Actionist·AI/ML API
AI/ML API

AI/ML API

· #287 most-used

Access 500+ AI models via a single unified API

ProductivityAnalyticsDeveloperAIAutomation

AI/ML API (aimlapi.com) is a unified gateway to 500+ AI models — covering text, image, video, speech, embeddings, and 3D generation — through a single OpenAI-compatible endpoint. Connect it to Actionist and your agents can run chat completions against any frontier model, generate images, transcribe audio, synthesise speech, compute semantic embeddings, and chain multi-modal tasks together, all without juggling separate provider credentials or SDKs.

Try AI/ML API with ActionistVisit AI/ML API
Average time saved
14 hours
per person · per month
≈ 2 workdays back

Eliminates manual work. Agents eliminate manual provider selection, credential juggling, and hand-stitched prompt pipelines by routing every AI task through a single configured connection.

Schedule

What your AI/ML API agent runs on autopilot

A week of scheduled jobs your Actionist agent will execute on your behalf.

28Scheduled jobs
7Agents at work
24/7Always on
Agents
Tue–Thu
Tue
Wed
Thu
7a
8a
9a
10a
11a
12p
1p
2p
3p
4p
5p
6p
Multi-app workflows

AI/ML API × every other app you use

End-to-end automations that span multiple apps — each one a real business outcome.

6Workflows
8Apps spanned
~44 hrsSaved / week
6Personas served
For sales
Featured3 apps

AI-drafted prospect email from CRM context

The Sales Agent pulls the prospect's industry, role, and recent news (via Web Search via AI Model), then calls Chat Completion to generate a personalised outreach email. The draft is posted as an internal note on the HubSpot deal for the rep to review and send — reducing email prep from 20 minutes to under two minutes per prospect.

~9 hrs

Time saved for your team — every week, on autopilot

The flow
Trigger·When a new lead is created or a deal stage changes in HubSpot
Trigger
Step 1
HubSpot
Detect new lead or deal-stage change
write
Step 2
AI/ML API
Web Search via AI Model — recent news about prospect company
write
Step 3
AI/ML API
Chat Completion — generate personalised outreach email draft
write
Step 4
HubSpot
Post email draft as internal note on deal record
write
Step 5
Slack
Notify rep in Slack that draft is ready for review
Result
Web Search via AI Model — recent news about prospect companyChat Completion — generate personalised outreach email draftPost email draft as internal note on deal recordNotify rep in Slack that draft is ready for review
The win
Saved per run
18 min
Runs / week
~30×
Every prospect email is personalised and context-rich before the rep even opens their inbox
Driven bySales Agent
ROI

Savings

What your team gets back — two angles: what you stop doing manually, and what that's worth.

Without Actionist

What you do manually today

With Actionist

What your agent runs for you

  • Sales
    120 min / week
    Manual email personalisation

    Reps research each prospect, open a separate AI tool, paste in context, copy the output, then edit for tone — 20 minutes of setup per outreach email before anything goes out.

    Sales Agent
    0 min
    Agent generates personalised drafts from CRM context

    When a deal stage changes, the Sales Agent automatically pulls CRM context, runs a live web search for company news, calls Chat Completion, and posts a ready-to-send draft on the deal record — the rep reviews and sends in under two minutes.

  • Marketing
    180 min / week
    Manual content brief-to-draft cycle

    A copywriter reads the brief, researches the topic, writes a first draft, and submits it for review — typically 3-4 hours per blog post before an editor sees it, creating a content calendar bottleneck.

    Marketing Agent
    0 min
    Agent delivers a first draft and header image on brief creation

    When a brief lands in Notion, the Marketing Agent calls Chat Completion for the draft and Generate Image for the header illustration — a complete first draft with art is in Google Docs within about a minute, ready for the editor to polish.

  • Customer Support
    160 min / week
    Rep reads ticket and writes reply from scratch

    Support reps read each ticket, search the knowledge base manually, and draft a reply — averaging 8 minutes per ticket on knowledge-base-resolvable issues, creating queue backlogs during volume spikes.

    Customer Support Agent
    0 min
    Agent grounds every reply in the knowledge base automatically

    The Support Agent embeds the ticket, retrieves the most relevant articles semantically, and posts a grounded draft on the ticket within about a minute. Reps review and send — handling more tickets in the same time.

