config

config

Configuration loading, hardware detection, and engine recommendation.

User configuration lives at ~/.openjarvis/config.toml. load_config() detects hardware, fills sensible defaults, then overlays any user overrides found in the TOML file.

Classes

GpuInfo dataclass

GpuInfo(vendor: str = '', name: str = '', vram_gb: float = 0.0, compute_capability: str = '', count: int = 0)

Detected GPU metadata.

HardwareInfo dataclass

HardwareInfo(platform: str = '', cpu_brand: str = '', cpu_count: int = 0, ram_gb: float = 0.0, gpu: Optional[GpuInfo] = None)

Detected system hardware.

OllamaEngineConfig dataclass

OllamaEngineConfig(host: str = '')

Per-engine config for Ollama.

VLLMEngineConfig dataclass

VLLMEngineConfig(host: str = 'http://localhost:8000')

Per-engine config for vLLM.

SGLangEngineConfig dataclass

SGLangEngineConfig(host: str = 'http://localhost:30000')

Per-engine config for SGLang.

LlamaCppEngineConfig dataclass

LlamaCppEngineConfig(host: str = 'http://localhost:8080', binary_path: str = '')

Per-engine config for llama.cpp.

MLXEngineConfig dataclass

MLXEngineConfig(host: str = 'http://localhost:8080')

Per-engine config for MLX.

LMStudioEngineConfig dataclass

LMStudioEngineConfig(host: str = 'http://localhost:1234')

Per-engine config for LM Studio.

ExoEngineConfig dataclass

ExoEngineConfig(host: str = 'http://localhost:52415')

Per-engine config for Exo.

NexaEngineConfig dataclass

NexaEngineConfig(host: str = 'http://localhost:18181', device: str = '')

Per-engine config for Nexa.

UzuEngineConfig dataclass

UzuEngineConfig(host: str = 'http://localhost:8000')

Per-engine config for Uzu.

AppleFmEngineConfig dataclass

AppleFmEngineConfig(host: str = 'http://localhost:8079')

Per-engine config for Apple Foundation Models.

EngineConfig dataclass

EngineConfig(default: str = 'ollama', ollama: OllamaEngineConfig = OllamaEngineConfig(), vllm: VLLMEngineConfig = VLLMEngineConfig(), sglang: SGLangEngineConfig = SGLangEngineConfig(), llamacpp: LlamaCppEngineConfig = LlamaCppEngineConfig(), mlx: MLXEngineConfig = MLXEngineConfig(), lmstudio: LMStudioEngineConfig = LMStudioEngineConfig(), exo: ExoEngineConfig = ExoEngineConfig(), nexa: NexaEngineConfig = NexaEngineConfig(), uzu: UzuEngineConfig = UzuEngineConfig(), apple_fm: AppleFmEngineConfig = AppleFmEngineConfig())

Inference engine settings with nested per-engine configs.

Attributes

ollama_host property writable

ollama_host: str

Deprecated: use engine.ollama.host.

vllm_host property writable

vllm_host: str

Deprecated: use engine.vllm.host.

llamacpp_host property writable

llamacpp_host: str

Deprecated: use engine.llamacpp.host.

llamacpp_path property writable

llamacpp_path: str

Deprecated: use engine.llamacpp.binary_path.

sglang_host property writable

sglang_host: str

Deprecated: use engine.sglang.host.

mlx_host property writable

mlx_host: str

Deprecated: use engine.mlx.host.

lmstudio_host property writable

lmstudio_host: str

Deprecated: use engine.lmstudio.host.

exo_host property writable

exo_host: str

Deprecated: use engine.exo.host.

nexa_host property writable

nexa_host: str

Deprecated: use engine.nexa.host.

uzu_host property writable

uzu_host: str

Deprecated: use engine.uzu.host.

apple_fm_host property writable

apple_fm_host: str

Deprecated: use engine.apple_fm.host.

