optimclaw/src/setup

Setup / Onboarding Specification

This document is the authoritative specification for IronClaw's onboarding wizard. Any code change to src/setup/ must keep this document in sync. If a future contributor or coding agent modifies setup behavior, update this file first, then adjust the code to match.

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Entry Points

ironclaw onboard [--skip-auth] [--channels-only]

Explicit invocation. Loads .env files, runs the wizard, exits.

ironclaw          (first run, no database configured)

Auto-detection via check_onboard_needed() in main.rs. Skips onboarding when ONBOARD_COMPLETED env var is set (written to ~/.ironclaw/.env by the wizard). Otherwise triggers when no database is configured:

• DATABASE_URL env var is set
• LIBSQL_PATH env var is set
• ~/.ironclaw/ironclaw.db exists on disk

The --no-onboard CLI flag suppresses auto-detection.

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Startup Sequence (main.rs)

1. Parse CLI args
2. If Command::Onboard  → load .env, run wizard, exit
3. If Command::Run or no command:
   a. Load .env files (dotenvy::dotenv() then load_ironclaw_env())
   b. check_onboard_needed() → run wizard if needed
   c. Config::from_env()     → build config from env vars
   d. Create SessionManager  → load session token
   e. ensure_authenticated() → validate session (NEAR AI only)
   f. ... rest of agent startup

Critical ordering: .env files must be loaded (step 3a) before Config::from_env() (step 3c) because bootstrap vars like DATABASE_BACKEND live in ~/.ironclaw/.env.

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The 8-Step Wizard

Overview

Step 1: Database Connection
Step 2: Security (master key)
Step 3: Inference Provider          ← skipped if --skip-auth
Step 4: Model Selection
Step 5: Embeddings
Step 6: Channel Configuration
Step 7: Extensions (tools)
Step 8: Background Tasks (heartbeat)
       ↓
   save_and_summarize()

--channels-only mode runs only Step 6, skipping everything else.

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Step 1: Database Connection

Module: wizard.rs → step_database()

Goal: Select backend, establish connection, run migrations.

Decision tree:

Both features compiled?
├─ Yes → DATABASE_BACKEND env var set?
│  ├─ Yes → use that backend
│  └─ No  → interactive selection (PostgreSQL vs libSQL)
├─ Only postgres feature → step_database_postgres()
└─ Only libsql feature  → step_database_libsql()

PostgreSQL path (step_database_postgres):

1. Check DATABASE_URL from env or settings
2. Test connection (creates deadpool_postgres::Pool)
3. Optionally run refinery migrations
4. Store pool in self.db_pool

libSQL path (step_database_libsql):

1. Offer local path (default: ~/.ironclaw/ironclaw.db)
2. Optional Turso cloud sync (URL + auth token)
3. Test connection (creates LibSqlBackend)
4. Always run migrations (idempotent CREATE IF NOT EXISTS)
5. Store backend in self.db_backend

Invariant: After Step 1, exactly one of self.db_pool or self.db_backend is Some. This is required for settings persistence in save_and_summarize().

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Step 2: Security (Master Key)

Module: wizard.rs → step_security()

Goal: Configure encryption for API tokens and secrets.

Decision tree:

SECRETS_MASTER_KEY env var set?
├─ Yes → use env var, done
└─ No  → try get_master_key() from OS keychain
   ├─ Ok(bytes) → cache in self.secrets_crypto, ask "use existing?"
   │  ├─ Yes → done (keychain)
   │  └─ No  → clear cache, fall through to options
   └─ Err   → fall through to options
              ├─ OS Keychain: generate + store + build SecretsCrypto
              ├─ Env variable: generate + print export command
              └─ Skip: disable secrets features

CRITICAL CAVEAT: macOS Keychain Dialogs

On macOS, security_framework::get_generic_password() can trigger TWO system dialogs:

1. "Enter your password to unlock the keychain" (keychain locked)
2. "Allow ironclaw to access this keychain item" (per-app authorization)

This is OS-level behavior we cannot prevent. To minimize pain:

• Use get_master_key() not has_master_key() in step 2. Both call the same underlying API, but get_master_key() returns the key bytes so we can cache them. has_master_key() throws them away, forcing a second keychain access later.

• Build SecretsCrypto eagerly. When the keychain key is retrieved, immediately construct SecretsCrypto and store in self.secrets_crypto. Later calls to init_secrets_context() check this field first, avoiding redundant keychain probes.

