# turnstone.toml — shared bootstrap configuration
#
# This file is read once at startup. Values here are overridden by
# environment variables, which are in turn overridden by CLI flags.
#
# All sections are optional. Missing sections use binary defaults.
# Config file location precedence:
#   1. --config flag
#   2. $TURNSTONE_CONFIG env var
#   3. ~/.config/turnstone/config.toml

# --- LLM API (turnstone, node, eval) ---

[api]
# base_url = ""                # API endpoint; empty = binary default
# api_key = ""                 # env: OPENAI_API_KEY or ANTHROPIC_API_KEY

# --- Default Model (turnstone, node, eval) ---

[model]
# name = ""                    # Model ID; empty = provider default (gpt-5 / claude-sonnet-4)
# temperature = 0.0            # 0 = provider default
# reasoning_effort = ""        # "none", "minimal", "low", "medium", "high", "xhigh", "max"
# context_window = 0           # 0 = auto-detect from provider capabilities
# max_tokens = 0               # 0 = provider default
#
# Sub-agent routing (task_agent tool).  Falls back to agent_model when
# unset, then to the session model.
# agent_model = ""             # legacy single-knob alias used as fallback
# task_model = ""              # task_agent override (e.g. "local" for cheap subtasks)
# task_effort = ""             # reasoning effort for task_agent (default: inherit session)
#
# At call time, the calling LLM may also pass `model="<alias>"` to
# task_agent to override these per-invocation.  Tool descriptions list
# available aliases dynamically; bad aliases return an error so the
# model retries with a valid choice.

# --- Named Models (turnstone, node, eval) ---
# Define model aliases with per-model overrides. Useful for local model
# servers or mixing providers. Reference by name with --model flag.
#
# [models.local]
# name = "llama-3-70b"
# provider = "openai"
# base_url = "http://localhost:8000/v1"
# context_window = 8192
#
# [models.local.capabilities]
# supports_vision = false
# supports_web_search = false
#
# [models.claude]
# name = "claude-opus-4-8"
# provider = "anthropic"

# --- Database (turnstone, node, console) ---

[database]
# url = ""                     # postgres://user:pass@host/db or /path/to.db
                               # env: TURNSTONE_DB_URL
# listen_url = ""              # direct-to-postgres URL for the console's
                               # dedicated LISTEN connection.  Set this when
                               # `url` points at pgbouncer in transaction
                               # pooling mode (LISTEN holds session state and
                               # is incompatible with transaction pooling —
                               # see docs/pgbouncer.md).  Defaults to `url`
                               # when unset.  env: TURNSTONE_DB_LISTEN_URL
# SSL params (passed through to SQLAlchemy connection):
# sslmode = "prefer"           # disable, allow, prefer, require, verify-ca, verify-full
# sslrootcert = ""             # path to CA cert for verify-ca/verify-full
# sslcert = ""                 # path to client cert (mTLS)
# sslkey = ""                  # path to client key (mTLS)

# --- Auth (node, console) ---

[auth]
# Auth is always enabled. JWT secret is required.
# jwt_secret = ""              # HS256 signing secret (min 32 bytes recommended)
                               # env: TURNSTONE_JWT_SECRET

# --- Logging (turnstone, node, console) ---

[log]
# level = ""                   # "debug", "info", "warn", "error"
                               # empty = binary default (warn for CLI, info for servers)
                               # env: TURNSTONE_LOG_LEVEL
# json = false                 # JSON output; auto-enabled when stderr is not a TTY

# --- Session (turnstone, node) ---

[session]
# instructions = ""            # Default system message
# compact_max_tokens = 32768   # Max tokens for context compaction summary
# auto_compact_pct = 0.8       # Trigger compaction at this % of context window

# --- Tools (turnstone, node) ---

[tools]
# timeout = 120                # Tool execution timeout in seconds
# skip_permissions = false     # Auto-approve all tool calls
#
# web_search backend (local/vLLM models only — commercial providers use their
# own native server-side search). The docker-compose stack bundles a SearxNG
# service and points at it by default.
# web_search_backend = ""      # "" (auto), "searxng", or "mcp:server:tool"
# searxng_url = "http://searxng:8080"  # SearxNG base URL; env: TURNSTONE_SEARXNG_URL
                               # (the admin Settings value, if set, wins; clear it
                               # there to disable web search)
# searxng_engines = ""         # comma-separated engines (e.g. "duckduckgo,wikipedia");
                               # empty = the instance's default mix
                               # env: TURNSTONE_SEARXNG_ENGINES
#
# workspace_dir = "/workspace" # directory surfaced to the model as its workspace
                               # (informational only — does not chdir or confine
                               # tools; skipped if the directory doesn't exist).
                               # The Docker image presets this to /workspace.
                               # env: TURNSTONE_WORKSPACE
#
# Reranking (optional, disabled by default). Turnstone runs no reranker itself —
# it POSTs to an external Cohere/Jina-compatible /rerank endpoint (self-hosted
# vLLM/TEI/llama.cpp, or hosted Cohere/Jina/Voyage) to reorder results by query
# relevance. The endpoint is a per-model definition: add a model in the admin
# Models tab with capability {"supports_rerank": true} and base_url set to the
# full /rerank endpoint, then pick it under Models -> Roles -> Reranker. The
# settings below are global knobs — there is no rerank_url-style endpoint setting.
# rerank_web_search = true     # rerank web_search results (when an endpoint is set)
# rerank_bm25 = true           # rerank BM25 retrieval: tool search, skill search, memory
# rerank_bm25_threshold = 0.0  # 0-1 relevance floor for proactive memory; 0 = off (reorder
                               # only). Per-model: set via `turnstone-admin rerank-calibrate`.
# rerank_instruction = ""      # for instruction-aware rerankers (Qwen3) when the endpoint
                               # does NOT apply the model's chat template, e.g. "Given a web
                               # search query, retrieve relevant passages that answer the
                               # query". Prefer vLLM's --chat-template; don't use both.
                               # NB: serving Qwen3-Reranker via vLLM REQUIRES --chat-template
                               # (the model's chat_template.jinja) or scores are near-random.

# --- Judge (turnstone, node) ---

[judge]
# enabled = true               # Enable intent validation
# smart_approvals = false      # Auto-approve high-confidence "approve" LLM verdicts (opt-in)
# confidence_threshold = 0.95  # Smart Approvals auto-approve bar (LLM recommendation=approve)
# output_guard = true          # Scan tool output for security signals
# redact_secrets = true        # Redact detected credentials in output

# --- Memory (turnstone, node) ---

[memory]
# relevance_k = 5              # Top-K memories for context injection
# fetch_limit = 50             # Max memories to fetch for ranking
# max_content = 32768          # Max memory content size in chars
# nudge_cooldown = 300         # Min seconds between metacognitive nudges
# nudges = true                # Enable memory nudges

# --- MCP (turnstone, node) ---

[mcp]
# config_path = ""             # Path to MCP servers config file (JSON)

# --- Server (node, console) ---

[server]
# max_workstreams = 50         # Maximum concurrent workstreams per node
                               # env: TURNSTONE_MAX_WORKSTREAMS