Add per-user filesystem sandbox via current_user_id ContextVar ...

- tools/base.py: add current_user_id ContextVar (set by Agent before
  every tool call, cleared after)
- core/agent.py: set current_user_id in run_stream from session.user_id
  and in run_ephemeral from parent_session.user_id; restore in finally
- tools/filesystem.py: _check_path resolves all paths inside
  user_data/<user_id>/ when current_user_id is present; legacy mode
  (no user_id) falls back to FS_ALLOWED_PATHS
- tools/share_file.py: validate source path is inside user sandbox

Co-Authored-By: Claude Opus 4.7 <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>

Autonomous reasoning improvements: budget, anchoring, anti-stall, validation ...

- AgentProfile: per-profile thinking mechanics flags (think_enabled,
  iteration_budget_enabled, goal_anchoring, anti_stall, step_validation,
  planning_reflect, adaptive_replan) — all profiles updated in config.json
- Iteration budget: inject remaining iterations into context so model knows
  when to wrap up; urgency levels at ≤7 and ≤3 remaining
- Goal anchoring: inject original goal + todo state every N iterations to
  prevent drift on long tasks
- Anti-stall: two signals — no todo progress for N iterations, or identical
  tool calls repeated N times; warning injected into context
- Todo step validation: marking done requires a validation field describing
  how result was verified; failed gets a soft nudge with tip for re-planning
- stream_complete: add think param to base class, ollama and openai backends
- Summarizer: raise max_tokens 1024→3000, expand system prompt with
  user-preferences section and verbatim-value instructions
- Compression card: persist to session.messages (is_compression flag on
  Message), show expandable summary in webclient with markdown body
- ToolResult.to_message_content: always include output on failure so
  tracebacks and error details reach the model (fixes silent Error: None)
- Developer profile: fix subagent profile secretary→developer, add write_tool
  to subagent_tools, clarify write_tool vs filesystem in system prompt

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

Add AIHelper + filesystem query/smart_edit AI actions ...

AIHelper (navi/core/ai_helper.py):
- Reusable LLM utility for AI-enhanced tools: ask() and ask_json()
- Reads current_model ContextVar (set per-turn) so tools always use
  the session's active model without extra wiring
- Robust JSON extraction: strips markdown fences, bracket-matching fallback

current_model ContextVar (navi/tools/base.py):
- New ContextVar set by run_stream() and run_ephemeral() after profile
  is resolved; AIHelper reads it to pick the right model automatically

filesystem query action:
- Natural language question about any file, chunked at ~20k tokens of
  content (~80k chars) with 30-line overlap between chunks
- Single-chunk: one LLM call; multi-chunk: partial answers accumulated
  then synthesized in a final call

filesystem smart_edit action:
- Natural language edit instruction on files up to ~200k chars
- LLM outputs JSON patch ops: replace / delete / insert (1-based lines)
- Ops validated then applied bottom-up to preserve line numbers
- Returns unified diff of changes; preserves trailing newline

registry: AIHelper created once, OllamaBackend reused (no double init),
FilesystemTool receives ai_helper at construction

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

Add stop button and fix context compression hang ...

Stop generation:
- Client: send button toggles to red ■ during streaming; sends {type:stop} via WS
- Server: _stream_recv concurrently reads incoming messages during streaming using
  asyncio.wait — stop signal is handled immediately without polling
- Cooperative stop via asyncio.Event (current_stop_event ContextVar): agent breaks
  out of LLM async-for cleanly so aclose() fires → Ollama stream closes gracefully,
  model stays in VRAM. No task.cancel() which would eject the model.
- StreamStopped event propagates through run_stream/run_ephemeral; sub-agents stop
  via the same shared stop_event inherited through task context

Context compression fix:
- compress_context passes think=False to llm.complete() — no extended reasoning
  during summarization which caused GPU hang
- Input truncated to 12k chars before sending to summarizer
- LLMBackend.complete() / OllamaBackend.complete() accept think: bool | None override

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

Live tool visibility: pending cards, sub-agent step log ...

Backend:
- ToolStarted event: emitted before tool execution begins so client
  can render a pending card with spinner immediately
- ToolEvent gains is_subagent flag; ToolStarted same
- current_event_sink ContextVar in tools/base.py — run_stream() sets it
  to an asyncio.Queue before create_task(); run_ephemeral() reads it and
  puts ToolStarted/ToolEvent into the queue as each sub-agent step runs
- run_stream() tool loop: sequential execution via create_task() +
  polling drain loop (20ms sleep); yields ToolStarted → sub-agent events
  from sink → ToolEvent (completed) for each tool call
- run_ephemeral() rewritten to inline sequential tool execution with
  sink emission (replaces _execute_tool_calls gather)
- _run_single_tool() helper extracted for run_stream()
- websocket.py handles tool_started and adds is_subagent to tool_call

Frontend:
- appendPendingToolCard(): creates card with spinner; spawn_agent opens
  body immediately to show sub-agent log as it fills
- finalizeToolCard(): fills result, removes spinner, adds toggle; strips
  "[Sub-agent result — ...]" reminder prefix from displayed text
- appendSubagentStep() / finalizeSubagentStep(): live step log inside
  spawn_agent card — each sub-agent tool call gets a ↳ row
- app.js: tool_started → pending card; tool_call → finalize card;
  is_subagent routing to sub-step vs main card; abandonStream() resets
  pendingToolCard/pendingSubStep
- CSS: .spinner-inline for card headers; .subagent-log / .subagent-step
  for nested step display; .tool-body-open for always-open spawn_agent
  body; .tool-card.pending suppresses chevron

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

SSH connection pooling: per-session, 20-minute TTL ...

- Pool keyed by session_id:host:port:username — parallel sessions share
  no state even when targeting the same server
- Per-key asyncio.Lock prevents concurrent connection creation races
- TTL (20 min) and is_closing() checked on every access; expired/closed
  connections are evicted and replaced transparently
- On disconnect error during command execution: evict + retry once with
  fresh connection
- current_session_id ContextVar (set by Agent before tool calls) is read
  in ssh_exec to build the pool key without changing tool signatures

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