fix(ai): agent reconnects instead of vanishing; spec the native harness
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The /ai agent held a single, un-shielded websocket with no retry. Any
close — server restart, ping/idle reap, laptop sleep, a transient blip —
ended the serve loop, so run_async returned and the process exited
silently: the agent dropped from the roster with no /ai stop and no
goodbye.

- run_async now wraps the connection in a backoff-reconnect loop (1s→30s,
  resets after a healthy ≥30s session). The server frees our session+name
  on drop, so each attempt re-runs SRP to mint a fresh token. Only Ctrl-C
  / process kill (KeyboardInterrupt / CancelledError, how /ai stop ends
  us) breaks the loop.
- _serve shields each frame via _handle_frame so one malformed/poisoned
  frame — or a handler error — can't unwind the loop; ConnectionClosed
  and cancellation propagate up to the reconnect loop.
- Forgiving keepalive (ping_interval=20, ping_timeout=60) so a heavy
  CPU-only Ollama generation doesn't trip a false drop.

Also adds docs/spec-native-harness.md: replace the heavyweight Goose
harness with a lightweight host-side Ollama-native tool-calling loop
(model runs host-side, only commands exec in the sandbox — the
slirp4netns→host-Ollama bug disappears), and a file-by-file plan to strip
all Goose integration. Supersedes the harness portion of
spec-goose-harness.md (Podman backend stays).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
leetcrypt
2026-06-08 12:49:04 -07:00
parent f93c8c5e4f
commit cf6b0b5b73
2 changed files with 288 additions and 41 deletions
+106 -41
View File
@@ -662,14 +662,66 @@ class AgentBridge(Client):
await worker
return "".join(parts)
# Reconnect policy. A session that stays up at least _RECONNECT_STABLE_SECS
# is treated as healthy: the next drop resets backoff so a one-off blip
# reconnects fast, while only rapid repeated failures back off (capped).
_RECONNECT_STABLE_SECS = 30.0
_RECONNECT_MAX_BACKOFF = 30.0
# Keepalive: ping every 20s but tolerate a slow pong, since a heavy CPU-only
# Ollama generation can briefly starve the event loop. A real drop is caught
# by the reconnect loop, so a forgiving timeout just avoids needless churn.
_PING_INTERVAL = 20.0
_PING_TIMEOUT = 60.0
async def run_async(self) -> None:
self.srp_authenticate()
"""Join the room and serve forever, reconnecting on any unexpected drop.
The agent used to hold a single websocket with no retry: any close —
server restart, idle/ping reap, laptop sleep, a transient network blip —
ended the serve loop, so ``run_async`` returned and the process exited
silently. The agent then vanished from the roster with no ``/ai stop`` and
no goodbye. We now wrap the connection in a backoff-reconnect loop. The
server frees our session + name when a socket drops, so each attempt
re-runs SRP to mint a fresh token before reopening. Only Ctrl-C / process
kill (KeyboardInterrupt / CancelledError — the latter is how ``/ai stop``
terminates us) ends the loop."""
backoff = 1.0
first = True
while True:
loop = asyncio.get_running_loop()
started = loop.time()
try:
await self._connect_and_serve(reconnect=not first)
except (KeyboardInterrupt, asyncio.CancelledError):
raise # intentional shutdown — never reconnect
except (websockets.ConnectionClosed, OSError) as e:
self.info(f"connection lost ({type(e).__name__}); reconnecting…")
except Exception as e: # noqa: BLE001 — auth/transient error: retry, don't die
self.info(f"agent loop error ({type(e).__name__}: {e}); reconnecting…")
else:
self.info("disconnected; reconnecting…") # clean close under us
if loop.time() - started >= self._RECONNECT_STABLE_SECS:
backoff = 1.0 # the session was healthy → reconnect promptly
first = False
await asyncio.sleep(backoff)
backoff = min(backoff * 2, self._RECONNECT_MAX_BACKOFF)
async def _connect_and_serve(self, reconnect: bool) -> None:
"""One connection lifecycle: (re)authenticate, open the socket, announce,
then serve frames until the socket closes. Raises on any drop so the outer
``run_async`` loop can decide whether to reconnect."""
