Fix subagent planning isolation and raise default max_iterations to 40 ...

- run_ephemeral signature default: max_iterations=20 → 40 (consistent
  with spawn_agent's explicit default)
- _run_planning accepts system_prompt_override; when called from
  run_ephemeral, passes the subagent's isolated system prompt instead of
  _build_system_prompt(profile) which includes the full orchestrator
  persona and profiles block — subagents now plan with only their own
  executor context

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

Fix core subagent misuse: enforce 1 plan step = 1 spawn_agent call ...

Root cause: nowhere was it stated that each AGENT step in the plan
maps to a separate spawn_agent call. Navi was bundling all AGENT steps
into a single call, dumping the full plan on one subagent.

spawn_agent description:
- Lead with: "Delegate EXACTLY ONE step of your plan"
- Explicit: "3 AGENT steps = 3 spawn_agent calls"
- Remove "multi-step sub-task" wording that invited bundling
- briefing: clarify as static context only (credentials, paths, instructions)
  Dynamic findings from prior steps → context_transfer, not briefing

Planning Phase 2 prompt:
- Add AGENT scoping rules: each step = one focused unit, not "do everything"
- Add good/bad examples of AGENT step granularity
- Show multiple AGENT steps in the format example

Secretary & server_admin system prompts:
- Add explicit 1:1 rule with counter-example
- Show correct multi-agent execution pattern with code example
- Clarify briefing vs context_transfer boundary everywhere

Persona:
- "ONE PLAN STEP = ONE spawn_agent CALL" as first sentence in SUB-AGENTS
- Field descriptions tightened: briefing = static, context_transfer = dynamic

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

Subagent system prompt rework: separate from parent, briefing as system context ...

run_ephemeral:
- Add briefing param (passed from spawn_agent, injected into system prompt)
- Subagent system prompt is now completely separate from parent's system_prompt:
    1. profile.subagent_system_prompt  (executor persona)
    2. custom_system_prompt            (role specialisation for this task)
    3. briefing                        (task context as system-level instruction)
  Fallback to profile.system_prompt only if subagent_system_prompt is not defined

spawn_agent:
- task → user message only (the goal)
- briefing → system prompt (credentials, context, instructions)
- system_prompt → role specialisation injected alongside briefing
- Removed old user-message composition (## Context / ## Task split)

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

Fix NameError in _run_planning: session.context → context after refactor ...

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

Improve subagent system: isolated tools, custom prompts, context transfer, timeout ...

AgentProfile:
- New fields: subagent_tools, subagent_planning_enabled, subagent_system_prompt
- loader.py: loads subagent_tools/subagent_planning_enabled from config.json,
  reads optional subagent_system_prompt.txt per profile

Profiles:
- Each profile now has a dedicated subagent_tools list (focused subset, no admin tools)
- subagent_planning_enabled: false (configurable per profile)
- New subagent_system_prompt.txt per profile with executor-focused instructions

run_ephemeral:
- Uses profile.subagent_tools instead of enabled_tools
- Builds subagent context without persona or profiles block (focused executor)
- Injects subagent_system_prompt after profile.system_prompt
- Accepts context_transfer: priming exchange injected before task message
- Wall-clock timeout (default 5 min) checked per iteration
- Returns (result_text, completed: bool) instead of bare string
- Optionally runs planning phase if profile.subagent_planning_enabled

spawn_agent:
- Removed briefing param; task is now fully self-contained
- Added system_prompt param: custom injected prompt for this specific task
- Auto-reads parent scratchpad context_transfer section via get_section()
- Result prefixed with [STATUS: completed|limit_reached]
- Timeout 300s

scratchpad:
- Added get_section(session_id, section) helper for cross-session reads

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

Planning phases, context compression, and tool improvements ...

