llms.txt
Compare with other sites' llms.txt files →# Tensorlake
> Tensorlake is the sandbox cloud for AI agents: stateful, AI-native
> microVM sandboxes with sub-second cold starts, snapshot,
> suspend/resume, and sandbox-native durable orchestration. Securely
> run untrusted, LLM-generated code in isolated sandboxes that scale
> from one to thousands. This file is a map for LLMs and coding
> agents building applications on Tensorlake — it points at the pages
> you need to install the SDK, authenticate, launch sandboxes, and
> orchestrate durable workflows.
Tensorlake's runtime is built on Firecracker microVMs with a custom
scheduler for agent workloads: isolated tool/code execution, stateful
checkpoints, per-second billing, and up to five million sandboxes per
project. The two surfaces you build against are **Sandboxes** and
**Applications** (sandbox-native durable orchestration).
## Product
- [Home](https://tensorlake.ai/): Product overview, capabilities, benchmarks, and customer signal.
- [Agent harnesses](https://tensorlake.ai/#product): Run full AI agents inside isolated, stateful sandboxes with filesystem, shell, and package state.
- [Capabilities](https://tensorlake.ai/#capabilities): Sandboxes, agent harnesses, isolated tool execution, RL environments, orchestration.
- [Benchmarks](https://tensorlake.ai/#bench): SQLite and filesystem benchmarks vs. E2B, Modal, Vercel, and Daytona.
- [Customer signal](https://tensorlake.ai/#customers): Production users and testimonials.
## Pricing and getting started
- [Pricing](https://tensorlake.ai/pricing): Plans and usage-based pricing.
- [Sign up](https://cloud.tensorlake.ai): Free tier — start building.
## Start here
- [Documentation home](https://docs.tensorlake.ai/introduction): Entry point. What Tensorlake is and which surface to pick for your use case.
- [Full docs index (llms.txt)](https://docs.tensorlake.ai/llms.txt): Machine-readable index of every page in the Tensorlake documentation — link-per-page for SDK, API reference, guides, and examples.
- [Dashboard / sign up](https://cloud.tensorlake.ai/login): Sign in to Tensorlake Cloud to get an API key. Free tier included, no credit card required.
- [Authentication](https://docs.tensorlake.ai/platform/authentication): How to create API keys and make authenticated requests to the Tensorlake APIs.
- [Agent Skills](https://docs.tensorlake.ai/agent-skills): Skill files that teach coding agents (Claude Code, Codex, OpenCode, Amp) how to ship production workflows on Tensorlake. Drop these into your agent's skill directory.
## Sandboxes — isolated, stateful execution
### Sandbox capability notes for coding agents
- Tensorlake Sandboxes expose SDKs and a REST API through each sandbox's proxy URL.
- REST process execution supports per-process environment variables via `env` on `POST /api/v1/processes` and `POST /api/v1/processes/run`.
- REST streaming stdin is supported for processes started with `stdin_mode: "pipe"`: write bytes with `POST /api/v1/processes/{pid}/stdin`, then send EOF with `POST /api/v1/processes/{pid}/stdin/close`.
- `POST /api/v1/processes/run` is the non-interactive one-request path: it streams stdout/stderr over Server-Sent Events and always starts with stdin closed.
- Use PTY sessions for interactive terminal semantics over WebSocket. Use TCP tunnels for raw binary WebSocket forwarding to sandbox-local TCP ports.
- The raw OpenAPI spec is the canonical machine-readable REST reference: https://docs.tensorlake.ai/api-reference/openapi.yaml
- [Sandboxes overview](https://docs.tensorlake.ai/sandboxes/introduction): Isolated, stateful Firecracker-backed execution environments for agents. Sub-second cold starts, suspend/resume, snapshots.
- [Sandboxes quickstart](https://docs.tensorlake.ai/sandboxes/quickstart): Install the SDK, authenticate, and run your first sandbox in under five minutes.
- [Sandbox lifecycle](https://docs.tensorlake.ai/sandboxes/lifecycle): States, creation, suspend/resume, cleanup.
- [Sandbox images](https://docs.tensorlake.ai/sandboxes/images): Define reusable named sandbox images in Python, TypeScript, or Dockerfiles. Valid base images include `tensorlake/ubuntu-minimal` and `tensorlake/ubuntu-systemd`.
