> ## Documentation Index > Fetch the complete documentation index at: https://docs.mka1.com/llms.txt > Use this file to discover all available pages before exploring further. # Upload an eval sample trajectory > Stores one coding-agent trial's session, in whatever interchange format the harness produced (record it in `schema_version`, e.g. `ATIF-v1.7`). `steps` is stored verbatim and never validated, so a harness format change needs no gateway release. Separate from the run import because a trajectory averages ~92 KB and a whole run's worth would not fit in one request. Upserts, so re-sending after a timeout replaces the trajectory rather than failing. Sample indices are unique per (task, model), not per run, so pass `task_id` and/or `model` when the run covers more than one — an ambiguous index returns 400 rather than writing to the wrong trial. Rejects a payload whose `steps` and `logs` together exceed 16 MiB with 413. ## OpenAPI ````yaml https://apigw.mka1.com/speakeasy.json put /api/v1/llm/evals/runs/{run_id}/samples/{sample_index}/trajectory openapi: 3.1.1 info: title: MKA1 API version: 1.1.0 description: >- The MKA1 API is a RESTful API that provides access to the MKA1 platform. Learn how to get started with the API and the TypeScript SDK [here](https://mka1.apidocumentation.com/guides/getting-started). license: name: Proprietary servers: - url: https://apigw.mka1.com description: MKA1 API Gateway - url: / description: Relative server URL (configurable via SDK constructor) security: [] tags: - name: Resource Authorization description: >- Manage permissions for LLM resources. Create resources, grant/revoke permissions, and delete resources. Only resource owners can grant, revoke, or delete permissions. x-displayName: Resource Authorization - name: Embeddings description: >- Text embedding API endpoints for generating vector representations of text. Create semantic embeddings for search, clustering, and similarity matching using various embedding models. x-displayName: Embeddings - name: Feedback description: >- User feedback API for rating and commenting on chat completions. Collect thumbs up/down ratings and detailed feedback to improve model responses and track user satisfaction. x-displayName: Feedback - name: Images description: >- Image generation API endpoints for creating images from text descriptions. Generate images with control over size, quality, and style. x-displayName: Images - name: MCP Vault description: >- MCP vault API for storing user-owned MCP server configurations and encrypted credentials. Agents reference vault IDs so secrets are resolved only at tool execution time. x-displayName: MCP Vault - name: Speech description: >- Speech API endpoints for audio processing. Convert text to natural-sounding speech (TTS) or transcribe speech to text (STT) in different languages. x-displayName: Speech - name: Usage description: >- Usage tracking and analytics API for monitoring token consumption, request counts, and cost analysis. View detailed statistics per user, model, and time period. x-displayName: Usage - name: Extract description: >- Structured data extraction API for extracting information from files. Define JSON schemas to extract structured data from images, PDFs, and documents. Supports reusable schema templates. x-displayName: Extract - name: Text Classification description: >- Text classification API for categorizing text into predefined labels. Use AI models to classify text content for sentiment analysis, topic categorization, and content moderation. x-displayName: Text Classification - name: Responses description: >- Agent-powered responses API for creating AI agents with autonomous tool usage. Build conversational assistants that can use web search, file operations, image generation, code execution, computer use simulation, and MCP integrations. Supports background processing, streaming, and real-time status tracking. x-displayName: Responses - name: Files description: >- File management API for uploading, storing, and managing files with automatic expiration and S3 integration. Upload files that can be used with Assistants, Vector Stores, and other features. Files are stored in S3 with metadata tracked in PostgreSQL. Supports automatic cleanup of expired files. x-displayName: Files - name: Vector Stores description: >- Vector store API for storing and searching documents using embeddings. Create vector stores, upload files with automatic chunking and embedding generation, and perform semantic search. Files are processed asynchronously using Temporal workflows for durability. Supports automatic cleanup of expired stores and LanceDB for efficient vector storage. x-displayName: Vector Stores - name: Conversations description: >- Conversation management API for storing and retrieving conversation state across Response API calls. Create conversations, add items (user messages, assistant messages, system messages), and maintain