Agent:
- Planning now a 3-phase async generator: Analysis → Execution plan → AIHelper critic
- Yield PlanningStatus events before each phase (UI progress labels)
- Phase 1 runs with think=True for deeper analysis
- Phase 2 includes available tool list so executor assignments are accurate
- Phase 3: independent critic pass validates and corrects TOOL: names against real tool list
- Planning converted from list return to async generator (fixes token accounting)

Backend:
- Context compression threshold: 80% → 70% to trigger earlier
- Compressor summary prompt: structured sections (goal, work state, key facts, outputs, errors)
- Terminal output capped at 5000 chars to prevent context flooding
- Web search: region=wt-wt for DDG, country=ALL for Brave, language=all for SearxNG
- Scratchpad: mandate writing a 'goal' section at start of multi-step tasks
- secretary max_iterations: 40→25, temperature: 0.7→0.5
- server_admin max_iterations: 40→20

Webclient:
- ThinkingCard strips <thought> XML tags leaked by Ollama
- planning_status WS event wired to chat.onPlanningStatus()

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

Add reflect tool: three parallel expert perspectives ...

ReflectTool runs Critic / Pragmatist / Detailer advisors concurrently
via asyncio.gather() + AIHelper.ask(). Each role has a distinct system
prompt designed to produce genuinely different analysis:
- Critic: challenges assumptions, surfaces risks and logical gaps
- Pragmatist: finds the simplest path, cuts unnecessary complexity
- Detailer: spots missing requirements, edge cases, ambiguities

Parameters: situation (required), assumptions (required list — the key
input that forces Navi to surface implicit beliefs), tried (optional).

Registered as a builtin with AIHelper injection. Added to all three
profiles. Persona updated with guidance on when to use it (complex or
ambiguous tasks before planning, or when stuck mid-execution).

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 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>

Make profile switching autonomous: switch immediately, inform after ...

Previously Navi asked for permission before switching profiles.
Updated both the injected profiles block in the system prompt and the
switch_profile tool description to explicitly say: switch on your own
judgment, do not ask, then inform the user which profile is active and why.

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

Add profile discoverability: list_profiles tool + system prompt injection ...

- AgentProfile: new short_description (1-line) and full_description (dict
  with specialization / when_to_use / key_tools) fields
- All 3 profile configs: structured descriptions added; list_profiles added
  to enabled_tools
- _build_system_prompt: now accepts full AgentProfile; injects compact
  "Available profiles" block into every system prompt so Navi always knows
  what other profiles exist and when to switch — dynamically, no hardcoding
- ListProfilesTool: new built-in; returns structured per-profile details
  (specialization, when_to_use, key_tools); accepts optional profile_id
  for single-profile lookup
- registry: register list_profiles_tool after profiles registry is built

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

Fix metrics: net token delta, subagent aggregation, ContextBar always visible ...

- run_stream: track _prev_tokens baseline before turn loop; compute net
  token cost as (context_tokens - prev) + subagent_tokens for per-message cost
- run_stream: intercept SubagentComplete in sink drain loop to accumulate
  subagent token and tool-call counts into the parent turn's totals
- run_ephemeral: already emitting SubagentComplete (from prior session)
- msg-meta-row: remove margin-left:auto from .msg-meta-time so time
  groups inline with elapsed/tools/tokens instead of floating right
- ContextBar: remove v-if guard so bar is always visible (not only after
  first LLM response with token data)

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

Add response metrics: elapsed time, tool calls, token count ...

Server:
- Message model: elapsed_seconds, tool_call_count, token_count fields
  (display-only, excluded from LLM context via exclude_none)
- StreamEnd event: carries same three fields
- agent.run_stream: tracks turn start time, counts ToolEvent completions,
  writes metrics onto the final assistant Message before saving to DB
- WebSocket: forwards metrics in stream_end payload

Client:
- chat.onStreamEnd: attaches elapsed_seconds, tool_call_count, token_count
  to the streaming message on completion
- buildMessageList: scans each assistant group for metrics from history
- AssistantMessage: renders .msg-meta-row below the response —
  timer icon + Xs · wrench icon + N tools · coins icon + Nk tokens · time
  (each item only shown if present; time pushed right via margin-left: auto)

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

Add session name generation via LLM ...