self.srp_authenticate() # fresh session each attempt; the old token dies on a drop
url = f"{self.ws_url}/ws/chat?user_id={self.user_id}&ws_token={self.ws_token}"
self.info(f"agent '{self.name}' connecting via {self.provider.name}/{self.provider.model}")
async with websockets.connect(url, ssl=self._ws_ssl_context()) as ws:
verb = "reconnecting" if reconnect else "connecting"
self.info(f"agent '{self.name}' {verb} via {self.provider.name}/{self.provider.model}")
async with websockets.connect(
url, ssl=self._ws_ssl_context(),
ping_interval=self._PING_INTERVAL, ping_timeout=self._PING_TIMEOUT,
) as ws:
self.running = True
announce = (
f"{self.name} (ai) online — {self.provider.name}/{self.provider.model}. "
f"{self.name} (ai) {'back online' if reconnect else 'online'} "
f"{self.provider.name}/{self.provider.model}. "
f"Ask me with /ai <question>; /ai {self.name} !<task> to act in the sandbox."
)
await ws.send(self.room_fernet.encrypt(announce.encode()).decode())
@@ -685,44 +737,57 @@ class AgentBridge(Client):
embed_task.cancel()
async def _serve(self, ws) -> None:
"""Drain frames until the socket closes. Each frame is handled under a
guard so a single malformed/poisoned frame — or a provider/handler error —
can never unwind the serve loop and drop the agent. A closed socket or a
cancellation propagates up to the reconnect loop / shutdown."""
async for raw in ws:
if not self.running:
break
try:
data = json.loads(raw)
except json.JSONDecodeError:
continue
mtype = data.get("type")
if mtype == "init":
self.users = data.get("users", [])
self._seed_transcript(data.get("messages", []))
continue
if mtype == "roster":
self.users = data.get("users", [])
continue
if mtype != "message":
continue
msg = self.decrypt_message(data.get("data", {}))
text = msg.get("text", "")
sender = msg.get("username", "?")
if sender == self.name:
continue # never react to our own messages
if text.startswith('{"_'):
self._handle_control(text) # track ACL grants; ignore other ctrl frames
continue
question = self._addressed_question(text)
if question is None:
# keep a short rolling transcript for context on future asks,
# and feed the line to long-term semantic memory
captured = Msg("user", f"{sender}: {text}")
self.transcript.append(captured)
self.transcript = self.transcript[-(self.context_window * 2):]
self._remember(captured)
elif question.startswith("!"):
self.info(f"{sender} → /ai !sbx: {question[1:].strip()}")
await self._run_in_sandbox(ws, question[1:].strip(), sender)
elif question.strip().lower() == "confirm":
await self._confirm_pending(ws, sender)
else:
self.info(f"{sender} → /ai: {question}")
await self._answer(ws, question, sender)
await self._handle_frame(ws, raw)
except (websockets.ConnectionClosed, asyncio.CancelledError):
raise # socket gone / shutting down → let run_async decide
except Exception as e: # noqa: BLE001 — one bad frame must not kill the agent
self.info(f"skipped frame after error ({type(e).__name__}: {e})")
async def _handle_frame(self, ws, raw) -> None:
"""Decode and act on one server frame (init/roster/message)."""
try:
data = json.loads(raw)
except json.JSONDecodeError:
return
mtype = data.get("type")
if mtype == "init":
self.users = data.get("users", [])
self._seed_transcript(data.get("messages", []))
return
if mtype == "roster":
self.users = data.get("users", [])
return
if mtype != "message":
return
msg = self.decrypt_message(data.get("data", {}))
text = msg.get("text", "")
sender = msg.get("username", "?")