IntelligenceConfig dataclass

IntelligenceConfig(default_model: str = '', fallback_model: str = '', model_path: str = '', checkpoint_path: str = '', quantization: str = 'none', preferred_engine: str = '', provider: str = '', temperature: float = 0.7, max_tokens: int = 1024, top_p: float = 0.9, top_k: int = 40, repetition_penalty: float = 1.0, stop_sequences: str = '')

The model — identity, paths, quantization, and generation defaults.

RoutingLearningConfig dataclass

RoutingLearningConfig(policy: str = 'heuristic', min_samples: int = 5)

Routing sub-policy config within Learning.

IntelligenceLearningConfig dataclass

IntelligenceLearningConfig(policy: str = 'none')

Intelligence sub-policy config within Learning.

AgentLearningConfig dataclass

AgentLearningConfig(policy: str = 'none', max_icl_examples: int = 20, advisor_confidence_threshold: float = 0.7)

Agent sub-policy config within Learning.

MetricsConfig dataclass

MetricsConfig(accuracy_weight: float = 0.6, latency_weight: float = 0.2, cost_weight: float = 0.1, efficiency_weight: float = 0.1)

Reward / optimization metric weights.

LearningConfig dataclass

LearningConfig(enabled: bool = False, update_interval: int = 100, auto_update: bool = False, routing: RoutingLearningConfig = RoutingLearningConfig(), intelligence: IntelligenceLearningConfig = IntelligenceLearningConfig(), agent: AgentLearningConfig = AgentLearningConfig(), metrics: MetricsConfig = MetricsConfig(), training_enabled: bool = False, training_schedule: str = '', lora_rank: int = 16, lora_alpha: int = 32, min_sft_pairs: int = 50, min_improvement: float = 0.02)

Learning system settings with per-primitive sub-policies.

Attributes

default_policy property writable

default_policy: str

Deprecated: use learning.routing.policy.

intelligence_policy property writable

intelligence_policy: str

Deprecated: use learning.intelligence.policy.

agent_policy property writable

agent_policy: str

Deprecated: use learning.agent.policy.

reward_weights property writable

reward_weights: str

Deprecated: use learning.metrics.*.

StorageConfig dataclass

StorageConfig(default_backend: str = 'sqlite', db_path: str = str(DEFAULT_CONFIG_DIR / 'memory.db'), context_top_k: int = 5, context_min_score: float = 0.1, context_max_tokens: int = 2048, chunk_size: int = 512, chunk_overlap: int = 64)

Storage (memory) backend settings.

MCPConfig dataclass

MCPConfig(enabled: bool = True, servers: str = '')

MCP (Model Context Protocol) settings.

BrowserConfig dataclass

BrowserConfig(headless: bool = True, timeout_ms: int = 30000, viewport_width: int = 1280, viewport_height: int = 720)

Browser automation settings (Playwright).

ToolsConfig dataclass

ToolsConfig(storage: StorageConfig = StorageConfig(), mcp: MCPConfig = MCPConfig(), browser: BrowserConfig = BrowserConfig(), enabled: str = '')

Tools primitive settings — wraps storage and MCP configuration.

AgentConfig dataclass

AgentConfig(default_agent: str = 'simple', max_turns: int = 10, tools: str = '', objective: str = '', system_prompt: str = '', system_prompt_path: str = '', context_from_memory: bool = True)

Agent harness settings — orchestration, tools, system prompt.

Attributes

default_tools property writable

default_tools: str

Deprecated: use agent.tools.

ServerConfig dataclass

ServerConfig(host: str = '0.0.0.0', port: int = 8000, agent: str = 'orchestrator', model: str = '', workers: int = 1)

API server settings.

TelemetryConfig dataclass

TelemetryConfig(enabled: bool = True, db_path: str = str(DEFAULT_CONFIG_DIR / 'telemetry.db'), gpu_metrics: bool = False, gpu_poll_interval_ms: int = 50, energy_vendor: str = '', warmup_samples: int = 0, steady_state_window: int = 5, steady_state_threshold: float = 0.05)

Telemetry persistence settings.

TracesConfig dataclass

TracesConfig(enabled: bool = False, db_path: str = str(DEFAULT_CONFIG_DIR / 'traces.db'))

Trace system settings.

