Pioneer Inference API

{
  "openapi": "3.1.0",
  "info": {
    "title": "Pioneer Inference API",
    "version": "1.0.0",
    "description": "Public inference API for Pioneer AI. Covers the Pioneer-native endpoint, OpenAI-compatible endpoints (/v1/chat/completions, /v1/completions, /v1/models), the Anthropic-compatible endpoint (/v1/messages), and inference history. Authenticate with an X-API-Key header (keys begin with pio_sk_).",
    "contact": {
      "name": "Pioneer AI",
      "url": "https://docs.pioneer.ai",
      "email": "support@pioneer.ai"
    }
  },
  "servers": [
    {
      "url": "https://api.pioneer.ai",
      "description": "Production"
    }
  ],
  "security": [
    { "ApiKeyAuth": [] },
    { "BearerAuth": [] }
  ],
  "tags": [
    {
      "name": "inference",
      "description": "Pioneer-native inference endpoint (encoder NER/classification/extraction and decoder text generation)."
    },
    {
      "name": "openai-compat",
      "description": "OpenAI-compatible endpoints. Use with the OpenAI SDK by setting base_url=https://api.pioneer.ai/v1."
    },
    {
      "name": "anthropic-compat",
      "description": "Anthropic-compatible endpoints. Use with the Anthropic SDK by setting base_url=https://api.pioneer.ai."
    },
    {
      "name": "inference-history",
      "description": "List and retrieve past inference records."
    }
  ],
  "paths": {
    "/inference": {
      "post": {
        "operationId": "run_inference",
        "summary": "Run inference (Pioneer native)",
        "description": "Unified inference endpoint for encoder tasks (NER, text classification, JSON extraction) and decoder tasks (text generation). Discriminated by the presence of a `messages` field: include `messages` for decoder generation; use `text` + `schema` for encoder tasks.",
        "tags": ["inference"],
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "oneOf": [
                  { "$ref": "#/components/schemas/EncoderInferenceRequest" },
                  { "$ref": "#/components/schemas/GenerateInferenceRequest" }
                ]
              },
              "examples": {
                "encoder_ner": {
                  "summary": "NER — extract entities",
                  "value": {
                    "model_id": "YOUR_MODEL_ID",
                    "text": "Apple launched the iPhone 16 in San Francisco.",
                    "schema": {
                      "entities": [
                        { "name": "organization" },
                        { "name": "product" },
                        { "name": "location" }
                      ]
                    }
                  }
                },
                "decoder_generate": {
                  "summary": "Decoder — text generation",
                  "value": {
                    "model_id": "YOUR_MODEL_ID",
                    "task": "generate",
                    "messages": [
                      { "role": "user", "content": "Summarize this document in one sentence." }
                    ],
                    "max_tokens": 256,
                    "temperature": 0.7
                  }
                }
              }
            }
          }
        },
        "responses": {
          "200": {
            "description": "Inference result. `type` is `\"encoder\"` for encoder tasks and `\"decoder\"` for generation.",
            "content": {
              "application/json": {
                "schema": {
                  "oneOf": [
                    { "$ref": "#/components/schemas/EncoderInferenceResponse" },
                    { "$ref": "#/components/schemas/GenerateInferenceResponse" }
                  ],
                  "discriminator": {
                    "propertyName": "type",
                    "mapping": {
                      "encoder": "#/components/schemas/EncoderInferenceResponse",
                      "decoder": "#/components/schemas/GenerateInferenceResponse"
                    }
                  }
                }
              }
            }
          },
          "401": { "$ref": "#/components/responses/Unauthorized" },
          "402": { "$ref": "#/components/responses/PaymentRequired" },
          "404": { "$ref": "#/components/responses/ModelNotFound" },
          "422": { "$ref": "#/components/responses/ValidationError" },
          "429": { "$ref": "#/components/responses/RateLimited" }
        }
      }
    },
    "/v1/chat/completions": {
      "post": {
        "operationId": "create_chat_completion",
        "summary": "Chat completions (OpenAI-compatible)",
        "description": "OpenAI-compatible chat completions endpoint. Works with the OpenAI Python SDK by setting `base_url=\"https://api.pioneer.ai/v1\"`. Set `stream=true` for SSE streaming. The response includes a `x_pioneer` extension field with `inference_id` and `routed_model`.",
        "tags": ["openai-compat"],
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": { "$ref": "#/components/schemas/ChatCompletionRequest" },
              "examples": {