• Never probe the keychain in read-only commands (e.g., ironclaw status). The status command reports "env not set (keychain may be configured)" rather than triggering system dialogs.

Invariant: After Step 2, self.secrets_crypto is Some if the user chose Keychain or generated a new key. It may be None if the user chose env-var mode or skipped secrets.

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Step 3: Inference Provider

Module: wizard.rs → step_inference_provider()

Goal: Choose LLM backend and authenticate.

Providers:

Provider	Auth Method	Secret Name	Env Var
NEAR AI Chat	Browser OAuth or session token	-	NEARAI_SESSION_TOKEN
NEAR AI Cloud	API key	llm_nearai_api_key	NEARAI_API_KEY
Anthropic	API key	anthropic_api_key	ANTHROPIC_API_KEY
OpenAI	API key	openai_api_key	OPENAI_API_KEY
Ollama	None	-	-
OpenRouter¹	API key	llm_compatible_api_key	LLM_API_KEY
OpenAI-compatible¹	Optional API key	llm_compatible_api_key	LLM_API_KEY

¹ OpenRouter and OpenAI-compatible share the same secret name and env var because OpenRouter is stored as llm_backend = "openai_compatible" under the hood. Switching between them overwrites the same credential slot.

OpenRouter (setup_openrouter):

• Pre-configured OpenAI-compatible preset with base URL https://openrouter.ai/api/v1
• Delegates to setup_api_key_provider() with a display name override ("OpenRouter")
• Sets llm_backend = "openai_compatible" and openai_compatible_base_url automatically
• Clears selected_model so Step 4 prompts for a model name (manual text input, no API-based model fetching)

API-key providers (setup_api_key_provider):

1. Check env var → if set, ask to reuse, persist to secrets store
2. Otherwise prompt for key entry via secret_input()
3. Store encrypted in secrets via init_secrets_context()
4. Cache key in self.llm_api_key for model fetching in Step 4

NEAR AI (setup_nearai):

• Calls session_manager.ensure_authenticated() which shows the auth menu:
  • Options 1-2 (GitHub/Google): browser OAuth → NEAR AI Chat mode (Responses API at private.near.ai, session token auth)
  • Option 4: NEAR AI Cloud API key → NEAR AI Cloud mode (Chat Completions API at cloud-api.near.ai, API key auth)
• NEAR AI Chat path: session token saved to ~/.ironclaw/session.json. Hosting providers can set NEARAI_SESSION_TOKEN env var directly (takes precedence over file-based tokens).
• NEAR AI Cloud path: NEARAI_API_KEY saved to ~/.ironclaw/.env (bootstrap) and encrypted secrets store (llm_nearai_api_key). LlmConfig::resolve() auto-selects ChatCompletions mode when the API key is present.

self.llm_api_key caching: The wizard caches the API key as Option<SecretString> so that Step 4 (model fetching) and Step 5 (embeddings) can use it without re-reading from the secrets store or mutating environment variables.

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Step 4: Model Selection

Module: wizard.rs → step_model_selection()

Goal: Choose which model to use.

Flow:

1. If model already set → offer to keep it
2. Fetch models from provider API (5-second timeout)
3. On timeout or error → use static fallback list
4. Present list + "Custom model ID" escape hatch
5. Store in self.settings.selected_model

Model fetchers pass the cached API key explicitly:

let cached = self.llm_api_key.as_ref().map(|k| k.expose_secret().to_string());
let models = fetch_anthropic_models(cached.as_deref()).await;

This avoids mutating environment variables. The fetcher checks the explicit key first, then falls back to the standard env var.

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Step 5: Embeddings

Module: wizard.rs → step_embeddings()

Goal: Configure semantic search for workspace memory.

Flow:

1. Ask "Enable semantic search?" (default: yes)
2. Detect available providers:
   • NEAR AI: if backend is nearai OR valid session exists
   • OpenAI: if OPENAI_API_KEY in env OR (backend is openai AND cached key)
3. If both available → let user choose
4. If only one → use it
5. If neither → disable embeddings

Default model: text-embedding-3-small (for both providers)

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Step 6: Channel Configuration

Module: wizard.rs → step_channels(), delegating to channels.rs

Goal: Enable input channels (TUI, HTTP, Telegram, etc.).

