Move providers.py + profiles.py from cmd_chat/agent/ to a shared
cmd_chat/ai/ package so the operator bridge and the /ai chat agent can
consume one model-agnostic Provider core (groundwork for harness-mode
operators). Pure refactor — no behaviour change.
- cmd_chat/ai/{providers,profiles}.py: the canonical modules (moved verbatim)
- cmd_chat/ai/__init__.py: re-exports the public API
- cmd_chat/agent/{providers,profiles}.py: thin back-compat shims re-exporting
from cmd_chat.ai (keeps `from cmd_chat.agent.providers import …` working,
e.g. hh/scripts/bench-native-harness.py)
- internal agent consumers (memory/bridge/__main__/__init__) point at cmd_chat.ai
125 tests pass; shim identity verified (re-exports are the same objects).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Reorder _CODER_MODELS to prefer the 3b coder build over 1.5b. The 3b roughly
doubles the ground-truth pass rate on the verify-then-repair native harness
(bench: 7/9 vs ~4/9 over the 9 non-net tasks) at a modest CPU-latency cost,
so it is auto-selected ahead of 1.5b when present. 7b was evaluated and
rejected: too slow to first-token inside the engage window on the CPU-only box.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement the bounded native harness from docs/spec-native-harness.md §1.3 and
make it the default granted-!task path. The model runs host-side (no container→
host Ollama hop); only its tool calls exec in the sandbox.
providers.py:
- OllamaProvider.complete_with_tools(system, messages, tools) -> (text, calls):
one non-streaming /api/chat turn with a `tools` schema; parses message.tool_calls
(dict or JSON-string arguments). Caches tool capability (_tools_ok / supports_tools).
- ToolsUnsupported raised when the model rejects `tools` ("does not support tools").
bridge.py:
- NATIVE_SYSTEM + a 3-tool schema (run_shell / write_file / read_file), turn/byte caps.
- _run_native: seed transcript window + task → loop up to max_turns; exec each tool
call in the sandbox, feed captured output back as a `tool` message; stop on a plain
answer or the cap; stream per-call progress to chat. Degrades to _run_simple when the
provider has no complete_with_tools or the model rejects tools.
- _exec_prefix/_exec_capture/_exec_tool: <engine> exec into docker/podman/multipass/local;
paths passed as positional args + content via stdin (no shell interpolation); combined
stdout+stderr byte-capped + time-bounded. run_shell is the only intentional shell.
- Guards: DESTRUCTIVE run_shell commands are blocked (not run — no human in the loop;
use simple + /ai confirm for destructive intent); MAX_COMMANDS budget per task.
- _run_in_sandbox dispatches native|simple; default harness flipped to native.
__main__.py: default harness native (self-degrades to simple, so safe).
Offline-tested: full write/run/read loop on the local backend; destructive block
(rm -rf never executed); ToolsUnsupported → simple fallback. Live Ollama wire
validation deferred to Phase 3 bench (daemon was down). py_compile clean.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove the Goose agentic harness across the codebase per
docs/spec-native-harness.md §3. Goose made N sequential model calls inside the
sandbox (slow on CPU-only hardware) and forced an in-container→host Ollama
gateway that tripped the rootless-Podman slirp4netns loopback bug.
- bridge.py: delete _run_goose/_goose_argv/_goose_present + GOOSE_* consts and
the present-cache; __init__ now takes harness="simple"/max_turns=5; granted
!task runs _run_simple until the native loop lands (Phase 2).
- __main__.py: --harness {native,simple} (was {goose,simple}); drop
--goose-max-turns, add --max-turns; default harness simple.
- app.rs: /ai start accepts native|simple (plain aliases simple) instead of a
bare plain flag; refresh harness comments.
- sbx.rs: remove the in-container Ollama gateway (Docker host-gateway / Podman
slirp4netns host-loopback) and the dk_bootstrap OLLAMA_HOST env — kills the
slirp4netns loopback bug; drop Goose comments.
- bootstrap.sh: drop goose from the prereq probe.
- bootstrap-ai.sh: remove the entire Goose install block, --no-goose flag,
GOOSE_INSTALLER_URL, host config writer, and goose_bin helper.
- sandbox-bootstrap.sh: remove the in-sandbox Goose binary install + config.
- spec-goose-harness.md: banner — harness portion superseded; Podman stays.
cargo check + py_compile clean. No Goose refs remain (headroom/ untouched).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add Podman as a rootless/daemonless sandbox backend alongside Docker,
Multipass and Local, and wire Goose in as the default agentic harness
for the granted `!task` path (bridge execs `<engine> exec <name> goose
run` and streams output to chat; auto-degrades to the simple one-shot
injector when goose is absent).
