Create AI agents that plan, execute, and delegate across your projects. Configure custom models, run autonomous discovery cycles, and generate ideas and assets 24/7 — while you focus on what matters.
No complex setup. Just create, assign, and review.
Configure a new agent with a name, model, system prompt, and capabilities. Choose from 200+ LLMs via OpenRouter.
Give your agent a task — or let it run autonomously. For complex work, use delegation strategies to distribute sub-tasks across multiple agents.
Browse outputs in the Agent Output Library. Approve, request revisions, or feed results back into the next cycle.
From agent creation to autonomous execution — built for teams that ship.
Create agents with custom names, model selection, system prompts, and capability toggles. Each agent is a specialised worker tuned for specific tasks.
Break complex tasks into sub-tasks and delegate across multiple agents simultaneously. Choose from 5 strategies: parallel, sequential, divide & conquer, and more.
Let agents run continuous discovery cycles on a schedule. They generate ideas, create multi-format assets, and log everything — no manual prompting required.
Browse all agent-generated content in card, table, graph, or timeline views. Search, filter, and export assets across your entire agent ecosystem.
Choose from GPT-4o, Claude 3.5, Gemini 1.5 Pro, Llama 3, and 200+ models via OpenRouter. Each agent can use a different model optimised for its role.
Get notified on completions, errors, and review requests. Agents can review each other's work in a quality-controlled pipeline before final approval.
Break complex tasks into sub-tasks and distribute them across multiple AI agents. Choose the strategy that matches your workflow.
Split work across agents by category — each agent handles a different domain in parallel. Perfect for multi-faceted research projects.
Assign agents different roles — researcher, writer, reviewer — to work simultaneously on the same task. Ideal for content pipelines.
Chain agents in sequence — each agent's output feeds into the next for multi-stage processing. Great for data enrichment workflows.
Split a large task into equal chunks and distribute across agents for parallel processing. Best for bulk data analysis or content generation.
Define your own delegation logic — assign specific sub-tasks to specific agents with custom instructions and review gates.
Turn on autonomous mode and let your AI agents run continuous discovery cycles — generating ideas, creating assets, and logging everything to your library. No manual prompting required.
Agents scan your project context and generate fresh ideas with confidence scoring, priority indicators, and category tagging — on a schedule you control.
From code snippets to blog posts, video scripts to images — agents create production-ready assets using DALL-E 3, Google Imagen, or your preferred LLM.
Every cycle, every idea, every asset is logged with timestamps, status, and error tracking. Search, filter, and review everything your agents produce.
The Agent Output Library is your central repository for everything your agents produce. Browse in card, table, graph, or timeline views — search, filter, and export across your entire agent ecosystem.

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