Collaborative Research Team with Round-Robin Turns
This example demonstrates how to use RoundRobin to facilitate collaborative discussion where agents take randomized turns and build on each other’s contributions — perfect for brainstorming, research synthesis, and cross-functional planning.
Step 1: Get Your API Key
- Visit https://swarms.world/platform/api-keys
- Sign in or create an account
- Generate a new API key
- Set it as an environment variable:
export SWARMS_API_KEY="your-api-key-here"
Step 2: Setup
import requests
import json
import os
API_BASE_URL = "https://api.swarms.world"
API_KEY = os.environ.get("SWARMS_API_KEY", "your_api_key_here")
headers = {
"x-api-key": API_KEY,
"Content-Type": "application/json"
}
Step 3: Define the Research Team
Create a team of domain experts who will take turns contributing their perspective. Each agent sees the full conversation history and builds on what others have said:
def run_roundtable(topic: str, max_loops: int = 1) -> dict:
"""Run a collaborative round-robin research discussion."""
swarm_config = {
"name": "Research Roundtable",
"description": "Collaborative research with round-robin agent turns",
"swarm_type": "RoundRobin",
"task": topic,
"agents": [
{
"agent_name": "Industry Researcher",
"description": "Gathers market data and industry trends",
"system_prompt": "You are an industry researcher. Provide data-driven market analysis, cite specific numbers and trends, and identify key players. Build on insights from other team members when available.",
"model_name": "gpt-4o",
"max_loops": 1,
"temperature": 0.4
},
{
"agent_name": "Technology Analyst",
"description": "Evaluates technical landscape and innovation",
"system_prompt": "You are a technology analyst. Assess the technical landscape, evaluate emerging technologies, and identify innovation opportunities. Reference and build upon the research data shared by other team members.",
"model_name": "gpt-4o",
"max_loops": 1,
"temperature": 0.4
},
{
"agent_name": "Strategy Advisor",
"description": "Synthesizes insights into actionable strategy",
"system_prompt": "You are a strategy advisor. Synthesize insights from the team into actionable strategic recommendations. Identify risks, opportunities, and provide a prioritized roadmap. Reference specific points made by other team members.",
"model_name": "gpt-4o",
"max_loops": 1,
"temperature": 0.5
}
],
"max_loops": max_loops
}
response = requests.post(
f"{API_BASE_URL}/v1/swarm/completions",
headers=headers,
json=swarm_config,
timeout=180
)
return response.json()
Step 4: Run the Roundtable
# Define the research topic
topic = """
Analyze the emerging autonomous AI agent market. Cover the current state of
the technology, major players and their approaches, enterprise adoption
barriers, and the most promising near-term use cases. Provide actionable
insights for a startup considering entering this space.
"""
# Run the roundtable discussion
result = run_roundtable(topic)
# Display the collaborative discussion
for output in result.get("output", []):
agent = output["role"]
content = output["content"]
print(f"\n{'='*60}")
print(f"{agent}")
print(f"{'='*60}")
# Handle content as string or list
if isinstance(content, list):
content = ' '.join(str(item) for item in content)
print(str(content)[:800] + "...")
============================================================
Industry Researcher
============================================================
## Autonomous AI Agent Market Analysis
### Market Overview
The autonomous AI agent market reached an estimated $4.2B in 2024 and is
projected to grow at 45% CAGR through 2028. Key segments include:
- Developer tools & coding agents (35% of market)
- Customer service automation (28%)
- Enterprise workflow agents (22%)
- Research & analysis agents (15%)
### Major Players
- **OpenAI** (GPT-based agents, Assistants API)
- **Anthropic** (Claude, tool use framework)
- **Google** (Gemini agents, Vertex AI)
- **Startups**: Cognition (Devin), Adept, Induced AI, CrewAI, Swarms
### Enterprise Adoption
Current penetration: ~12% of Fortune 500 in production...
============================================================
Technology Analyst
============================================================
Building on the Industry Researcher's market data, let me assess the
technical landscape:
### Core Technology Stack
The agent frameworks broadly fall into three categories:
1. **Single-agent loops** (ReAct, function calling) — mature but limited
2. **Multi-agent orchestration** (Swarms, CrewAI, AutoGen) — growing fast
3. **Code-generation agents** (Devin, Cursor) — highest enterprise demand
### Key Technical Differentiators
Drawing from the market segments identified above:
- Tool use reliability (currently 85-92% accuracy)
- Context window management for long-running tasks
- Multi-step planning and self-correction capabilities
- Sandboxed execution environments for safety...
============================================================
Strategy Advisor
============================================================
Synthesizing the market data from our Industry Researcher and the technical
assessment from our Technology Analyst, here are my strategic recommendations:
### Entry Strategy (Priority Order)
1. **Target the multi-agent orchestration gap** — As noted, this segment is
growing fastest at 45% CAGR, and tool use reliability (85-92%) leaves
room for differentiation through better orchestration
2. **Focus on enterprise workflow agents** — The 22% market share with only
12% Fortune 500 penetration signals massive headroom
3. **Build on open-source adoption** — CrewAI and Swarms have proven the
community-first model works for developer tools
### Key Risks
- Commoditization risk as foundation model providers add native agent features
- Enterprise security and compliance requirements add 6-12 months to sales cycles...
Step 5: Multi-Loop Refinement (Optional)
Run multiple rounds so agents can iterate on each other’s contributions:
# Run 2 loops — agents go around twice, refining their analysis each time
deep_result = run_roundtable(
topic="Evaluate the competitive positioning of Anthropic vs OpenAI vs Google in the enterprise AI market. Assess technical capabilities, pricing strategy, ecosystem lock-in, and likely market share in 3 years.",
max_loops=2
)
# Show the final contributions after 2 rounds of refinement
for output in deep_result.get("output", []):
print(f"\n{output['role']}:")
content = output["content"]
if isinstance(content, list):
content = ' '.join(str(item) for item in content)
print(str(content)[:600] + "...")
RoundRobin creates a collaborative dynamic where each agent sees the full conversation history and naturally builds on prior contributions. Agent order is randomized each loop, so every agent gets a chance to lead the conversation. Use max_loops > 1 when you want the team to iteratively refine their analysis across multiple rounds.