  • Human Resources
    90 min / week
    Manual interview note-taking and write-up

    HR coordinators take notes during interviews, transcribe recordings manually, and write summaries — 45 minutes of admin per interview, with inconsistent formatting that makes cross-candidate comparison difficult.

    Human Resources Agent
    0 min
    Agent transcribes, structures, and writes the summary automatically

    The HR Agent transcribes the interview recording via Speech to Text, extracts a structured summary with Structured Output Generation, and writes it to the candidate record — consistent, formatted notes ready for the hiring manager within about a minute of upload.

  • Finance
    140 min / week
    Manual invoice data entry from PDF

    AP staff open each PDF invoice, read the figures, and type party name, line items, total, and due date into the tracking spreadsheet — averaging 7 minutes per invoice, with regular transcription errors under high volume.

    Finance Agent
    0 min
    Agent extracts invoice fields from PDFs via vision model

    The Finance Agent sends each invoice page to Vision Analysis, receives structured JSON, and writes a clean row to the AP spreadsheet — invoices processed with no human data entry and zero transcription errors.

  • Operations
    120 min / week
    Weekly ops report written manually every Friday

    An ops manager collects KPI data from multiple sources, writes narrative sections for each department, assembles the report, and publishes it — a 2-hour task that often slips to Monday when people leave early.

    Operations Agent
    0 min
    Agent generates and publishes the full report automatically

    At 4 PM Friday the Operations Agent reads Google Sheets KPIs, calls Batch Text Completions to generate all department narratives in parallel, assembles an executive summary, and posts the finished report to Notion before the team closes their laptops.

  • Legal
    90 min / week
    Manual contract clause review against standard

    Legal counsel reads each incoming contract clause by clause, compares it against the standard clause library mentally, and flags deviations — 2-3 hours per vendor contract before any negotiation can start.

    Legal Agent
    0 min
    Agent flags non-standard clauses via semantic similarity

    The Legal Agent embeds each clause and compares it against pre-embedded standard clauses using Create Embedding — non-conforming clauses are flagged with similarity score and the matching standard text, so counsel reviews deviations only, not the entire document.

+ 100s of other AI/ML API automations
Average time saved
90 hrs / person / month
Calculator

Calculate what your team saves

Team size
5 people
Hourly rate
$75 / hr
Hours saved / week
18
Hours saved / year
875
Annual ROI
$65,625

Based on AI/ML API's typical team usage — the visible tasks plus a few other automations the agent runs: ~3.5 hrs / person / week of admin work automated.

Connect

How to plug AI/ML API into Actionist

Pick the connection method that suits your environment.

Generate an API key from your AI/ML API dashboard and paste it into Actionist. Every agent call is authenticated with your key — no OAuth flow needed.

1
Create your AI/ML API account

Sign up at aimlapi.com and navigate to the Dashboard. Free-tier accounts include a usage credit to test API calls before upgrading.

2
Generate an API key

In the Dashboard, open API Keys and click Create key. Name it (e.g. 'Actionist') and copy it immediately — it won't be shown again.

3
Paste into Actionist

Open the Apps tab, find AI/ML API, click Connect, and paste the key into the API Key field. Actionist runs a test Chat Completion call to confirm the handshake.

Credentials you'll need
API Key*
aimlapi.com → Dashboard → API Keys → Create key
Actions

15 actions your agent can call

Read and write operations available to your Actionist agent.

Triggers

0 events your agent can react to

Events your agent watches for, and the actions it kicks off in response.

This app has no triggers yet.
Skills

Skills that pair with AI/ML API

Reusable agent skills that work well alongside this app.

arXiv Research Assistant

Search, download, and summarise academic papers from arXiv. Built for AI/ML researchers.

GitHub AI Trends

Generate GitHub AI trending project reports as formatted text leaderboards. Fetches top-starred AI/ML/LLM repos by daily, weekly, or monthly period.

Trend Watcher

Monitors GitHub Trending and tech communities to track and analyse emerging tools in CLI, AI/ML, automation, and developer categories.

Competitive Intelligence & Market Research

B2B SaaS competitive intelligence with 24 scenarios across Sales/HR/Fintech/Ops Tech.

Research Cog

AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, and literature reviews.

