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.

Supported agents and frameworks | Docs

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Supported agents and frameworks

Connect supported agent CLIs, LangChain, LangGraph, OpenAI/OpenGPT-style agents, and custom CLI agents to Tether through source adapters and normalized event ingestion.

Install Setup steps

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

Source model

Tether is source-adapter based. A source can be local logs, callback events, OpenAI-compatible proxy traffic, or a wrapped CLI process. Each source is normalized into the same local execution graph.

text

tether capture -- <agent command>
  -> wrapper starts or uses local proxy
  -> agent traffic plus tool, file, shell, and test events are captured
  -> local trace DB
  -> macOS or Linux desktop execution graph

Connection paths

Source	How it connects
Local agent logs	Supported local logs can be ingested by a source adapter and normalized into execution events.
Agent CLIs	Run tether capture -- <agent command> so the wrapper can collect process, shell, file, and proxy events.
LangChain	Use callbacks for tool and chain events, or route OpenAI-compatible model traffic through the local proxy.
LangGraph	Use graph callbacks for node transitions, or proxy model traffic through Tether when the model client supports base_url.
OpenAI/OpenGPT-style agents	Point OpenAI-compatible base_url traffic at the local Tether proxy.
Custom CLI agents	Connect custom local workflows through the capture wrapper or a source adapter.

What gets normalized

User request and turn-scoped prompt text.
LLM requests and responses with provider metadata.
Tool calls, file reads, file writes, shell commands, test runs, git diffs, and errors.
Replay and rollback evidence where the selected source supports it.