- [Snapshots](https://docs.tensorlake.ai/sandboxes/snapshots): Save and restore filesystem, memory, and running processes.
- [Execute commands](https://docs.tensorlake.ai/sandboxes/commands): Run commands with output capture, streaming, and error handling.
- [Process management](https://docs.tensorlake.ai/sandboxes/processes): Start, monitor, and manage background processes.
- [PTY sessions](https://docs.tensorlake.ai/sandboxes/pty-sessions): Interactive shell sessions over WebSocket with live terminal I/O.
- [File operations](https://docs.tensorlake.ai/sandboxes/file-operations): Copy, read, write, and manage files inside a sandbox.
- [Environment variables](https://docs.tensorlake.ai/sandboxes/environment-variables): Per-command and per-PTY env vars in CLI, Python, and TypeScript.
- [Networking](https://docs.tensorlake.ai/sandboxes/networking): Route internet traffic into sandbox applications and control outbound access.
- [AI code execution](https://docs.tensorlake.ai/sandboxes/ai-code-execution): Run LLM-generated code in isolated containers with resource limits; integrate sandboxes as agent tools.
- [Computer use](https://docs.tensorlake.ai/sandboxes/computer-use): Launch `tensorlake/ubuntu-vnc` sandboxes and drive their desktop from Python or JavaScript.
- [Skills in sandboxes](https://docs.tensorlake.ai/sandboxes/skills-in-sandboxes): Pre-load Tensorlake skill files inside sandbox images so coding agents auto-discover them at startup.
## Applications — durable orchestration for agents
- [Applications introduction](https://docs.tensorlake.ai/applications/introduction): Add serverless orchestration to any agent.
- [Applications quickstart](https://docs.tensorlake.ai/applications/quickstart): Ship a first durable agent endpoint.
- [Programming agents (overview)](https://docs.tensorlake.ai/applications/overview): Core concepts and common patterns for running agents on Tensorlake.
- [SDK reference (functions, apps, decorators)](https://docs.tensorlake.ai/applications/concepts): Functions, applications, decorators, request context, and lifecycle.
- [Architecture](https://docs.tensorlake.ai/applications/architecture): How the Application Runtime runs your code under the hood.
- [Building workflows](https://docs.tensorlake.ai/applications/building-workflows): Multi-step data workflows with parallel execution.
- [Durable execution](https://docs.tensorlake.ai/applications/durability): How crashes and restarts preserve progress.
- [Crash recovery](https://docs.tensorlake.ai/applications/crash-recovery): How agents survive failures and resume without losing work.
- [Futures](https://docs.tensorlake.ai/applications/futures): Run multiple function calls in parallel.
- [Map-Reduce](https://docs.tensorlake.ai/applications/map-reduce): Fan-out / fan-in patterns.
- [Parallel sub-agents](https://docs.tensorlake.ai/applications/parallel-sub-agents): Fan out work to specialist agents running in parallel.
- [Async functions](https://docs.tensorlake.ai/applications/async-functions): Python `async`/`await` inside Tensorlake functions.
- [Sandboxes from Applications](https://docs.tensorlake.ai/applications/sandboxes): Two patterns for running agents with isolated code execution from an Application.
- [Retries & rate limits](https://docs.tensorlake.ai/applications/retries): Handle LLM rate limits, transient failures, and validation with durable retries.
- [Timeouts](https://docs.tensorlake.ai/applications/timeouts): How function timeouts work and how progress updates reset them.
- [Error handling](https://docs.tensorlake.ai/applications/error-handling).
- [Secrets](https://docs.tensorlake.ai/applications/secrets): Provide secrets to Tensorlake functions.
- [Container images](https://docs.tensorlake.ai/applications/images): Custom images for Applications.
- [Cron scheduler](https://docs.tensorlake.ai/applications/cron-scheduler): Schedule recurring invocations of your Orchestration endpoints.
- [Scale-out & queuing](https://docs.tensorlake.ai/applications/scale-out-queuing): Automatic scaling per function.
- [Autoscaling](https://docs.tensorlake.ai/applications/scaling-agents): Autoscaling guide for orchestration endpoints.