Backend:
- Session model gets name: str | None field
- SQLite migration: ADD COLUMN name TEXT
- PostgreSQL: ADD COLUMN IF NOT EXISTS name TEXT (applied on pool init)
- SessionStore: add set_name() abstract method, implemented in all stores
- navi/core/name_generator.py: LLM worker that reads user messages and
  returns a 3–6 word title or None if content isn't substantial yet
- POST /sessions/{id}/generate-name endpoint: fires LLM, saves and
  returns name; skips if session already named or has no user messages
- GET /sessions and GET /sessions/{id} now include name field

Client:
- api.generateSessionName(id) — calls the new endpoint
- sessions store: updateName(id, name) mutation
- chat store: after stream_end, _tryGenerateName() runs fire-and-forget;
  skips silently if session already has a name or if request fails
- SessionItem already displays session.name (falls back to id prefix)

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

Persist thinking and plan cards across session reloads ...

- Message: add thinking and is_plan fields (display-only, not sent to LLM)
- Agent main loop: accumulate thinking per iteration, save with assistant message
- _run_planning: also append plan to session.messages with is_plan=True so UI
  can render plan cards after page reload (context already had the plan)

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

Fix Ollama connection leak and empty message bug in agent ...

- _iter_stream_guarded: track chunk_task as nullable, cancel in finally
  block to prevent zombie HTTP connections accumulating under load
- Final turn: use `content or None` so empty text isn't saved to DB
- client/index.html: point to new Vue webclient build
- profiles: add email_manager tool

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

Migrate storage to PostgreSQL with SQLite fallback; misc fixes ...

- Add PgSessionStore (asyncpg pool) and PgMemoryStore replacing aiosqlite
- Keep SqliteSessionStore + SqliteMemoryStore for zero-dependency quick start
- Selection logic in deps.py: DATABASE_URL set → PG, else → SQLite
- Add asyncpg>=0.29 to dependencies; add DATABASE_URL / DB_PATH to config
- Add RESPONSE HYGIENE rule to persona: never echo tool output or plan state
- Add developer profile user tools: weather, internal_monitor
- Update README: developer profile, DB section, current tool/profile state

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

Add delete_tool: trash-based tool removal with restore support ...

Moves tool files to tools/.trash/ instead of deleting permanently.
Actions: remove (trash + unregister), restore (recover + re-register), list.
Data files are intentionally left in place on both remove and restore.
Available only in the developer profile.

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

Add developer profile; replace write_tool pattern with direct filesystem approach ...

- New TestToolTool: runs a user tool's execute() from disk in isolation,
  returns result or full traceback. No stale module cache — always fresh import.
- New developer profile: full architecture knowledge in system prompt
  (format rules, file locations, workflow, data persistence, common mistakes),
  test_tool + reload_tools + filesystem/terminal/code_exec toolset, spawn_agent
  for API research only.
- Remove write_tool and reload_tools from server_admin and smart_home profiles.
- persona.txt: drop SELF-EXTENSION block; add one-liner to switch to developer
  profile when the user asks to create/edit a tool.

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>

Expand summarization budget for better context quality ...

- _MAX_SUMMARY_INPUT_CHARS: 12k → 24k chars (2x input fed to summarizer)
- context_keep_recent: 10 → 8 turns (2 more turns go into each summary batch)
- Summarizer prompt: replace "Be brief" with "Be thorough" — capture code/config
  snippets and enough detail to continue the conversation without original messages

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

Consolidate memory_search/save/forget into single memory tool ...

Three separate tools → one tool with action enum (save/search/forget/list).
Reduces tool-slot pressure; same functionality, same MemoryStore backend.

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

Add memory_save tool for proactive fact persistence ...

Navi previously had no way to write to memory mid-conversation — she
could only search and forget. Facts were extracted automatically after
sessions went idle for 30+ min, so important context shared by the user
could be lost or delayed.

