Reddit Research Agent
Under maintenancePricing
from $1.00 / 1,000 llm usages
Give the agent a research task, choose your preferred language model, and receive results in the JSON format you define.
You can access the Reddit Research Agent programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.
{ "openapi": "3.0.1", "info": { "version": "0.1", "x-build-id": "AuxasKCbXctZGqZKy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/agenticscraper~reddit-research-agent/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-agenticscraper-reddit-research-agent", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/agenticscraper~reddit-research-agent/runs": { "post": { "operationId": "runs-sync-agenticscraper-reddit-research-agent", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/agenticscraper~reddit-research-agent/run-sync": { "post": { "operationId": "run-sync-agenticscraper-reddit-research-agent", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "task" ], "properties": { "task": { "title": "Research task", "type": "string", "description": "Question or research task." }, "model": { "title": "OpenRouter model", "enum": [ "aion-labs/aion-2.0", "aion-labs/aion-3.0", "aion-labs/aion-3.0-mini", "aion-labs/aion-rp-llama-3.1-8b", "amazon/nova-2-lite-v1", "amazon/nova-lite-v1", "amazon/nova-micro-v1", "amazon/nova-premier-v1", "amazon/nova-pro-v1", "anthracite-org/magnum-v4-72b", "anthropic/claude-3-haiku", "anthropic/claude-fable-5", "anthropic/claude-fable-5.1", "anthropic/claude-fable-5.1:batch", "anthropic/claude-fable-5:batch", "anthropic/claude-haiku-4.5", "anthropic/claude-haiku-4.5:batch", "anthropic/claude-opus-4", "anthropic/claude-opus-4.1", 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"google/gemini-2.5-pro:batch", "google/gemini-3-flash-preview", "google/gemini-3-flash-preview:batch", "google/gemini-3-pro-image", "google/gemini-3-pro-image-preview", "google/gemini-3.1-flash-image", "google/gemini-3.1-flash-image-preview", "google/gemini-3.1-flash-lite", "google/gemini-3.1-flash-lite-image", "google/gemini-3.1-flash-lite-preview", "google/gemini-3.1-flash-lite:batch", "google/gemini-3.1-pro-preview", "google/gemini-3.1-pro-preview-customtools", "google/gemini-3.1-pro-preview:batch", "google/gemini-3.5-flash", "google/gemini-3.5-flash-lite", "google/gemini-3.5-flash-lite:batch", "google/gemini-3.5-flash:batch", "google/gemini-3.6-flash", "google/gemini-3.6-flash:batch", "google/gemini-3.7-flash", "google/gemini-3.7-flash:batch", "google/gemini-3.8-flash", "google/gemini-3.8-flash:batch", "google/gemma-2-27b-it", "google/gemma-3-12b-it", "google/gemma-3-27b-it", "google/gemma-3-4b-it", "google/gemma-4-26b-a4b-it", "google/gemma-4-26b-a4b-it:free", 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"meta/muse-glimmer-30b", "meta/muse-glimmer-30b:batch", "meta/muse-spark-1.1", "meta/muse-spark-1.2", "meta/muse-spark-1.2-contributor", "meta/muse-spark-1.3", "meta/muse-spark-1.3-contributor", "microsoft/phi-4", "microsoft/wizardlm-2-8x22b", "minimax/minimax-01", "minimax/minimax-m1", "minimax/minimax-m2", "minimax/minimax-m2-her", "minimax/minimax-m2.1", "minimax/minimax-m2.5", "minimax/minimax-m2.7", "minimax/minimax-m3", "minimax/minimax-m3:batch", "mistralai/codestral-2508", "mistralai/codestral-2508:batch", "mistralai/devstral-2512", "mistralai/ministral-14b-2512", "mistralai/ministral-3b-2512", "mistralai/ministral-8b-2512", "mistralai/ministral-8b-2512:batch", "mistralai/mistral-large", "mistralai/mistral-large-2407", "mistralai/mistral-large-2512", "mistralai/mistral-large-2512:batch", "mistralai/mistral-medium-3", "mistralai/mistral-medium-3-5", "mistralai/mistral-medium-3-5:batch", "mistralai/mistral-medium-3.1", "mistralai/mistral-medium-3.1:batch", "mistralai/mistral-nemo", "mistralai/mistral-saba", "mistralai/mistral-small-24b-instruct-2501", "mistralai/mistral-small-2603", "mistralai/mistral-small-2603:batch", "mistralai/mistral-small-3.1-24b-instruct", "mistralai/mistral-small-3.2-24b-instruct", "mistralai/mixtral-8x22b-instruct", "mistralai/voxtral-small-24b-2507", "moonshotai/kimi-k2", "moonshotai/kimi-k2-0905", "moonshotai/kimi-k2-thinking", "moonshotai/kimi-k2.5", "moonshotai/kimi-k2.6", "moonshotai/kimi-k2.7-code", "moonshotai/kimi-k3", "moonshotai/kimi-k3:batch", "morph/morph-v3-fast", "morph/morph-v3-large", "nex-agi/nex-n2.5-mini:free", "nex-agi/nex-n2.5-pro:free", "nousresearch/hermes-3-llama-3.1-405b", "nousresearch/hermes-3-llama-3.1-70b", "nousresearch/hermes-4-405b", "nvidia/nemotron-3-nano-30b-a3b", "nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free", "nvidia/nemotron-3-super-120b-a12b", "nvidia/nemotron-3-super-120b-a12b:free", "nvidia/nemotron-3-ultra-550b-a55b", "nvidia/nemotron-3-ultra-550b-a55b:free", "nvidia/nemotron-3.5-content-safety", "nvidia/nemotron-3.5-content-safety:free", "nvidia/nemotron-3.5-lightning", "nvidia/nemotron-3.5-lightning:free", "openai/gpt-3.5-turbo", "openai/gpt-3.5-turbo-0613", "openai/gpt-3.5-turbo-16k", "openai/gpt-3.5-turbo-instruct", "openai/gpt-3.5-turbo:batch", "openai/gpt-4", "openai/gpt-4-turbo", "openai/gpt-4-turbo-preview", "openai/gpt-4-turbo:batch", "openai/gpt-4.1", "openai/gpt-4.1-mini", "openai/gpt-4.1-mini:batch", "openai/gpt-4.1-nano", "openai/gpt-4.1-nano:batch", "openai/gpt-4.1:batch", "openai/gpt-4o", "openai/gpt-4o-2024-05-13", "openai/gpt-4o-2024-08-06", "openai/gpt-4o-2024-11-20", "openai/gpt-4o-mini", "openai/gpt-4o-mini-2024-07-18", "openai/gpt-4o-mini:batch", "openai/gpt-4o:batch", "openai/gpt-5", "openai/gpt-5-image", "openai/gpt-5-image-mini", "openai/gpt-5-mini", "openai/gpt-5-mini:batch", "openai/gpt-5-nano", "openai/gpt-5-nano:batch", "openai/gpt-5-pro", "openai/gpt-5-pro:batch", "openai/gpt-5.1", "openai/gpt-5.1-codex", "openai/gpt-5.1-codex-max", "openai/gpt-5.1-codex-mini", "openai/gpt-5.1:batch", "openai/gpt-5.2", "openai/gpt-5.2-chat", "openai/gpt-5.2-codex", "openai/gpt-5.2-pro", "openai/gpt-5.2-pro:batch", "openai/gpt-5.2:batch", "openai/gpt-5.3-codex", "openai/gpt-5.4", "openai/gpt-5.4-image-2", "openai/gpt-5.4-mini", "openai/gpt-5.4-mini:batch", "openai/gpt-5.4-nano", "openai/gpt-5.4-nano:batch", "openai/gpt-5.4-pro", "openai/gpt-5.4-pro:batch", "openai/gpt-5.4:batch", "openai/gpt-5.5", "openai/gpt-5.5-pro", "openai/gpt-5.5-pro:batch", "openai/gpt-5.5:batch", "openai/gpt-5.6-luna", "openai/gpt-5.6-luna-pro", "openai/gpt-5.6-luna-pro:batch", "openai/gpt-5.6-luna:batch", "openai/gpt-5.6-sol", "openai/gpt-5.6-sol-pro", "openai/gpt-5.6-sol-pro:batch", "openai/gpt-5.6-sol:batch", "openai/gpt-5.6-terra", "openai/gpt-5.6-terra-pro", "openai/gpt-5.6-terra-pro:batch", "openai/gpt-5.6-terra:batch", "openai/gpt-5:batch", "openai/gpt-6-astra", "openai/gpt-6-astra-pro", "openai/gpt-6-astra-pro:batch", "openai/gpt-6-astra:batch", "openai/gpt-audio", "openai/gpt-audio-mini", "openai/gpt-chat-latest", "openai/gpt-oss-120b", "openai/gpt-oss-120b:batch", "openai/gpt-oss-20b", "openai/gpt-oss-20b:batch", "openai/gpt-oss-safeguard-20b", "openai/o1", "openai/o1-pro", "openai/o3", "openai/o3-mini", "openai/o3-mini-high", "openai/o3-mini:batch", "openai/o3-pro", "openai/o3:batch", "openai/o4-mini", "openai/o4-mini-high", "openai/o4-mini:batch", "openrouter/auto", "openrouter/auto-beta", "openrouter/bodybuilder", "openrouter/free", "openrouter/fusion", "openrouter/pareto-code", "perceptron/perceptron-mk1", "perplexity/sonar", "perplexity/sonar-deep-research", "perplexity/sonar-pro", "perplexity/sonar-pro-search", "perplexity/sonar-reasoning-pro", "poolside/laguna-s-2.1", "poolside/laguna-s-2.1:free", "poolside/laguna-xs-2.1", "poolside/laguna-xs-2.1:free", "qwen/qwen-2.5-72b-instruct", "qwen/qwen-2.5-7b-instruct", "qwen/qwen-2.5-coder-32b-instruct", "qwen/qwen-plus", 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"thinkingmachines/inkling-small:free", "thinkingmachines/inkling:batch", "thinkingmachines/inkling:free", "undi95/remm-slerp-l2-13b", "upstage/solar-pro-3", "upstage/solar-pro4", "writer/palmyra-x5", "x-ai/grok-4.20", "x-ai/grok-4.20-multi-agent", "x-ai/grok-4.3", "x-ai/grok-4.3:batch", "x-ai/grok-4.5", "x-ai/grok-4.6", "x-ai/grok-build-0.1", "xiaomi/mimo-v2.5", "xiaomi/mimo-v2.5-pro", "z-ai/glm-4.5", "z-ai/glm-4.5-air", "z-ai/glm-4.5v", "z-ai/glm-4.6", "z-ai/glm-4.6v", "z-ai/glm-4.7", "z-ai/glm-4.7-flash", "z-ai/glm-5", "z-ai/glm-5-turbo", "z-ai/glm-5.1", "z-ai/glm-5.2", "z-ai/glm-5.2:batch", "z-ai/glm-5.3", "z-ai/glm-5.3-flash", "z-ai/glm-5.3-flash:batch", "z-ai/glm-5.3:batch", "z-ai/glm-5v-turbo", "~anthropic/claude-fable-latest", "~anthropic/claude-haiku-latest", "~anthropic/claude-opus-latest", "~anthropic/claude-sonnet-latest", "~deepseek/deepseek-flash-latest", "~deepseek/deepseek-pro-latest", "~deepseek/deepseek-v4-flash-latest", "~google/gemini-flash-latest", "~google/gemini-pro-latest", "~moonshotai/kimi-latest", "~openai/gpt-astra-latest", "~openai/gpt-luna-latest", "~openai/gpt-mini-latest", "~openai/gpt-sol-latest", "~openai/gpt-terra-latest", "~x-ai/grok-latest", "~z-ai/glm-flash-latest", "~z-ai/glm-latest" ], "type": "string", "description": "Any model ID from GET /models. Defaults to OpenRouter Auto Router.", "default": "openrouter/auto" }, "output_schema": { "title": "Output JSON schema", "type": "object", "description": "JSON schema for the research response.", "default": { "type": "object" } }, "max_tokens": { "title": "Max output tokens", "minimum": 128, "maximum": 16000, "type": "integer", "description": "Maximum number of generated tokens.", "default": 3000 }, "max_api_calls": { "title": "Maximum Reddit API calls", "minimum": 1, "maximum": 10, "type": "integer", "description": "Hard maximum number of MCP tool calls per run.", "default": 5 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } }}OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.
OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.
By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.
You can download the OpenAPI definitions for Reddit Research Agent from the options below:
If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.
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