AI agent infrastructure: dedup, cost prediction, validation, governance, failure intelligence.
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Get today's tracked LLM spend, per-model breakdown, projection, and budget alerts. Free — no credits charged.
Optional. Daily budget in USD (e.g., 10.0 for a $10/day cap). Enables budget alerts and remaining-balance calculation.
Get today's tracked LLM spend, per-model breakdown, projection, and budget alerts. Free — no credits charged.
| Argument | Type | Description |
|---|---|---|
| daily_limit | number | Optional. Daily budget in USD (e.g., 10.0 for a $10/day cap). Enables budget alerts and remaining-balance calculation. |
{
"type": "object",
"properties": {
"daily_limit": {
"type": "number",
"description": "Optional. Daily budget in USD (e.g., 10.0 for a $10/day cap). Enables budget alerts and remaining-balance calculation."
}
},
"additionalProperties": false
}Before executing a multi-step agent plan, estimate the total LLM cost. Returns per-step breakdown and optimization suggestions. If the estimate exceeds your budget, pipe the same plan into burnrate_optimize. Costs 1 credit.
| Argument | Type | Description |
|---|---|---|
| plan* | array | Array of plan steps with provider, model, and token estimates. |
{
"type": "object",
"required": [
"plan"
],
"properties": {
"plan": {
"type": "array",
"items": {
"type": "object",
"required": [
"step",
"provider",
"model",
"estimated_input_tokens",
"estimated_output_tokens"
],
"properties": {
"step": {
"type": "string",
"description": "Step identifier — string label or number."
},
"model": {
"type": "string",
"description": "Model name: claude-sonnet-4-6, gpt-4o, etc."
},
"provider": {
"type": "string",
"description": "LLM provider: anthropic, openai, google, etc."
},
"search_calls": {
"type": "number",
"description": "Optional. Number of grounding/search calls (e.g. Gemini Search)."
},
"cache_read_tokens": {
"type": "number",
"description": "Optional. Cached prompt tokens for providers with prompt caching."
},
"estimated_input_tokens": {
"type": "number",
"description": "Estimated prompt token count."
},
"estimated_output_tokens": {
"type": "number",
"description": "Estimated completion token count."
}
}
},
"description": "Array of plan steps with provider, model, and token estimates."
}
},
"additionalProperties": false
}Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your budget. Returns the optimized plan with substituted models, new per-step costs, total savings, and whether the target_budget is met. Optionally set target_budget to constrain the optimization. Costs 1 credit.
| Argument | Type | Description |
|---|---|---|
| plan* | array | Array of plan steps. Same schema as burnrate_estimate: each step needs step, provider, model, estimated_input_tokens, estimated_output_tokens. |
| target_budget | number | Optional. Target total cost in USD. |
{
"type": "object",
"required": [
"plan"
],
"properties": {
"plan": {
"type": "array",
"items": {
"type": "object",
"required": [
"step",
"provider",
"model",
"estimated_input_tokens",
"estimated_output_tokens"
],
"properties": {
"step": {
"type": "string",
"description": "Step identifier — string label or number."
},
"model": {
"type": "string",
"description": "Model name."
},
"provider": {
"type": "string",
"description": "LLM provider."
},
"estimated_input_tokens": {
"type": "number",
"description": "Estimated input tokens."
},
"estimated_output_tokens": {
"type": "number",
"description": "Estimated output tokens."
}
}
},
"description": "Array of plan steps. Same schema as burnrate_estimate: each step needs step, provider, model, estimated_input_tokens, estimated_output_tokens."
},
"target_budget": {
"type": "number",
"description": "Optional. Target total cost in USD."
