.sb-flow, .intel-flow { height: auto !important; } .sb-sticky, .intel-sticky { position: static !important; height: auto !important; overflow: visible !important; padding: 4.5rem 0 !important; } .sb-tile { opacity: 1 !important; } .sb-line { stroke-dashoffset: 0 !important; } .sb-node, .sb-node-label { opacity: 1 !important; } .istage-area { min-height: 0 !important; } .istage { position: static !important; inset: auto !important; grid-template-columns: 1fr !important; gap: 1.4rem !important; opacity: 1 !important; pointer-events: auto !important; margin-bottom: 2.5rem !important; } .istage .icopy, .istage .ipane { opacity: 1 !important; transform: none !important; } .ship-fan { position: static !important; height: auto !important; display: grid !important; gap: 1rem !important; } .ship-card { position: static !important; width: auto !important; opacity: 1 !important; transform: none !important; } .intel-step { display: none !important; }

Run agents, intelligently. Make them better.

Tangle runs your agents in isolated sandboxes and turns every trace into proven improvements.

Open the workbench Read the docs

Give every agent an isolated workspace.

Coding harnesses, tool-calling agents, long-running workflows: each gets a real computer in a Tangle Sandbox with files, a terminal, and browser access.

Claude Code $ claude running

Codex $ codex running

OpenCode $ opencode running

Hermes $ hermes running

OpenClaw $ openclaw running

NanoClaw $ nanoclaw running

Kimi Code $ kimi running

Pi $ pi running

Turn each run into a reviewable record.

Capture the run

Record model calls, tool use, timing, tokens, and cost so a result can be inspected after the agent finishes.

illustrative trace · checkout-agent38.2s

run · fix flaky checkout test 38.2s

claude-sonnet-5 · plan 2.1s

read · checkout.test.ts 0.5s

pnpm test · full suite 12.8s

edit · retry-policy.ts 1.9s

pnpm test · full suite 11.4s

edit · checkout.test.ts 0.9s

pnpm test · full suite 8.9s

Find what repeats

Analyze multiple runs to find recurring failures and expensive steps worth testing as candidate changes.

illustrative trace · checkout-agent38.2s

run · fix flaky checkout test 38.2s

claude-sonnet-5 · plan 2.1s

read · checkout.test.ts 0.5s

pnpm test · full suite 12.8s

edit · retry-policy.ts 1.9s

pnpm test · full suite 11.4s

edit · checkout.test.ts 0.9s

pnpm test · full suite 8.9s

Review changes where you work

Turn measured findings into a pull request, issue, skill proposal, or team summary. A person decides what ships.

Pull requestexample

tangle/faster-test-loop

Run the failing test file after each edit. Full suite once before finishing.

Issueexample

TAN-312

Adopt the failing-test-first loop across all coding agents.

Skill proposalreview

skills/testing.md

Proposed testing guidance is ready for review.

#eng-agentsdraft

summary

Candidate passed the selected cases; review the results before rollout.

Infrastructure for running, measuring, and deploying AI agents.

Sandboxes

Isolated computers for agents: shell, files, browser, any coding harness.

sandbox.tangle.tools

Intelligence

Analyze traces and compare candidate changes against held-out cases.

intelligence.tangle.tools

Inference

OpenAI-compatible access to multiple model providers through one API.

router.tangle.tools

Simple, usage-based pricing.

Start free. Every plan includes monthly credit and pays the same per-second compute rates.

Free

$0/mo

Get started with Tangle.

$2 in monthly credit
1 concurrent sandbox
60 requests / minute
Community support

Start free

Run agents, intelligently.

Get started Read the docs