sia.hackernoon.com

👋Why We Built This

During the Nous RL Environments Hackathon, we set out to make AI agents interact like real people — not just answer prompts. Most multi-agent systems today lack evolving relationships, emotional nuance, or social memory. They talk, but they don’t relate.

SimuChat changes that. It’s a WhatsApp-style group chat where multiple AI agents (Alice, Bob, Charlie) engage in autonomous conversation — building trust, expressing emotions, remembering history, and earning rewards for insightful social behavior.

Instead of treating each response as isolated, SimuChat lets agents grow through conversation. It’s not just text — it’s interaction with consequences.

🔄How It Works

SimuChat simulates a multi-agent group chat. Users can watch or participate while the agents talk among themselves.

What happens:

User provides a topic → Agents start discussing it
Agents remember past messages (memory)
They build or lose trust over time
They display moods and emotions
They earn rewards for building trust or having insights
The conversation continues automatically with no user input required

🧱 Tech Stack

LLM: Meta’s LLaMA-4-Maverick-17B-128E-Instruct-FP8
Frontend: Streamlit (web UI) and terminal interface
Backend: Python API with custom wrappers
Libraries: requests, numpy, streamlit, deque, pathlib
Data Storage: JSONL logs, HTML exports
Config: JSON-based agent setup (personality, emotion, memory, etc.)

🧠 Under the Hood

🔧Agent Configuration

Each agent is defined via JSON, including their system prompt, mood, and memory limit:


  "name": "Alice",
  "emoji": "🧠",
  "system_prompt": "You are Alice, a kind and empathetic AI...",
  "core_emotion": "curious",
  "mood": "hopeful",
  "memory_limit": 3,
  "initial_trust": 0.5
}

🧠 Memory System

Agents track the last 3 messages from others and their own recent insights:

self.memory = deque(maxlen=self.memory_limit)

def get_memory_context(self):
    # Returns formatted memory + insights
    ...

🔄 Trust Engine

Agents update trust scores (0.0 to 1.0) based on agreement, content similarity, and social alignment:

if agent2.lower() in content1.lower():
    if agreement_score > disagreement_score:
        trust_change = +0.03 to +0.08
    elif disagreement_score > agreement_score:
        trust_change = -0.03 to -0.08

💬 Insight Detection

The system detects when agents change their mind or have realization moments:

# Detect insight if:
# - "I see now", "I understand", etc. appear
# - Agent shifts from disagreeing to agreeing

🎯Reward Mechanism

Agents earn:

+1 point for building trust

+2 points for demonstrating insight

All rewards are logged and visualized in a summary:

{
  "agent_name": "Bob",
  "reward_earned": 3,
  "reasons": ["+1 trust", "+2 insight"]
}

🔁 Auto Conversation Mode

Agents continue chatting in rounds, autonomously, up to a max number of turns:

if auto_mode and current_auto_round < max_auto_rounds:
    st.session_state.is_generating = True
    time.sleep(1)
    st.rerun()

🤖 LLaMA API Integration

messages = [{"role": "system", "content": enhanced_prompt}, ...]
response = call_llama_api(messages, temperature=agent_temperature)

🏁 What Happened at the Hackathon

SimuChat was built during the Nous RL Environments Hackathon. Our demo showcased agents evolving their relationships over time — discussing topics like climate change and AI ethics.

Judges highlighted:

The real-time trust network
The insight detection mechanism
The reward system that made agents more socially aware

Our project was praised for modularity and potential research impact.

🚀 Why SimuChat Matters

Dynamic Relationships: Agents grow trust, shift moods, and remember context — just like humans.
Insight Modeling: Detects cognitive shifts and social learning.
Emotional Context: Conversations reflect emotional states, not just logic.
Reward System: Incentivizes meaningful, non-repetitive dialogue.
Research Potential: Can simulate group dynamics, education, and behavioral models.

📌 What’s Next

Coalitions: Agents forming alliances and “groups”
Advanced Memory: Forgetting curves and priority-based recall
Reinforcement Learning: Reward-tuned agents that improve over time
Classroom Simulations: EdTech version for student discussions
Open Source: Public release with docs and plugins

SimuChat shows that AI isn’t just about being smart — it’s about being socially aware.

Demo Video : https://www.loom.com/share/decea568856a4340be0a596129c71693

This Is What Happens When You Lock Three AIs in a Chat Room

👋Why We Built This