TelegramChannelConfig dataclass

TelegramChannelConfig(bot_token: str = '', allowed_chat_ids: str = '', parse_mode: str = 'Markdown')

Per-channel config for Telegram.

DiscordChannelConfig dataclass

DiscordChannelConfig(bot_token: str = '')

Per-channel config for Discord.

SlackChannelConfig dataclass

SlackChannelConfig(bot_token: str = '', app_token: str = '')

Per-channel config for Slack.

WebhookChannelConfig dataclass

WebhookChannelConfig(url: str = '', secret: str = '', method: str = 'POST')

Per-channel config for generic webhooks.

EmailChannelConfig dataclass

EmailChannelConfig(smtp_host: str = '', smtp_port: int = 587, imap_host: str = '', imap_port: int = 993, username: str = '', password: str = '', use_tls: bool = True)

Per-channel config for email (SMTP/IMAP).

WhatsAppChannelConfig dataclass

WhatsAppChannelConfig(access_token: str = '', phone_number_id: str = '')

Per-channel config for WhatsApp Cloud API.

SignalChannelConfig dataclass

SignalChannelConfig(api_url: str = '', phone_number: str = '')

Per-channel config for Signal (via signal-cli REST API).

GoogleChatChannelConfig dataclass

GoogleChatChannelConfig(webhook_url: str = '')

Per-channel config for Google Chat webhooks.

IRCChannelConfig dataclass

IRCChannelConfig(server: str = '', port: int = 6667, nick: str = '', password: str = '', use_tls: bool = False)

Per-channel config for IRC.

WebChatChannelConfig dataclass

WebChatChannelConfig()

Per-channel config for in-memory webchat.

TeamsChannelConfig dataclass

TeamsChannelConfig(app_id: str = '', app_password: str = '', service_url: str = '')

Per-channel config for Microsoft Teams (Bot Framework).

MatrixChannelConfig dataclass

MatrixChannelConfig(homeserver: str = '', access_token: str = '')

Per-channel config for Matrix.

MattermostChannelConfig dataclass

MattermostChannelConfig(url: str = '', token: str = '')

Per-channel config for Mattermost.

FeishuChannelConfig dataclass

FeishuChannelConfig(app_id: str = '', app_secret: str = '')

Per-channel config for Feishu (Lark).

BlueBubblesChannelConfig dataclass

BlueBubblesChannelConfig(url: str = '', password: str = '')

Per-channel config for BlueBubbles (iMessage bridge).

WhatsAppBaileysChannelConfig dataclass

WhatsAppBaileysChannelConfig(auth_dir: str = '', assistant_name: str = 'Jarvis', assistant_has_own_number: bool = False)

Per-channel config for WhatsApp via Baileys protocol.

ChannelConfig dataclass

ChannelConfig(enabled: bool = False, default_channel: str = '', default_agent: str = 'simple', telegram: TelegramChannelConfig = TelegramChannelConfig(), discord: DiscordChannelConfig = DiscordChannelConfig(), slack: SlackChannelConfig = SlackChannelConfig(), webhook: WebhookChannelConfig = WebhookChannelConfig(), email: EmailChannelConfig = EmailChannelConfig(), whatsapp: WhatsAppChannelConfig = WhatsAppChannelConfig(), signal: SignalChannelConfig = SignalChannelConfig(), google_chat: GoogleChatChannelConfig = GoogleChatChannelConfig(), irc: IRCChannelConfig = IRCChannelConfig(), webchat: WebChatChannelConfig = WebChatChannelConfig(), teams: TeamsChannelConfig = TeamsChannelConfig(), matrix: MatrixChannelConfig = MatrixChannelConfig(), mattermost: MattermostChannelConfig = MattermostChannelConfig(), feishu: FeishuChannelConfig = FeishuChannelConfig(), bluebubbles: BlueBubblesChannelConfig = BlueBubblesChannelConfig(), whatsapp_baileys: WhatsAppBaileysChannelConfig = WhatsAppBaileysChannelConfig())

Channel messaging settings.

CapabilitiesConfig dataclass

CapabilitiesConfig(enabled: bool = False, policy_path: str = '')

RBAC capability system settings.

