Remove code-specific scoping rules from planning prompt ...

Keep only the universal comma test heuristic — code-specific rules
were too narrow and cluttered the prompt.

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

Tighten AGENT step scoping in planning prompt ...

Added comma test heuristic: if a step description lists things with
'and' or commas, each item is a separate step. Added code-specific
guidance: one step = one file or one focused feature addition, never
scaffold + logic + helpers combined. Replaced abstract good/bad
examples with concrete code implementation examples.

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

Adaptive re-plan on todo step failure ...

When a todo step is newly marked failed, queue a targeted system message
for the next iteration prompting the model to revise its remaining pending
steps before continuing. Enabled by adaptive_replan_enabled flag (on by
default in developer profile). Zero overhead when no failure occurs.

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>

Planning debug panel, todo auto-populate, scratchpad/persona improvements ...

- Planning debug panel: new Planning tab in debug/index.html shows raw
  phase 1/2 outputs and token counts per planning run, stored in
  session.planning_logs (new column in both SQLite and PostgreSQL)
- New GET /sessions/{id}/planning API endpoint
- PlanningDebugData internal event wires _run_planning() output into
  session storage; never forwarded to WebSocket clients
- Phase 3 (plan critic) disabled — to be reworked with reflect integration
- Todo tool: auto-populated from plan steps after phase 2; model only
  needs to call update/view, not set
- Scratchpad: clarified description and persona instructions; removed
  context_transfer from user-facing docs (internal mechanism only)
- web_search: switched to ddgs package, SearXNG as primary backend,
  DDG html-only fallback; added find_up action to filesystem tool
- Persona: added SCRATCHPAD and TODO sections with clear usage rules;
  added NAVI.md project context instructions
- chat.js: fixed subagent planning event fallthrough into parent UI;
  statusLabel cleared on first stream delta

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

Route subagent planning events into spawn_agent card in the UI ...

Previously PlanningStatus/PlanReady had no is_subagent flag, so subagent
planning spinners and plan cards rendered as top-level Navi planning UI.

Backend:
- Add is_subagent field to PlanningStatus and PlanReady events
- _run_planning accepts is_subagent param, passes it through all yields
- run_ephemeral calls _run_planning with is_subagent=True
- websocket.py forwards is_subagent in planning_status and plan_ready messages

Frontend (chat.js):
- onPlanningStatus: if is_subagent, set planningLabel on the last spawn_agent
  card instead of msg.statusLabel
- onPlanReady: if is_subagent, push plan into spawn card steps and clear
  planningLabel; otherwise behave as before

Frontend (ToolCard.vue):
- Render subagent-planning-indicator (spinner + label) when planningLabel set
- Render plan cards inside subagent steps using the same plan-card pattern

Also includes leftover session changes: spawn_agent default 40 in description
and manual, updated manual content.

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

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>