Navi Code: force context compression via typed /compact control message ...

Forced /compact previously sent a chat message, which ran a full agent turn
that only produced summary text instead of running the real context compressor.
Worse, even when wired correctly, forced compact always reported "Nothing to
compact yet — the context is still small" regardless of context size: the typical
navi_code shape is a single long autonomous turn (1 user message + many tool
iterations = one turn), and partition_messages finds nothing to summarize when
turns <= keep_recent. The midturn auto-compress path already bypassed this via
keep_recent_messages (intra-turn split), but compact_stream passed
keep_recent_messages=None, so the fallback was disabled.

Changes:
- WS protocol: {"type":"compact"} control message (distinct from {"type":
  "message"}); rejected while an agent turn is active to avoid racing the agent.
- Agent.compact_stream: forced compression that bypasses the token threshold
  but still runs the real compressor; passes keep_recent_messages=max(12,
  context_keep_recent*2) so a single long turn compresses via intra-turn split
  (mirrors midturn auto-compress). Raises NothingToCompactError when context is
  genuinely too small.
- Orchestrator.run_compact + clear_run: broadcast agent events to subscribers,
  end with done marker, surface NothingToCompactError as an error event.
- Terminal client: ws_client.send accepts str|dict; CompactCommand enqueues
  {"type":"compact"}; TUI distinguishes forced compact (no stream_start) from
  in-turn auto-compress via the _streaming flag.
- Tests: compact_stream (incl. single-long-turn regression), WS handler
  dispatch/rejection, run_compact event/error broadcasting, ws_client send,
  compact command.

Co-Authored-By: Claude <noreply@anthropic.com>

Add Ollama multi-server fallback with in-memory blacklisting ...

- New FallbackOllamaBackend (navi/llm/fallback.py): tries servers and
  models in priority order; on LLMConnectionError blacklists the server
  for the process lifetime, on LLMModelNotFoundError blacklists the
  (server, model) pair — eliminates latency from repeated failed probes
- OllamaBackend now raises typed LLMConnectionError / LLMModelNotFoundError
  instead of bare LLMBackendError; accepts list[str] | str | None for model
- AgentProfile.model changed from str to list[str] (str auto-normalised);
  all profiles updated to ["gemma4:31b-cloud", "gemma4:26b-a4b-it-q4_K_M"]
- New config field OLLAMA_BACKENDS_FILE: path to [{host, api_key?}] JSON;
  when set, registry creates FallbackOllamaBackend instead of OllamaBackend
- ollama_backends.json template added (gitignored — contains API key)
- current_model ContextVar type widened to list[str] | str | None

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Fix LLM hang: stop button during prefill, context guard, timeouts ...

Root cause: during prefill (processing input tokens), Ollama emits no
HTTP chunks. The `async for chunk in stream_complete()` loop body never
executes, so stop_event is never checked — Stop button has no effect.
Same issue with complete() calls (planning, compression): blocking await
with no cancellation path.

Fixes:

_iter_stream_guarded() (agent.py, module-level):
  Wraps any stream_complete() generator. Polls stop_event every 1s while
  waiting for the next chunk using asyncio.wait() — so Stop works even
  during multi-minute prefill. On stop or timeout, calls aclose() on the
  generator which closes the HTTP connection to Ollama → generation halts
  → GPU drops to idle. Applied to both run_stream() and run_ephemeral().

_check_context_size() (Agent method):
  Estimates context tokens (chars/4 + 500 per image) before every LLM
  call. Raises ContextTooLargeError (new NaviError subclass) at 92% of
  ollama_num_ctx — before Ollama ever receives the request.

_run_planning() timeouts:
  Both complete() calls (phase 1 and 2) wrapped with asyncio.wait_for().
  Timeout logged and planning skipped gracefully — execution continues.

New config (config.py):
  llm_complete_timeout = 120s
  llm_stream_first_chunk_timeout = 180s  (prefill budget)
  llm_stream_chunk_timeout = 60s         (inter-token budget)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Initial implementation of the agent system core ...

- FastAPI server with REST API and WebSocket streaming
- Modular LLM backend abstraction (Ollama implemented, OpenAI stub)
- Tool system: web_search (ddgs), filesystem, http_request, code_exec, terminal
- Agent profiles: smart_home, server_admin, secretary
- Tool-calling loop with concurrent tool execution
- In-memory session store with SessionStore ABC for future persistence
- Registry pattern for tools, profiles, and backends
- Orchestrator stub as foundation for multi-agent scenarios