- [Observability](https://docs.tensorlake.ai/applications/observability): Built-in tracing and execution timelines.
- [Logging](https://docs.tensorlake.ai/applications/guides/logging).
- [Streaming progress updates](https://docs.tensorlake.ai/applications/guides/streaming-progress): Stream real-time progress from functions.
- [Troubleshooting](https://docs.tensorlake.ai/applications/production/troubleshooting): Common issues and how to debug them.
## API reference (v2)
- [API introduction](https://docs.tensorlake.ai/api-reference/v2/introduction): Conventions, authentication, and errors.
- [Sandboxes — create](https://docs.tensorlake.ai/api-reference/v2/sandboxes/create), [get](https://docs.tensorlake.ai/api-reference/v2/sandboxes/get), [list](https://docs.tensorlake.ai/api-reference/v2/sandboxes/list), [update](https://docs.tensorlake.ai/api-reference/v2/sandboxes/update), [suspend](https://docs.tensorlake.ai/api-reference/v2/sandboxes/suspend), [resume](https://docs.tensorlake.ai/api-reference/v2/sandboxes/resume), [snapshot](https://docs.tensorlake.ai/api-reference/v2/sandboxes/snapshot), [restore](https://docs.tensorlake.ai/api-reference/v2/sandboxes/restore), [delete](https://docs.tensorlake.ai/api-reference/v2/sandboxes/delete).
- [Processes — start](https://docs.tensorlake.ai/api-reference/v2/processes/start), [run](https://docs.tensorlake.ai/api-reference/v2/processes/run), [get](https://docs.tensorlake.ai/api-reference/v2/processes/get), [list](https://docs.tensorlake.ai/api-reference/v2/processes/list), [kill](https://docs.tensorlake.ai/api-reference/v2/processes/kill), [signal](https://docs.tensorlake.ai/api-reference/v2/processes/signal), [stdout](https://docs.tensorlake.ai/api-reference/v2/processes/stdout), [stderr](https://docs.tensorlake.ai/api-reference/v2/processes/stderr), [output](https://docs.tensorlake.ai/api-reference/v2/processes/output), [follow-output (SSE)](https://docs.tensorlake.ai/api-reference/v2/processes/follow-output), [stdin](https://docs.tensorlake.ai/api-reference/v2/processes/stdin), [close-stdin](https://docs.tensorlake.ai/api-reference/v2/processes/close-stdin).
- [PTY — create](https://docs.tensorlake.ai/api-reference/v2/pty/create), [get](https://docs.tensorlake.ai/api-reference/v2/pty/get), [list](https://docs.tensorlake.ai/api-reference/v2/pty/list), [resize](https://docs.tensorlake.ai/api-reference/v2/pty/resize), [kill](https://docs.tensorlake.ai/api-reference/v2/pty/kill), [WebSocket attach](https://docs.tensorlake.ai/api-reference/v2/pty/websocket).
- [Sandbox files — read](https://docs.tensorlake.ai/api-reference/v2/sandbox-files/read), [write](https://docs.tensorlake.ai/api-reference/v2/sandbox-files/write), [list](https://docs.tensorlake.ai/api-reference/v2/sandbox-files/list), [delete](https://docs.tensorlake.ai/api-reference/v2/sandbox-files/delete).
- [Runtime metadata — health](https://docs.tensorlake.ai/api-reference/v2/runtime/health), [info](https://docs.tensorlake.ai/api-reference/v2/runtime/info).
- [TCP tunnel WebSocket](https://docs.tensorlake.ai/api-reference/v2/tunnels/tcp): Authenticated binary WebSocket tunnel to sandbox-local TCP ports.
- [SSH lifecycle — status](https://docs.tensorlake.ai/api-reference/v2/ssh/status), [enable](https://docs.tensorlake.ai/api-reference/v2/ssh/enable), [disable](https://docs.tensorlake.ai/api-reference/v2/ssh/disable): In-sandbox SSH daemon used by Tensorlake's SSH proxy.
- [OpenAPI spec](https://docs.tensorlake.ai/api-reference/openapi.yaml): Machine-readable OpenAPI definition.
## Platform
- [Access control](https://docs.tensorlake.ai/platform/access-control): Organizations, projects, roles, users.
