compressor: structured summaries, profile-aware compression, adaptive keep_recent ...

- Replace free-form summary with strict Markdown template (Goal, Active Files,
  Decisions, Completed Work, Pending Work/Todo, Errors, Key Values).
- Keep filesystem/code_exec/terminal tool results and messages with
  is_compression_critical=True verbatim during compression instead of 300-char truncation.
- Make compression profile-aware: AgentProfile gains compression_keep_recent,
  compression_max_tokens, compression_prompt_file. navi_code uses dedicated
  compression prompt and larger keep_recent/max_tokens.
- Adaptive partition_messages(): important turns (user corrections, errors,
  critical tools) survive longer; filler/social turns compress sooner.
- Increase default context_summary_max_tokens from 3000 to 4000.
- Propagate active profile changes to ContextCompressor and SubAgentRunner.

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

Refactor profile tool config to explicit agent/subagent structure ...

Replaces the confusing mix of enabled_tools + mcp_servers + subagent_tools
with a single explicit structure:

    tools: {
      agent:   {native: [...], mcp: {server: [groups]}},
      subagent:{native: [...], mcp: {server: [groups]}}
    }

Why:
- Old fields mixed native and MCP names (mcp__server__tool) in one list,
  making it impossible to tell at a glance what a subagent actually gets.
- subagent_runner.py had 25 lines of runtime MCP filtering logic that
  was hard to follow and error-prone.

Changes:
- AgentProfile: add ToolConfig / ToolScopeConfig pydantic models,
  keep old fields (enabled_tools, mcp_servers, subagent_tools) for
  auto-migration via _migrate_tools validator.
- loader.py: read new "tools" key, auto-migrate legacy configs.
- agent.py: _tool_list now accepts ToolScopeConfig.
- subagent_runner.py: simplified — profile.get_subagent_tools() returns
  the exact scope, no runtime filtering needed.
- context_builder.py, list_tools.py, spawn_agent.py: updated to use
  profile.get_agent_tools() / get_subagent_tools().
- All 6 profile config.json files migrated to new schema.
- Secretary subagent now explicitly gets navi-web MCP tools for web search.

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

Add inherit_system_prompt and is_subagent_only mechanisms ...

- inherit_system_prompt: subagent parameter to prepend parent's system
  prompt as a base layer before subagent specialisation
- is_subagent_only: profile flag blocking switch_profile, allowing
  spawn_agent only; shown with [subagent only] tag in list_profiles
- Document both in docs/profiles.md and docs/tools.md

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

Refactor MCP integration: server groups in profiles ...

- mcp_servers.json: add 'groups' (read/write/admin) for gnexus-book
- AgentProfile: new 'mcp_servers' field (server_name -> group list)
- Profile loader: parse and persist 'mcp_servers' in config.json
- Agent._tool_list(): expands mcp_servers into concrete tool names
  via McpManager.resolve_group(), wildcard '*' supported
- /agents/profiles API: includes 'mcp_servers' in response
- Profiles no longer list individual mcp_ tools in 'enabled_tools'
- discuss: gnexus-book read group
- server_admin: gnexus-book read+write+admin groups

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

Multi-user auth via gnexus-auth OAuth + hybrid role/permission model ...

- Integrate gnexus-auth-client-py (GAuthClient) for OAuth flow, token refresh,
  and webhook parsing
- Add navi/auth/ package: User model, Fernet encryptor, client singleton,
  deps (get_current_user, require_admin, require_permission)
- New tables: navi_users, user_auth_sessions (auto-created on startup)
- Session/memory isolation by user_id with legacy NULL support
- Cookie-based auth proxy: /auth/login, /callback, /logout, /me
- Webhook receiver /webhooks/gnexus-auth handling user events, global logout,
  session revocation, role/permission changes
- Admin endpoints (/admin/*) gated by role + permissions
- Webclient auth store with isAdmin/hasPermission guards
- Admin-only profile filtering in /agents/profiles
- 200/200 tests passing

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

Disable thinking stalls for 3D subagents

Architecture extensibility — event bus, middleware, auto-discovery, Pydantic profiles ...

- EventBus: async pub/sub for AgentEvents, WebSocket subscribes instead of direct yield
- Declarative serialization: AgentEvent.to_wire() on all event types
- Auto-discovery for LLM backends (_discover_backends) and workers (scan navi/workers/*.py)
- AgentProfile: Pydantic BaseModel with extra='allow', @field_validator for model coercion
- Tool middleware chain: pre/post execute hooks via ToolRegistry.add_middleware()
- LoggingMiddleware: built-in, logs every tool call
- Fix pg_trgm DDL: conditional GIN indexes via DO $$ block, no CREATE EXTENSION
- New files: event_bus.py, middleware.py, logging_middleware.py

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

changed llm & new ollama param

Add structured planning review and adaptive depth

Add context providers: dynamic system message injection per LLM call ...

- navi/context_providers/ registry + built-in public_url provider (global, always injected)
- context_providers/ user directory, hot-reloaded via reload_tools
- AgentProfile.context_providers field for per-profile opt-in providers
- Agent._collect_context_injections() called before every tool-calling loop
- reload_tools now reloads both user tools and user context providers
- manuals/write_context_provider.md for Navi, docs/context_providers.md reference

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

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 per-phase planning flags and planning_mandatory ...

- planning_mandatory: disables DIRECT shortcut, forces all phases to run
- planning_phase1_enabled / phase2_enabled / phase3_enabled: per-phase toggles
- planning_phase2_enabled replaces planning_reflect_enabled (migrated in loader with backward compat)
- Migrate all profile configs; rewrite docs/profiles.md as full config reference

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>

Use gemma4 cloud model by default

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>

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>

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>

Add planning phase and scratchpad tool for smarter task execution ...

- ScratchpadTool: session-scoped working notepad with named sections
  (write/append/read/clear). Lets Navi capture intermediate findings
  between tool calls instead of losing track of them.

- Planning phase: when profile.planning_enabled=True, a fast pre-loop
  LLM call (think=False, no tools) outlines a numbered plan before
  any actions are taken. The plan is injected into session context as
  an assistant message so the model naturally continues from it.

- PlanReady event + plan_ready WebSocket message + plan card in UI
  (green-tinted, collapsible, mirroring thinking card design).

- secretary and server_admin profiles: planning_enabled=True,
  scratchpad added to enabled_tools, system prompts updated with
  explicit execution discipline instructions.

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