Add an optional GUI sandbox track (XFCE + TigerVNC + websockify/noVNC on
:6080) summoned via `/sbx <engine> gui`, plus container-side provisioning
in sandbox-bootstrap.sh and a host-side ensure-podman.sh prereq helper.
Refresh the in-app command help to the backend-led `/sbx <engine> [gui]`
grammar and minor ui tweaks.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Agents now join under their model tag (e.g. "qwen2.5:3b" — model name +
parameter size) or profile label, derived at /ai start and tracked on
App.agent_name, so the roster shows what's actually answering. Updates the
broker re-grant + /ai stop revoke paths, the Python --name default
(falls back to provider.model), and the demo/bootstrap/README/docs refs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- /sbx save|load|snaps: docker commit → hh-snap:<label> image that
survives /sbx stop; load relaunches a fresh sandbox from it; multipass
delegates to `multipass snapshot`. Local backend unsupported.
- Help overlay redesigned into topical clusters (SANDBOX, AI AGENTS,
PERMISSIONS, FILES, APPEARANCE, KEYS, ROSTER GLYPHS), collapsed by
default; up/down highlight a cluster, left/right/Enter expand-collapse
it (tmux-style), PgUp/PgDn scroll overflow, Esc closes.
- docstring: example uses --model qwen2.5:3b (the locally-pulled model),
not llama3.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tier A/B/C wins for the CPU-only Ollama box (no GPU → optimize TTFT and
tokens/sec, not VRAM):
- Separate qwen2.5-coder provider for the sandbox `!task` path; chat keeps
the general model. Auto-selected when chat is Ollama and a coder build is
present, override with --code-model.
- OllamaProvider num_ctx default 8192→4096 (8192 was a GPU-mindset default
that inflates prefill/TTFT on CPU); expose num_thread; add --num-ctx,
--num-thread, --num-predict. token_budget default 3000→2000 to fit.
- OllamaProvider.stream() generator over Ollama's stream=True chat endpoint
(provider half of token streaming; agent/Rust rendering is a follow-up).
- Few-shot request→shell exemplars in SANDBOX_SYSTEM to anchor the small
model's fenced-command output.
- Matryoshka embedding truncation: OllamaEmbedder truncate_dim=256 (--embed-dim)
for faster pure-Python cosine and less RAM; query+stored share the dim.
- docs/ai-perf-plan.md records all 8 items with status and the server-side
env (OLLAMA_NUM_PARALLEL=1, keep_alive) that must be set where ollama serve runs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Give the agent recall of things said beyond the verbatim window, without
breaking the RAM-only philosophy — nothing is persisted to disk.
- MemoryIndex: a capped, in-memory pool of embedded messages with pure-Python
cosine search (no numpy). Retains far more than the rolling transcript so old
lines can be surfaced on demand; oldest evicted past the cap to bound RAM.
- OllamaEmbedder: local embeddings via nomic-embed-text, on by default and
independent of the chat provider (reuses the Ollama host when chat is Ollama).
- Bridge: captured room messages (live + backfilled) are embedded on a
background worker so a slow embedder can't stall frame draining. On a /ai
question the agent retrieves top-k relevant lines, drops weak (<min_score) and
windowed-duplicate hits, and prepends them as a clearly-fenced "recalled
context" preamble — kept at user role, never elevated to system, so untrusted
room text informs without instructing. Falls back to recency-only if the
embedder is unreachable.
- CLI: --no-rag, --embed-model, --embed-host, --rag-top-k.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The server already ships the full RAM message backlog in the init frame; the
agent was discarding it. _seed_transcript now decrypts that history with the
room key (skipping our own lines, control frames, and undecryptable blobs) so
the agent has context the moment it joins instead of starting amnesiac.
_window() replaces the fixed last-12 slice on both the answer and sandbox
paths: it walks newest-to-oldest and keeps messages up to --token-budget
(approx, ~4 chars/token), still capped at --context-window count. Keeps small
local models inside their effective context. Nothing touches disk.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Make connecting any model a config step, not a code change:
- models.toml named profiles (api_key_env names an env var, never the key)
- providers gain available_models(); add preflight + --list-models/--check
- /ai list and /ai models in-room; client probes local Ollama for
/ai models when no agent is running, and /ai list hints to summon one
- docs/providers.md provider guide + examples/echo_provider.py
- README: command table, AI section, layout updated
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add cmd_chat/agent: a headless client that joins a room via SRP, decrypts
broadcasts, and answers /ai <question> through a pluggable model provider
(ollama default + anthropic + openai-compatible + module:Class). Server and
zero-knowledge guarantees unchanged; the agent is just another encrypted client.
Also pin the lets-hack demo to a detached worktree of main (default) so running
it from dev still demos stable main without touching the working checkout.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
leetcrypt