if sender == self.name:
return # never react to our own messages
if text.startswith('{"_'):
self._handle_control(text) # track ACL grants; ignore other ctrl frames
return
question = self._addressed_question(text)
if question is None:
# keep a short rolling transcript for context on future asks,
# and feed the line to long-term semantic memory
captured = Msg("user", f"{sender}: {text}")
self.transcript.append(captured)
self.transcript = self.transcript[-(self.context_window * 2):]
self._remember(captured)
elif question.startswith("!"):
self.info(f"{sender} → /ai !sbx: {question[1:].strip()}")
await self._run_in_sandbox(ws, question[1:].strip(), sender)
elif question.strip().lower() == "confirm":
await self._confirm_pending(ws, sender)
else:
self.info(f"{sender} → /ai: {question}")
await self._answer(ws, question, sender)
+182
View File
@@ -0,0 +1,182 @@
# hack-house → Native lightweight harness (strip Goose) — Spec
> **Status:** Draft v1 · **Date:** 2026-06-08
> **Scope:** Replace the **Goose** agentic harness on the `/ai <name> !<task>`
> path with a **lightweight, host-side, Ollama-native harness**, and **strip all
> Goose integration** from the codebase + bootstrap scripts.
> **Baseline reviewed:** `cmd_chat/agent/bridge.py`, `cmd_chat/agent/__main__.py`,
> `cmd_chat/agent/providers.py`, `hh/src/sbx.rs`, `hh/src/app.rs`,
> `hh/scripts/bootstrap.sh`, `hh/scripts/bootstrap-ai.sh`,
> `hh/scripts/sandbox-bootstrap.sh` @ `main` (f93c8c5).
> **Supersedes:** `docs/spec-goose-harness.md` (Goose harness portion only — the
> Podman backend it also introduced **stays**).
> **Related (do NOT touch):** `headroom/` references Goose for an unrelated CLI
> wrapper experiment; out of scope here.
---
## 0. Why
| Problem with Goose | Consequence on this project |
|---|---|
| Agentic loop = **N sequential model calls** (`--max-turns 15`) | On a CPU-only box (i5-8350U, no GPU) each call is already slow; Goose multiplies per-call latency by N → "takes forever". |
| Heavyweight external **Rust binary** baked into every sandbox | Extra install in `sandbox-bootstrap.sh` + host install in `bootstrap-ai.sh`; a moving release dependency. |
| In-container Goose must reach **host Ollama** | The documented **slirp4netns loopback bug**: rootless Podman's `host.containers.internal` resolves to the host LAN IP and can't reach a `127.0.0.1`-bound Ollama. Forced the `--network=slirp4netns:allow_host_loopback=true` + `OLLAMA_HOST=10.0.2.2` workaround in `sbx.rs`. |
| Opaque: we relay raw stdout, no control of the tool schema | Hard to bound, hard to render, hard to reason about. |
**What we already have that works decently** (commit `47019dd`, still alive today
as `_run_simple`): a **one-shot keystroke injector***one* model call → a
```` ```sh ```` block → typed into the shared sandbox PTY via the existing
`_sbx:input` frames, guarded by a destructive-command regex + blast-radius caps,
echoed to the room first as an audit trail. It is fast (single call), needs **no
external binary**, and reuses transport that already exists. Its only real
limitation: it types **blind** — it never reads command output back.
**Decision:** keep the proven injector as the floor, optionally add a **bounded,
host-side, Ollama-native tool-calling loop** as the ceiling, and remove Goose
entirely.
---
## 1. Native harness design
### 1.1 Key architectural shift: host-side model, container-side execution
Goose ran the *whole* loop **inside** the sandbox (model calls + command
execution), which is why the container needed to reach host Ollama. The native
harness splits these:
- **Model call → host.** The bridge (co-located with the broker, already on the
host) calls Ollama on the host directly via `providers.py`. No container→host
Ollama hop.
- **Command execution → sandbox.** Commands run in the sandbox via the engine the
broker advertises (`_sbx:status` → `engine` + `name`), using the same
`<engine> exec <name> …` plumbing Goose used for its probe.