                "simple": {
                  "summary": "Simple chat completion",
                  "value": {
                    "model": "Qwen/Qwen3-8B",
                    "messages": [{ "role": "user", "content": "Hello!" }]
                  }
                },
                "streaming": {
                  "summary": "Streaming chat completion",
                  "value": {
                    "model": "Qwen/Qwen3-8B",
                    "messages": [{ "role": "user", "content": "Tell me a short story." }],
                    "stream": true,
                    "max_tokens": 512
                  }
                },
                "pioneer_auto": {
                  "summary": "Use pioneer/auto router",
                  "value": {
                    "model": "pioneer/auto",
                    "messages": [{ "role": "user", "content": "Classify this text." }]
                  }
                }
              }
            }
          }
        },
        "responses": {
          "200": {
            "description": "Chat completion result. Returns `application/json` when `stream=false` (default) and `text/event-stream` SSE when `stream=true`. Each SSE event is `data: {...}\\n\\n` terminated by `data: [DONE]\\n\\n`.",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/ChatCompletionResponse" }
              },
              "text/event-stream": {
                "schema": { "$ref": "#/components/schemas/ChatCompletionStreamChunk" },
                "example": "data: {\"id\":\"chatcmpl-abc\",\"object\":\"chat.completion.chunk\",\"created\":0,\"model\":\"Qwen/Qwen3-8B\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"Hi\"},\"finish_reason\":null}]}\n\ndata: [DONE]\n\n"
              }
            }
          },
          "401": { "$ref": "#/components/responses/Unauthorized" },
          "402": { "$ref": "#/components/responses/PaymentRequired" },
          "404": { "$ref": "#/components/responses/ModelNotFound" },
          "422": { "$ref": "#/components/responses/ValidationError" },
          "429": { "$ref": "#/components/responses/RateLimited" }
        }
      }
    },
    "/v1/completions": {
      "post": {
        "operationId": "create_text_completion",
        "summary": "Text completions (OpenAI-compatible)",
        "description": "OpenAI-compatible legacy text completions endpoint. Accepts a `prompt` string and returns a completion. Set `stream=true` for SSE streaming.",
        "tags": ["openai-compat"],
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": { "$ref": "#/components/schemas/TextCompletionRequest" },
              "examples": {
                "simple": {
                  "summary": "Simple text completion",
                  "value": {
                    "model": "Qwen/Qwen3-8B-Base",
                    "prompt": "The capital of France is",
                    "max_tokens": 32
                  }
                }
              }
            }
          }
        },
        "responses": {
          "200": {
            "description": "Text completion result. `application/json` when `stream=false`, `text/event-stream` SSE when `stream=true`.",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/TextCompletionResponse" }
              },
              "text/event-stream": {
                "schema": { "$ref": "#/components/schemas/TextCompletionStreamChunk" },
                "example": "data: {\"id\":\"cmpl-abc\",\"object\":\"text_completion\",\"created\":0,\"model\":\"Qwen/Qwen3-8B-Base\",\"choices\":[{\"index\":0,\"text\":\" Paris.\",\"finish_reason\":\"stop\"}]}\n\ndata: [DONE]\n\n"
              }
            }
          },
          "401": { "$ref": "#/components/responses/Unauthorized" },
          "402": { "$ref": "#/components/responses/PaymentRequired" },
          "404": { "$ref": "#/components/responses/ModelNotFound" },
          "422": { "$ref": "#/components/responses/ValidationError" },
          "429": { "$ref": "#/components/responses/RateLimited" }
        }
      }
    },
    "/v1/models": {
      "get": {
        "operationId": "list_models",
        "summary": "List available models",
        "description": "Returns the Pioneer model catalog including base encoder models (GLiNER), base decoder LLMs, and any fine-tuned models you have deployed. Compatible with both Anthropic and OpenAI model-list consumers.",
        "tags": ["anthropic-compat"],
        "parameters": [
          {
            "name": "limit",
            "in": "query",
            "schema": { "type": "integer", "minimum": 1 },
            "description": "Maximum number of models to return. Omit for the full catalog."
          },
          {
            "name": "before_id",
            "in": "query",
            "schema": { "type": "string" },
            "description": "Cursor for paginating backward (Anthropic-style)."
          },
          {
            "name": "after_id",
            "in": "query",
            "schema": { "type": "string" },
            "description": "Cursor for paginating forward (Anthropic-style)."