SecurityConfig dataclass

SecurityConfig(enabled: bool = True, scan_input: bool = True, scan_output: bool = True, mode: str = 'warn', secret_scanner: bool = True, pii_scanner: bool = True, audit_log_path: str = str(DEFAULT_CONFIG_DIR / 'audit.db'), enforce_tool_confirmation: bool = True, merkle_audit: bool = True, signing_key_path: str = '', ssrf_protection: bool = True, rate_limit_enabled: bool = False, rate_limit_rpm: int = 60, rate_limit_burst: int = 10, capabilities: CapabilitiesConfig = CapabilitiesConfig())

Security guardrails settings.

SandboxConfig dataclass

SandboxConfig(enabled: bool = False, image: str = 'openjarvis-sandbox:latest', timeout: int = 300, workspace: str = '', mount_allowlist_path: str = '', max_concurrent: int = 5, runtime: str = 'docker', wasm_fuel_limit: int = 1000000, wasm_memory_limit_mb: int = 256)

Container sandbox settings.

SchedulerConfig dataclass

SchedulerConfig(enabled: bool = False, poll_interval: int = 60, db_path: str = '')

Task scheduler settings.

WorkflowConfig dataclass

WorkflowConfig(enabled: bool = False, max_parallel: int = 4, default_node_timeout: int = 300)

Workflow engine settings.

SessionConfig dataclass

SessionConfig(enabled: bool = False, max_age_hours: float = 24.0, consolidation_threshold: int = 100, db_path: str = str(DEFAULT_CONFIG_DIR / 'sessions.db'))

Cross-channel session settings.

A2AConfig dataclass

A2AConfig(enabled: bool = False)

Agent-to-Agent protocol settings.

OperatorsConfig dataclass

OperatorsConfig(enabled: bool = False, manifests_dir: str = '~/.openjarvis/operators', auto_activate: str = '')

Operator lifecycle settings.

SpeechConfig dataclass

SpeechConfig(backend: str = 'auto', model: str = 'base', language: str = '', device: str = 'auto', compute_type: str = 'float16')

Speech-to-text settings.

OptimizeConfig dataclass

OptimizeConfig(max_trials: int = 20, early_stop_patience: int = 5, optimizer_model: str = 'claude-sonnet-4-6', optimizer_provider: str = 'anthropic', benchmark: str = '', max_samples: int = 50, judge_model: str = 'gpt-5-mini-2025-08-07', db_path: str = str(DEFAULT_CONFIG_DIR / 'optimize.db'))

Configuration optimization settings.

AgentManagerConfig dataclass

AgentManagerConfig(enabled: bool = True, db_path: str = str(DEFAULT_CONFIG_DIR / 'agents.db'))

Persistent agent manager settings.

JarvisConfig dataclass

JarvisConfig(hardware: HardwareInfo = HardwareInfo(), engine: EngineConfig = EngineConfig(), intelligence: IntelligenceConfig = IntelligenceConfig(), learning: LearningConfig = LearningConfig(), tools: ToolsConfig = ToolsConfig(), agent: AgentConfig = AgentConfig(), server: ServerConfig = ServerConfig(), telemetry: TelemetryConfig = TelemetryConfig(), traces: TracesConfig = TracesConfig(), channel: ChannelConfig = ChannelConfig(), security: SecurityConfig = SecurityConfig(), sandbox: SandboxConfig = SandboxConfig(), scheduler: SchedulerConfig = SchedulerConfig(), workflow: WorkflowConfig = WorkflowConfig(), sessions: SessionConfig = SessionConfig(), a2a: A2AConfig = A2AConfig(), operators: OperatorsConfig = OperatorsConfig(), speech: SpeechConfig = SpeechConfig(), optimize: OptimizeConfig = OptimizeConfig(), agent_manager: AgentManagerConfig = AgentManagerConfig())

Top-level configuration for OpenJarvis.

Attributes

memory property writable

memory: StorageConfig

Backward-compatible accessor — canonical location is tools.storage.

Functions

detect_hardware

detect_hardware() -> HardwareInfo

Auto-detect hardware capabilities with graceful fallbacks.

