Paperclip Integration
AVELIN API is compatible with Paperclip — an open-source AI agent management platform for running teams of AI agents as structured companies.
Paperclip is an open-source platform that treats AI agents like employees in a real organization. Its killer feature is the corporate governance layer: org charts, budgets, goals, and reporting structures for AI agent teams. You can run "zero-human companies" where agents have defined roles, report to manager agents, follow budgets, and work toward company goals — all without human intervention. There's also a community OpenRouter adapter on GitHub that makes it easy to plug in any compatible provider.
Designed for researchers, AI enthusiasts, and forward-thinking operators experimenting with autonomous business operations and multi-agent coordination, Paperclip turns abstract agent swarms into manageable, auditable organizations. Point it at AVELIN's agentic models for reliable multi-step reasoning and tool-calling across your entire agent hierarchy.
Why AVELIN + Paperclip? Leverage AVELIN's frontier models with 1M-token context windows, automatic failover, and competitive pricing for multi-agent company simulations and automated business operations.
Quick Setup
Step 1: Get your AVELIN API key
Sign up at avelin.ai and generate an API key. It will look like sk-ave...xxxx.
Step 2: Install Paperclip
Clone the repository and install dependencies:
git clone https://github.com/paperclip-org/paperclip cd paperclip # Follow the README for installation instructions
Step 3: Configure AVELIN as the API provider
Paperclip uses an OpenRouter-compatible API. Set the base URL to AVELIN and provide your API key. If using the community OpenRouter adapter, configure it to point to AVELIN:
export OPENROUTER_BASE_URL=https://api.avelin.ai/v1 export OPENROUTER_API_KEY=sk-avelin-YOUR_KEY
API Reference
Recommended Models
Why AVELIN for Paperclip?
- Agentic models: Purpose-built for tool-calling and multi-step planning across agent hierarchies
- Automatic failover: Your agent company keeps running if a provider is down — no single point of failure
- 1M context: Agents can process large documents, reports, and conversation histories without losing track
- Cost optimization: Prompt caching reduces costs by ~80% on repeated prompts — critical when running many agents simultaneously
- Model variety: Mix high-reasoning models for leadership agents with fast models for worker agents to optimize spend
Troubleshooting
Related
- API Quickstart — First API call in 5 minutes
- Agent Zero Guide — Visual agent builder
- Quests Guide — Rapid app prototyping
- Model Catalog — Full comparison