Why not just use print() or logging?

Logging shows fragments. Tether connects the prompt, action, file diff, command output, failed test, and recovery point in one local execution graph.

Can I use this with production code?

Yes. It's a local proxy, so your real code doesn't change. Use it locally for debugging, or keep it running. Tether only stores traces locally and never sends anything anywhere.

How much money does caching actually save?

It depends on your agent. If you're iterating on prompt logic and re-running the same retrieval steps, caching saves you 50-90% of API spend while you debug. Each cached hit costs $0.0000.

Does Tether add latency to my agent?

Negligible. Tether runs locally on your machine. The only overhead is the proxy hop, which is less than 1ms. Real LLM calls are the bottleneck, not Tether.

Can I share traces with my team?

Not yet. Each developer runs their own Tether instance locally. Export as JSON is coming in a later release.

How does Tether intercept LLM calls without SDK changes?

Tether runs a local HTTP proxy on your machine. You point your AI client's base_url at http://localhost:8080/v1 — that's the only change required. Tether forwards requests to the real provider and records everything locally.

Does Tether send my prompts or API keys to the cloud?

No. Tether is fully air-gapped. Your prompts and responses stay on your machine. macOS secrets use Keychain-aware storage, Linux uses the same local proxy model, and traces stay local.

Which LLM providers does Tether support?

Tether supports OpenAI, Anthropic, Ollama, LM Studio, and any provider that accepts an OpenAI-compatible base_url. It also works with LangChain, LangGraph, LlamaIndex, and similar frameworks.

Yes. Tether is free during the alpha period. The core proxy is open source.

Home/Docs/Replay and cache

Using Tether

Replay and cache

Use cached execution state to keep local iteration cheap and replay a downstream branch from a selected node.

Install Setup steps

Release: Alpha channel
Runtime: macOS + Linux + local proxy
Docs: 19 guides

Cache loop

Cache keys are derived from the HTTP method, path, query, and request body. A cache hit is returned to the app and written to the trace graph as a cached node.

Use cache when iterating on downstream logic that repeats the same prompt.
Clear cache when prompt changes should reach the provider again.
Treat cached hits as local development artifacts, not production truth.

Replay boundary

Replay loads the selected node request, sends it to the configured provider, persists the replacement response, and marks downstream nodes stale so the graph shows which branch needs fresh execution.