Why not just use print() or logging?

Logging shows you what happened. Tether shows you why. You see the exact point where your agent failed, what response broke it, and you replay with a fix in seconds without re-running the whole chain.

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.

Will Tether work with my stack?

If your SDK uses a configurable base_url, including OpenAI SDK, LangChain, LangGraph, LlamaIndex, or Anthropic SDK, it works with one line change. If you use a different provider or custom setup, Tether still works as a transparent proxy.

Does Tether add latency to my agent?

Negligible. Tether runs locally on your Mac. 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, responses, and API keys never leave your Mac. API keys are encrypted in the macOS Keychain.

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.

Local-first agent observability

Product

Tether gives AI builders a single Mac-native workspace for tracing, inspecting, caching, and replaying LLM calls.

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Built around the real shape of agent work

Agent runs are not linear logs. They branch, retry, call tools, hit caches, fail in strange places, and recover in ways that are hard to see from a terminal. Tether turns those runs into a readable product surface.

Visual call trees
Provider metadata
Prompt and response inspection
Replay-ready traces

Private by default

The product is designed for sensitive workflows where prompts, customer data, and API keys should stay local. Tether keeps traces on your machine and puts secrets in macOS Keychain.