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The plugin bundles the each::labs MCP connector with twelve skills for common media tasks: image and video generation and editing, music, voice, fashion, product visuals and workflows.
Install it from the eachlabs/plugin marketplace: under Customize > Plugins in claude.ai and Claude Desktop, with claude plugin install eachlabs@eachlabs in Claude Code, and with codex plugin add eachlabs@eachlabs in Codex.
Sign-in, billing and the tools are the same as with the connector alone. Steps are on the Claude and Codex pages.
Run any each::labs model, not only video tools: search_models and get_model_schema find a model and its inputs, and run_model runs it, with up to 8 variants of one request.
Run your organization's workflows: list_workflows, get_workflow, trigger_workflow and get_workflow_execution.
Interactive cards in AI apps with a chat interface that support MCP Apps: upload files, view and download results, choose one of several variants, adjust a model's settings, fill in a workflow's inputs, compare an edit with the original, and trim or crop by dragging. Apps without a chat interface, such as terminals, have no cards: they show results as links and ask you in the chat instead.
A long job comes back as running with its id; check it with get_job, or get_workflow_execution for a workflow, until it is done.
New list_recent_jobs lists your organization's recent runs (id, model, status, time and result link) so the AI can pick up a run it started earlier.
New permissions workflows:run, workflows:read and uploads:create. Existing connections keep working for the video tools; reconnect to approve the new permissions and see the new tools.
POST /v1/chat/completions accepts any chat model ID from OpenRouter's model list. Send the ID exactly as listed.
Available Models now lists our featured models and every other available model, and shows the matching OpenRouter ID when a featured model's ID differs.
Pricing is the model's list price plus the same standard platform fee OpenRouter charges, nothing more.
The model catalog API reference moved to each::router.
Open a webhook on the Webhooks page and choose Redeliver to send the same payload to your endpoint again, without re-running the execution.
The button is offered only when a redelivery would be accepted, and names the reason when it would not: a delivery still in flight, a short cooldown after a previous redelivery, or a payload that is no longer stored.
A redelivery carries X-Webhook-Redelivery: true, which an ordinary delivery never sends, so your endpoint can tell the two apart.
Signed webhooks are signed again when the redelivery is sent, so it passes your timestamp tolerance rather than looking like a replayed request. See Redeliveries are signed again.
The body is unchanged, so handle deliveries idempotently and key on execution_id.
POST /v1/decisions answers typed questions over one shared context and returns structured results instead of prose: a noul question gives you a probability, a choice question a named option, and a score question a rating on an ordered scale.
Send model: "typesafe/jev-1.13" with state and questions, authenticated with the same Bearer API key as the rest of each::api. The response carries answers keyed by your question names plus billable usage.
The model is routed through OpenRouter to TypeSafe Jev. This API is in alpha and its contract may change — see the Decisions API page.
Webhook deliveries are now signed with HMAC-SHA256
API
Webhook deliveries are now signed with HMAC-SHA256
Set a webhook_secret when creating a prediction or triggering a workflow, and every delivery for that execution now carries X-Webhook-Signature and X-Webhook-Timestamp.
The signature is an HMAC-SHA256 over <X-Webhook-Timestamp>.<raw request body>, hex-encoded and prefixed with sha256=.
The plain-text X-Webhook-Secret header is still sent, so receivers comparing it keep working. Verify the signature instead — it also proves the body was not modified.
Deliveries triggered without a webhook_secret are unchanged and remain unsigned.
GET /v1/models/{slug} is now the single route for retrieving model details. It returns the same payload as before, including the nullable cost guidance.
The deprecated GET /v1/model?slug= query route has been removed. Update any integration that used it to GET /v1/models/{slug}.
Control execution and storage retention for your organization
Organization Settings
Control execution and storage retention for your organization
Organization Settings now puts execution data and stored file retention in your hands. Choose how long each type of data is kept to match your team's needs.
Manage execution retention and storage retention separately, with automatic cleanup based on your organization's settings.
Review and update your retention preferences in one place as your team's data policies evolve.
An easier LLM Router interface and errors by model
LLM Router
An easier LLM Router interface and errors by model
LLM Router now uses the same model-aware form on model pages and in both workflow editors. Edit prompts, upload images, and adjust supported options through familiar controls that adapt to your selected model.
In Canvas Workflows, add and connect LLM Router like any other model card. The shared interface makes it easier to move from trying a model to using it in a workflow.
Error Analytics now breaks down LLM Router errors by the requested model, so you can see which models are failing and focus your investigation.
GET /v2/executions lists organization-wide execution history in descending creation order with the same filters as V1, a default limit of 20, and a maximum limit of 100. Authenticate with your API key as a Bearer token.
Follow the opaque next_cursor only while has_more is true. Keep the same filters and limit for each page; restart without a cursor to change them.
GET /v1/executions is deprecated but remains available for shallow compatibility. Requests beyond the 1,000-row prefix (offset <= 1000 - appliedLimit) return {"error":"V1 execution pagination is limited to 1000 rows; use GET /v2/executions with cursor pagination"}.
The dashboard now has Activity → Alerts for organization-wide spending alerts.
Create threshold rules for rolling charged spend, enable or disable them, and review the 20 most recent triggers from the same page.
Alert emails are sent to the organization owner and billing managers, with the charged spend, threshold, rolling window, and evaluation time included for reconciliation.
Replay re-runs a sample of successful past executions on a compatible test model, so you can compare outputs and cost on your real inputs before switching.
Pick a time window and sample size, then confirm the suggested field mapping. The preview shows which runs are eligible, explains any skips, and gives you the original sample's baseline cost before you start.
Set a soft cost cap, pause or resume an active test, and inspect the original and replayed inputs and outputs side by side. The final totals show how the test model's actual cost compares with the original runs.
You can also keep the same model and change its mapped inputs to test a parameter variant without changing live traffic.
Direct-model fallback lets an async prediction recover when its primary model fails. Compatible fallback models are tried in the order you choose, and the first success completes the original request; when the primary succeeds, the backups do not run.
Open a model from AI Models and choose Fallbacks to create one or more named chains. Start from a curated recommendation when one is available, or pick compatible models yourself, reorder them, and confirm how the primary model's inputs map to each fallback.
Every chain has a stable request selector. Add "fallback_selector": "your-chain" to an async prediction request to use it. Only enabled, saved chains run, and changes apply to new requests.
GET /v1/models/{slug} and GET /v1/model?slug={slug} include a nullable cost field with a fixed estimate or concise usage-based guidance when a reliable public estimate is available.
Estimates may change with provider rates, promotions, or model configuration. Use metrics.cost on the completed prediction or workflow execution for the settled value.