}
},
"additionalProperties": false
}Log the actual cost of an LLM call after execution. Call this after every LLM request to build calibration data that improves burnrate_estimate accuracy over time. Free — no credits charged. Returns the recorded cost entry with computed margin versus the prior estimate when one exists for this model and token range.
| Argument | Type | Description |
|---|---|---|
| model* | string | Model identifier as returned by the provider. Examples: claude-sonnet-4-6, gpt-4o, gemini-2.0-flash, mistral-large-latest. Unknown models are accepted but cost may show as $0. |
| task_id | string | Optional task ID for cross-referencing spend with DedupQ deduplication results. Use the same task_id passed to dedupq_check to link cost tracking with deduplication. |
| provider* | string | LLM provider identifier. Supported: anthropic, openai, google, mistral, cohere, deepseek, together, fireworks, groq. Must match the provider of the model used. |
| input_tokens* | number | Actual prompt tokens used. Must be >= 0. |
| output_tokens* | number | Actual completion tokens used. Must be >= 0. |
| cache_read_tokens | number | Optional. Cache-read tokens. |
Before executing any LLM task, check if an identical or semantically similar task has already been completed. Returns cached result on hit, saving one LLM call. On a miss, execute your task and call dedupq_complete to cache the result for future hits. Costs 1 credit.
| Argument | Type | Description |
|---|---|---|
| content* | string | The task content to check for duplicates. This is hashed and embedded for matching. |
| task_id | string | Optional caller task ID for tracing and cross-referencing with BurnRate. |
| hash_only | boolean | If true, skip vector similarity search and use exact hash matching only. Default: false. |
| similarity_threshold | number | Cosine similarity threshold for semantic matching, 0.0 to 1.0. Default: 0.80. |
{
"type": "object",
"required": [
"content"
],
"properties": {
"content": {
"type": "string",
"description": "The task content to check for duplicates. This is hashed and embedded for matching."
},
"task_id": {
"type": "string",
"description": "Optional caller task ID for tracing and cross-referencing with BurnRate."
},
"hash_only": {
"type": "boolean",
"description": "If true, skip vector similarity search and use exact hash matching only. Default: false."
},
"similarity_threshold": {
"type": "number",
"description": "Cosine similarity threshold for semantic matching, 0.0 to 1.0. Default: 0.80."
}
},
"additionalProperties": false
}After executing a task, store the result so future identical or similar tasks return a cache hit via dedupq_check. Costs 2 credits.
| Argument | Type | Description |
|---|---|---|
| result* | any | The task result to cache. Can be any JSON value. |
| content* | string | Original task content. Used to compute hash and embedding for future matching. |
| task_id | string | Optional task ID. Used as the database row ID if provided. |
| hash_only | boolean | If true, skip embedding generation. Default: false. |
{
"type": "object",
"required": [
"content",
"result"
],
"properties": {
"result": {
"description": "The task result to cache. Can be any JSON value."
},
"content": {
"type": "string",
"description": "Original task content. Used to compute hash and embedding for future matching."
},
"task_id": {
"type": "string",
"description": "Optional task ID. Used as the database row ID if provided."
},
"hash_only": {
"type": "boolean",
"description": "If true, skip embedding generation. Default: false."
}
},
"additionalProperties": false
}Evaluate a proposed agent action against your governance policies. Returns allow or deny with the matched policy reason. Requires at least one active policy created via guardrail_create_policy. Deterministic rule evaluation — no LLM. Costs 1 credit.
| Argument | Type | Description |
|---|---|---|
| agent_id* | string | Agent identifier. |
| proposed_action* | object | Action to evaluate. Must contain a 'type' field. Example: {"type": "http_request", "url": "https://external.example.com"} or {"type": "file_write", "path": "/etc/config"}. |
{
"type": "object",
"required": [
"agent_id",
"proposed_action"
],
"properties": {
"agent_id": {
"type": "string",
"description": "Agent identifier."
},
"proposed_action": {
"type": "object",
"required": [
"type"
],
"properties": {
"type": {
"type": "string",
"description": "Action type: http_request, delete_file, send_email, etc."
}
},
"description": "Action to evaluate. Must contain a 'type' field. Example: {\"type\": \"http_request\", \"url\": \"https://external.example.com\"} or {\"type\": \"file_write\", \"path\": \"/etc/config\"}."
