config.example.toml

# config.example.toml — AgentHandover configuration
# Copy to the OS-appropriate location:
#   macOS:   ~/Library/Application Support/agenthandover/config.toml
#   Linux:   ~/.config/agenthandover/config.toml

[observer]
t_dwell_seconds = 3
t_scroll_read_seconds = 8
capture_screenshots = true
screenshot_max_per_minute = 20
multi_monitor_mode = "focused_window"
screenshot_format = "jpeg"       # "jpeg" (half-res, recommended) or "png" (full-res)
screenshot_quality = 70          # JPEG quality 1-100 (only used for jpeg format)
screenshot_scale = 0.5           # 0.5 = half resolution (recommended for VLM)
dhash_threshold = 10             # perceptual hash dedup threshold (lower = stricter)

[privacy]
enable_inline_secret_redaction = true
enable_clipboard_preview = false
clipboard_preview_max_chars = 200
secure_field_drop = true

[browser]
extension_id = "jpemkdcihaijkolbkankcldmiimmmnfo"
native_host_name = "com.agenthandover.host"
deny_network_egress = true

[storage]
retention_days_raw = 14
retention_days_episodes = 90
sqlite_wal_mode = true
vacuum_min_free_gb = 5
vacuum_safety_multiplier = 2.5

[idle_jobs]
require_ac_power = true
min_battery_percent = 50
max_cpu_percent = 30
max_temp_c = 80
run_window_local_time = "01:00-05:00"

[vlm]
enabled = true
max_jobs_per_day = 50
max_queue_size = 500
job_ttl_days = 7
max_compute_minutes_per_day = 20
# backend = "mlx-vlm"         # "mlx-vlm", "llama-cpp-python", "ollama", "openai-compat", "mock"
# model_name = "mlx-community/llava-1.5-7b-4bit"
# timeout_seconds = 120
# --- llama-cpp-python specific ---
# model_path = "/path/to/model.gguf"
# clip_model_path = "/path/to/clip-model.gguf"
# n_ctx = 2048
# --- ollama specific ---
# ollama_host = "http://localhost:11434"
# --- openai-compat specific ---
# api_key = ""                 # or set AGENTHANDOVER_API_KEY / OPENAI_API_KEY env var
# base_url = ""                # for local providers (vLLM, LM Studio, etc.)
# --- remote mode (cloud APIs) ---
# mode = "local"               # "local" (default) or "remote" (cloud API)
# provider = "openai"          # "openai" | "anthropic" | "google"
# model = "gpt-4o-mini"        # provider-specific model name
# api_key_env = "OPENAI_API_KEY"  # env var name (NEVER store keys directly!)
# --- v2 scene annotation pipeline ---
# Model tiers (auto-detected by RAM during onboarding):
#   8GB:  qwen3.5:2b + qwen3.5:4b (6.1 GB, standard)
#   16GB: gemma4:12b-it-qat (7.2 GB, recommended — 12B dense, single model)
#   24GB: gemma4:12b-it-qat (7.2 GB, performance — headroom for more frames)
#   48GB: gemma4:31b-it-qat (18 GB, max quality — frontier intelligence)
# QAT (Gemma 4 16/24/48GB tiers) requires Ollama 0.30.6+.  The 8GB Qwen
# tier works on older Ollama.  Settings per model are auto-tuned via
# model_profiles.py.
annotation_model = "qwen3.5:2b"   # model for per-frame annotation
sop_model = "qwen3.5:4b"          # model for SOP generation
annotation_enabled = true          # enable continuous scene annotation pipeline
stale_skip_count = 3               # skip after N consecutive non-workflow same-app frames
sliding_window_max_age_sec = 600   # 10 min max age for context window

[llm]
enhance_sops = true
max_enhancements_per_day = 20
# model = "llama3.2:3b"        # lighter text model for local; empty = inherit from vlm
# timeout_seconds = 60
# temperature = 0.3
# max_tokens = 800

[openclaw]
workspace_path = "~/.openclaw/workspace"
sop_output_dir = "memory/apprentice/sops"
index_path = "memory/apprentice/index.md"
atomic_writes = true

[sop]
# When false (default), generated SOPs are saved as drafts for human review
# in the app before being exported. Set to true to auto-approve and export
# immediately — use only if you trust the pipeline's output quality.
auto_approve = false

[export]
adapter = "openclaw"           # "openclaw" or "generic"
json_export = false            # also write .json alongside .md
# generic_output_dir = "~/Library/Application Support/agenthandover/sops"

# ─── Phase 2+: Knowledge base, trust & privacy zones ───────────────

[knowledge]
# Root directory for the portable knowledge base.
# Any AI agent can read this directory to execute your workflows.
root_dir = "~/.agenthandover/knowledge"
# Time to run daily batch processing (HH:MM, local time)
daily_batch_time = "23:30"
# Minimum days of data before pattern detection kicks in
pattern_detection_min_days = 3
# Enable the local HTTP query API for agents (default: true)
query_api_enabled = true
query_api_port = 9477
# Enable knowledge base sync (Phase 5)
sync_enabled = false
# sync_remote = ""             # rsync-style remote path or URL

[trust]
# Default trust level for new procedures:
#   "observe" | "suggest" | "draft" | "execute_with_approval" | "autonomous"
default_trust_level = "observe"
# Minimum observations before a procedure can be promoted beyond "observe"
min_observations_for_promotion = 3
# Minimum success rate (0.0–1.0) before suggesting promotion
min_success_rate_for_suggestion = 0.90
# Never auto-promote — only suggest (human must approve)
auto_promote = false

[constraints]
# Global guardrails applied to ALL procedures
# max_spend_usd_without_approval = 100
# blocked_domains = []         # domains agent must never interact with
# require_confirmation_for = ["email", "payment", "deletion"]
# max_autonomous_actions_per_hour = 20

[privacy.zones]
# Apps that are always observed at full fidelity
# full_observation = ["com.apple.Safari", "com.google.Chrome"]
# Apps where only metadata (app name, timestamp) is captured — no screenshots
# metadata_only = ["com.apple.Messages"]
# Apps that are completely blocked from observation (glob patterns)
# blocked = ["1Password*", "Bitwarden*", "LastPass*", "KeePassXC*", "Keychain Access"]
# URL patterns that are always blocked (glob patterns, case-insensitive)
# blocked_urls = ["*bank*", "*.gov/*", "*payroll*", "*login*password*"]
# Time windows during which all observation is paused (HH:MM-HH:MM, local time)
# auto_pause = ["12:00-13:00"]

# ---- Feature Toggles ----
# Enable/disable Phase 3-6 features independently.
# All default to true. Set to false to disable.
[features]
# 8-class activity taxonomy (Phase 1)
activity_classification = true
# Task continuity graph across interruptions (Phase 2)
continuity_tracking = true
# 7-state procedure lifecycle state machine (Phase 3)
lifecycle_management = true
# Curation queue with merge/upgrade/drift detection (Phase 5)
curation = true
# Runtime environment validation via pgrep (Phase 6)
runtime_validation = true