**Consequence:** the entire in-container Ollama gateway surface
(`--add-host=host.docker.internal:host-gateway`, `--network=slirp4netns:allow_host_loopback=true`,
in-container `OLLAMA_HOST`) becomes **dead** and is removed — **the slirp4netns
loopback bug ceases to exist.**
### 1.2 Two harness levels (both host-side model, grant-gated)
The grant tiers from the Goose spec are unchanged (`/grant` is the switch):
**not granted → advisory chat-only**; **granted → act in the sandbox**. What
changes is *how* the granted path acts:
| Level | What it does | Reads output? | Model calls | Default |
|---|---|---|---|---|
| **`simple`** | One model call → ```` ```sh ```` block → type into the shared PTY (`_sbx:input`). The proven `47019dd` injector. | No (blind) | 1 | fallback |
| **`native`** | Bounded tool-calling loop: Ollama `/api/chat` with a small `tools` schema; bridge execs each tool call in the sandbox, captures stdout, feeds it back; cap at **35 turns**. | Yes | ≤ turns | **default** |
`native` degrades to `simple` automatically when the model doesn't advertise tool
support (so it is default but never load-bearing — same safety property Goose
had, minus the binary).
### 1.3 The `native` loop (Ollama-native function calling)
`qwen2.5` (our default) supports function calling through Ollama's `/api/chat`
`tools` field — we just aren't using it yet (`providers.py:104/119` send no
`tools`). Add an opt-in tool-calling completion to `OllamaProvider`:
```
POST /api/chat
{ "model": …, "messages": [...], "tools": [ <schema below> ], "stream": false }
→ message.tool_calls: [ {function:{name, arguments}}, … ]
```
**Minimal tool schema** (the whole surface — deliberately tiny):
| Tool | Args | Maps to (sandbox exec) |
|---|---|---|
| `run_shell` | `command: string` | `<engine> exec <name> sh -c "<command>"`, capture stdout+stderr+rc |
| `write_file` | `path: string, content: string` | heredoc write via exec (no host fs) |
| `read_file` | `path: string` | `cat` via exec |
Loop (per `!task`, granted):
1. Seed messages with `SANDBOX_SYSTEM` + the task + recent transcript window.
2. Call Ollama with `tools`. If `tool_calls` present → run each through the
**same guards** (`DESTRUCTIVE` regex + `/confirm`, `MAX_COMMANDS`/`MAX_BYTES`),
exec in the sandbox, append a `tool` role message with captured output.
3. Repeat until the model returns a plain answer or the **turn cap** (35) is hit.
4. Stream progress to chat (reuse `_send_stream`); post the final line; append a
capped summary to the transcript.
**Containment unchanged from the Goose spec §2.1:** execution reach == the
sandbox namespace. Container/VM backends isolate it; `local` is the host by
explicit, warned choice. Output is byte-capped + throttled. Room text stays
untrusted; the sandbox is the blast radius.
### 1.4 Why this fixes "takes forever"
- **We own the turn budget** (35, not 15) → bounded worst case.
- **Model runs host-side** with the CPU-tuned `OllamaProvider` knobs already in
place (`num_ctx`/`num_thread`/`num_predict`, plus the `qwen2.5-coder` code path).
- A trivial task that the model nails in one tool call costs ~1 round-trip — same
ballpark as the old one-shot injector.
---
## 2. Nomenclature
Drop the Goose-implicit grammar (where `plain` *disabled* Goose and there was no
positive keyword). New, symmetric harness selector:
```
/ai start [profile|model] [native|simple] [allow]
```
- **harness word** (optional): `native` (default) or `simple`. `plain` kept as a
back-compat alias for `simple`.
- **`allow`** (unchanged): pre-grant sandbox drive on spawn (owner only).
- CLI: `--harness {native,simple}` on `python -m cmd_chat.agent`
(replaces `{goose,simple}`); drop `--goose-max-turns`, add
`--max-turns` (default 5) for the `native` loop.
- `models.toml`: `harness = "native"|"simple"` (was `"goose"|"simple"`).