Sub-steps:

6a. Tunnel setup (if webhook channels needed)
6b. Discover WASM channels from ~/.ironclaw/channels/
6c. Build channel options: discovered + bundled + registry catalog
6d. Multi-select: CLI/TUI, HTTP, all available channels
6e. Install missing bundled channels (copy WASM binaries)
6f. Install missing registry channels (download artifacts, fallback to source build)
6g. Initialize SecretsContext (for token storage)
6h. Setup HTTP webhook (if selected)
6i. Setup each WASM channel (secrets, owner binding)

Channel sources (priority order for installation):

1. Already installed in ~/.ironclaw/channels/
2. Bundled channels (pre-compiled in channels-src/)
3. Registry channels (registry/channels/*.json, download-first with source fallback)

Tunnel setup (setup_tunnel):

• Options: ngrok, Cloudflare Tunnel, localtunnel, custom URL
• Validates HTTPS requirement
• Stored in self.settings.tunnel.public_url

WASM channel setup (setup_wasm_channel):

• Reads capabilities.json for setup.required_secrets
• For each secret: check existing, prompt or auto-generate, validate regex
• Save each secret via SecretsContext

Telegram special case (setup_telegram):

• Validates bot token via Telegram getMe API
• Owner binding: polls getUpdates for 120s to capture sender's user ID
• Optional webhook secret generation

SecretsContext creation (init_secrets_context):

1. Check self.secrets_crypto (set in Step 2) → use if available
2. Else try SECRETS_MASTER_KEY env var
3. Else try get_master_key() from keychain (only in channels_only mode)
4. Create backend-appropriate secrets store (respects selected database backend)

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Step 7: Extensions (Tools)

Module: wizard.rs → step_extensions()

Goal: Install WASM tools from the extension registry.

Flow:

1. Load RegistryCatalog from registry/ directory
2. If registry not found, print info and skip
3. List all tool manifests from the catalog
4. Discover already-installed tools in ~/.ironclaw/tools/
5. Multi-select: show all registry tools with display name, auth method, and description. Pre-check tools tagged "default" and already installed.
6. For each selected tool not yet installed, install via RegistryInstaller::install_with_source_fallback() (download-first, fallback to source build)
7. Print consolidated auth hints (deduplicated by provider, e.g. one hint for all Google tools sharing google_oauth_token)

Registry lookup (load_registry_catalog): Searches for registry/ directory in order:

1. Current working directory
2. Next to the executable
3. CARGO_MANIFEST_DIR (compile-time, dev builds)

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Step 8: Heartbeat

Module: wizard.rs → step_heartbeat()

Goal: Configure periodic background execution.

Flow:

1. Ask "Enable heartbeat?" (default: no)
2. If yes: interval in minutes (default: 30), notification channel
3. Store in self.settings.heartbeat

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Settings Persistence

Two-Layer Architecture

Settings are persisted in two places:

Layer 1: ~/.ironclaw/.env (bootstrap vars)

Contains only the settings needed BEFORE database connection. Written by save_bootstrap_env() in bootstrap.rs.

DATABASE_BACKEND="libsql"
LIBSQL_PATH="/Users/name/.ironclaw/ironclaw.db"
LLM_BACKEND="openai_compatible"
LLM_BASE_URL="http://my-vllm:8000/v1"

Or for PostgreSQL + NEAR AI:

DATABASE_BACKEND="postgres"
DATABASE_URL="postgres://user:pass@localhost/ironclaw"
LLM_BACKEND="nearai"

Or for Ollama:

LLM_BACKEND="ollama"
OLLAMA_BASE_URL="http://localhost:11434"

Why separate? Chicken-and-egg: you need DATABASE_BACKEND to know which database to connect to, and LLM_BACKEND to know whether to attempt NEAR AI session auth -- neither can be stored in the database.

Layer 2: Database settings table (everything else)

All other settings are stored as key-value pairs in the settings table, keyed by (user_id, key). Written by set_all_settings().