Source code in src/openjarvis/core/config.py

def detect_hardware() -> HardwareInfo:
    """Auto-detect hardware capabilities with graceful fallbacks."""
    gpu = _detect_nvidia_gpu() or _detect_amd_gpu() or _detect_apple_gpu()
    return HardwareInfo(
        platform=platform.system().lower(),
        cpu_brand=_detect_cpu_brand(),
        cpu_count=os.cpu_count() or 1,
        ram_gb=_total_ram_gb(),
        gpu=gpu,
    )

recommend_engine

recommend_engine(hw: HardwareInfo) -> str

Suggest the best inference engine for the detected hardware.

Source code in src/openjarvis/core/config.py

def recommend_engine(hw: HardwareInfo) -> str:
    """Suggest the best inference engine for the detected hardware."""
    gpu = hw.gpu
    if gpu is None:
        return "llamacpp"
    if gpu.vendor == "apple":
        return "mlx"
    if gpu.vendor == "nvidia":
        # Datacenter cards (A100, H100, L40, etc.) → vllm; consumer → ollama
        datacenter_keywords = ("A100", "H100", "H200", "L40", "A10", "A30")
        if any(kw in gpu.name for kw in datacenter_keywords):
            return "vllm"
        return "ollama"
    if gpu.vendor == "amd":
        return "vllm"
    return "llamacpp"

recommend_model

recommend_model(hw: HardwareInfo, engine: str) -> str

Suggest the largest Qwen3.5 model that fits the detected hardware.

Uses llmfit-style VRAM estimation: Q4_K_M quantization is ~0.5 bytes/param with 10% overhead. For MoE models Ollama loads full model weights, so we use parameter_count_b (total), not active_parameter_count_b.

Source code in src/openjarvis/core/config.py

def recommend_model(hw: HardwareInfo, engine: str) -> str:
    """Suggest the largest Qwen3.5 model that fits the detected hardware.

    Uses llmfit-style VRAM estimation: Q4_K_M quantization is ~0.5 bytes/param
    with 10% overhead.  For MoE models Ollama loads full model weights, so we
    use ``parameter_count_b`` (total), not ``active_parameter_count_b``.
    """
    from openjarvis.intelligence.model_catalog import BUILTIN_MODELS

    # Determine available memory in GB
    gpu = hw.gpu
    if gpu and gpu.vram_gb > 0:
        available_gb = gpu.vram_gb * max(gpu.count, 1) * 0.9
    elif hw.ram_gb > 0:
        available_gb = (hw.ram_gb - 4) * 0.8
    else:
        return ""

    # Filter Qwen3.5 models compatible with the chosen engine
    candidates = [
        spec
        for spec in BUILTIN_MODELS
        if spec.provider == "alibaba"
        and spec.model_id.startswith("qwen3.5:")
        and engine in spec.supported_engines
    ]

    # Sort by parameter count descending — pick the largest that fits
    candidates.sort(key=lambda s: s.parameter_count_b, reverse=True)

    for spec in candidates:
        estimated_gb = spec.parameter_count_b * 0.5 * 1.1
        if estimated_gb <= available_gb:
            return spec.model_id

    return ""

load_config

load_config(path: Optional[Path] = None) -> JarvisConfig

Detect hardware, build defaults, overlay TOML overrides.

PARAMETER	DESCRIPTION
path	Explicit config file. If not set, uses OPENJARVIS_CONFIG when set, otherwise ~/.openjarvis/config.toml. TYPE: Optional[Path] DEFAULT: None

Source code in src/openjarvis/core/config.py

def load_config(path: Optional[Path] = None) -> JarvisConfig:
    """Detect hardware, build defaults, overlay TOML overrides.

