AI Agents
smolvm
An embeddable, portable, branchable microVM that runs AI agents safely on your own machine.
★ 6.6k starsApache-2.0
View guide →Durable, composable AI agents — flows that crash and resume, retry safely, and explain every step.
julep is an open-source ai agents project by julep-ai (julep-ai/julep on GitHub). It has 6,577 GitHub stars and 968 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 6 August 2026.
Sketch the multi-step agent flow you need — the tools it may call, the decisions it must make, and where it must be able to resume after a failure.
Install the julep Python package (the current release line is pre-release, so use the documented --pre flag).
Register your tools or MCP servers and keep production secrets in your deployment environment or Kubernetes Secrets, as the docs require.
Define a @flow, dry-run it locally with in-memory tools, then deploy it and watch each step execute.
Flows are built to crash and resume and to retry safely, so long-running agent work is not lost when a process dies.
Define flows in Python: registered tools and pure functions, think/cond/switch/each/reschedule steps, and record merging with the | operator.
Every step of a flow is recorded so you can see what the agent did and why, rather than getting an opaque final answer.
Call MCP servers from inside a flow with mcp_tool(server, tool), alongside natively registered tools.
The julep CLI supports plan, apply and status with composable selectors (tags, state, graph traversal) across environments.
dry_run executes a flow locally with in-memory tools and deterministic fake reasoners, so you can test logic before deploying.
Clone Julep to explore the source, examples and docs.
git clone https://github.com/julep-ai/julep.git
cd julepInstall the current Julep Python package from PyPI.
pip install --pre julepRun one of the bundled @flow examples locally to see a durable flow execute step by step.
python examples/episode_summary_flow.py # see README for the exact commandjulep-ai maintains Julep; documentation is published at docs.julep.ai.
The open-source project is licensed under Apache-2.0. Julep also offers a hosted platform, but the repository code itself is free to use under that license.
Flows call LLMs through reasoners, so in production you typically supply your own model provider credentials; local dry runs can use deterministic fake reasoners instead.
Both build graph-like agent workflows. Julep emphasizes durable execution — crash-and-resume flows, safe retries and step-by-step explanations — managed through its CLI and deployment model.
pip install --pre julep, then work through the Quickstart and the example flows in the examples directory.
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Free and open — grab the source, star the repo, and start creating.