Settings are serialized via Settings::to_db_map() as dotted paths:

database_backend = "libsql"
llm_backend = "nearai"
selected_model = "anthropic/claude-sonnet-4-5"
embeddings.enabled = "true"
embeddings.provider = "nearai"
channels.http_enabled = "true"
heartbeat.enabled = "true"
heartbeat.interval_secs = "300"

Incremental Persistence

Settings are persisted after every successful step, not just at the end. This prevents data loss if a later step fails (e.g., the user enters an API key in step 3 but step 5 crashes — they won't need to re-enter it).

persist_after_step() is called after each step in run() and:

1. Writes bootstrap vars to ~/.ironclaw/.env via write_bootstrap_env()
2. Writes all current settings to the database via persist_settings()
3. Silently ignores errors (e.g., if called before Step 1 establishes a DB)

try_load_existing_settings() is called after Step 1 establishes a database connection. It loads any previously saved settings from the database using get_all_settings("default") → Settings::from_db_map() → merge_from(). This recovers progress from prior partial wizard runs.

Ordering after Step 1 is critical:

step_database()                        → sets DB fields in self.settings
let step1 = self.settings.clone()      → snapshot Step 1 choices
try_load_existing_settings()           → merge DB values into self.settings
self.settings.merge_from(&step1)       → re-apply Step 1 (fresh wins over stale)
persist_after_step()                   → save merged state

This ordering ensures:

• Prior progress (steps 2-7 from a previous partial run) is recovered
• Fresh Step 1 choices override stale DB values (not the reverse)
• The first DB persist doesn't clobber prior settings with defaults

save_and_summarize()

Final step of the wizard:

1. Mark onboard_completed = true
2. Call persist_settings() for final write (idempotent — ensures
   onboard_completed flag is saved)
3. Call write_bootstrap_env() for final .env write (idempotent)
4. Print configuration summary

Bootstrap vars written to ~/.ironclaw/.env:

• DATABASE_BACKEND (always)
• DATABASE_URL (if postgres)
• LIBSQL_PATH (if libsql)
• LIBSQL_URL (if turso sync)
• LLM_BACKEND (always, when set)
• LLM_BASE_URL (if openai_compatible)
• OLLAMA_BASE_URL (if ollama)
• NEARAI_API_KEY (if API key auth path)
• ONBOARD_COMPLETED (always, "true")

Invariant: Both Layer 1 and Layer 2 must be written. If the database write fails, the wizard returns an error and the .env file is not written.

Legacy Migration

bootstrap.rs handles one-time upgrades from older config formats:

• bootstrap.json → extracts DATABASE_URL, writes .env, renames to .migrated
• settings.json → migrated to database via migrate_disk_to_db()

---

Settings Struct

Module: settings.rs

pub struct Settings {
    // Meta
    pub onboard_completed: bool,

    // Step 1: Database
    pub database_backend: Option<String>,    // "postgres" | "libsql"
    pub database_url: Option<String>,
    pub libsql_path: Option<String>,
    pub libsql_url: Option<String>,

    // Step 2: Security
    pub secrets_master_key_source: KeySource, // Keychain | Env | None

    // Step 3: Inference
    pub llm_backend: Option<String>,         // "nearai" | "anthropic" | "openai" | "ollama" | "openai_compatible"
    pub ollama_base_url: Option<String>,
    pub openai_compatible_base_url: Option<String>,

    // Step 4: Model
    pub selected_model: Option<String>,

    // Step 5: Embeddings
    pub embeddings: EmbeddingsSettings,      // enabled, provider, model

    // Step 6: Channels
    pub tunnel: TunnelSettings,              // provider, public_url
    pub channels: ChannelSettings,           // http config, telegram owner, etc.

    // Step 7: Heartbeat
    pub heartbeat: HeartbeatSettings,        // enabled, interval, notify

    // Advanced (not in wizard, set via `ironclaw config set`)
    pub agent: AgentSettings,
    pub wasm: WasmSettings,
    pub sandbox: SandboxSettings,
    pub safety: SafetySettings,
    pub builder: BuilderSettings,
}

KeySource enum: Keychain | Env | None

---

Secrets Flow

SecretsContext

Thin wrapper for setup-time secret operations:

pub struct SecretsContext {
    store: Arc<dyn SecretsStore>,
    user_id: String,
}

Created by init_secrets_context() which:

1. Gets SecretsCrypto from self.secrets_crypto or loads from keychain/env
2. Creates the appropriate backend store:
   • If both features compiled: respects self.settings.database_backend
   • Tries selected backend first, falls back to the other
3. Returns SecretsContext wrapping the store

Secret Storage

Secrets are encrypted with AES-256-GCM using the master key, then stored in the database secrets table. The wizard writes secrets like:

telegram_bot_token    → encrypted bot token
telegram_webhook_secret → encrypted webhook HMAC secret
anthropic_api_key     → encrypted API key

