Apps hedra.com

Skill file

Set up Hedra with an AI agent

Generate images, video and audio with multimodal models through an API.

Raw file

Website
hedra.com
Original file
www.hedra.com/docs/skill.md
Last changed
2026-11-07
Last checked
2026-11-07
History
Every change
Data
app.json

Make videosGenerate images

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CLI, SDK, API, Skill
Sign-in
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API key
Yes
Mentions cost
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The setup file

Written by hedra.com, shown exactly as it was checked.

name: hedra
description: Use when building integrations with multimodal AI models for
  image/video/audio generation, vision-language reasoning, or chat completions.
  Reach for this skill when working with asynchronous inference jobs, managing
  API credentials, handling webhooks, or optimizing costs through the prepaid
  USD wallet.
metadata:
  mintlify-proj: hedra
  version: "1.0"

Hedra API v3 Skill

Product summary

Hedra is a managed inference platform providing access to frontier multimodal models through a REST API. Agents use it to submit asynchronous jobs for image, video, and audio generation; run vision-language reasoning through OpenAI-compatible chat endpoints; and manage inference workflows with webhooks and job polling. The platform uses a prepaid USD API wallet (separate from Studio credits) and exposes models through https://api.hedra.com/v3. Key files: API keys stored in HEDRA_API_KEY environment variable as <key_id>:<secret>. SDKs available for Python (hedra-sdk), TypeScript (@hedra/sdk), and CLI (@hedra/cli). Primary docs: https://www.hedra.com/docs

When to use

  • Submitting media generation jobs: When a user requests image, video, or audio generation from text or reference inputs
  • Vision-language inference: When analyzing images or reasoning over visual content using models like GLM-5.3 or Kimi-K3
  • Asynchronous workflows: When building systems that submit jobs and retrieve results later (jobs return 202 with job_id)
  • Webhook integration: When you need to avoid polling and receive completion notifications at a callback URL
  • Cost estimation: Before submitting expensive jobs, estimate the charge with POST /v3/models/{model}/estimate
  • Chaining generations: When using outputs from one model as inputs to another (reuse asset_id from completed jobs)
  • Error recovery: When handling rate limits, insufficient balance, or retryable failures with bounded retry logic

Quick reference

Authentication
Method Format Use case
Direct HTTP Authorization: Key <key_id>:<secret> cURL, raw HTTP requests
Python SDK Pass api_key parameter from hedra import Hedra
TypeScript SDK Pass apiKey parameter new HedraClient({ apiKey })
OpenAI-compatible Pass as api_key, base URL https://api.hedra.com/v3 Use existing OpenAI/Anthropic clients
API scopes (least-privilege)
  • chat:write — Language model calls
  • jobs:read, jobs:write — Submit and poll jobs
  • files:write — Upload media files
  • webhooks:manage — Configure webhook endpoints
  • usage:read — Inspect billing and usage
  • keys:manage — Manage API keys
Job lifecycle
State Meaning Next action
IN_QUEUE Waiting to run Poll or wait for webhook
IN_PROGRESS Running Poll or wait for webhook
COMPLETED Finished successfully Inspect outputs[], download URLs
FAILED Job failed Read error envelope, inspect logs
Common endpoints
Endpoint Purpose
POST /v3/models/{model} Submit a job (returns 202 with job_id)
GET /v3/jobs/{job_id} Poll job status and retrieve outputs
GET /v3/jobs/{job_id}/status Quick status check (lighter than full job fetch)
POST /v3/models/{model}/estimate Estimate cost before submitting
POST /v3/files Upload media for use as model input
POST /v3/chat/completions Synchronous chat/vision inference (OpenAI format)
PUT /v3/webhooks/default Set account-wide webhook endpoint
GET /v3/balance Check API wallet balance
GET /v3/usage Inspect usage and charges
Model selection
Task Starting model Check
Image generation gpt-image-2 Aspect ratio, resolution, quality, output format
Video generation veo-3 Duration, start frame, resolution, audio support
Audio/speech elevenlabs-v3 Available voices, text length, language
Vision reasoning glm-5.3 Max images per request (up to 8), context length
Chat/reasoning glm-5.3, kimi-k3, deepseek-v4-pro-0813 Context, output tokens, reasoning effort

Decision guidance

When to use polling vs webhooks
Scenario Approach Reason
Low-volume, interactive requests Polling with GET /v3/jobs/{job_id}/status Simple, no infrastructure needed
High-volume batch processing Webhooks with PUT /v3/webhooks/default Reduces API calls, scales better
Real-time user-facing requests Polling with short timeout User expects immediate feedback
Background/async processing Webhooks Decouple submission from result handling
When to use chat vs model jobs
Use case Endpoint Why
Text reasoning, image understanding POST /v3/chat/completions Synchronous, returns immediately, OpenAI-compatible
Image/video/audio generation POST /v3/models/{model} Asynchronous, returns job_id, requires polling/webhooks
Chaining outputs Model jobs with asset_id Generated outputs can be reused as inputs
When to estimate vs submit directly
Condition Action
First time with a model Always estimate first
Production workload Estimate on representative inputs before switching models
Cost-sensitive application Estimate before each submission
Rapid prototyping Skip estimation, handle 402 INSUFFICIENT_BALANCE

