Security & Auth Configuration

Configuration for external authentication, Vertex AI, OAuth, auth profiles, tool policies, proactive memory, context engine, audit logging, health checks, plugins, prompt intelligence, and environment variables reference.

[external_auth]

Configures OAuth2/OIDC external authentication, allowing users to log in via identity providers like Google, GitHub, Okta, Auth0, or Keycloak.

[external_auth]
enabled = true
issuer_url = "https://accounts.google.com"
client_id = "your-client-id.apps.googleusercontent.com"
client_secret_env = "LIBREFANG_OAUTH_CLIENT_SECRET"
redirect_url = "http://127.0.0.1:4545/api/auth/callback"
scopes = ["openid", "profile", "email"]
allowed_domains = ["example.com"]
session_ttl_secs = 86400

For multi-provider setups, use [[external_auth.providers]] with fields: id, display_name, issuer_url, auth_url, token_url, userinfo_url, jwks_uri, client_id, client_secret_env, redirect_url, scopes, allowed_domains, audience.

[terminal]

Configures access control for the interactive terminal WebSocket endpoint.

[terminal]
enabled = true
allow_remote = false
allowed_origins = ["https://dashboard.example.com"]
tmux_enabled = true
max_windows = 16
# tmux_binary_path = "/usr/local/bin/tmux"

Notes:

• Missing Origin is allowed for non-browser clients.
• allow_remote = true does not disable auth; if API keys or dashboard credentials are configured, remote clients still need valid auth.
• Prefer explicit HTTPS origins over "*" for browser access.
• The ws_terminal_messages_per_minute rate limit (default: 3600) in the [rate_limit] section controls per-connection WebSocket message throughput for interactive terminal sessions.

[vertex_ai]

Configures Google Cloud Vertex AI as an LLM provider.

[vertex_ai]
project_id = "my-gcp-project"
region = "us-central1"
credentials_path = "/path/to/service-account.json"

Credentials are resolved in this order:

1. credentials_path in config (JSON string or file path)
2. VERTEX_AI_SERVICE_ACCOUNT_JSON env var
3. GOOGLE_APPLICATION_CREDENTIALS env var (file path)
4. gcloud auth print-access-token CLI fallback

[oauth]

Configures OAuth client IDs for PKCE (Proof Key for Code Exchange) flows used by the dashboard.

[oauth]
google_client_id = "your-google-client-id.apps.googleusercontent.com"
github_client_id = "your-github-app-client-id"
microsoft_client_id = "your-azure-app-client-id"
slack_client_id = "your-slack-app-client-id"

[auth_profiles]

Configures multiple API key profiles per provider to enable key rotation when one key is rate-limited or exhausted.

[auth_profiles]
anthropic = [
  {name = "primary", api_key_env = "ANTHROPIC_API_KEY_1", priority = 0},
  {name = "secondary", api_key_env = "ANTHROPIC_API_KEY_2", priority = 1},
]
openai = [
  {name = "main", api_key_env = "OPENAI_API_KEY", priority = 0},
]

The value is a map from provider name to a list of AuthProfile objects:

[tool_policy]

Configures global tool access rules, groups, and recursion depth limits. References the ToolPolicy type.

[tool_policy]
subagent_max_depth = 10
subagent_max_concurrent = 5

[[tool_policy.global_rules]]
pattern = "shell_*"
effect = "deny"

[[tool_policy.groups]]
name = "web_tools"
tools = ["web_search", "web_fetch"]

ToolPolicyRule fields:

ToolGroup fields:

[proactive_memory]

Configures proactive memory extraction (mem0-style automatic memory management). References the ProactiveMemoryConfig type.

[proactive_memory]
enabled = true
auto_memorize = true
auto_retrieve = true
max_retrieve = 10
extraction_threshold = 0.7
# extraction_model = "gpt-4o-mini"             # uses default provider
# extraction_model = "anthropic/claude-haiku-4" # targets a specific provider
# extraction_model = "anthropic:claude-haiku-4" # colon form also works
extract_categories = ["user_preference", "important_fact", "task_context", "relationship"]
session_ttl_hours = 24
duplicate_threshold = 0.5
confidence_decay_rate = 0.01
max_memories_per_agent = 1000

[context_engine]

Configures the pluggable context assembly engine that controls how agent memory is recalled and assembled into prompts.

[context_engine]
engine = "default"
# plugin = "qdrant-recall"    # resolves to ~/.librefang/plugins/qdrant-recall/

[context_engine.hooks]
# ingest = "~/.librefang/scripts/my_recall.py"
# after_turn = "~/.librefang/scripts/my_indexer.py"
# runtime = "python"   # python (default) | v | node | deno | go | native

[[context_engine.plugin_registries]]
name = "Official"
github_repo = "librefang/librefang-registry"

[audit]

Configures audit log retention.

[audit]
retention_days = 90

[health_check]

Configures periodic health checks for LLM providers.

[health_check]
health_check_interval_secs = 60

[plugins]

Configures additional plugin registries to search for installable context engine plugins.

[plugins]
plugin_registries = ["acme-corp/librefang-plugins"]

[prompt_intelligence]

Configures prompt versioning and A/B experiment support. When enabled, LibreFang automatically tracks prompt version history and supports running A/B experiments to compare prompt variants. See the Prompt Intelligence guide for full documentation.

[prompt_intelligence]
enabled = false
hash_prompts = true
max_versions_per_agent = 50

Environment Variables

Complete table of all environment variables referenced by the configuration. None of these are read by the config file itself -- they are read at runtime by the kernel and channel adapters.

LLM Provider Keys

Web Search Keys

Channel Tokens