conversation history. Supports metadata tracking and multi-turn dialogue state management. x-displayName: Conversations - name: Guardrails description: >- AI safety guardrails API for configuring content moderation and security policies. Set up ban word lists, prompt injection detection, and system prompt leakage prevention. Guardrails apply to all requests from an account and can be tested before deployment. x-displayName: Guardrails - name: Models description: >- Model listing API for discovering available models. Returns model IDs, ownership, and metadata for all registered models in the gateway. x-displayName: Models - name: Skills description: >- Skills API for managing versioned bundles of instructions and files following the Agent Skills standard. Create, version, and download reusable skill packages that include SKILL.md manifests for agent environments. x-displayName: Skills - name: Chat Completions description: >- **Deprecated: Use the Responses API (`/api/v1/llm/responses`) instead.** Chat completion endpoints with support for streaming, tool calls, and multiple providers. x-deprecated: true x-displayName: Chat Completions - name: Batches x-displayName: Batches - name: Evals x-displayName: Evals - name: Fine-Tuning x-displayName: Fine-Tuning - name: Memory Stores x-displayName: Memory Stores - name: Prompts x-displayName: Prompts - name: API Key x-displayName: API Key - name: Session x-displayName: Session - name: Organization x-displayName: Organization - name: Cluster Admin x-displayName: Cluster Admin - name: Sessions description: Create, inspect, access, and terminate sandbox sessions. x-displayName: Sessions - name: Browser description: >- Connect to browser sessions through the gateway port proxy. Browser sessions expose a Chrome DevTools Protocol endpoint on port 9222. x-displayName: Browser - name: Execution description: Run shell commands and code inside an existing sandbox session. x-displayName: Execution - name: Workspace description: >- Inspect the workspace manifest, transfer files or archives, and download generated artifacts. x-displayName: Workspace - name: Sandbox Usage description: >- Aggregate sandbox usage statistics across sessions, execution, and workspace operations. x-displayName: Sandbox Usage - name: Sandbox Pricing description: >- Cluster-admin management of the sandbox compute rate card used for budgeted spend. x-displayName: Sandbox Pricing - name: schema-3_other x-displayName: other - name: Agents description: Create and manage reusable agent definitions. x-displayName: Agents - name: Agent Versions description: Inspect an agent's configuration history and roll back to a prior version. x-displayName: Agent Versions - name: Agent Runs description: Execute saved agents and inspect persisted run results. x-displayName: Agent Runs - name: Agent Connectors description: >- Connect saved agents to external messaging channels such as Telegram, including text, photo, and supported document exchange. x-displayName: Agent Connectors - name: Agent Schedules description: Create and manage scheduled or recurring saved agent runs. x-displayName: Agent Schedules - name: schema-4_other x-displayName: other - name: Budgets x-displayName: Budgets - name: Settings x-displayName: Settings - name: Compute Jobs x-displayName: Compute Jobs - name: Compute Services x-displayName: Compute Services - name: Compute Catalog x-displayName: Compute Catalog - name: Compute Volumes x-displayName: Compute Volumes - name: Compute Pricing x-displayName: Compute Pricing - name: Compute Secrets x-displayName: Compute Secrets - name: Compute Usage x-displayName: Compute Usage - name: Compute Tenants x-displayName: Compute Tenants - description: Create, list, read, update, and delete repositories. name: Repositories x-displayName: Repositories paths: /api/v1/llm/evals/runs/{run_id}/samples/{sample_index}/trajectory: put: tags: - Evals summary: Upload an eval sample trajectory description: >- Stores one coding-agent trial's session, in whatever interchange format the harness produced (record it in `schema_version`, e.g. `ATIF-v1.7`). `steps` is stored verbatim and never validated, so a harness format change needs no gateway release. Separate from the run import because a trajectory averages ~92 KB and a whole run's worth would not fit in one request. Upserts, so re-sending after a timeout replaces the trajectory rather than failing. Sample indices are unique per (task, model), not per run, so pass `task_id` and/or `model` when the run covers more than one — an ambiguous index returns 400 rather than writing to the wrong trial. Rejects a payload whose `steps` and `logs` together exceed 16 MiB with 413. operationId: uploadEvalSampleTrajectory parameters: - name: run_id in: path required: true schema: type: string example: eval_run_aa87e2b1112a455b8deabed784372198 - name: sample_index in: path required: true schema: type: integer minimum: 0 maximum: 2147483647 example: 