alice.toml

[runtime]
default_model = "openai:gpt-4o-mini"
max_steps = 12
turn_timeout_ms = 90000
dispatch_mode = "native_preferred"

# Agent backend selection: "bob" (default) or "acp" (requires --features acp-agent).
# [agent]
# backend = "bob"
# auto_orchestrate = false
# acp_command = "claude-agent-acp"
# acp_args = []
# acp_working_dir = "."
#
# # Optional named ACP profiles for orchestration
# # Set auto_orchestrate = true to fan out ordinary natural-language turns
# # across all non-primary ACP profiles.
# primary_profile = "manager"
#
# [agent.acp_profiles.manager]
# command = "opencode"
# args = ["serve", "--acp"]
# working_dir = "."
#
# [agent.acp_profiles.writer]
# command = "codex"
# args = ["--acp"]
# working_dir = "."

[memory]
db_path = "./.alice/memory.db"
recall_limit = 6
bm25_weight = 0.3
vector_weight = 0.7
vector_dimensions = 384
enable_vector = true

# Alice now keeps lightweight long-term user profiles in the same memory DB.
# Profiles are refreshed automatically from durable self-descriptions such as
# preferences, project context, and workflow constraints.

# Skill system configuration — auto-selects relevant SKILL.md files per turn.
# [skills]
# enabled = true
# max_selected = 3
# token_budget = 1800
#
# [[skills.sources]]
# path = "./skills"
# recursive = true

# Optional post-turn reflection. When enabled, Alice asks a hidden reflector
# to turn reusable workflows into learned `SKILL.md` files under the directory
# below. Point the directory under one of your configured skill sources so the
# learned skills are eligible for future turns.
# [reflection]
# enabled = true
# learned_skills_dir = "./skills/learned"

# Channel adapters — enable multi-channel input beyond the default CLI REPL.
# Discord requires ALICE_DISCORD_TOKEN env var.
# Telegram requires ALICE_TELEGRAM_TOKEN env var.
# [channels.discord]
# enabled = false
# [channels.telegram]
# enabled = false

# Background scheduler for persisted recurring tasks. The loop runs in
# long-lived `chat` and `channel` sessions when enabled, and will post task
# results back to the active bound thread when a live channel adapter exists.
# [scheduler]
# enabled = false
# poll_interval_ms = 30000

# Built-in tools (local/file_read, local/file_write, local/file_list,
# local/shell_exec) are always available. Additional MCP servers can be
# configured below.

# [[mcp.servers]]
# id = "filesystem"
# command = "npx"
# args = ["-y", "@modelcontextprotocol/server-filesystem", "."]
# tool_timeout_ms = 15000