Human

The world's slowest LLM. Astonishing reasoning. Terrible latency. Unionized.

An OpenAI/Anthropic-compatible model server where the model is a person.

简体中文

Your coding agent calls POST /v1/chat/completions like it always does. The request shows up in someone's browser. They read it, type an answer, maybe send back native tool calls for the agent to run in its own workspace, and hit deliver. The agent gets a normal SSE stream and is none the wiser.

One request, both sides

The browser is the Human's control room: conversation, native tool calls, Tasks, and reviewed workspace delivery.

The Agent side stays ordinary. Here OpenCode receives the plan and executes the file write in its own workspace.

Yes, we know how this sounds. We built an entire idempotent, crash-recovering, TLA+-verified pipeline so that a human can type "have you tried restarting it" at 2 tokens per second.

What it verifiably does (real-CLI test doors and all):

• Human-in-the-loop where the human can actually do things: answer, ask back, run commands through the agent's own execution gate, edit files in a live mirror and deliver them as native write/edit calls. The working tree stays on the agent's side.
• Wizard-of-Oz prototyping: your product doesn't have its AI yet? Ship a human. Protocol compatibility means existing agents, harnesses, and clients connect unchanged.

Other uses we're not comfortable putting in official documentation, so you'll have to discover them yourself. For instance: live-demoing "our in-house foundation model" to a client; taking over a stuck agent at 2am (assuming the gateway was set up beforehand, not at 2am while reading this README); recording your own model-seat sessions as evaluation data (it's all in SQLite, the export tool doesn't exist yet, you know what to do); and, should the A2A ecosystem ever want to hire an actual human — we happen to have an endpoint ready.

The plumbing is the serious part: fail-closed everywhere, byte-exact replay, durable remote-worker journals, 91 formal gates, fault-injection doors that run the real OpenCode CLI. See docs/ if that's your thing.

Run it

You need Go (or a release binary), a browser, and a human.

brew install vibe-agi/tap/human

Official release binaries and runtime CI currently cover macOS and Linux on amd64 and arm64. Windows remains compile-checked, but is not yet a supported runtime: its durable locking, ACL, path, and SQLite contracts do not have the same evidence, so we do not publish a misleading Windows binary.

human local --workspace ~/human-workspace

It prints the Human-side base working directory followed by two URLs:

Human workspace base: /home/human/human-workspace
model base URL: http://127.0.0.1:19080/v1
human side (browser): http://127.0.0.1:19081/?token=...

Open the second one. That's your inbox.

--workspace is only the base directory on the Human user's machine; it is not the Agent user's cwd. Every workspace-capable harness session gets a stable session-<hash> child by default. After accepting a request, the Human can switch that conversation to any existing repo in Web. The repo's current contents become the baseline, so switching never queues the whole repo for delivery. Human and Agent only agree that their two directories represent the same logical project: delivered tool calls use project-relative paths and the Agent resolves them in its own cwd. Absolute paths need not match and never cross the model protocol.

Then point an agent at the first one. OpenCode config:

// opencode.json
"human": {
  "npm": "@ai-sdk/openai-compatible",
  "name": "Human",
  "options": {
    "baseURL": "http://127.0.0.1:19080/v1",
    "apiKey": "{env:HUMAN_CALLER_TOKEN}"
  },
  "models": { "human-expert": { "name": "Human Expert" } }
}

export HUMAN_CALLER_TOKEN="$(human local credentials --workspace ~/human-workspace --token-only)"
opencode --model human/human-expert

If the Agent and Human are different OS users or machines, transfer the caller token through a secure channel. The Agent neither needs nor should know the Human working directory.

Ask it something. Your browser pings. You're the model now — take your time, the agent will wait.

No agent handy? curl works:

curl -N http://127.0.0.1:19080/v1/chat/completions \
  -H "Authorization: Bearer $HUMAN_CALLER_TOKEN" \
  -H "Content-Type: application/json" -H "Idempotency-Key: try-1" \
  -d '{"model":"human-expert","stream":true,"messages":[{"role":"user","content":"hello"}]}'

The curl hangs until you answer in the browser.

The same local endpoint also serves POST /v1/responses and POST /v1/messages. It accepts OpenAI-style Authorization: Bearer and Anthropic-style X-Api-Key; conflicting duplicate credentials fail closed. Wire compatibility is implemented for all three APIs. Harness-level claims are tracked separately: an API decoding successfully does not imply that every Workspace or tool feature of every CLI has been validated.

The core wire contract is also exercised through the official Go clients, currently pinned to openai-go/v3 v3.37.0 and anthropic-sdk-go v1.58.1: official client serialization enters Human, and the same clients decode its aggregate responses, SSE text and function/tool calls, tool-result continuations, standard error envelopes, and Anthropic count_tokens response. Compatibility intentionally targets model invocation, not every provider administration API or provider-hosted tool.

Remote setup is two commands: human gateway --listen :8080 on a server (put TLS in front), then human worker --gateway wss://.../internal/v1/worker/ws --caller-scope <caller-id> --workspace-scope <opaque-workspace-key> on the Human's machine. The scope binds that Human-owned mirror to one authenticated Agent-user workspace; it is not a directory path.

Embedding

human.NewLLM() / human.NewAgent() are transport-neutral cores. Store, auth, codecs, transports, and KMS are replaceable ports with public conformance suites in humantest; examples/custom-framework runs entirely on its own store, auth, and transport. The web UI is a stateless projection over the workerkit domain layer, so you can replace it too.

Docs: goals, gateway, embedding, operations, TLA+ model, framework contract, the human-side stack.

Status, honestly: all three advertised APIs now have a default product test for aggregate and streaming calls completed through the Web human API. Real-client gates give Claude Code 2.1.217, OpenCode 1.17.18, and Codex 0.145.0 the same baseline: final, second-process session resume, successful caller-side command, failed command result followed by recovery to final, and bounded Web rejection. Claude, OpenCode, and Codex additionally prove Workspace create and modify through their native file tools, with separate Human/Agent directories and byte-exact caller-side results. Codex uses the Responses custom freeform apply_patch tool and receives a Workspace profile only when the request declares the exact compatible tool and grammar. Through the structured Web Tasks panel, Claude runs TaskCreate → TaskUpdate → TaskList, while OpenCode todowrite and Codex update_plan each run pending→in-progress→completed continuations. The structured Web Command panel likewise uses exact, replaceable profiles for Claude Bash, OpenCode bash, and Codex exec_command; unknown versions, missing Codex model metadata, or behavioral schema drift fail closed to Chat/RemoteTools and the generic declared-tool editor. Claude and Codex also recover after a forced SSE disconnect following a complete Human progress frame; that evidence is not extrapolated to Workspace fault recovery. A separate Testcontainers gate pins all three CLIs in Linux; its independent direct-protocol layer also calls all three APIs in aggregate and streaming modes. Both layers route through the host local stack and can use a host OpenAI-compatible model as the Web operator; see the operations runbook.