}
},
"additionalProperties": false
}Create a persistent governance policy that guardrail_check evaluates on every subsequent call. Define rules using and/or/not operators over action types, resource patterns, and budget thresholds. Call this before using guardrail_check — checks require at least one active policy. Policies persist until explicitly deleted. Duplicate policy names return an error. Returns the created policy with its ID and active status.
| Argument | Type | Description |
|---|---|---|
| name* | string | Unique policy name per org. Examples: 'no-delete-in-prod', 'budget-cap-50', 'pii-block'. |
| rules* | array | Array of rule objects evaluated against the proposed_action in guardrail_check. Leaf operators: eq, starts_with, contains, gt, lt (compare field to value). Compound operators: and, or, not (nest sub-rules in a rules array). Example: [{operator:'eq', field:'type', value:'file_write'}] blocks all file writes. Nested example: [{operator:'and', rules:[{operator:'eq',field:'type',value:'api_call'},{operator:'contains',field:'url',value:'prod'}]}] blocks prod API calls. |
| priority | number | Optional. Evaluation order. Default: 0. |
| description | string | Optional human-readable summary of what this policy enforces. Returned in guardrail_check responses and guardrail_list_policies output for auditability. |
| action_types | array | Optional. Restrict this policy to only evaluate when proposed_action.type matches one of these values. Examples: ['file_write', 'api_call', 'db_delete']. Omit to apply the policy to all action types regardless of type field. |
Check for known failure patterns before executing a task type. Returns pitfalls with severity, fix suggestions, and confidence scores. After your agent runs, submit failures via pitfalldb_report so others benefit. Costs 2 credits.
| Argument | Type | Description |
|---|---|---|
| filters | object | Optional filters. |
| task_type* | string | Task category: code_generation, web_search, data_analysis, etc. |
| task_description | string | Optional. Natural-language task description for semantic search. |
{
"type": "object",
"required": [
"task_type"
],
"properties": {
"filters": {
"type": "object",
"properties": {
"language": {
"type": "string",
"description": "Programming language."
},
"provider": {
"type": "string",
"description": "LLM provider."
},
"framework": {
"type": "string",
"description": "Framework."
}
},
"description": "Optional filters."
},
"task_type": {
"type": "string",
"description": "Task category: code_generation, web_search, data_analysis, etc."
},
"task_description": {
"type": "string",
"description": "Optional. Natural-language task description for semantic search."
}
},
"additionalProperties": false
}Report an agent failure. PII-scrubbed before storage. Linked to existing pitfalls if similar. Free — no credits charged.
| Argument | Type | Description |
|---|---|---|
| failure* | object | Failure details. |
| task_type* | string | Task category. |
| task_description* | string | Description of the failed task. |
{
"type": "object",
"required": [
"task_type",
"task_description",
"failure"
],
"properties": {
"failure": {
"type": "object",
"properties": {
"language": {
"type": "string",
"description": "Programming language."
},
"provider": {
"type": "string",
"description": "LLM provider."
},
"framework": {
"type": "string",
"description": "Framework."
},
"error_type": {
"type": "string",
"description": "Error category: tool_call_ignored, syntax_error, etc."
},
"fix_worked": {
"type": "boolean",
"description": "Whether the fix worked."
},
"root_cause": {
"type": "string",
"description": "Root cause analysis (PII-scrubbed)."
},
"fix_applied": {
"type": "string",
"description": "Fix applied (PII-scrubbed)."
},
"error_message": {
"type": "string",
"description": "Error message (PII-scrubbed)."
}
},
"description": "Failure details."
},
"task_type": {
"type": "string",
"description": "Task category."
},
"task_description": {
"type": "string",
"description": "Description of the failed task."
}
},
"additionalProperties": false
}After your agent generates output, validate it against your rules before shipping. Runs deterministic checks (regex, JSON schema, syntax) plus optional LLM-powered tone and factual analysis. Returns a structured verdict (pass, warn, or fail) with a 0-100 score and per-check issue details. Use qualitygate_trends to spot recurring failure patterns over time. Variable cost: 1 credit per deterministic check, 8 credits per LLM check.
| Argument | Type | Description |
|---|---|---|
| output* | string | The agent output text to validate. |
| schema | object | JSON Schema to validate output against. |
| language | string | Code language for syntax check: json, python, javascript, typescript. |
| override | boolean | Force pass. Requires override_reason. |
| directives | array | Directive objects. Types: must_include, must_not_include, must_match, must_not_match, must_contain, must_not_contain, min_length, max_length. |
| check_types | array | Checks to run. Auto-inferred if omitted. |
| override_reason | string | Required when override is true. |
{
"type": "object",
"required": [
"output"
],
"properties": {
"output": {
"type": "string",
"description": "The agent output text to validate."
},
"schema": {
"type": "object",
"description": "JSON Schema to validate output against."
},
"language": {
"type": "string",
"description": "Code language for syntax check: json, python, javascript, typescript."
},
"override": {
"type": "boolean",
"description": "Force pass. Requires override_reason."
},
"directives": {
"type": "array",
"items": {
"type": "object",
"required": [
"type",
"value"
],
"properties": {
"name": {
"type": "string",
"description": "Optional directive name."
},
"type": {
"type": "string",
"description": "Directive type."
},
"value": {
"type": "string",
"description": "Directive value — string pattern, regex, keyword array (as comma-separated string), or number (as string). Interpreted based on directive type."
}
}
},
"description": "Directive objects. Types: must_include, must_not_include, must_match, must_not_match, must_contain, must_not_contain, min_length, max_length."
},
"check_types": {
"type": "array",
"items": {
"enum": [
"directive_compliance",
"schema_validation",
"code_syntax",
"tone",
"factual_claims"
],
"type": "string"
},
"description": "Checks to run. Auto-inferred if omitted."
},
"override_reason": {
"type": "string",
"description": "Required when override is true."
}
},
"additionalProperties": false
}Execute a structured workflow end-to-end. Call rigor_plan first (free) to preview the step sequence and cost estimate before committing credits. Classifies the task, selects the optimal tool sequence, and executes each step with the right LLM model. Returns a complete deliverable — solution designs, competitive analyses, governance documents, and more. Supports SSE streaming for real-time progress, webhook callback, or polling. For atomic work — classification, scoring, ranking, entity extraction, query parsing — set preferences.execution to 'direct' and declare preferences.output_contract to get validated JSON records from a single call, routed to the cheapest model that holds the schema.
| Argument | Type | Description |
|---|---|---|
| context | object | Additional context for the workflow. |
| delivery | object | Delivery method. Default: polling (MCP clients typically can't handle SSE). |
| task_type | string | Optional hint to bypass automatic classification. Passing it also removes the slowest classification tiers from the critical path, so send it whenever you know the shape. Multi-step deliverable types: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation. Atomic single-call types, which auto-select direct execution: tag, score, rerank, compose, extract_entities, parse_query, quick_research, quick_classification, quick_extraction, quick_scoring. Call GET /api/rigor/task-types for the full vocabulary with each type's shape. |
| preferences | object |
Before executing a complex task, get a structured workflow plan with per-step cost estimates. Classifies your task, selects the optimal framework sequence, and returns the full plan without executing anything. The response's allowed_modes tells you whether this plan is eligible for direct execution. Free — no credits charged.
| Argument | Type | Description |
|---|---|---|
| task_type | string | Optional hint to bypass automatic classification. Passing it also removes the slowest classification tiers from the critical path, so send it whenever you know the shape. Multi-step deliverable types: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation. Atomic single-call types, which auto-select direct execution: tag, score, rerank, compose, extract_entities, parse_query, quick_research, quick_classification, quick_extraction, quick_scoring. Call GET /api/rigor/task-types for the full vocabulary with each type's shape. |
| preferences | object | Optional workflow preferences. |
| context_fields | array | Optional, with prior_workflow_id: the fields to carry over. Pass ["full_step_results"] for every step output in full. |
| task_description* | string | Natural language description of the task, at most 4,000 characters. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.' |
Check the status of a running or completed Rigor workflow. Returns progress, step results, and the full deliverable when complete. Use after rigor_execute with polling delivery to retrieve results.
| Argument | Type | Description |
|---|---|---|
| workflow_id* | string | The workflow ID returned by rigor_execute (format: wr_xxx). |
{
"type": "object",
"required": [
"workflow_id"
],
"properties": {
"workflow_id": {
"type": "string",
"description": "The workflow ID returned by rigor_execute (format: wr_xxx)."