          }
        ],
        "responses": {
          "200": {
            "description": "Combined Anthropic-compatible and OpenAI-compatible model catalog.",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/ModelListResponse" }
              }
            }
          },
          "401": { "$ref": "#/components/responses/Unauthorized" }
        }
      }
    },
    "/v1/messages": {
      "post": {
        "operationId": "create_message",
        "summary": "Messages (Anthropic-compatible)",
        "description": "Anthropic Messages API compatible endpoint. Works with the Anthropic Python SDK by setting `base_url=\"https://api.pioneer.ai\"`. Set `stream=true` for SSE streaming in Anthropic event format. Extended-thinking is supported via the `thinking` field.",
        "tags": ["anthropic-compat"],
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": { "$ref": "#/components/schemas/AnthropicMessagesRequest" },
              "examples": {
                "simple": {
                  "summary": "Simple message",
                  "value": {
                    "model": "claude-sonnet-4-6",
                    "max_tokens": 1024,
                    "messages": [{ "role": "user", "content": "Hello, Claude!" }]
                  }
                },
                "with_system": {
                  "summary": "Message with system prompt",
                  "value": {
                    "model": "claude-sonnet-4-6",
                    "max_tokens": 512,
                    "system": "You are a helpful assistant.",
                    "messages": [{ "role": "user", "content": "What is the weather like?" }]
                  }
                },
                "streaming": {
                  "summary": "Streaming message",
                  "value": {
                    "model": "claude-sonnet-4-6",
                    "max_tokens": 1024,
                    "stream": true,
                    "messages": [{ "role": "user", "content": "Write a haiku about inference routing." }]
                  }
                }
              }
            }
          }
        },
        "responses": {
          "200": {
            "description": "Message response. `application/json` when `stream=false`, `text/event-stream` SSE (Anthropic event format) when `stream=true`.",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/AnthropicMessagesResponse" }
              },
              "text/event-stream": {
                "schema": { "type": "string" },
                "example": "event: message_start\ndata: {\"type\":\"message_start\",\"message\":{\"id\":\"msg_abc\",\"type\":\"message\",\"role\":\"assistant\",\"content\":[],\"model\":\"claude-sonnet-4-6\",\"stop_reason\":null,\"usage\":{\"input_tokens\":10,\"output_tokens\":0}}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"Hello\"}}\n\nevent: message_stop\ndata: {\"type\":\"message_stop\"}\n\n"
              }
            }
          },
          "401": { "$ref": "#/components/responses/Unauthorized" },
          "402": { "$ref": "#/components/responses/PaymentRequired" },
          "404": { "$ref": "#/components/responses/ModelNotFound" },
          "422": { "$ref": "#/components/responses/ValidationError" },
          "429": { "$ref": "#/components/responses/RateLimited" }
        }
      }
    },
    "/inferences": {
      "get": {
        "operationId": "list_inferences",
        "summary": "List inference history",
        "description": "Paginated list of past inference records for the authenticated user's team. Supports filtering by model, project, task type, latency range, and LLM-as-Judge score.",
        "tags": ["inference-history"],
        "parameters": [
          {
            "name": "limit",
            "in": "query",
            "schema": { "type": "integer", "minimum": 1, "maximum": 500, "default": 100 },
            "description": "Maximum records to return."
          },
          {
            "name": "offset",
            "in": "query",
            "schema": { "type": "integer", "minimum": 0, "default": 0 },
            "description": "Records to skip for pagination."
          },
          {
            "name": "model_id",
            "in": "query",
            "schema": { "type": "string" },
            "description": "Filter by model ID."
          },
          {
            "name": "project_id",
            "in": "query",
            "schema": { "type": "string" },
            "description": "Filter by project ID."
          },
          {
            "name": "training_job_id",
            "in": "query",
            "schema": { "type": "string" },
            "description": "Filter by training job UUID."
          },
          {
            "name": "task",
            "in": "query",
            "schema": { "type": "string" },
            "description": "Filter by task type (extract_entities, classify_text, extract_json, generate)."
          },
          {
            "name": "latency_min",
            "in": "query",
            "schema": { "type": "number", "minimum": 0 },
            "description": "Minimum latency in ms."
          },
          {
            "name": "latency_max",
            "in": "query",
            "schema": { "type": "number", "minimum": 0 },
            "description": "Maximum latency in ms."
          },
          {
            "name": "llmaj_score_min",
            "in": "query",
            "schema": { "type": "number", "minimum": 0.0, "maximum": 1.0 },
            "description": "Minimum LLM-as-Judge score [0.0, 1.0]."