---
## 3. Strip plan — file by file
> `headroom/**` is a different project; **excluded**.
| File | Change |
|---|---|
| `cmd_chat/agent/bridge.py` | Remove `_run_goose`, `_goose_argv`, `_goose_present`, `_goose_present_cache`, and the `GOOSE_*` consts. Rewrite `_run_in_sandbox`: granted → `native` loop (new `_run_native`) with `simple` fallback; not granted → `_advise` (unchanged). Default `self.harness = "native"`. Keep `_run_simple`, `_advise`, `_inject`, `_extract_commands`, `_confirm_pending`, the destructive gate, and blast caps. Keep `_handle_control` reading `_sbx:status` `engine`+`name` (the `native` exec path uses them). |
| `cmd_chat/agent/providers.py` | Add tool-calling completion to `OllamaProvider` (`complete_with_tools(system, messages, tools) -> (text, tool_calls)`); add a capability probe (does the model accept `tools`). Chat/`stream` paths unchanged. |
| `cmd_chat/agent/__main__.py` | `--harness {native,simple}` (was `{goose,simple}`); remove `--goose-max-turns` + `GOOSE_MAX_TURNS` import; add `--max-turns`. Default harness `native`. |
| `hh/src/app.rs` | Update the `/ai start` parser (≈26282651): accept `native|simple` (+ `plain` alias) instead of just `plain`; pass `--harness native|simple`. Refresh the comment at 3248. |
| `hh/src/sbx.rs` | Remove the in-container Ollama gateway: the `--add-host=host.docker.internal:host-gateway` / `--network=slirp4netns:allow_host_loopback=true` block (≈780792) and the in-container `OLLAMA_HOST` set in `dk_bootstrap` (≈1249). Drop the Goose-reaches-host comments (688, 780, 1249). **Verify** nothing else depends on the gateway before deleting. |
| `hh/scripts/bootstrap.sh` | Remove `goose` from the prereq probe loop (line 52). |
| `hh/scripts/bootstrap-ai.sh` | Remove the entire Goose install block + `--no-goose` flag + `GOOSE_INSTALLER_URL` + host `~/.config/goose/config.yaml` writer + the `goose_bin()` helper and its status lines. Update header/usage. |
| `hh/scripts/sandbox-bootstrap.sh` | Remove the Goose section (≈5380): binary install + container-side `~/.config/goose/config.yaml`. (Mirrors the noVNC strip already done.) |
| `models.toml` | `harness` values doc: `goose` → `native`. |
| `docs/spec-goose-harness.md` | Add a banner: **Harness section superseded by `spec-native-harness.md`; the Podman backend portion still stands.** |
**Keep (not Goose-specific):** Podman backend, `_sbx:status` `engine`+`name`
fields (the `native` exec path needs them), the grant/ACL gate, `_advise`,
`_run_simple`, destructive gate + blast caps, CPU tuning + `qwen2.5-coder` path.
---
## 4. Phasing
| Phase | Scope |
|---|---|
| **0 (done)** | Fix the agent disconnect bug (reconnect loop + per-frame shield in `bridge.py`). |
| **1** | Strip Goose: `bridge.py`/`__main__.py` harness plumbing, `app.rs`/`sbx.rs`, bootstrap scripts, `models.toml`, supersede banner. Default falls back to `simple` until Phase 2 lands. `cargo check` + `py_compile` clean. |
| **2** | Implement the `native` tool-calling loop: `OllamaProvider.complete_with_tools` + tool probe; `_run_native` with the 3-tool schema, turn cap, guards, exec-capture, stream→chat, `simple` fallback. |
| **3** | Bench `native` vs the old `simple`/Goose on this box (latency, success on a few canonical `!task`s); tune turn cap + tool schema; update help/usage + memory. |
---
## 5. Open questions
1. **Tool schema breadth:** start with `run_shell` only (closest to the proven
injector, simplest), or ship `write_file`/`read_file` from the start?
2. **Turn cap default:** 3 (snappy) vs 5 (more self-correction) — decide after the
Phase 3 bench.
3. **`native` for non-Ollama providers** (Anthropic/OpenAI also do tool calls):
in scope now, or Ollama-only first and treat cloud as a later generalization?
4. **`simple` fate:** keep indefinitely as the zero-dependency fallback (recommended),
or retire once `native` is proven?