}
},
"additionalProperties": false
}List and search Rigor workflows for your organization, with filtering and pagination. Returns status, progress, capacity usage, and available actions per workflow. Use to monitor workflow state, understand concurrent limit usage, identify stuck or completed workflows, and — via q — find prior work on a subject before commissioning it again. Pair a q hit with rigor_status to read that workflow's deliverable.
| Argument | Type | Description |
|---|---|---|
| q | string | Search the workflow title and task description. Every whitespace-separated term must appear in one or the other, as a case-insensitive substring — so "vector search postgres" matches a task described as "add vector search to an existing Postgres-backed SaaS app". Substring matching, not full-text: there is no stemming and no ranking, so "migrating" does not match "migration". Max 200 characters and 8 terms; punctuation is treated as a separator and wildcards are not supported. |
| limit | number | Page size (default 20, max 100) |
| cursor | string | Pagination cursor (created_at timestamp from previous page) |
| status | string | Filter by status (comma-separated). Valid values: executing, step_executing, completed, failed, halted, pending_approval, cancelled. E.g. "halted,failed,pending_approval" |
| folder_id | string | Filter by folder ID. Pass "unassigned" for workflows in no folder |
| task_type |
{
"type": "object",
"required": [
"provider",
"model",
"input_tokens",
"output_tokens"
],
"properties": {
"model": {
"type": "string",
"description": "Model identifier as returned by the provider. Examples: claude-sonnet-4-6, gpt-4o, gemini-2.0-flash, mistral-large-latest. Unknown models are accepted but cost may show as $0."
},
"task_id": {
"type": "string",
"description": "Optional task ID for cross-referencing spend with DedupQ deduplication results. Use the same task_id passed to dedupq_check to link cost tracking with deduplication."
},
"provider": {
"type": "string",
"description": "LLM provider identifier. Supported: anthropic, openai, google, mistral, cohere, deepseek, together, fireworks, groq. Must match the provider of the model used."
},
"input_tokens": {
"type": "number",
"description": "Actual prompt tokens used. Must be >= 0."
},
"output_tokens": {
"type": "number",
"description": "Actual completion tokens used. Must be >= 0."
},
"cache_read_tokens": {
"type": "number",
"description": "Optional. Cache-read tokens."
}
},
"additionalProperties": false
}{
"type": "object",
"required": [
"name",
"rules"
],
"properties": {
"name": {
"type": "string",
"description": "Unique policy name per org. Examples: 'no-delete-in-prod', 'budget-cap-50', 'pii-block'."
},
"rules": {
"type": "array",
"items": {
"type": "object",
"required": [
"operator"
],
"properties": {
"field": {
"type": "string",
"description": "Field path on proposed_action (e.g. 'action_type', 'path', 'amount')."
},
"rules": {
"type": "array",
"description": "Nested rules for compound operators (and/or/not)."
},
"value": {
"type": "string",
"description": "Comparison value for leaf operators. String, number, or boolean as string."
},
"operator": {
"type": "string",
"description": "Rule operator: eq | starts_with | contains | gt | lt | and | or | not."
}
}
},
"description": "Array of rule objects evaluated against the proposed_action in guardrail_check. Leaf operators: eq, starts_with, contains, gt, lt (compare field to value). Compound operators: and, or, not (nest sub-rules in a rules array). Example: [{operator:'eq', field:'type', value:'file_write'}] blocks all file writes. Nested example: [{operator:'and', rules:[{operator:'eq',field:'type',value:'api_call'},{operator:'contains',field:'url',value:'prod'}]}] blocks prod API calls."
},
"priority": {
"type": "number",
"description": "Optional. Evaluation order. Default: 0."
},
"description": {
"type": "string",
"description": "Optional human-readable summary of what this policy enforces. Returned in guardrail_check responses and guardrail_list_policies output for auditability."
},
"action_types": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional. Restrict this policy to only evaluate when proposed_action.type matches one of these values. Examples: ['file_write', 'api_call', 'db_delete']. Omit to apply the policy to all action types regardless of type field."