          },
          {
            "name": "llmaj_score_max",
            "in": "query",
            "schema": { "type": "number", "minimum": 0.0, "maximum": 1.0 },
            "description": "Maximum LLM-as-Judge score [0.0, 1.0]."
          },
          {
            "name": "since",
            "in": "query",
            "schema": { "type": "string", "format": "date-time" },
            "description": "Return records created at or after this ISO 8601 timestamp."
          },
          {
            "name": "until",
            "in": "query",
            "schema": { "type": "string", "format": "date-time" },
            "description": "Return records created at or before this ISO 8601 timestamp."
          }
        ],
        "responses": {
          "200": {
            "description": "Paginated list of inference records.",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/InferenceListResponse" }
              }
            }
          },
          "401": { "$ref": "#/components/responses/Unauthorized" }
        }
      }
    },
    "/inferences/{inference_id}": {
      "get": {
        "operationId": "get_inference",
        "summary": "Get an inference record",
        "description": "Retrieve a single inference record by ID. Includes LLM-as-Judge verdict and score when judging has completed (fields are null until then).",
        "tags": ["inference-history"],
        "parameters": [
          {
            "name": "inference_id",
            "in": "path",
            "required": true,
            "schema": { "type": "string" },
            "description": "The inference record UUID returned in `inference_id` on any inference response."
          }
        ],
        "responses": {
          "200": {
            "description": "The inference record.",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/InferenceRecord" }
              }
            }
          },
          "401": { "$ref": "#/components/responses/Unauthorized" },
          "404": { "$ref": "#/components/responses/NotFound" }
        }
      }
    },
    "/inferences/{inference_id}/feedback": {
      "post": {
        "operationId": "submit_inference_feedback",
        "summary": "Submit inference feedback",
        "description": "Submit a human correction on a past inference. Corrections are used as labeled examples for Adaptive Inference — Pioneer's continuous improvement loop that retrains your model on corrected live-traffic examples.",
        "tags": ["inference-history"],
        "parameters": [
          {
            "name": "inference_id",
            "in": "path",
            "required": true,
            "schema": { "type": "string" },
            "description": "The inference record UUID to annotate."
          }
        ],
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": { "$ref": "#/components/schemas/InferenceFeedbackRequest" },
              "examples": {
                "correct": {
                  "summary": "Mark as correct",
                  "value": { "verdict": "correct" }
                },
                "incorrect": {
                  "summary": "Mark as incorrect with correction",
                  "value": {
                    "verdict": "incorrect",
                    "corrected_output": {
                      "entities": [
                        { "text": "Apple", "label": "organization", "start": 0, "end": 5 },
                        { "text": "iPhone", "label": "product", "start": 18, "end": 24 }
                      ]
                    },
                    "notes": "Missed the product entity"
                  }
                }
              }
            }
          }
        },
        "responses": {
          "200": {
            "description": "Feedback recorded.",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/InferenceFeedbackResponse" }
              }
            }
          },
          "401": { "$ref": "#/components/responses/Unauthorized" },
          "404": { "$ref": "#/components/responses/NotFound" },
          "422": { "$ref": "#/components/responses/ValidationError" }
        }
      }
    }
  },
  "components": {
    "securitySchemes": {
      "ApiKeyAuth": {
        "type": "apiKey",
        "in": "header",
        "name": "X-API-Key",
        "description": "Pioneer API key. Generate one at https://agent.pioneer.ai/settings/api-keys. Keys begin with `pio_sk_`."
      },
      "BearerAuth": {
        "type": "http",
        "scheme": "bearer",
        "bearerFormat": "JWT",
        "description": "Supabase access token or Pioneer API key as a Bearer token."
      }
    },
    "responses": {
      "Unauthorized": {
        "description": "Missing or invalid API key.",
        "content": {
          "application/json": {
            "schema": { "$ref": "#/components/schemas/ErrorResponse" }
          }
        }
      },
      "PaymentRequired": {
        "description": "Insufficient credits or no active billing plan.",
        "content": {
          "application/json": {
            "schema": { "$ref": "#/components/schemas/ErrorResponse" }
          }
        }
      },
      "NotFound": {
        "description": "Resource not found.",
        "content": {
          "application/json": {
            "schema": { "$ref": "#/components/schemas/ErrorResponse" }
          }
        }
      },
      "ModelNotFound": {
        "description": "Model ID not found or not yet deployed.",
        "content": {
          "application/json": {
            "schema": { "$ref": "#/components/schemas/ErrorResponse" }
          }
        }
      },
      "ValidationError": {
        "description": "Request body failed schema validation.",
        "content": {
          "application/json": {
            "schema": { "$ref": "#/components/schemas/ValidationErrorResponse" }
          }
        }
      },
      "RateLimited": {
        "description": "Rate limit exceeded. Retry after the duration in the `Retry-After` response header.",
        "headers": {
          "Retry-After": {
            "schema": { "type": "integer" },
            "description": "Seconds to wait before retrying."
