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THE AGENT CLOUD

Deploy your agent. Make every run count.

Run the agent you've already built in the cloud without managing infrastructure. Lagrange handles deployment and execution, then uses data from real runs to identify and test ways to improve speed, cost, and performance.

Deploy your agent How it works
Codex · ~/example-store/support-bot

Deploy this agent to LagrangeCloud

  1. Read AGENTS.md, 2 skills and a script
  2. Added MCP servers linear and stripe
  3. Stored 2 keys as secrets, without printing them
  4. Deployed support-bot v1 to LagrangeCloud using Codex

Live https://lgr.tensorblock.co/v1/invoke/ag_5d02c7a3b81e

Example store supportlive

Where is order 42?

It shipped yesterday and arrives Friday.

Lagrange found an optimizationLook up each order once Cost per run$0.031$0.013 Median time4.8s2.0s Promoted. support-bot v2 is live.
DEPLOYMENT

Take your agent from local to live.

Deploy your existing agent with its instructions, tools, and dependencies. Lagrange handles the infrastructure, stores credentials as secrets, and gives you one place to manage and inspect executions.

Your agentCodex app, Codex CLI or Claude Code

Lagrange CloudHosted execution

Where it runsOne deploy, three ways

$ npx -y @openai/codex plugin marketplace add https://github.com/TensorBlock/lagrange-plugins
$ npx -y @openai/codex plugin add lagrange@tensorblock
Then, in your agent's folder: "Deploy this agent to LagrangeCloud"
support-botv1 live
Endpoint
/v1/invoke/ag_5d02c7a3b81e
Harness
Codex or Claude Code
Secrets
2 keys stored
Executions
Every run, inspectable
  • From your appCall your agent through an APIPOST /v1/invoke
  • On your websiteMake it available to usersstore.example
  • On a scheduleRun recurring tasks automatically0 8 * * *
CONTEXT & SECRETS

Deploys exactly as you built it. API keys stay in secrets, never in chat.

LagrangeCloud hosts your agent. A harness runs its instructions and tools: Codex or Claude Code today, with our self-improving Lagrange harness coming soon.

~/example-store/support-bot
AGENTS.md
skills/orders/SKILL.md
skills/refunds/SKILL.md
scripts/lookup_order.py
.codex/config.toml  linear, stripe
.env                LINEAR_API_KEY, STRIPE_SECRET_KEY
support-botv1 live
Instructions
AGENTS.md
Skills
orders, refunds
Files
lookup_order.py
MCP servers
linear, stripe
Secrets
LINEAR_API_KEY, STRIPE_SECRET_KEY
Harness
Codex in this example
CONTINUOUS OPTIMIZATION

Infrastructure that improves with your agent.

  • Learns from every run

    Most agent infrastructure stays the same after deployment until someone changes it by hand. Every execution generates data that can reveal better ways to run your agent.

  • Proposes and tests changes

    Lagrange analyzes execution traces to find ways to reduce latency, lower costs, and improve reliability, then tests each change against the current version.

  • You approve what ships

    Review what changed, why it was proposed, and how it performed. Each accepted improvement becomes the baseline for the next round of optimization.

Optimization proposalPending review

Example

Use a lighter model for classification

Route a straightforward classification step to a smaller model, while keeping the remaining workflow unchanged.

steps.classify:
- model: large
+ model: small
steps.*: unchanged

Current and proposed

CurrentProposed

Execution time−28% 8.2s 5.9s
Cost per run−31% $0.042 $0.029
Output qualityUnchanged 98% 98%

Changes require your approval

Illustrative example, not measured Lagrange results

HOW LAGRANGE WORKS

Each accepted improvement becomes the baseline for the next cycle.

  1. Deploy once

    01

    Deploy

    Get your agent running

    support-bot v1 live

  2. Then on every run

    02

    Run

    Execute real tasks

    APIWebsiteSchedule
  3. 03

    Trace

    Capture execution data

  4. v1v2v3…

    04

    Improve

    Test and approve changes

    v2 proposed
GET STARTED

Try it on your agent.

Choose how you want to deploy your agent to Lagrange. Then, once it's running, Lagrange will begin collecting execution data and identifying opportunities for improvement.

$ npx -y @openai/codex plugin marketplace add https://github.com/TensorBlock/lagrange-plugins
$ npx -y @openai/codex plugin add lagrange@tensorblock
Then, in your agent's folder: "Deploy this agent to LagrangeCloud"
Get startedSign in
PRICING

Pay for what you use.

Runs, instances, and fleets from

$0.05/ sandbox hour

  • Free credits to get started
  • Model tokens billed separately at list price