}
},
"additionalProperties": false
}| Optional workflow preferences. |
| context_fields | array | Optional, with prior_workflow_id: the fields to carry over. Pass ["full_step_results"] for every step output in full. |
| task_description* | string | Natural language description of the task, at most 4,000 characters. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.' |
| prior_workflow_id | string | Optional: the workflow_id (wr_…) of a finished workflow in your workspace to build on. Its results are passed to this run as context. It is charged as a new run at the normal quote. |
{
"type": "object",
"required": [
"task_description"
],
"properties": {
"context": {
"type": "object",
"properties": {
"additional_context": {
"type": "string",
"description": "Free-form context the workflow steps can reference."
}
},
"description": "Additional context for the workflow."
},
"delivery": {
"type": "object",
"properties": {
"method": {
"type": "string",
"description": "sse | webhook | polling. Default for MCP: polling."
},
"webhook_url": {
"type": "string",
"description": "Required if method is webhook. Must be HTTPS."
}
},
"description": "Delivery method. Default: polling (MCP clients typically can't handle SSE)."
},
"task_type": {
"type": "string",
"description": "Optional hint to bypass automatic classification. Passing it also removes the slowest classification tiers from the critical path, so send it whenever you know the shape. Multi-step deliverable types: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation. Atomic single-call types, which auto-select direct execution: tag, score, rerank, compose, extract_entities, parse_query, quick_research, quick_classification, quick_extraction, quick_scoring. Call GET /api/rigor/task-types for the full vocabulary with each type's shape."
},
"preferences": {
"type": "object",
"properties": {
"execution": {
"enum": [
"direct"
],
"type": "string",
"description": "Set to 'direct' to compose the plan's content frameworks into a single LLM call and route cost-first, using a per-task-type model floor that moves when a cheaper model earns the work. Research steps, process steps (classification-verify, review protocol, synthesis) and the quality review each remain separate calls, so this is not a one-call-per-workflow guarantee: for atomic task types, which have a single content framework, the call count matches standard execution and the saving is the model. Supplying output_contract replaces the quality-review call with deterministic validation, which is one fewer call. No intermediate outputs. Available at every tier. Auto-selected for atomic task types when no execution preference is given. Falls back to standard execution when combined with require_approval or interactive mode, or when the plan exceeds the composition size limit. With attachments or prior_workflow_id, the workflow runs as standard execution instead, so the attachments and the earlier results are applied."
},
"rigor_level": {
"type": "string",
"description": "quick | standard (default) | thorough. Controls analysis depth and cost."
},
"max_budget_usd": {
"type": "number",
"description": "Spending ceiling in USD. If the plan's quote is still above it after Rigor fits the plan (cheaper models, optional steps dropped), rigor_plan returns a max_budget_exceeded warning and rigor_execute refuses with 402 max_budget_exceeded before anything runs or is charged."
},
"output_contract": {
"type": "object",
"required": [
"task_type",
"shape",
"fields"
],
"properties": {
"count": {
"type": "object",
"properties": {
"max": {
"type": "number"
},
"min": {
"type": "number"
}
},
"description": "Bounds on the number of entries. Only read when shape is \"array\"."
},
"shape": {
"enum": [
"object",
"array"
],
"type": "string",
"description": "\"object\" for 1 record, \"array\" for 1 entry per input item."
},
"fields": {
"type": "array",
"items": {
"type": "object",
"required": [
"name",
"type"
],
"properties": {
"enum": {
"type": "array",
"items": {
"type": "string"
},
"description": "Restricts a string field to a fixed set of values."
},
"name": {
"type": "string",
"description": "The JSON key."
},
"type": {
"enum": [
"string",
"number",
"integer",
"boolean",
"array",
"object"
],
"type": "string",
"description": "The value's type."
},
"maximum": {
"type": "number",
"description": "Upper bound for a numeric field."
},
"minimum": {
"type": "number",
"description": "Lower bound for a numeric field."
},
"required": {
"type": "boolean",
"description": "Defaults to true. Set false for a field that may be absent."
},
"description": {
"type": "string",
"description": "Passed to the model as the field's description."
}
}
},
"description": "The schema for 1 record."
},
"selection": {
"type": "object",
"properties": {
"maxCount": {
"type": "number"
},
"minCount": {
"type": "number"
},
"minScore": {
"type": "number"
},
"scoreField": {
"type": "string"
}
},
"description": "Use when you want the model to over-generate candidates and Rigor to sort, threshold, and cap them before the count bounds are checked."
