navi: a token count that matches the model, a type for every file, and a socket that admits it is dead ...

Compression decided everything in chars // 3, and on this workload that sits
~1.8x below reality — 101k real prompt tokens against a ~56k estimate — so the
trigger, the target, the truncate budgets and the pre-call size check all ran
in a unit half the size of the real one. TokenCalibration now fits
overhead + chars / chars_per_token per model from the prompt_eval_count that
every call already reports, and a profile declares its own context_window
(sent to Ollama as num_ctx) instead of every model being held to OLLAMA_NUM_CTX.

Superseded summaries were never demoted in the database: save() only updates
flags for rows in session.messages, a summary is is_display=False, so it lived
in session.context alone and every reload resurrected it — prod sessions
carried up to 15 live summaries. PgSessionStore.set_context_flag() writes the
flip for those rows directly.

Five extension tables disagreed about what a file is. navi/content_types.py is
the one detector — signature, then extension, then a text probe — it decides
the MIME the download route serves, and the session file list now carries the
type the viewers branch on.

The idle receive loop answers client pings and sends its own heartbeat. A
half-open socket keeps readyState at OPEN and never fires close, so traffic
coming back is the only proof the connection still works.

tui: show the currently-served model in the status panel ...

The status panel's Model line was fed the global ollama_default_model, not the
session/profile model, and the server never told the client which model
actually served a call. Now:

- Backends stamp the resolved model onto LLMChunk (first chunk) / LLMResponse.
  The fallback backend reports the model that survived its server+model
  priority list (may differ from the profile's first choice).
- New ModelInfo event ({"type":"model_info","model":...}) emitted once per
  turn from agent._consume_stream, re-emitted only when the model changes
  across iterations. Additive WS event — old clients ignore it.
- TUI: attach_session/switch fetch the profile's configured model (first of
  profile.model) via api.get_profile_model so the panel shows a value before
  the first request; model_info then refines it to the actually-served model.
  Not forwarded to the chat panel. raw CLI prints "[model] ...".

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

Add structured logging for Ollama chat errors ...

Log model, message count, tools count, and raw error string whenever
self._client.chat() raises an exception. This makes it possible to
reconstruct the exact request payload that triggered a 500 from
Ollama Cloud — critical for diagnosing transient vs systemic failures.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Defensive image cleanup in Ollama backend to prevent 'unknown format' errors ...

- _clean_base64_image() strips data URI prefix and validates non-empty result
- _to_ollama_messages() filters out invalid/empty images before sending to Ollama
- Prevents 500 errors when models receive malformed or unsupported image data

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Remove dead LLMBackend.stream() method ...

The method was defined on all backends (Ollama, FallbackOllama, OpenAI)
and in the base LLMBackend interface, but was never called by agent.py
or messages.py. stream_complete() covers all streaming use cases.

- navi/llm/base.py: remove abstract stream() method
- navi/llm/ollama.py: remove OllamaBackend.stream()
- navi/llm/fallback.py: remove FallbackOllamaBackend.stream()
- navi/llm/openai_backend.py: remove OpenAIBackend.stream()
- docs/tech_debt_review: mark item 54 as fixed

236 tests passing.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Improve content publishing UX

Align Ollama HTTP timeout with LLM timeouts

Stability fixes batch — tech debt review 2026-04-29 ...

Critical:
- Concurrent WS run race guard (#1)
- Tool task cancellation on generator teardown (#2)
- StopAsyncIteration kills fallback chain (#3)
- Session loading race with _lastLoadId guard (#4)
- ContentCard .match() crash on non-string result (#5)
- Image data type guard in buildMessageList (#6)

High:
- Cap WS replay buffer at 500 events (#7)
- Deduplicate memory extraction task with asyncio.Lock (#9)
- TTL-based fallback blacklisting (5 min) (#10)
- Subagent tool exception isolation (#11)
- Inline image size/count validation on WS (#12)
- Clean up orphaned file on DB insert failure (#13)
- Deep watch streamingMsg for auto-scroll (#14)
- WS_SCHEME wss:// support for HTTPS (#15)
- Sending guard against duplicate message sends (#16)
- Global unhandledrejection listener in API layer (#17)

Medium:
- Cap planning_logs at 20 entries (#22)
- Store cleanup_loop task reference (#23)
- BaseException → Exception in _run_with_sentinel (#24)
- Propagate SystemExit in agent loop (#25)
- Configurable output_reserve_tokens (#26)
- Always reloadSession on session_sync (#30)
- FIFO queue for confirm dialogs (#31)
- Reset body.overflow on ImageLightbox unmount (#32)
- try/finally in fallback copy (#33)
- _isConnecting guard in WS send() (#34)

Low:
- Lazy-init deps.py singletons (#36)
- Replace __import__ with direct imports (#38)
- Preserve token count 0 in ollama.py (#39)
- Clear orphaned streamingMsg on reconnect reload (#43)
- Escape single quote in UserMessage (#44)
- Polyfill-free findLast replacement (#48)
- Match <table> tags with attributes in markdown (#49)
- Attach copy buttons only when msg.done (#50)
- Fix hasMeta falsy-0 bug (#53)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Wire pgvector semantic search into memory system ...

