AgentENV

AgentENV (AENV) is a platform for running agent environments at scale, powering agentic RL training for Kimi K3.

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Why AgentENV

• Scale across diverse environments: AENV runs massive numbers of Firecracker environments across machines, loading diverse OCI-compatible images on demand via overlaybd and scaling to 1.5 million images in production. Local disk acts as a bounded cache, retaining hot data and evicting cold, so the aggregate image and snapshot footprint can exceed local disk capacity by several orders of magnitude while startup stays fast cluster-wide, without pre-warming every host.
• Make idle environments inexpensive: Snapshot-backed environments boot or resume in under 50 ms and pause in under 100 ms. Idle environments can quickly release CPU and memory, then return when new work arrives.
• Native snapshot and fork support: AENV snapshots memory and filesystem changes incrementally, completing in under 100 ms even under heavy disk modification. A running environment can fork into multiple independent sandboxes for parallel agent workflows. Snapshots persist to S3-compatible object storage or a shared distributed filesystem to prevent data loss.
• Preserve performance and density over time: AENV delivers high-performance I/O via ublk while sharing the host page cache across storage and memory-snapshot data. Memory ballooning returns reclaimable guest memory to the host, achieving a 9.6x memory overcommit ratio in production as environments run longer and diverge.

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Prerequisites

• Linux kernel 6.8+
• /dev/kvm access for Firecracker microVM execution

If your server does not support standard KVM, see the PVM deployment guide before installing.

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⚡ Quick Start (Single Node)

[!WARNING] AgentENV authenticates API requests but does not encrypt traffic. Do not send the API key over an untrusted plaintext network. Run AgentENV on a trusted network or terminate HTTPS at a reverse proxy or load balancer.

1. Install and start the server

Option A — install script

Install both the server and the aenv CLI, then start the server as a systemd service:

curl -fsSL https://raw.githubusercontent.com/kvcache-ai/AgentENV/main/scripts/install.sh | sudo bash
sudo systemctl start aenv

Option B — Docker

Set up the server:

curl -fsSL https://raw.githubusercontent.com/kvcache-ai/AgentENV/main/scripts/docker-setup.sh | sudo bash
docker pull ghcr.io/kvcache-ai/aenv-server:latest
docker run -d --name aenv-server --privileged -v /dev:/dev -p 8000:8000 ghcr.io/kvcache-ai/aenv-server:latest

The server is accessible at http://127.0.0.1:8000 by default.

2. Install the aenv CLI (skip if you used Option A in step 1)

Install separately if you used the Docker method above, or if you are running the CLI on a different machine from the server. Supports Linux and macOS on x86_64 and arm64:

curl -fsSL https://raw.githubusercontent.com/kvcache-ai/AgentENV/main/scripts/install-cli.sh | bash

3. Authenticate

The server generates an API key on its first startup. Retrieve it for the installation method used in step 1:

# Native install
sudo cat /var/lib/aenv/secrets/api-key

# Docker
docker exec aenv-server cat /workspace/env/secrets/api-key

Then run aenv auth and paste that key:

aenv auth
# AENV server URL [http://localhost:8000]: http://127.0.0.1:8000
# API key: <paste the generated key>

4. Pull a template and run a sandbox

aenv pull ubuntu:22.04 --name ubuntu
aenv start ubuntu            # starts a sandbox and attaches an interactive shell

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Deployment

For Docker Compose / Kubernetes cluster deployment and build-from-source instructions, see Deployment.

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E2B compatibility

AgentENV exposes an E2B-compatible HTTP API. Point E2B_API_URL at your server and use the standard E2B Python / TypeScript SDK without any code changes. See E2B integration for setup details.

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aenv CLI reference

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aenv start accepts a template UUID or human-readable name/alias. aenv list outputs a table on TTY and JSON when piped; override with --output table|json.

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Contributing

Bug reports, feature proposals, documentation fixes, and pull requests are welcome. Please read CONTRIBUTING.md before opening an issue or submitting a change.

Follow SECURITY.md to report security vulnerabilities privately; do not disclose them in a public issue.