    Parameters
    ----------
    path:
        Explicit config file. If not set, uses ``OPENJARVIS_CONFIG`` when set,
        otherwise ``~/.openjarvis/config.toml``.
    """
    hw = detect_hardware()
    cfg = JarvisConfig(hardware=hw)
    cfg.engine.default = recommend_engine(hw)

    if path is not None:
        config_path = Path(path)
    elif os.environ.get("OPENJARVIS_CONFIG"):
        config_path = Path(os.environ["OPENJARVIS_CONFIG"]).expanduser().resolve()
    else:
        config_path = DEFAULT_CONFIG_PATH
    if config_path.exists():
        with open(config_path, "rb") as fh:
            data = tomllib.load(fh)

        # Run backward-compat migrations before applying
        _migrate_toml_data(data, cfg)

        # All top-level sections — recursive _apply_toml_section handles
        # nested sub-configs (engine.ollama, learning.routing, channel.*, etc.)
        top_sections = (
            "engine", "intelligence", "learning", "agent",
            "server", "telemetry", "traces", "security",
            "channel", "tools", "sandbox", "scheduler",
            "workflow", "sessions", "a2a", "operators",
            "speech", "optimize", "agent_manager",
        )
        for section_name in top_sections:
            if section_name in data:
                _apply_toml_section(
                    getattr(cfg, section_name), data[section_name],
                )

        # Memory: accept [memory] (old) → maps to tools.storage
        if "memory" in data:
            _apply_toml_section(cfg.tools.storage, data["memory"])

    return cfg

generate_minimal_toml

generate_minimal_toml(hw: HardwareInfo) -> str

Render a minimal TOML config with only essential settings.

Source code in src/openjarvis/core/config.py

def generate_minimal_toml(hw: HardwareInfo) -> str:
    """Render a minimal TOML config with only essential settings."""
    engine = recommend_engine(hw)
    model = recommend_model(hw, engine)
    gpu_comment = ""
    if hw.gpu:
        mem_label = (
            "unified memory" if hw.gpu.vendor == "apple" else "VRAM"
        )
        gpu_comment = (
            f"\n# GPU: {hw.gpu.name}"
            f" ({hw.gpu.vram_gb} GB {mem_label})"
        )
    return f"""\
# OpenJarvis configuration
# Hardware: {hw.cpu_brand} ({hw.cpu_count} cores, {hw.ram_gb} GB RAM){gpu_comment}
# Full reference config: jarvis init --full

[engine]
default = "{engine}"

[intelligence]
default_model = "{model}"

[agent]
default_agent = "simple"

[tools]
enabled = ["code_interpreter", "web_search", "file_read", "shell_exec"]
"""

generate_default_toml

generate_default_toml(hw: HardwareInfo) -> str

Render a commented TOML string suitable for ~/.openjarvis/config.toml.

Source code in src/openjarvis/core/config.py

def generate_default_toml(hw: HardwareInfo) -> str:
    """Render a commented TOML string suitable for ``~/.openjarvis/config.toml``."""
    engine = recommend_engine(hw)
    model = recommend_model(hw, engine)
    gpu_line = ""
    if hw.gpu:
        gpu_line = f"# Detected GPU: {hw.gpu.name} ({hw.gpu.vram_gb} GB VRAM)"

    model_comment = ""
    if model:
        model_comment = "  # recommended for your hardware"

    return f"""\
# OpenJarvis configuration
# Generated by `jarvis init`
#
# Hardware: {hw.cpu_brand} ({hw.cpu_count} cores, {hw.ram_gb} GB RAM)
{gpu_line}

[engine]
default = "{engine}"

[engine.ollama]
host = "http://localhost:11434"

[engine.vllm]
host = "http://localhost:8000"

[engine.sglang]
host = "http://localhost:30000"

# [engine.llamacpp]
# host = "http://localhost:8080"
# binary_path = ""

[engine.mlx]
host = "http://localhost:8080"

# [engine.lmstudio]
# host = "http://localhost:1234"

# [engine.exo]
# host = "http://localhost:52415"

# [engine.nexa]
# host = "http://localhost:18181"
# device = ""  # cpu, gpu, npu

# [engine.uzu]
# host = "http://localhost:8080"

# [engine.apple_fm]
# host = "http://localhost:8079"

[intelligence]
default_model = "{model}"{model_comment}
fallback_model = ""
# model_path = ""              # Local weights (HF repo, GGUF file, etc.)
# checkpoint_path = ""         # Checkpoint/adapter path
# quantization = "none"        # none, fp8, int8, int4, gguf_q4, gguf_q8
# preferred_engine = ""        # Override engine for this model (e.g., "vllm")
# provider = ""                # local, openai, anthropic, google
temperature = 0.7
max_tokens = 1024
# top_p = 0.9
# top_k = 40
# repetition_penalty = 1.0
# stop_sequences = ""