---

Prompt Utilities

Module: prompts.rs

Function	Description
select_one(label, options)	Numbered single-choice menu
select_many(label, options, defaults)	Checkbox multi-select (raw terminal mode)
input(label)	Single line text input
optional_input(label, hint)	Text input that can be empty
secret_input(label)	Hidden input (shows * per char), returns SecretString
confirm(label, default)	[Y/n] or [y/N] prompt
print_header(text)	Bold section header with underline
print_step(n, total, text)	[1/7] Step Name
print_success(text)	Green ✓ prefix (ANSI color), message in default color
print_error(text)	Red ✗ prefix (ANSI color), message in default color
print_info(text)	Blue ℹ prefix (ANSI color), message in default color

select_many uses crossterm raw mode for arrow key navigation. Must properly restore terminal state on all exit paths.

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Platform Caveats

macOS Keychain

• get_generic_password() triggers system dialogs (unlock + authorize)
• Two dialogs per call is normal, not a bug
• Cache the result after first access to avoid repeat prompts
• Never probe keychain in read-only commands (status, --help)
• Service name: "ironclaw", account: "master_key"

Linux Secret Service

• Uses GNOME Keyring or KWallet via secret-service crate
• May need gnome-keyring daemon running
• Collection unlock may prompt for password

Remote Server Authentication

On remote/VPS servers, the browser-based OAuth flow for NEAR AI may not work because http://127.0.0.1:9876 is unreachable from the user's local browser.

Solutions:

1. NEAR AI Cloud API key (option 4 in auth menu): Get an API key from https://cloud.near.ai and paste it into the terminal. No local listener is needed. The key is saved to ~/.ironclaw/.env and the encrypted secrets store. Uses the OpenAI-compatible ChatCompletions API mode.

2. Custom callback URL: Set IRONCLAW_OAUTH_CALLBACK_URL to a publicly accessible URL (e.g., via SSH tunnel or reverse proxy) that forwards to port 9876 on the server:

   export IRONCLAW_OAUTH_CALLBACK_URL=https://myserver.example.com:9876
   

The callback_url() function in oauth_defaults.rs checks this env var and falls back to http://127.0.0.1:{OAUTH_CALLBACK_PORT}.

URL Passwords

• # is common in URL-encoded passwords (%23 decoded)
• .env values must be double-quoted to preserve #
• Display masked: postgres://user:****@host/db

Telegram API

• Bot token format: 123456:ABC-DEF...
• Token goes in URL path: https://api.telegram.org/bot{TOKEN}/method
• Webhook secret header: X-Telegram-Bot-Api-Secret-Token
• Owner binding polls getUpdates (must delete webhook first)

---

Testing

Tests live in mod tests {} at the bottom of each file.

What to test when modifying setup:

• Settings round-trip: to_db_map() then from_db_map() preserves values
• Bootstrap .env: dotenvy can parse what save_bootstrap_env() writes
• Model fetchers: static fallback works when API is unreachable
• Channel discovery: handles missing dir, invalid JSON, deduplication
• Prompt functions: not tested (interactive I/O), but ensure error paths don't panic

Run setup tests:

cargo test --lib -- setup
cargo test --lib -- bootstrap

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Modification Checklist

When changing the onboarding flow:

1. Update this README first with the intended behavior change
2. If adding a new wizard step:
   • Add to the step enum in run(), adjust total_steps
   • Add corresponding settings fields to Settings
   • Add to_db_map / from_db_map serialization
   • If the setting is needed before DB connection, add to save_bootstrap_env()
3. If adding a new provider or channel:
   • Add to the selection menu in the appropriate step
   • Add authentication flow (API key or OAuth)
   • Add model fetcher with static fallback + 5s timeout
4. If touching keychain:
   • Cache the result, never call get_master_key() twice
   • Test on macOS (dialog behavior differs from Linux)
5. If touching secrets:
   • Ensure init_secrets_context() respects the selected database backend
   • Test with both postgres and libsql features
6. Run the full shipping checklist:

   cargo fmt
   cargo clippy --all --benches --tests --examples --all-features -- -D warnings
   cargo test --lib -- setup bootstrap
   

7. Test a fresh onboarding: rm -rf ~/.ironclaw && cargo run