- [Billing](https://docs.tensorlake.ai/platform/billing): Per-second sandbox billing, plan limits, usage.
- [EU data residency](https://docs.tensorlake.ai/platform/eu-data-residency): Regional endpoints and residency controls.
- [Security policies](https://docs.tensorlake.ai/platform/security): Kernel boundaries, tenancy, compliance posture.
- [Single Sign-On (SSO)](https://docs.tensorlake.ai/platform/sso): OIDC and SAML 2.0.
- [Webhooks overview](https://docs.tensorlake.ai/platform/webhooks/overview), [configuration](https://docs.tensorlake.ai/platform/webhooks/configuration), [signature verification](https://docs.tensorlake.ai/platform/webhooks/signature-verification), [testing](https://docs.tensorlake.ai/platform/webhooks/testing).
- [Webhook payload — workflows](https://docs.tensorlake.ai/platform/webhooks/payloads/workflows).
## Examples & cookbooks
- [Examples overview](https://docs.tensorlake.ai/examples/overview).
- [Agent with tool calling](https://docs.tensorlake.ai/examples/agentic-applications/agent-with-tools): Claude agent orchestrating workflows via tool calls.
- [Code interpreter agent](https://docs.tensorlake.ai/examples/agentic-applications/code-interpreter): Secure code-execution agent on Tensorlake + OpenAI.
- [Deep research agent](https://docs.tensorlake.ai/examples/agentic-applications/deep-research): Multi-agent deep research pipeline.
- [Personal finance manager](https://docs.tensorlake.ai/examples/agentic-applications/personal-finance-manager).
- [Weather agent](https://docs.tensorlake.ai/examples/agentic-applications/weather-agent).
- [Web scraper → MongoDB Atlas](https://docs.tensorlake.ai/examples/agentic-applications/web-scraper).
- [Workflow tutorial — Hello World](https://docs.tensorlake.ai/examples/tutorials/workflow-tutorial).
## FAQs
- [Workflows FAQ](https://docs.tensorlake.ai/faqs/workflows-faq).
- [Sandbox and platform FAQ](https://tensorlake.ai/faq): Plain-language answers on sandboxes, security, pricing, and how Tensorlake compares to E2B, Modal, and Daytona.
## Open source
- [Indexify](https://docs.tensorlake.ai/opensource/indexify): Open-source compute engine for agentic data applications.
- [Indexify — deployment](https://docs.tensorlake.ai/opensource/deployment), [configuration](https://docs.tensorlake.ai/opensource/configuration), [monitoring](https://docs.tensorlake.ai/opensource/monitoring).
- [GitHub — tensorlakeai](https://github.com/tensorlakeai): SDK source, benchmarks, and open-source projects.
- [Slack community](https://join.slack.com/t/tensorlakecloud/shared_invite/zt-32fq4nmib-gO0OM5RIar3zLOBm~ZGqKg): Ask questions and share what you're building.
## Blog
- [A sandbox that holds a whole environment](https://tensorlake.ai/blog/agentenv-tensorlake-sandbox-provider): Tensorlake is now a built-in sandbox provider in Scale AI’s AgentEnv Framework.
- [Run Cursor Cloud Agents on Tensorlake Sandboxes](https://tensorlake.ai/blog/cursor-cloud-agents-tensorlake-sandbox): Cursor keeps the agent loop and the model. Every command, file edit, build, and checkout runs in a Tensorlake sandbox you own, one sandbox per agent, suspended when idle.
- [Run multi-container agent evals on Tensorlake with Harbor](https://tensorlake.ai/blog/harbor-multi-container-tasks-on-tensorlake): Harbor 0.23.0 can run Docker Compose tasks on Tensorlake. Each task boots as one MicroVM with a Docker daemon inside, so an agent’s MCP server, database, browser, or game server runs next to it as its own container.
- [Dynamic Firewall Policies for Sandboxes.](https://tensorlake.ai/blog/updating-egress-policies-on-running-sandboxes): Every Tensorlake sandbox that carries a network policy sits behind an egress firewall on the host. A walk through the enforcement path, plus two recent improvements: live policy updates and per-connection hostname checks.
- [Strengthening agent evaluation environments.](https://tensorlake.ai/blog/dynamic-network-policies-in-harbor): Tensorlake Sandboxes now support dynamic network policies in Harbor: public, allowlist, or no-network per trial phase, switched on a running sandbox without a restart.
