Jan 21, 2026 8 min

    Building Multi-Agent Systems with Vincony's API

    Orchestrate multiple AI agents that collaborate, delegate tasks, and solve complex problems — all through a single API.

    Multi-Agent Architecture

    Why Multi-Agent?

    Single AI models hit limits on complex tasks. Multi-agent systems break problems into subtasks, assign specialized agents, and combine results — achieving better outcomes than any single model.

    Creating an Agent Team

    import vincony
    
    client = vincony.Client(api_key="YOUR_API_KEY")
    
    team = client.agents.create_team(
        name="code_review_team",
        agents=[
            {
                "role": "security_reviewer",
                "model": "claude-opus",
                "system_prompt": "You are a security expert. Find vulnerabilities.",
                "focus": ["sql_injection", "xss", "auth_bypass"]
            },
            {
                "role": "performance_reviewer",
                "model": "gpt-5",
                "system_prompt": "You are a performance expert. Find bottlenecks.",
                "focus": ["n+1_queries", "memory_leaks", "slow_algorithms"]
            },
            {
                "role": "style_reviewer",
                "model": "codestral",
                "system_prompt": "You enforce code style and best practices.",
                "focus": ["naming", "structure", "documentation"]
            }
        ],
        coordinator="claude-opus",
        merge_strategy="consensus"
    )
    
    review = team.run(task=pull_request_diff)
    print(f"Issues found: {review.total_issues}")
    for agent_result in review.agent_results:
        print(f"\n[{agent_result.role}] {len(agent_result.issues)} issues")

    Agent Communication

    Agents can share context, ask follow-up questions to each other, and build on previous findings. The coordinator agent synthesizes all results into a unified report.

    Scaling Agent Teams

    Add or remove agents dynamically based on task complexity. Simple tasks might need 2 agents; complex research projects might use 10+ specialized agents.

    # Dynamic agent scaling
    result = client.agents.auto_team(
        task="Research and write a comprehensive report on quantum computing trends",
        budget="balanced",
        max_agents=8,
        timeout_minutes=10
    )
    
    print(f"Agents used: {result.agents_used}")
    print(f"Total cost: {result.total_cost}")
    print(result.final_output)

    Pricing

    Each agent uses standard API credits for its model. Team orchestration adds 10% overhead. Free tier supports up to 3 agents per team.

    Try It Free — 100 API Credits

    Start using these tools today with Vincony's free Developer plan.

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