0 - name: task_id in: query schema: type: string description: >- Disambiguate the sample index, which is unique per (task, model) and not per run. Required when the index matches trials from more than one task. allowEmptyValue: true - name: model in: query schema: type: string description: >- Disambiguate the sample index when the run covers more than one model. allowEmptyValue: true - name: X-On-Behalf-Of in: header required: false schema: type: string description: Optional external end-user identifier forwarded by the API gateway. requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/UploadEvalSampleTrajectoryRequest' example: schema_version: ATIF-v1.7 steps: - step_id: 1 source: agent reasoning_content: >- The test fails because the fixture directory is not writable. tool_calls: - function_name: bash arguments: command: ls -la /workdir responses: '200': description: OK content: application/json: schema: $ref: '#/components/schemas/EvalSampleTrajectoryUploadedObject' example: object: eval.sample.trajectory.uploaded run_id: eval_run_aa87e2b1112a455b8deabed784372198 sample_index: 0 task_id: fix-permissions model: meetkai:functionary-urdu-mini-pak step_count: 42 size_bytes: 94208 security: - bearerAuth: [] x-codeSamples: - lang: python label: Python (SDK) source: |- from meetkai_mka1 import SDK with SDK( bearer_auth="", ) as sdk: res = sdk.llm.evals.upload_sample_trajectory(run_id="eval_run_aa87e2b1112a455b8deabed784372198", sample_index=0, steps=[ { "step_id": 1, "source": "agent", "reasoning_content": "The test fails because the fixture directory is not writable.", "tool_calls": [ { "function_name": "bash", "arguments": { "command": "ls -la /workdir", }, }, ], }, ], schema_version="ATIF-v1.7") # Handle response print(res) - lang: typescript label: Typescript (SDK) source: |- import { SDK } from "@meetkai/mka1"; const sdk = new SDK({ bearerAuth: "", }); async function run() { const result = await sdk.llm.evals.uploadSampleTrajectory({ runId: "eval_run_aa87e2b1112a455b8deabed784372198", sampleIndex: 0, uploadEvalSampleTrajectoryRequest: { steps: [ { "step_id": 1, "source": "agent", "reasoning_content": "The test fails because the fixture directory is not writable.", "tool_calls": [ { "function_name": "bash", "arguments": { "command": "ls -la /workdir", }, }, ], }, ], }, }); console.log(result); } run(); - lang: csharp label: CSharp (SDK) source: >- using MeetKai.MKA1; using MeetKai.MKA1.Types.Components; using System.Collections.Generic; var sdk = new SDK(bearerAuth: ""); Types.Requests.UploadEvalSampleTrajectoryRequest req = new MeetKai.MKA1.Types.Requests.UploadEvalSampleTrajectoryRequest() { RunId = "eval_run_aa87e2b1112a455b8deabed784372198", SampleIndex = 0, Body = new MeetKai.MKA1.Types.Components.UploadEvalSampleTrajectoryRequest() { Steps = new List() { new Dictionary() { { "step_id", 1 }, { "source", "agent" }, { "reasoning_content", "The test fails because the fixture directory is not writable." }, { "tool_calls", new List() { new Dictionary() { { "function_name", "bash" }, { "arguments", new Dictionary() { { "command", "ls -la /workdir" }, } }, }, } }, }, }, }, }; var res = await sdk.Llm.Evals.UploadSampleTrajectoryAsync(req); // handle response components: schemas: UploadEvalSampleTrajectoryRequest: type: object properties: schema_version: type: string minLength: 1 maxLength: 32 default: ATIF-v1.7 description: >- Interchange format of `steps`. Stored per row so a harness upgrade leaves older trajectories readable. steps: type: array items: {} description: >- Steps exactly as the harness wrote them. Stored verbatim and never validated, so a format change needs no gateway release. logs: anyOf: - type: object propertyNames: type: string minLength: 1 maxLength: 255 additionalProperties: type: string - type: 'null' description: >- Bounded tails of the trial's log files, keyed by their path in the trial dir — `trial.log` (the harness's own account of the trial), `verifier/test-stdout.txt` / `test-stderr.txt`, `exception.txt`. The harness tails each file before sending; the whole payload still counts against the trajectory size limit. required: - steps EvalSampleTrajectoryUploadedObject: type: object properties: object: const: eval.sample.trajectory.uploaded run_id: type: string sample_index: type: integer minimum: -9007199254740991 maximum: 9007199254740991 task_id: type: string model: type: string step_count: type: integer minimum: -9007199254740991 maximum: 9007199254740991 size_bytes: type: integer minimum: -9007199254740991 maximum: 9007199254740991 description: >- Measured from the stored payload, so it describes what was actually written. required: - object - run_id - sample_index - task_id - model - step_count - size_bytes securitySchemes: bearerAuth: type: http scheme: bearer bearerFormat: API Key description: >- Gateway auth: send `Authorization: Bearer `. For multi-user server-side integrations, you can also send `X-On-Behalf-Of: `. ````