- [Run Crabbox Test Suites in a Tensorlake Sandbox](https://tensorlake.ai/blog/crabbox-tensorlake-provider): Crabbox's Tensorlake provider drops any command into a Firecracker microVM: warm a box, sync your working tree, run the suite. One flag, no extra wiring.
- [Run DeepSeek Harness in a Tensorlake Sandbox](https://tensorlake.ai/blog/deepseek-harness-tensorlake-sandbox): The @tensorlakeai/dsh-sandbox bundle moves DeepSeek Harness file, subprocess, Bash, terminal, and LSP operations into one short-lived Tensorlake microVM. No changes to the Harness installation.
- [Run OpenCode in a Tensorlake Sandbox](https://tensorlake.ai/blog/opencode-tensorlake-sandbox): One plugin routes OpenCode's bash, read, write, and edit tools into an isolated Firecracker microVM. The model loop stays on your machine; its hands run in a sandbox you control.
- [Best AI Coding Agents in 2026: I Tested Claude Code, Codex, OpenCode, and Pi for 14 Days](https://tensorlake.ai/blog/best-ai-coding-agents-2026): Same model, four harnesses, 14 days of real work plus a 30-task agentic benchmark. The wrapper around the model turned out to decide cost, speed, and safety more than the model did.
- [Build Your Own CI infrastructure](https://tensorlake.ai/blog/build-your-own-ci-infrastructure): You get a GitHub runner, and you get a GitHub runner...
- [Introducing Tensorlake BYOC: Serverless Sandboxes in Your Private Cloud](https://tensorlake.ai/blog/introducing-tensorlake-byoc): Run Tensorlake Sandboxes on compute you own across AWS, GCP, Azure, CoreWeave, Nebius, and bare metal. Same APIs, images, and SDKs as Tensorlake Cloud, with compute, storage, and workload traffic inside your own network.
- [Run Devin Outposts on Tensorlake Sandboxes.](https://tensorlake.ai/blog/devin-outposts-on-tensorlake): Cognition keeps the agent loop; execution runs inside a Tensorlake sandbox you control. We built the integration, and you can clone and run it today.
- [Firecracker disk snapshots in O(changed bytes), not O(disk size)](https://tensorlake.ai/blog/firecracker-disk-snapshots-o-changed-bytes): We added copy-on-write image layering inside Firecracker's block device, and the disk got faster, not slower. Snapshots pause the VM for tens of milliseconds and cost only what changed, so a running Postgres barely notices them, at any disk size.
- [Zero-copy TLS ingress with kTLS and splice(2) for sandboxes](https://tensorlake.ai/blog/near-zero-overhead-sandbox-networking)
- [Accelerating Harbor with Tensorlake.](https://tensorlake.ai/blog/accelerating-harbor-with-tensorlake): Tensorlake runs the real task Docker images for Harbor on microVM sandboxes. On Terminal-Bench 2.1 the oracle passes all 89 tasks in 36m 44s — every image published, cold starts in seconds.
- [Tensorlake Sandboxes as the runtime for Claude Managed Agents.](https://tensorlake.ai/blog/claude-managed-agents): Run Claude Managed Agents on Tensorlake Sandboxes: you keep the control of self-hosting, and drop the always-on box. The short version — the server goes away.
- [Claude Opus 4.7 vs. Kimi K2.6: Real-World Coding Test](https://tensorlake.ai/blog/claude-opus-4-7-vs-kimi-k2-6-real-world-coding-test): A practical comparison between the premium closed frontier model and the cheaper open-weight agentic coder, on the same two-part coding task.
- [Filter and rank: how we schedule sandboxes across every cloud](https://tensorlake.ai/blog/multi-cloud-scheduling): One primitive — filter, then rank — runs through statefulness, multi-cloud, BYOC, and the data path. A tour of the scheduler behind Tensorlake's sandbox fleet.
- [Browser Harness: direct Chrome DevTools Protocol access and a self-healing harness for browser agents](https://tensorlake.ai/blog/browser-harness-cdp-self-healing): Browser-use strips their harness to 592 lines and gives the LLM a raw WebSocket to Chrome's 