MCP servers

MCP servers that work with AI/ML API

Connect Actionist to MCP servers built for or around this app.

checklist.day Registry

Registry of Python AI/ML libraries: correct imports, quickstart code, and known footguns.

FAQs

Questions about AI/ML API + Actionist

How does Actionist connect to AI/ML API?
Open the Apps tab in Actionist, find AI/ML API, and click Connect. You'll be asked for an API key — generate one from the AI/ML API dashboard (aimlapi.com → Dashboard → API Keys → Create key). Paste the key into Actionist, and the agent runs a test Chat Completion call to confirm the connection is live. No OAuth flow is needed; the key persists securely in your workspace credential store.
Which AI models can agents use through AI/ML API?
AI/ML API currently provides access to 500+ models across text, image, video, audio, embedding, and 3D modalities. On the text side this includes models from OpenAI (GPT series), Anthropic (Claude series), Meta (Llama), Google (Gemini), Mistral, DeepSeek, and dozens more. For images: FLUX, Stable Diffusion variants, DALL-E. For video: Kling, Wan. The full list is returned by the List Available Models action and updated continuously. Within Actionist you can specify the model ID in any AI/ML API action, letting different agent tasks use the model best suited to the job.
Is the AI/ML API endpoint compatible with the OpenAI SDK?
Yes — AI/ML API is designed to be OpenAI-compatible. You can point the OpenAI SDK or any OpenAI-compatible client at https://api.aimlapi.com/v1 and use your AI/ML API key in place of the OpenAI key. This means existing prompts, tool schemas, and streaming configurations work without modification. Actionist's AI/ML API integration uses this compatibility layer under the hood, so all standard parameters (temperature, max_tokens, response_format, tools) behave as expected.
How do I choose which model to use for a specific agent task?
Use the model ID parameter in any Chat Completion, Generate Image, or other AI/ML API action within Actionist. For cost-sensitive, high-volume tasks (support triage, lead classification) smaller and faster models keep costs low. For complex reasoning or long-context tasks (contract review, financial narrative) use a frontier reasoning model. For creative generation (blog drafts, marketing copy) mid-tier models with strong instruction-following score best on value. Call List Available Models to see what is currently live and check the AI/ML API pricing page for per-token costs before committing a model to a high-volume workflow.
Can agents use AI/ML API to process images and documents, not just text?
Yes. The Vision Analysis action accepts an image URL alongside a text prompt and returns a text response from a vision-capable model — useful for invoice extraction, screenshot classification, chart reading, and visual QA. For PDFs, the agent converts pages to images first (via a read action), then sends each page to Vision Analysis. The Generate Image action works in the reverse direction: text in, image out. AI/ML API's vision endpoints follow the same message format as OpenAI's vision API, so passing base64 or URL-based images to the model works consistently.
What is the difference between Chat Completion and Structured Output Generation?
Chat Completion returns free-form text — best for drafting, summarising, explaining, or answering in natural language. Structured Output Generation constrains the model's response to a JSON schema you define, so the output is always valid, parseable JSON matching your exact field names and types. Use Structured Output Generation whenever the result feeds directly into another system (a CRM write, a spreadsheet row, an API call) and you need machine-readable data rather than human-readable prose. Both actions call the same AI/ML API endpoint; Structured Output Generation sets the response_format parameter to your schema.
How does AI/ML API handle rate limits and what happens if a model is unavailable?
Each AI/ML API plan has request-per-minute and token-per-day limits. Actionist surfaces these errors in the agent run log so you can identify bottlenecks. For high-volume tasks, use the Batch Text Completions action rather than firing individual requests — batches are processed asynchronously and have higher effective throughput. If a specific model is temporarily unavailable, use the List Available Models action at the start of a workflow to confirm availability and route to a fallback model ID dynamically rather than hard-coding a single model throughout your agent logic.
Is my data sent to AI/ML API kept private and used for model training?
AI/ML API does not use API request data to train its models, and routes requests to the underlying model providers under enterprise-grade data-handling agreements where applicable. Prompts and completions are not stored longer than is operationally necessary for the API call. For sensitive workloads (legal documents, financial data, HR records), review the AI/ML API data processing agreement and consider whether to redact or anonymise data before sending it in prompts — Actionist lets you add a preprocessing step to any workflow to do this before the AI/ML API call is made.