},
"task_type": {
"type": "string",
"description": "Your own label for the work. Echoed into telemetry. Not read as a framework name and does not change routing."
}
},
"description": "Only read when execution is \"direct\". Declares the JSON shape you want back, so the answer is generated against your schema and validated against it before return, instead of returned as prose you have to parse. A conforming run also skips the quality-review call, costing 1 LLM call rather than 2. The schema is closed: a record carrying an undeclared key is rejected exactly like one missing a required key."
}
},
"description": "Optional workflow preferences."
},
"context_fields": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional, with prior_workflow_id: the fields to carry over. Pass [\"full_step_results\"] for every step output in full."
},
"task_description": {
"type": "string",
"description": "Natural language description of the task, at most 4,000 characters. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.'"
},
"prior_workflow_id": {
"type": "string",
"description": "Optional: the workflow_id (wr_…) of a finished workflow in your workspace to build on. Its results are passed to this run as context. It is charged as a new run at the normal quote."
}
},
"additionalProperties": false
}| prior_workflow_id | string | Optional: the workflow_id (wr_…) of a finished workflow in your workspace to build on. Its results are passed to this run as context. It is charged as a new run at the normal quote. |
{
"type": "object",
"required": [
"task_description"
],
"properties": {
"task_type": {
"type": "string",
"description": "Optional hint to bypass automatic classification. Passing it also removes the slowest classification tiers from the critical path, so send it whenever you know the shape. Multi-step deliverable types: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation. Atomic single-call types, which auto-select direct execution: tag, score, rerank, compose, extract_entities, parse_query, quick_research, quick_classification, quick_extraction, quick_scoring. Call GET /api/rigor/task-types for the full vocabulary with each type's shape."
},
"preferences": {
"type": "object",
"properties": {
"execution": {
"enum": [
"direct"
],
"type": "string",
"description": "Set to 'direct' to compose the plan's content frameworks into a single LLM call and route cost-first, using a per-task-type model floor that moves when a cheaper model earns the work. Research steps, process steps (classification-verify, review protocol, synthesis) and the quality review each remain separate calls, so this is not a one-call-per-workflow guarantee: for atomic task types, which have a single content framework, the call count matches standard execution and the saving is the model. Supplying output_contract replaces the quality-review call with deterministic validation, which is one fewer call. No intermediate outputs. Available at every tier. Auto-selected for atomic task types when no execution preference is given. Falls back to standard execution when combined with require_approval or interactive mode, or when the plan exceeds the composition size limit. With attachments or prior_workflow_id, the workflow runs as standard execution instead, so the attachments and the earlier results are applied."
},
"rigor_level": {
"enum": [
"quick",
"standard",
"thorough"
],
"type": "string",
"description": "Review depth. Default auto-detected from task complexity."
},
"add_frameworks": {
"type": "array",
"items": {
"type": "string"
},
"description": "Inject additional frameworks into the plan."
},
"max_budget_usd": {
"type": "number",
"description": "Spending ceiling in USD. If the plan's quote is still above it after Rigor fits the plan (cheaper models, optional steps dropped), rigor_plan returns a max_budget_exceeded warning and rigor_execute refuses with 402 max_budget_exceeded before anything runs or is charged."
},
"only_frameworks": {
"type": "array",
"items": {
"type": "string"
},
"description": "Restrict the plan to these frameworks (display name or ID). Applied before skip_frameworks; unknown names are reported in warnings."
},
"output_contract": {
"type": "object",
"required": [
"task_type",
"shape",
"fields"
],
"properties": {
"count": {
"type": "object",
"properties": {
"max": {
"type": "number"
},
"min": {
"type": "number"
}
},
"description": "Bounds on the number of entries. Only read when shape is \"array\"."
},
"shape": {
"enum": [
"object",
"array"
],
"type": "string",
"description": "\"object\" for 1 record, \"array\" for 1 entry per input item."
},
"fields": {
"type": "array",
"items": {
"type": "object",
"required": [
"name",
"type"
],
"properties": {
"enum": {
"type": "array",
"items": {
"type": "string"
},
"description": "Restricts a string field to a fixed set of values."
