> ## 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. # Export replay prompts > Export traces needing replay as a downloadable file. Returns a list of prompts (user inputs) from traces that need to be re-run against the current agent version. Users download this file, run the prompts against their agent externally, then submit the new traces back to DecimalAI. Each row includes a ``replay_context`` object explaining *why* the trace needs replay — what surfaces changed, which skills/tools were involved, and the compat reason. This metadata is informational and does not control agent behavior during replay. Filter by verdict to get specific subsets: - replay: traces that can't be mechanically repaired - drop: traces that are incompatible - repair: traces that need schema fixes ## OpenAPI ````yaml /openapi.json get /api/v1/replay/export 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/replay/export: get: tags: - replay summary: Export replay prompts description: |- Export traces needing replay as a downloadable file. Returns a list of prompts (user inputs) from traces that need to be re-run against the current agent version. Users download this file, run the prompts against their agent externally, then submit the new traces back to DecimalAI. Each row includes a ``replay_context`` object explaining *why* the trace needs replay — what surfaces changed, which skills/tools were involved, and the compat reason. This metadata is informational and does not control agent behavior during replay. Filter by verdict to get specific subsets: - replay: traces that can't be mechanically repaired - drop: traces that are incompatible - repair: traces that need schema fixes operationId: export_replay_prompts_api_v1_replay_export_get parameters: - name: agent_name in: query required: true schema: type: string description: Agent to export replay prompts for title: Agent Name description: Agent to export replay prompts for - name: verdict in: query required: false schema: anyOf: - type: string - type: 'null' description: 'Filter by verdict: replay, drop, repair' title: Verdict description: 'Filter by verdict: replay, drop, repair' - name: format in: query required: false schema: type: string description: 'Export format: jsonl or json' default: jsonl title: Format description: 'Export format: jsonl or json' - name: limit in: query required: false schema: type: integer maximum: 5000 minimum: 1 title: Limit default: 500 - 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 responses: '200': description: Successful Response content: application/json: schema: {} example: prompts: - trace_id: trc_001 input: What flights go to Tokyo? manifest_id: mfst_old verdict: replay total: 50 limit: 10 '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' x-codeSamples: - lang: Python label: Python SDK source: >- import decimalai decimalai.init(api_key="dai_sk_...") prompts = decimalai.get_replay_prompts(agent_name="my-agent", limit=10) 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) ````