          }
        },
        "content": {
          "application/json": {
            "schema": { "$ref": "#/components/schemas/ErrorResponse" }
          }
        }
      }
    },
    "schemas": {
      "ErrorResponse": {
        "type": "object",
        "properties": {
          "detail": {
            "oneOf": [{ "type": "string" }, { "type": "object" }],
            "description": "Human-readable error message or structured detail object."
          }
        }
      },
      "ValidationErrorResponse": {
        "type": "object",
        "properties": {
          "detail": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "loc": { "type": "array", "items": { "oneOf": [{ "type": "string" }, { "type": "integer" }] } },
                "msg": { "type": "string" },
                "type": { "type": "string" }
              }
            }
          }
        }
      },
      "ReasoningConfig": {
        "type": "object",
        "description": "Opt-in reasoning / extended-thinking controls. Normalized across providers — Anthropic (`thinking`), OpenAI/Fireworks (`reasoning_effort`), OpenRouter (`reasoning`). Pioneer does not enable reasoning by default.",
        "properties": {
          "enabled": {
            "type": "boolean",
            "default": true,
            "description": "Set false to explicitly disable thinking on models that have it on by default."
          },
          "effort": {
            "type": "string",
            "enum": ["minimal", "low", "medium", "high", "xhigh", "none"],
            "description": "OpenAI/Grok-style effort tier. Mutually exclusive with `max_tokens`."
          },
          "max_tokens": {
            "type": "integer",
            "minimum": 1,
            "description": "Anthropic-style reasoning budget in tokens. Mutually exclusive with `effort`."
          },
          "mode": {
            "type": "string",
            "enum": ["manual", "adaptive"],
            "description": "Anthropic extended-thinking dispatch mode. Leave unset to let Pioneer pick the per-model default."
          },
          "display": {
            "type": "string",
            "enum": ["summarized", "omitted"],
            "description": "Whether thinking text streams back (`summarized`) or is omitted to save latency (`omitted`)."
          },
          "exclude": {
            "type": "boolean",
            "default": false,
            "description": "Model reasons internally but reasoning tokens are not returned to the caller."
          }
        }
      },
      "EncoderInferenceRequest": {
        "type": "object",
        "required": ["model_id", "text", "schema"],
        "description": "Request for encoder (GLiNER) tasks. Discriminated from GenerateInferenceRequest by the absence of a `messages` field.",
        "properties": {
          "model_id": {
            "type": "string",
            "description": "Training job UUID, project name, or base encoder model ID (e.g. `fastino/gliner2-base-v1`)."
          },
          "text": {
            "oneOf": [
              { "type": "string" },
              { "type": "array", "items": { "type": "string" } }
            ],
            "description": "Input text or list of texts for batch processing."
          },
          "schema": {
            "oneOf": [
              { "type": "object", "description": "Unified extraction schema dict with keys: entities, classifications, structures, relations." },
              { "type": "array", "items": { "type": "string" }, "description": "Deprecated flat entity label list. Use the dict form instead." }
            ],
            "description": "Extraction schema. Use the unified dict form: `{\"entities\": [{\"name\": \"organization\"}]}`."
          },
          "threshold": {
            "type": "number",
            "minimum": 0.0,
            "maximum": 1.0,
            "default": 0.5,
            "description": "Confidence threshold for predictions."
          },
          "include_confidence": { "type": "boolean", "default": true },
          "include_spans": { "type": "boolean", "default": true, "description": "Include character-level start/end positions." },
          "store": { "type": "boolean", "default": true, "description": "Persist to inference history. Set false to opt out." },
          "project_id": { "type": "string", "description": "Project ID for attribution and auto-improvement." }
        }
      },
      "GenerateInferenceRequest": {
        "type": "object",
        "required": ["model_id", "task", "messages"],
        "description": "Request for decoder text generation. Discriminated from EncoderInferenceRequest by the presence of a `messages` field.",
        "properties": {
          "model_id": {
            "type": "string",
            "description": "Training job UUID, project name, or base decoder model ID (e.g. `Qwen/Qwen3-8B`)."