},
"name": {
"type": "string",
"description": "The JSON key."
},
"type": {
"enum": [
"string",
"number",
"integer",
"boolean",
"array",
"object"
],
"type": "string",
"description": "The value's type."
},
"maximum": {
"type": "number",
"description": "Upper bound for a numeric field."
},
"minimum": {
"type": "number",
"description": "Lower bound for a numeric field."
},
"required": {
"type": "boolean",
"description": "Defaults to true. Set false for a field that may be absent."
},
"description": {
"type": "string",
"description": "Passed to the model as the field's description."
}
}
},
"description": "The schema for 1 record."
},
"selection": {
"type": "object",
"properties": {
"maxCount": {
"type": "number"
},
"minCount": {
"type": "number"
},
"minScore": {
"type": "number"
},
"scoreField": {
"type": "string"
}
},
"description": "Use when you want the model to over-generate candidates and Rigor to sort, threshold, and cap them before the count bounds are checked."
},
"task_type": {
"type": "string",
"description": "Your own label for the work. Echoed into telemetry. Not read as a framework name and does not change routing."
}
},
"description": "Only read when execution is \"direct\". Declares the JSON shape you want back, so the answer is generated against your schema and validated against it before return, instead of returned as prose you have to parse. A conforming run also skips the quality-review call, costing 1 LLM call rather than 2. The schema is closed: a record carrying an undeclared key is rejected exactly like one missing a required key."
},
"skip_frameworks": {
"type": "array",
"items": {
"type": "string"
},
"description": "Frameworks to exclude, by the display name the plan shows or by ID, any case. Unknown names are reported in warnings."
},
"require_approval": {
"type": "boolean",
"description": "Pause at pending_approval before the final step."
},
"approval_before_step": {
"type": "array",
"items": {
"type": "number"
},
"description": "Zero-based step indices where approval gates are inserted."
}
},
"description": "Optional workflow preferences."
},
"context_fields": {
"type": "array",
"items": {
"type": "string"
},
"description": "Optional, with prior_workflow_id: the fields to carry over. Pass [\"full_step_results\"] for every step output in full."
},
"task_description": {
"type": "string",
"description": "Natural language description of the task, at most 4,000 characters. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.'"
},
"prior_workflow_id": {
"type": "string",
"description": "Optional: the workflow_id (wr_…) of a finished workflow in your workspace to build on. Its results are passed to this run as context. It is charged as a new run at the normal quote."
}
},
"additionalProperties": false
}| string |
| Filter by classified task type |
| created_after | string | ISO timestamp — only workflows created after this time |
| created_before | string | ISO timestamp — only workflows created before this time |
| counts_toward_limit | string | Filter to workflows counting toward the concurrent limit |
{
"type": "object",
"required": [],
"properties": {
"q": {
"type": "string",
"description": "Search the workflow title and task description. Every whitespace-separated term must appear in one or the other, as a case-insensitive substring — so \"vector search postgres\" matches a task described as \"add vector search to an existing Postgres-backed SaaS app\". Substring matching, not full-text: there is no stemming and no ranking, so \"migrating\" does not match \"migration\". Max 200 characters and 8 terms; punctuation is treated as a separator and wildcards are not supported."
},
"limit": {
"type": "number",
"description": "Page size (default 20, max 100)"
},
"cursor": {
"type": "string",
"description": "Pagination cursor (created_at timestamp from previous page)"
},
"status": {
"type": "string",
"description": "Filter by status (comma-separated). Valid values: executing, step_executing, completed, failed, halted, pending_approval, cancelled. E.g. \"halted,failed,pending_approval\""
},
"folder_id": {
"type": "string",
"description": "Filter by folder ID. Pass \"unassigned\" for workflows in no folder"
},
"task_type": {
"type": "string",
"description": "Filter by classified task type"
},
"created_after": {
"type": "string",
"description": "ISO timestamp — only workflows created after this time"
},
"created_before": {
"type": "string",
"description": "ISO timestamp — only workflows created before this time"
},
"counts_toward_limit": {
"enum": [
"true",
"false"
],
"type": "string",
"description": "Filter to workflows counting toward the concurrent limit"
}
},
"additionalProperties": false
}