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/Reading traces

Using Tether

Reading traces

Understand the graph, sidebar, inspector, and status metadata for each execution step shown by the desktop app.

Install Setup steps

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

Trace node model

Every captured call becomes an AgentNode. Nodes carry provider metadata, prompt and response text, cache state, latency, cost, token counts, and optional error details.

success: upstream returned a 2xx response.
cached: the proxy served a local cache hit.
running: reserved UI state for active work.
error: upstream or command failure captured as a trace node.

Inspector panes

Pane	Use
Prompt	Read system and user input that reached the model.
Response	Inspect or edit model output for replay workflows.
Metadata	Review latency, tokens, cost, cache, file diffs, provider, and request ids.