          },
          "task": { "type": "string", "enum": ["generate"] },
          "messages": {
            "type": "array",
            "minItems": 1,
            "items": {
              "type": "object",
              "required": ["role", "content"],
              "properties": {
                "role": { "type": "string", "enum": ["system", "user", "assistant"] },
                "content": { "type": "string" }
              }
            },
            "description": "Chat messages. The last message must have role `\"user\"`."
          },
          "max_tokens": { "type": "integer", "minimum": 1, "maximum": 131072 },
          "temperature": { "type": "number", "minimum": 0.0, "maximum": 2.0 },
          "top_p": { "type": "number", "minimum": 0.0, "maximum": 1.0 },
          "reasoning": { "$ref": "#/components/schemas/ReasoningConfig" },
          "include_reasoning_trace": {
            "type": "boolean",
            "default": false,
            "description": "Return extracted `<think>` trace text separately in the response."
          },
          "store": { "type": "boolean", "default": true, "description": "Persist to inference history. Set false to opt out." },
          "project_id": { "type": "string" }
        }
      },
      "EncoderInferenceResponse": {
        "type": "object",
        "required": ["type", "inference_id", "result", "model_id", "latency_ms", "token_usage", "model_used"],
        "properties": {
          "type": { "type": "string", "enum": ["encoder"] },
          "inference_id": { "type": "string", "description": "Unique inference ID. Use with GET /inferences/{inference_id} or POST /inferences/{inference_id}/feedback." },
          "result": {
            "oneOf": [{ "type": "object" }, { "type": "array" }],
            "description": "Extraction result. Shape depends on the schema and task."
          },
          "model_id": { "type": "string" },
          "latency_ms": { "type": "number", "description": "Server-side inference latency in milliseconds." },
          "token_usage": { "type": "integer", "description": "Input tokens processed." },
          "model_used": { "type": "string", "description": "Resolved model identifier." }
        }
      },
      "GenerateInferenceResponse": {
        "type": "object",
        "required": ["type", "inference_id", "completion", "model_id", "latency_ms"],
        "properties": {
          "type": { "type": "string", "enum": ["decoder"] },
          "inference_id": { "type": "string", "description": "Unique inference ID." },
          "completion": { "type": "string", "description": "Generated text." },
          "reasoning_trace": { "type": "string", "description": "Extracted `<think>` reasoning trace when `include_reasoning_trace=true`." },
          "model_id": { "type": "string" },
          "latency_ms": { "type": "number" }
        }
      },
      "PioneerExtension": {
        "type": "object",
        "description": "Pioneer-specific extension on OpenAI-compatible responses.",
        "properties": {
          "inference_id": {
            "type": "string",
            "description": "Pioneer inference record ID. Use with GET /inferences/{inference_id} to poll for async LLM-judge results. Null when persistence was disabled."
          },
          "routed_model": {
            "type": "string",
            "description": "Actual backend model selected by a router project (e.g. `pioneer/auto`). Null when not routed."
          }
        }
      },
      "ChatCompletionRequest": {
        "type": "object",
        "required": ["model", "messages"],
        "properties": {
          "model": { "type": "string", "description": "Model ID, project name, or `pioneer/auto` for automatic routing." },
          "messages": {
            "type": "array",
            "minItems": 1,
            "items": {
              "type": "object",
              "properties": {
                "role": { "type": "string" },
                "content": { "oneOf": [{ "type": "string" }, { "type": "array", "items": { "type": "object" } }] },
                "tool_calls": { "type": "array" },
                "tool_call_id": { "type": "string" }
              }
            }
          },
          "temperature": { "type": "number" },
          "max_tokens": { "type": "integer", "minimum": 1, "maximum": 131072 },
          "stream": { "type": "boolean", "default": false },
          "stop": { "oneOf": [{ "type": "string" }, { "type": "array", "items": { "type": "string" } }] },
          "response_format": { "type": "object" },
          "tools": { "type": "array" },
          "tool_choice": { "oneOf": [{ "type": "string" }, { "type": "object" }] },
          "top_p": { "type": "number" },
          "store": { "type": "boolean", "default": true, "description": "Persist to inference history. Set false to opt out." },
          "metadata": { "type": "object", "description": "Pioneer reads `metadata.project_id` for attribution." },
          "reasoning": {
            "type": "object",
            "description": "Opt-in reasoning controls (OpenRouter-n

# --- truncated at 32 KB (45 KB total) ---
# Full source: https://raw.githubusercontent.com/api-evangelist/fastino-labs/refs/heads/main/openapi/pioneer-openapi-original.json