- Add vector(768) column + HNSW index to memory_facts

- Add LLMBackend.embed() with Ollama + fallback implementation

- MemoryStore: cosine-distance search with ILIKE fallback

- New memory tool params: source, confidence, expires_days, source_context

- Update extractor, sqlite_store, deps wiring

- Add pgvector to requirements

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

changed llm & new ollama param

Set temperature=1.0, top_k=64, top_p=0.95 for all profiles (Google recommended for gemma4) ...

Also fixes discuss profile memory tools: use combined "memory" tool name, not nonexistent split variants.

Co-Authored-By: Claude Sonnet 4.6 <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>

Support Ollama Cloud API key

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>

Count AIHelper tokens in session metrics ...

Adds prompt/completion token fields to LLMResponse, populated by
OllamaBackend.complete(). AIHelper emits AIHelperTokensUsed into the
current event sink after each LLM call; run_stream drains it into
_subagent_tokens so AIHelper usage is reflected in the turn token delta.

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

Add explicit output token budget for summarizer (context_summary_max_tokens) ...

Previously there was no num_predict set for the summarization LLM call,
so Ollama used its server default (often 128 tokens — very short summaries).

- Add max_tokens param to LLMBackend.complete() and OllamaBackend (→ num_predict)
- Add context_summary_max_tokens: int = 1024 to config
- Thread it through compress_context() and CompressionWorker

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>

Major feature batch: visibility, planning, file uploads, streaming ...

- stream_complete(): streaming with tools for all LLM turns — thinking
  now streams as ThinkingDelta/ThinkingEnd in real-time during tool-
  selection turns, not just on the final response
- todo built-in tool: session-scoped plan manager (set/view/update/clear);
  persona + all profiles updated with mandatory planning instructions
- TurnThinking event: sub-agent thinking forwarded to parent sink as a
  collapsible block in the spawn_agent card
- File uploads: non-image files uploaded via XHR, shown as badges in
  message bubble; SVG treated as regular file (not base64 image)
- session_files: POST /sessions/{id}/files, TTL cleanup, forbidden exts
- WebSocket reconnect: _AgentRun broadcast pattern, re-attach mid-stream
- UI: favicon, sidebar logo, turn-thinking cards, subagent thinking blocks,
  token counter, draft persistence, file progress bar
- Removed AgentNote (content is always None alongside tool_calls)
- Ollama stream_complete: tool_calls captured from non-final chunk (done=False)

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

Add context token counter: 64k default, live UI display ...

- config: ollama_num_ctx default 8192 → 65536
- LLMChunk: add prompt_tokens / completion_tokens fields
- OllamaBackend.stream: populate token counts from final chunk
  (prompt_eval_count + eval_count when chunk.done)
- StreamEnd: add context_tokens and max_context_tokens
- Agent.run_stream: capture token counts, pass to StreamEnd
- websocket: include context_tokens / max_context_tokens in stream_end
- index.html: split chat-header into title span + token-counter span
- sidebar.js: updateChatHeader targets #chat-header-title, not innerHTML
- app.js: updateTokenCounter() shows "X/Y (Z%) tokens", colors:
  gray <50%, amber 50–79%, red ≥80%
- style.css: .token-counter, .warn, .danger styles

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

Server review fixes: profile model routing, sorting, datetime, cleanup ...

- LLMBackend.complete/stream: add model param; OllamaBackend uses it
  over self.model, enabling per-profile model selection
- BackendRegistry.get(): remove unused model param
- Agent: pass profile.model to complete() and stream()
- Profiles: correct model to gemma4:e2b-it-q8_0 (was leftover e4b)
- InMemorySessionStore.list_all(): fix sort (pinned+newest first,
  was pinned+oldest) — now consistent with SQLite ORDER BY
- session.py, sqlite_session_store.py: datetime.utcnow() →
  datetime.now(timezone.utc) (deprecated since Python 3.12)
- _base_options(): accept temperature param, remove dead default
- deps.py: rename _registries → get_registries (public API)
- websocket.py: update import accordingly

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

Add thinking/reasoning streaming support ...

Enable Ollama think param and stream reasoning chunks to client.
New agent events: ThinkingDelta, ThinkingEnd. Config gains ollama_think
and ollama_num_ctx settings. WebSocket protocol updated accordingly.

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

Add multimodal image support and client UX improvements ...

Server:
- Add ImageViewTool (load image from file/URL, returns base64)
- Add images field to Message model with created_at timestamp
- Agent run/run_stream accept images param; inject image messages after image_view tool calls
- WebSocket handler accepts images array from client, strips data URI prefix
- All profiles include image_view tool
- Fix tool call serialization (model_dump mode=json for datetime)
- Add no-store cache headers for static files

Client:
- Image attachment: file picker button + clipboard paste + preview strip with remove
- Images rendered in chat bubbles; loaded from history
- Tool cards rebuilt as div+CSS toggle (fixes details/overflow-hidden collapse bug)
- Tool cards appear before response bubble (lazy bubble creation on first stream_delta)
- Typing indicator persists through tool calls, removed only when text starts streaming
- Tool cards restored from history on page reload
- Message timestamps stored via created_at field, shown correctly in history
- Session ID reflected in URL hash for bookmarking; restored on page load
- Remove localStorage session tracking (server last_active used instead)

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