[agent]
default_agent = "simple"
max_turns = 10
# tools = ""                   # Comma-separated tool names
# objective = ""               # Concise purpose string
# system_prompt = ""           # Inline system prompt
# system_prompt_path = ""      # Path to system prompt file
context_from_memory = true

[tools.storage]
default_backend = "sqlite"

[tools.mcp]
enabled = true

# [tools.browser]
# headless = true
# timeout_ms = 30000
# viewport_width = 1280
# viewport_height = 720

[server]
host = "0.0.0.0"
port = 8000
agent = "orchestrator"

[learning]
enabled = false
update_interval = 100
# auto_update = false

[learning.routing]
policy = "heuristic"
# min_samples = 5

# [learning.intelligence]
# policy = "none"              # "sft" to learn from traces

# [learning.agent]
# policy = "none"              # "agent_advisor" | "icl_updater"

# [learning.metrics]
# accuracy_weight = 0.6
# latency_weight = 0.2
# cost_weight = 0.1
# efficiency_weight = 0.1

[telemetry]
enabled = true
# gpu_metrics = false
# gpu_poll_interval_ms = 50

[traces]
enabled = false

[channel]
enabled = false
default_agent = "simple"

# [channel.telegram]
# bot_token = ""  # Or set TELEGRAM_BOT_TOKEN env var

# [channel.discord]
# bot_token = ""  # Or set DISCORD_BOT_TOKEN env var

# [channel.slack]
# bot_token = ""  # Or set SLACK_BOT_TOKEN env var

# [channel.webhook]
# url = ""

# [channel.whatsapp]
# access_token = ""      # Or set WHATSAPP_ACCESS_TOKEN env var
# phone_number_id = ""   # Or set WHATSAPP_PHONE_NUMBER_ID env var

# [channel.signal]
# api_url = ""            # signal-cli REST API URL
# phone_number = ""       # Or set SIGNAL_PHONE_NUMBER env var

# [channel.google_chat]
# webhook_url = ""        # Or set GOOGLE_CHAT_WEBHOOK_URL env var

# [channel.irc]
# server = ""
# port = 6667
# nick = ""
# use_tls = false

# [channel.teams]
# app_id = ""             # Or set TEAMS_APP_ID env var
# app_password = ""       # Or set TEAMS_APP_PASSWORD env var

# [channel.matrix]
# homeserver = ""         # Or set MATRIX_HOMESERVER env var
# access_token = ""       # Or set MATRIX_ACCESS_TOKEN env var

# [channel.mattermost]
# url = ""                # Or set MATTERMOST_URL env var
# token = ""              # Or set MATTERMOST_TOKEN env var

# [channel.feishu]
# app_id = ""             # Or set FEISHU_APP_ID env var
# app_secret = ""         # Or set FEISHU_APP_SECRET env var

# [channel.bluebubbles]
# url = ""                # Or set BLUEBUBBLES_URL env var
# password = ""           # Or set BLUEBUBBLES_PASSWORD env var

[security]
enabled = true
mode = "warn"
scan_input = true
scan_output = true
secret_scanner = true
pii_scanner = true
enforce_tool_confirmation = true
ssrf_protection = true
# rate_limit_enabled = false
# rate_limit_rpm = 60
# rate_limit_burst = 10

# [sandbox]
# enabled = false
# image = "openjarvis-sandbox:latest"
# timeout = 300
# max_concurrent = 5
# runtime = "docker"

# [scheduler]
# enabled = false
# poll_interval = 60
# db_path = ""                # Defaults to ~/.openjarvis/scheduler.db

# [channel.whatsapp_baileys]
# auth_dir = ""               # Defaults to ~/.openjarvis/whatsapp_auth
# assistant_name = "Jarvis"
# assistant_has_own_number = false
"""