274 lines
9.3 KiB
YAML
274 lines
9.3 KiB
YAML
edge:
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id: "edge-example"
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name: "Example Edge"
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server:
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listen: "0.0.0.0:9090"
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# advertise_host is the host other components (Node bootstrap, OpenAI client)
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# use to reach this edge. Empty means resolve automatically from interfaces.
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advertise_host: ""
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# bootstrap is the artifact/bootstrap channel iop-edge can render in
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# bootstrap commands and `env` output. Leave artifact_base_url empty until a
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# field channel is provisioned.
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bootstrap:
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artifact_base_url: ""
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tls:
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enabled: false
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logging:
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level: "info"
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pretty: true
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# path is the edge log file. Empty falls back to <binary-dir>/logs/edge.log
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# for the bundled dev binary; explicit values win.
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path: ""
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metrics:
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port: 19092
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# control_plane connects this Edge to a Control Plane instance.
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# Set enabled: true and wire_addr to the Control Plane TCP endpoint to
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# activate the outbound connector. Leave enabled: false (default) for
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# standalone / local-only Edge deployments.
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control_plane:
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enabled: false
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wire_addr: ""
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reconnect_interval_sec: 5
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# refresh enables the Edge-local admin API used by `iop-edge config refresh`.
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# Keep it loopback-only unless an operator-specific access control layer is added.
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refresh:
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enabled: false
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listen: "127.0.0.1:19093"
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a2a:
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enabled: false
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listen: "0.0.0.0:8081"
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path: "/a2a"
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node: ""
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adapter: "cli"
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session_id: "a2a"
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timeout_sec: 120
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bearer_token: ""
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openai:
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enabled: false
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listen: "0.0.0.0:18081"
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bearer_token: ""
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node: ""
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adapter: "ollama"
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target: ""
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models: []
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# model_routes is the current compatibility route catalog. It maps external
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# model ids to internal adapter/target routing.
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# When set, /v1/models exposes these ids and /v1/chat/completions + /v1/responses
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# resolve adapter/target per-entry. Entries not matched fall back to target/adapter above.
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# workspace_required: true marks CLI agent routes that require a workspace path from the caller.
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# max_queue and queue_timeout_ms configure the Edge-owned queue policy for the model alias.
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#
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# Provider-pool migration direction:
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# - Keep the external model id as the future models[].id canonical routing key.
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# - Represent a 1:1 model_route as one models[] entry with one provider id.
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# - Use the route target as models[].providers[provider_id], the concrete served model.
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# - Continue sending adapter + target to Node after Edge rewrites the selected provider target.
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# model_routes (legacy/adapter/type reference):
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# - model: "codex"
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# adapter: "cli"
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# target: "codex"
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# workspace_required: true
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# - model: "llama3"
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# adapter: "ollama"
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# target: "llama3:8b"
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# max_queue: 10
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# queue_timeout_ms: 30000
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#
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# # adapter: "vllm" — legacy path: node runs the built-in vllm adapter
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# # (direct HTTP-to-vLLM-engine). Deprecated/discouraged for new deploys.
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# # New deploys should use openai_compat adapter with provider="vllm"
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# # instead, so that auth/headers/options can be configured per-route.
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# - model: "qwen3"
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# adapter: "vllm" # legacy — prefer openai_compat+provider:vllm
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# target: "qwen3-72b"
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# node: "node-gpu-01"
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#
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# - model: "lemonade"
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# adapter: "openai_compat"
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# target: "lemonade-served-model"
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# node: "node-lemonade-01"
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#
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# Legacy/compatibility adapter-backed provider override example:
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# Use only when explicit adapter instance override or compatibility before compilation is required.
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# - alias: "qwen3.6:35b", served model: "nvidia/Qwen3.6-35B-A3B-NVFP4"
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# model_routes:
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# - model: "qwen3.6:35b"
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# adapter: "openai_compat"
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# target: "nvidia/Qwen3.6-35B-A3B-NVFP4"
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# node: "node-vllm-01"
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# max_queue: 10
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# queue_timeout_ms: 30000
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#
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# === Provider-pool (models[] / nodes[].providers[]) example (recommended) ===
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# Top-level models[] defines canonical routing keys and their provider-pool mapping.
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# Each entry id is the external model id; providers map maps provider id → served model.
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# models:
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# - id: "qwen3.6:35b"
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# display_name: "Qwen 3.6 35B"
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# providers:
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# vllm-gpu: "nvidia/Qwen3.6-35B-A3B-NVFP4"
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# ollama-local: "qwen3.6:35b"
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#
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# nodes[].providers[] defines each provider candidate with catalog and execution fields (Provider-First).
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# nodes[].providers[].id is the stable provider identity referenced by models[].providers keys.
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# nodes[].providers[].type — runtime type (vllm, ollama, lemonade, sglang, openai_api, cli).
