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.
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.
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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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.
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.
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.
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.
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.
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.
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.
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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.
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