeat themselves.

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Automated Research and Analysis Pipelines: Deploy research agents that search multiple sources in parallel, accumulate domain knowledge over time in persistent memory, connect related findings through knowledge graphs, and produce comprehensive reports.

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Multi-Tenant SaaS Agent Infrastructure: Power SaaS platforms where each customer gets dedicated AI agents with isolated data and memories, sharing underlying compute while maintaining strict tenant boundaries — well-suited to regulated industries that require data sovereignty.

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Educational Tutoring Systems with Learning Memory: Create tutoring agents that track student progress, adapt teaching strategies based on past performance, identify knowledge gaps across sessions, and provide personalized curriculum recommendations.

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24/7 Monitoring and Operations Agents: Run financial, infrastructure, or compliance monitoring agents continuously, with automatic retries on transient failures and human escalation only when issues exceed automated recovery.

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Julep AI doesn't handle well:

  • ⚠Hosted cloud service discontinued on December 31, 2025 — requires self-hosting with container infrastructure
  • ⚠Founding team attention has shifted to memory.store, which may slow Julep community development and roadmap velocity
  • ⚠Significant DevOps investment needed for production deployment, scaling, and ongoing maintenance
  • ⚠Steeper learning curve for workflow definition compared to simpler frameworks like LangChain or CrewAI
  • ⚠Overkill for simple single-interaction chatbot use cases where a managed service would be more cost-effective

Pros & Cons

✓ Pros

  • ✓Fully open-source with zero licensing or per-API-call costs for self-hosted deployments
  • ✓Sophisticated persistent memory system with semantic search and knowledge-graph traversal — well beyond conversation history
  • ✓Multi-step workflow engine supports conditional branching, loops, and parallel execution defined in YAML, Python, or Node.js
  • ✓Long-running task support spanning hours, days, or weeks with pause/resume and durable state
  • ✓Built-in self-healing, automatic retries, and error recovery for production reliability
  • ✓Native multi-tenant architecture with strict data isolation for SaaS use cases
  • ✓Complete data sovereignty when self-hosted — important for healthcare, finance, and other regulated industries

✗ Cons

  • ✗Hosted cloud service and dashboard were sunset on December 31, 2025 — self-hosting is now the only option
  • ✗Significant DevOps overhead to deploy, scale, and maintain containerized infrastructure
  • ✗Steeper learning curve than lighter agent frameworks like LangChain or CrewAI
  • ✗Founding team has redirected focus to memory.store, which may slow Julep's roadmap and community responsiveness
  • ✗Overkill for simple chatbot or single-interaction agent use cases where a managed service would suffice

Frequently Asked Questions

Is the Julep hosted cloud service still available?+

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.

How does Julep's memory differ from simple conversation history storage?+

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.

What infrastructure do I need to self-host Julep?+

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.

How does Julep compare to LangChain, CrewAI, and Letta?+

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.

What is the relationship between Julep AI and memory.store?+

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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What's New in 2026

On December 31, 2025, Julep sunset its hosted cloud backend and dashboard, transitioning fully to an open-source, self-hosted model. The founding team launched memory.store, described as an MCP-compatible memory layer for AI tools like Claude, ChatGPT, and Cursor. The Julep codebase remains available on GitHub for self-hosted deployment, though community activity should be monitored to assess ongoing project health.

Alternatives to Julep AI

Mem0

AI agent memory

Memory infrastructure for AI agents and applications, available as an open-source framework and managed platform.

Zep

AI Memory & Search

Enterprise agent memory built on temporal Context Graphs (Graphiti) with millisecond retrieval, SOC 2 Type II, and HIPAA BAA.

Letta

AI Memory & Search

Letta is the open-source successor to MemGPT — a stateful agent platform with persistent memory, tool use, and a visual Agent Development Environment.

LangChain

AI Agent Builders

The industry-standard framework for building production-ready LLM applications with comprehensive tool integration, agent orchestration, and enterprise observability through LangSmith.

CrewAI

AI Agents

Open-source Python framework for orchestrating role-playing, autonomous AI agents that collaborate as a 'crew' to complete complex tasks.

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