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# nodes[].providers[].category — "api", "cli", or "local_inference".
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# nodes[].providers[].endpoint / base_url / command — type-specific execution fields.
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# nodes[].providers[].models — served model names this provider can serve.
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# nodes[].providers[].health — observed health state string.
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# nodes[].providers[].capacity — provider-pool max concurrent execution slots; 0 is not dispatchable.
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# nodes[].providers[].max_queue — max queue depth (per-provider).
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# nodes[].providers[].queue_timeout_ms — queue timeout in milliseconds.
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# nodes[].providers[].lifecycle_capabilities — coarse lifecycle capabilities list.
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# nodes[].providers[].enabled — on/off dispatch switch; omit or true = enabled, false = excluded from
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# dispatch pool. Disabled providers appear in status with status=disabled, capacity=0. Does not
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# stop adapter processes. Classified as live-apply (no restart required) on config refresh.
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#
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# Example node with provider pool (Provider-First, recommended):
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# nodes:
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# - id: "node-gpu-01"
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# alias: "gpu-node"
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# providers:
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# - id: "vllm-gpu"
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# type: "vllm"
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# category: "api"
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# endpoint: "http://127.0.0.1:8000/v1"
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# models:
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# - "nvidia/Qwen3.6-35B-A3B-NVFP4"
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# health: "healthy"
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# capacity: 4
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# max_queue: 16
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# queue_timeout_ms: 30000
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# request_timeout_ms: 120000
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# lifecycle_capabilities: ["scale_up", "scale_down"]
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# # enabled: false # set to exclude this provider from dispatch pool (live-apply)
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# - id: "ollama-local"
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# type: "ollama"
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# category: "local_inference"
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# base_url: "http://127.0.0.1:11434"
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# models:
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# - "qwen3.6:35b"
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# health: "healthy"
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# capacity: 2
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# max_queue: 8
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# queue_timeout_ms: 30000
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#
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# # Legacy/compatibility adapter-backed provider override setup (discouraged for new deployments):
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# - id: "node-vllm-01"
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# alias: "vllm-gpu-node"
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# token: "<node-token>"
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# adapters:
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# openai_compat_instances:
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# - name: "vllm-gpu"
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# enabled: true
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# provider: "vllm"
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# endpoint: "http://127.0.0.1:8000/v1"
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# capacity: 4
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# max_queue: 16
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# queue_timeout_ms: 30000
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# request_timeout_ms: 120000
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# providers:
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# - id: "vllm-gpu-legacy"
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# type: "vllm"
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# category: "api"
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# adapter: "vllm-gpu"
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# models:
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# - "nvidia/Qwen3.6-35B-A3B-NVFP4"
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session_id: "openai"
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timeout_sec: 120
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strict_output: true
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strict_stream_buffer: false
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console:
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adapter: "cli"
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target: "codex"
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session_id: "default"
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background: false
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timeout_sec: 240
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nodes:
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# id is the stable node identity; omitting it falls back to an auto UUID (dev only).
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# agent_kind selects the registration kind; omitting it defaults to "generic-node".
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# Allowed values: "generic-node" (default).
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#
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# Provider-First example (recommended):
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- id: "node-example-01"
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alias: "example-node"
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token: "<node-token>"
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agent_kind: "generic-node"
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providers:
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- id: "claude-tui"
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type: "cli"
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category: "cli"
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command: "claude"
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args:
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- "--dangerously-skip-permissions"
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env:
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- "TERM=xterm-256color"
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mode: "persistent-lazy"
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capacity: 1
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- id: "codex"
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type: "cli"
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category: "cli"
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command: "codex"
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args:
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- "app-server"
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mode: "codex-app-server"
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capacity: 1
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- id: "codex-exec"
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type: "cli"
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category: "cli"
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command: "codex"
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args:
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- "exec"
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- "--json"
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resume_args:
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- "exec"
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- "resume"
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- "--json"
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output_format: "codex-json"
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mode: "codex-exec"
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capacity: 1
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# Legacy adapters configuration (compat override example):
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# adapters:
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# cli:
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# enabled: true
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# profiles:
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# claude-tui:
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# command: "claude"
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# args:
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# - "--dangerously-skip-permissions"
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# env:
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# - "TERM=xterm-256color"
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# persistent: true
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# terminal: true
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# response_idle_timeout_ms: 5000
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# startup_idle_timeout_ms: 5000
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# mode: "persistent-lazy"
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# codex:
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# command: "codex"
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# args:
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# - "app-server"
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# mode: "codex-app-server"
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# codex-exec:
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# command: "codex"
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# args:
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# - "exec"
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# - "--json"
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# resume_args:
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# - "exec"
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# - "resume"
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# - "--json"
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# output_format: "codex-json"
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# mode: "codex-exec"
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runtime:
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concurrency: 1
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