Ever wondered what it would be like to have a dream research team at your fingertips? I recently built something that scratches that exact itch: a virtual laboratory where AI agents take on the roles of different scientists and actually collaborate on research questions. Think of it as your personal interdisciplinary research team that never sleeps and is thinking for you all the time. I watched a video in the earlier days of LLMs where somebody connected different LLMs together through a rest APi through a game and made them role play different kings and queens of the ages. This serves as my motivation along with my experience in academia.
The Team #
The heart of this system is five distinct AI personalities, each with their own expertise and quirks. I spent a lot of time crafting their personalities because, let’s be honest, real scientists have very different approaches to problems.
Dr. Sarah serves as our Principal Investigator. She’s the strategic mastermind with 15 years of experience running interdisciplinary teams. Sarah thinks about funding, timelines, and how to translate complex ideas for different audiences. She’s the one asking “What would the reviewers say about this?” and “How does this fit into our three-year plan?”
Dr. Marcus is our immunologist, and he’s absolutely obsessed with controls and statistical power. Marcus brings that medical precision where every detail matters. He gets genuinely excited about unexpected results but stays conservative about interpretation. You’ll hear him say things like “The immune system is telling us…” and “Have we controlled for HLA variations?”
Dr. Molly is our machine learning specialist who came from Google DeepMind. She speaks in model architectures and performance metrics. Her expertise is always asking about data quality and feature engineering, and she gets visibly excited about transformer models and graph neural networks.
Dr. James bridges the computational and biological worlds as our systems biologist. He thinks in networks and pathways, always considering upstream and downstream effects. James is the guy who can take a pile of multi-omics data and somehow make biological sense of it.
Finally, Dr. Elena serves as our scientific critic and quality assurance. She’s the professional skeptic who asks the uncomfortable but necessary questions. Elena spots methodological flaws from a mile away and makes sure we don’t embarrass ourselves in peer review.
How the Magic Happens #
The real fun begins when these agents interact. I designed the system to simulate actual research team meetings with multiple phases:
def team_meeting(self, research_question: str) -> Dict[str, Any]:
"""Simulate a team meeting discussion with progress tracking"""
self._update_progress("Initialization")
# Phase 1: Initial responses from each specialist
agent_order = ['PI', 'Immunologist', 'ML_Specialist', 'Comp_Bio']
for agent_key in agent_order:
agent = self.agents[agent_key]
response = agent.generate_response(prompt, meeting_context)
meeting_context += f"\n{agent.name}: {response}\n"
# Phase 2: Scientific Critic Review
critic_response = self.agents['Critic'].generate_response(critic_prompt)
# Phase 3: PI Synthesis and Summary
pi_synthesis = self.agents['PI'].generate_response(pi_synthesis_prompt)
The beautiful thing is watching how each agent builds on what others have said. Marcus might point out immunological considerations that is incorporated into machine learning approach. James connects everything to existing biological pathways, while Elena keeps everyone honest about statistical rigor.
Features That Actually Matter #
What started as a fun experiment turned into something surprisingly useful. The system saves everything locally, so you build up this growing knowledge base of discussions. I can go back and see how the team approached similar problems months ago.
The GUI shows real-time progress as agents “think” and respond. There’s something oddly satisfying about watching that progress bar fill up as each team member weighs in. It feels like you’re actually running a meeting.
def _update_progress(self, phase: str, agent_completed: str = None):
"""Update the progress tracking system"""
self.current_phase = phase
if agent_completed:
self.progress_tracker["agents_completed"].append(agent_completed)
# Calculate overall progress
phase_weights = {"Initialization": 10, "Expert Responses": 50,
"Critical Analysis": 80, "PI Synthesis": 100}
self.progress_tracker["overall_progress"] = phase_weights.get(phase, 0)
Individual consultations are another killer feature. Sometimes you just want to bounce an idea off the machine learning expert without convening the whole team. These one-on-one sessions feel remarkably natural and often lead to insights I wouldn’t have considered.
The export functionality means I can generate proper meeting reports that actually look professional. The system structures everything logically: individual responses, critical analysis, and strategic summary. It’s like having a research assistant who never forgets to take notes.
The Technical Reality #
Under the hood, this is all powered by OpenAI’s GPT model (4.1-mini for the costs) with carefully crafted system prompts. I can always choose a much more expensive model, but I decided against it to keep the costs low since the performance gain is not so much for the increase in costs. Each agent has a detailed personality that includes their background, communication style, and specific quirks:
agents['Immunologist'] = VirtualLabAgent(
name="Dr. Marcus",
role="Senior Immunologist",
expertise="Adaptive immunity, vaccine development, autoimmune diseases",
personality="""You are a meticulous immunologist with deep expertise...
You love explaining the elegant complexity of the immune system.
You frequently draw diagrams and use analogies to military strategy..."""
)
The whole system is built in Python with a tkinter GUI for the visual interface because I want it to resemble a old school GUI. You’ll need an OpenAI API key, but beyond that, it’s pretty straightforward to set up. The code handles all the conversation history and progress tracking automatically. The conversation history builds context over time, so later meetings reference earlier discussions. It’s not just isolated Q&A sessions but an ongoing collaborative relationship. But I quickly noticed that the hallucinations also increased with the larger context size.
Room for Growth #
This is definitely version 1.0 territory. Despite the marketing, The conversations are not at the level that can be classified as reaching close to even a junior scientist. I’d love to add more specialized agents (maybe a biostatistician or a regulatory affairs expert), integrate with actual research databases, or even connect to lab instruments for real experimental design. Each of these agents are not at the level where this can replace real scientists. It is pointless to think of that.
The dream would be linking this to literature search APIs so the agents could cite actual papers in their responses. Imagine the bot saying there is this paper we need to look at. I have problems with the LLMs challenging me and I don’t have arguments with my virtual colleagues.
Beyond the cool factor, there’s something genuinely valuable about having diverse perspectives challenge your thinking. This system provides that collaborative energy even when you’re working solo.
It’s also a fantastic learning tool for training scientists. Watching how different experts approach the same problem helps a junior develop a more interdisciplinary mindset. You can learn to ask better questions and considering angles that you would have missed.
Whether you’re a researcher looking for a brainstorming buddy, a student trying to understand how scientific collaboration works, or just someone who thinks AI agents sounds cool but it is not a level to replace serious scientists. My guess is they will slowly evolve over time.