> ## Documentation Index > Fetch the complete documentation index at: https://docs.decimal.ai/llms.txt > Use this file to discover all available pages before exploring further. # Run a regression check for a candidate manifest against the baseline > Run a regression check for a candidate manifest against the baseline. Request body: { "agent_name": "support-agent", "candidate_manifest_id": "mfst_xyz", "pr_context": { # optional "repo": "acme/support-bot", "pr_number": 42, "branch": "fix/refund-prompt", "commit_sha": "abc123" }, "trace_window_days": 30 # optional, default 30 } Query params: dry_run=true: compute the impact report but do NOT persist a RegressionCheck row and do NOT consume the org's metered quota. Useful for local exploration. The response has no `id` field and the `pr_context` is ignored. Response: see ImpactReport structure in manifest_impact_service.py. Behavior on edge cases: - No baseline exists → returns verdict='first_run', registers candidate as baseline, status='completed' with zero impacts. (Decision #11) (Dry-run mode does not register the candidate as baseline either.) - Candidate manifest doesn't exist → 404 - Candidate manifest belongs to a different agent → 400 ## OpenAPI ````yaml /openapi.json post /api/v1/regression-check openapi: 3.1.0 info: title: DecimalAI Platform description: Agent Dataset Lifecycle Platform — Backend API version: 0.1.0 servers: [] security: - BearerAuth: [] tags: - name: traces description: >- Ingest, search, and inspect agent execution traces. A trace is the atomic unit of the platform — every other feature (evaluation, compatibility scoring, dataset building) operates on traces. Each trace is auto-tagged with the manifest hash of the agent that produced it. - name: eval-scores description: >- Push, fetch, and aggregate per-trace evaluation scores. Scores carry source provenance (built-in, DeepEval, LangSmith, custom) and feed the unified decision engine, which produces a keep / repair / replay / drop verdict per trace. - name: skills description: >- Manage reusable instruction blocks (SKILL.md files) that modify agent behavior. Skills are first-class manifest components — changes to a skill appear in your regression-check impact reports. Use these endpoints to create, version, fork, and sync skills with your local SKILL.md files. - name: manifests description: >- Register and inspect manifest versions. A manifest is a snapshot of your agent's structural identity (tools, prompts, models, skills) at a point in time. The SDK registers manifests automatically; these endpoints expose the underlying records. - name: datasets description: >- Build versioned SFT/DPO training datasets from manifest-classified, eval-scored traces. Datasets are reproducible — each version locks the manifest and filter set used to build it. Export as JSONL, Parquet, or push to HuggingFace Hub. - name: replay description: >- Re-execute historical traces against a new manifest version. Replay is the deferred-future capability for behavioral verification of agent changes. The SDK creates a replay batch, your worker executes each task, then submits results back here for eval scoring. - name: registry description: >- Browse and install community skills from the public skills registry. Each registry skill carries a SkillScore — an evidence-tiered quality composite computed from real-world evals, benchmarks, and ratings across organizations. Installing creates a fork in your org — edits don't affect the public version. - name: agents description: >- List and inspect agents, plus their multi-agent topology (orchestrator → sub-agent edges discovered from traces). Agents are identified by a stable agent_name string set in your SDK init() call. - name: import description: >- Bulk-import historical traces from another observability platform or a JSONL backup. Useful for migrating from LangSmith / Braintrust / Langfuse. Duplicate trace IDs are silently skipped — re-running an import is safe. paths: /api/v1/regression-check: post: tags: - regression summary: Run a regression check for a candidate manifest against the baseline description: |- Run a regression check for a candidate manifest against the baseline. Request body: { "agent_name": "support-agent", "candidate_manifest_id": "mfst_xyz", "pr_context": { # optional "repo": "acme/support-bot", "pr_number": 42, "branch": "fix/refund-prompt", "commit_sha": "abc123" }, "trace_window_days": 30 # optional, default 30 } Query params: dry_run=true: compute the impact report but do NOT persist a RegressionCheck row and do NOT consume the org's metered quota. Useful for local exploration. The response has no `id` field and the `pr_context` is ignored. Response: see ImpactReport structure in manifest_impact_service.py. Behavior on edge cases: - No baseline exists → returns verdict='first_run', registers candidate as baseline, status='completed' with zero impacts. (Decision #11) (Dry-run mode does not register the candidate as baseline either.) - Candidate manifest doesn't exist → 404 - Candidate manifest belongs to a different agent → 400 operationId: create_regression_check_api_v1_regression_check_post parameters: - name: dry_run in: query required: false schema: type: boolean description: Compute the report without persisting or metering. default: false title: Dry Run description: Compute the report without persisting or metering. - name: Authorization in: header required: false schema: type: string title: Authorization - name: decimal_session in: cookie required: false schema: anyOf: - type: string - type: 'null' title: Decimal Session requestBody: required: true content: application/json: schema: type: object additionalProperties: true title: Payload responses: '200': description: Successful Response content: application/json: schema: {} '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' components: schemas: HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type input: title: Input ctx: type: object title: Context type: object required: - loc - msg - type title: ValidationError securitySchemes: BearerAuth: type: http scheme: bearer description: Enter your API key (e.g. dai_sk_test_key_001) ````