Workflow

1. Set up credentials and funding
2. Inspect the model
  • Fetch GET /v3/models/{model} to read input schema, limits, and pricing
  • For media models, fetch GET /v3/models/{model}/openapi.json for detailed input/output contracts
  • Check required fields, enums, file size limits, and duration limits
3. Estimate the cost (optional but recommended)
  • Send the exact input payload to POST /v3/models/{model}/estimate
  • Compare prices across models or quality levels before committing
  • Estimate does not create a job
4. Upload media if needed
  • Use POST /v3/files to upload images, video, or audio
  • Returns url (upload handle), content_type, and expires_at (typically 1 hour)
  • Pass the returned url in the model's media input field
5. Submit the job
  • POST to /v3/models/{model} with model-specific input object
  • Optionally pass webhook URL for this job, or rely on account default
  • Response: 202 with job_id (e.g., job_5f3a…)
  • Store the job_id for tracking and recovery
6. Wait for completion
  • Polling: Call GET /v3/jobs/{job_id}/status every 5–10 seconds until COMPLETED or FAILED
  • Webhooks: Register endpoint with PUT /v3/webhooks/default, verify ed25519 signature on delivery, deduplicate on X-Hedra-Webhook-Id
  • Streaming: Use GET /v3/jobs/{job_id}/stream for SSE updates
7. Retrieve and download outputs
  • Fetch GET /v3/jobs/{job_id} to get full job details
  • Check each output's status (should be COMPLETED)
  • Download from outputs[].url without forwarding API credentials
  • Signed URLs expire; download immediately or store the asset_id for reuse
8. Reuse outputs (optional)
  • For chaining jobs, use {"source":"asset","asset_id":"asset_..."} in the next model's input
  • Do not derive asset_id from job_id; read it from the completed output
9. Handle errors and retry
  • Check HTTP status and error code (e.g., INSUFFICIENT_BALANCE, RESOURCE_EXHAUSTED)
  • Use idempotency_key when retrying the same logical job
  • Respect Retry-After header for rate limits
  • Do not retry invalid inputs indefinitely

Common gotchas

  • Confusing API wallet with Studio credits: v3 uses a separate prepaid USD wallet. Studio subscription credits do not fund API requests.
  • Embedding long-lived keys in browser code: Store keys server-side only. Use POST /v3/tokens to mint ephemeral tokens for browser access.
  • Resubmitting on timeout: A local timeout does not cancel the job. Store the job_id and resume polling; do not submit a fresh job.
  • Not verifying webhook signatures: Always verify ed25519 signature on webhook deliveries before processing. Use GET /v3/webhooks/public-key to fetch the public key.
  • Deriving asset_id from job_id: Asset IDs are server-issued and separate from job IDs. Read them from completed outputs, not constructed.
  • Ignoring output status: A completed job can contain outputs with status FAILED or EXPIRED. Check each output's status before using it.
  • Polling too aggressively: Use webhooks or SSE streams for sustained workloads. Aggressive polling triggers rate limits (429).
  • Not handling 402 INSUFFICIENT_BALANCE: This error has two causes: insufficient balance (not retryable, add funds) or in-flight requests holding the balance (retryable after Retry-After).
  • Passing external URLs as media inputs: Only Hedra upload handles (from POST /v3/files) work as media inputs. External URLs are not equivalent.
  • Assuming model-specific input schemas are interchangeable: Each model has its own input contract. Payloads for gpt-image-2 do not work for veo-3.
  • Not checking model availability: Models change; inspect the live catalog before hardcoding model IDs. Use GET /v3/models to list current models.
  • Forgetting to set HEDRA_API_KEY: SDKs read this environment variable. If a legacy v2 integration uses it, use a separate terminal or command-scoped assignment for v3.

Verification checklist

Before submitting work:

  • API key created with required scopes (chat:write for chat, jobs:write for media jobs)
  • API wallet funded and balance checked with GET /v3/balance
  • Model ID confirmed in live catalog (GET /v3/models)
  • Input payload validated against model's schema (GET /v3/models/{model}/openapi.json)
  • Cost estimated with POST /v3/models/{model}/estimate (for production workloads)
  • Media files uploaded with POST /v3/files if needed; URLs stored before expiry
  • Webhook endpoint configured and tested with POST /v3/webhooks/default/test (if using webhooks)
  • Idempotency key generated for retryable submissions
  • Job ID stored for tracking and recovery
  • Error handling in place for 402, 429, 5xx responses
  • Webhook signature verification implemented (if using webhooks)
  • Output status checked before downloading or reusing

Resources

For additional documentation and navigation, see: https://www.hedra.com/docs/llms.txt