- New MemorySaveTool (navi/tools/memory_save.py): upsert a fact by
  category/key/value; overwrites existing key so no separate forget needed
- Registered as builtin alongside memory_search/memory_forget
- Added to all three profiles (secretary, server_admin, smart_home)
- persona.txt: explicit "call memory_save immediately when..." guidance
  so Navi saves stable facts as they arrive, not only post-session

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

Expose compression summary as collapsible debug card in chat UI ...

ContextCompressed event now carries the full summary text produced by the
LLM. Compression notice in chat becomes a <details> element showing
message count (before→after) with the summary expandable on click.
Rendered as markdown via marked.js.

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

Add share_file tool and session-lifetime file storage ...

Session file directories now live until the session is deleted, not
24h TTL. Cleanup loop only removes orphaned dirs (session gone from DB).

New share_file tool: copies any file to the session directory and returns
a clickable download URL. Navi can call this after generating any file
the user will want to keep.

New GET /sessions/{id}/files/{filename} endpoint serves files with
correct Content-Disposition (inline for images/HTML/PDF, attachment
for everything else).

Added PUBLIC_URL config key for building correct download links behind
reverse proxies.

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

Improve filesystem, web search, context guard, and subagent narration ...

filesystem: add find (glob), info (stat), move, append actions; read now
supports offset/limit with hard 1MB guard; list shows sizes, dates,
optional recursion.

web_search: retry DDG across auto/html/lite backends; add optional Brave
Search API and SearXNG fallbacks configured via .env.

agent: fix ContextTooLargeError to surface as Navi response instead of
raw system error; fix _check_context_size to calculate from remaining
budget (window - output_reserve) rather than a fixed 92% threshold.

persona: add ReAct narration instruction to subagent briefing template.

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>

Improve planning: two-phase pipeline and orchestrator discipline ...

agent.py:
- _run_planning() now runs two sequential LLM calls:
  Phase 1 (analysis): reformulate task, identify subtasks and unknowns;
  skip immediately if DIRECT.
  Phase 2 (execution plan): assign each subtask an executor —
  TOOL/AGENT/SELF — using a structured ## Plan format.
  Phase 2 context = analysis (embedded in system prompt) + last user
  message only; full history excluded to keep focus on plan structure.
- Warn in logs when plan lacks TOOL/AGENT/SELF executor assignments.

persona.txt:
- MANDATORY sequence: step 0 = scratchpad init before anything else;
  todo tasks must mirror plan steps exactly (same order, same executors).
- PLAN → EXECUTION BINDING: explicit rule — never switch an AGENT step
  to inline execution silently.
- SCRATCHPAD: initialize sections at task start, not after first tool call;
  write context to scratchpad before briefing subagents.
- Fix typo in BRIEFING ("sub-lagent" → "sub-agent").
- Replace stale Knowledge Retrieval Protocol with accurate one-liner.

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

Strengthen Navi planning/delegation, unify toolsets, isolate subagent scratchpad ...

persona.txt:
- DELEGATION: 'default to spawning, not to doing inline' — stronger default,
  clearer triggers, explicit when-not-to-spawn rules
- PLANNING: ties automatic planning phase to mandatory todo(op='set') as first
  tool call; reconciles pre-loop plan with in-loop execution discipline
- SCRATCHPAD: new section — when to write, section naming conventions,
  mandatory read before final answer

Profiles (secretary, server_admin, smart_home):
- All three now share the same 18-tool set (each file independent)
- planning_enabled=True on all three
- scratchpad and web_search added to smart_home
- System prompts updated with scratchpad/todo execution discipline sections

agent.py run_ephemeral:
- Each subagent gets a unique session ID (subagent_<uuid>) for scratchpad
  isolation — parallel or sequential subagents no longer share working notes

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

Skip planning phase for simple/direct requests ...

The planning prompt now asks the model to respond with "DIRECT" if the
request doesn't need multiple steps. Added a regex fallback: if the
response has no numbered steps it's also discarded. This prevents plan
cards appearing for conversational replies that would just duplicate
the final message.

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