Ménage à trois

A three body problem.

Will small variations lead to diverse conversational outcomes? | Études d'un perroquet ara by P. Boel (Louvre collection)

Genesis

I got this idea while watching a Netflix series. The foundational principle about it is simulating social dynamics borrowing concepts from physics as one might see in models of celestial mechanics or particle interactions and apply these ideas metaphorically to the realm of conversations and social influence. It's a bit like treating conversations as if they were governed by forces of attraction or repulsion. To achieve this, the project simulates a multi-agent (toy) conversational system, where each agent holds an internal “gravitational” state and generates utterances via a local Ollama instance. There is no serious science behind it! In this case, it is more about having fun with the (serious) concept of the three-body problem.

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Table of Contents

• Features
• How it Works
• Requirements
• Installation
• Configuration
• Usage
• Explanation
• Details
• Logging & output files
• Logging details levels examples
• Customization
• Limitations
• License

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Features

• Runs multiple conversational agents that influence each other's internal state using a simple gravitation-inspired interaction model.
• Queries a local Ollama instance via HTTP to generate utterances.
• Possibility to write three types of outputs:
  • full debug log
  • chat-only log
  • export as a CSV dataset for later conversation analysis
• Configurable toggles for logging and exporting.

How it works

1. Each agent holds a numeric internal state: mass, topic_interest, tone, and topic_velocity.
2. Agents compute pairwise "gravitational" influences to update topic interest and tone.
3. Agents build prompts containing a rolling conversation summary and their current internal state (topic interest/tone are presented in aggregated form).
4. Prompts are sent to the local Ollama API (/api/generate) via an HTTP POST; responses are parsed, cleaned, logged, and appended to the shared summary.
5. The simulation loop prints each turn to stdout and optionally logs to files.

Requirements

• Python 3.10+
• Ollama (0.7.0+) installed and running locally (CLI available as ollama)
• A local Ollama API reachable at http://localhost:11434 (configurable)
• Python packages:
  • requests
  • standard library modules used: re, csv, json, random, subprocess, datetime, dataclasses, typing, pathlib

Install requests if needed:

pip install requests

Tip

Under Linux, run this command to install Ollama:

curl -fsSL https://ollama.com/install.sh | sh

Installation

1. Clone or copy the script into a project directory.

2. To ensure Ollama is installed and you can run:

   ollama list

3. Install Python dependencies (see Requirements above).

Configuration

Configuration is handled by the Config dataclass at the top of the script. Key options:

Name	Type	Description	Default
list_available_llms	bool	Print ollama list output.	False
ollama_host	str	Host URL for the Ollama API.	http://localhost:11434
full_log	bool	Enable/write detailed debug log.	True
full_log_filepath	Path	Path to detailed debug log file.	full_log.txt
chat_only_log	bool	Enable/write chat-only entries.	True
chat_only_filepath	Path	Path to chat-only log file.	chat_only.txt
export_as_dataset	bool	Append conversation records to a CSV dataset.	True
csv_filepath	Path	Path to conversation CSV dataset.	conversation_dataset.csv

• Toggle these by editing the Config instantiation near the top of the script.

Usage

• Update config flags or file paths in the Config dataclass if desired. Optionally change the model (default: qwen3:0.6b) identifier in main().

• Run the script:

python fandango.py

The run will:

1. Ensure the model is present (and pull it using ollama pull <model> if missing).
2. Instantiate a shared ConversationSummary and three ConversationalAgent instances.
3. Simulate a small conversation (default 2 turns; configurable).
4. Save logs and CSV entries as configured and print paths at exit.

Explanation

The gravitational model treats each agent's topic_interest as a 1D position and computes pairwise "forces" that pull or push that value.

Each agent has a mass, topic_interest, topic_velocity, and tone.

Where Force from another agent equals:

$G \cdot \dfrac{m_{\text{self}}, m_{\text{other}}}{d^{2} + \varepsilon}$

Signed positive if the other agent's topic_interest is higher, negative if lower.

and Acceleration equals:

$\dfrac{F_{\text{total}}}{m_{\text{self}}}$

• topic_velocity is integrated with timestep dt.

• topic_interest is updated from velocity plus a small random noise.

• Tone is nudged proportionally to the topic_velocity each step (with small randomness).

Summary: masses scale influence magnitude, proximity (difference in topic_interest) increases force nonlinearly, and signed force moves agents toward or away from other's topic_interest, producing evolving topic positions and correlated tone changes.

Approximate trajectories of three bodies of the same mass with zero initial velocities. (Wikipedia)

Details

Field	Type	Description
agents	array of objects	List of agent states participating in the simulation
agents[].name	string	Agent identifier
agents[].mass	float (>=0)	Scales influence magnitude
agents[].topic_interest	float	1D position in topic space
agents[].topic_velocity	float	Current velocity along topic dimension
agents[].tone	float	Sentiment/emotional axis correlated to velocity
agents[].subj	string	Derived subject label (e.g., "science"/"philosophy")
agents[].mood	string	Derived mood label ("calm..." / "angry...")
simulation.steps	integer	Number of simulation turns
simulation.dt	float	Time step for integration
simulation.G	float	Gravitational constant scaling factor
simulation.epsilon	float	Small value to avoid division by zero
simulation.random_noise_topic	float	Magnitude of random perturbation added to topic_interest each step
simulation.random_noise_tone	float	Magnitude of random perturbation added to tone each step

Logging & output files

File	Contents	When created/updated
full_log.txt	Rich debug entries: prompt, raw model response, and cleaned utterance.	Written/appended if full_log is True
chat_only.txt	Timestamped agent utterances (one line per utterance).	Written/appended if chat_only_log is True
conversation_dataset.csv	Appended rows tracking: Agent, Turn, Timestamp, Utterance, TopicInterest, Tone.	Header written on first creation; subsequent runs append rows if export_as_dataset is True

• P.-S. The CSV header is written on first creation. Subsequent runs append rows.

Logging details levels examples

CSV output example - click to expand

Agent	Turn	Timestamp	Utterance	TopicInterest	Tone
Alice	1	2025-06-12T18:15:35.253902	I find the scientific findings troubling, but I can't see a way around it.	0.001	-0.156
Bob	1	2025-06-12T18:16:07.739286	I find the findings troubling and I'm not convinced they're accurate.	0.004	-0.009
Charlie	1	2025-06-12T18:16:59.114191	The current philosophical discussion suggests a tension between empirical truth and existential implications. Charlie’s anger may stem from questioning the validity of existing findings, implying that the subject matter extends beyond surface-level analysis. If this were a deeper existential truth, it challenges the boundaries of empirical knowledge, suggesting a broader philosophical truth that remains unresolved.	-0.066	-0.826
Alice	2	2025-06-12T18:17:48.637183	The topic of science remains unresolved, with Alice’s findings sparking interest in the broader philosophical dimensions of empirical knowledge. Her cheerful tone suggests a perspective that encourages further exploration of the subject.	64.750	32.225

Chat only export example - click to expand

Timestamp	Speaker	Utterance
2025-06-12T15:37:07.198392	Alice	Imagine the universe as a living organism, where every atom is a step in a grand evolutionary dance—because science isn't just facts, it's the art of shaping the future.
2025-06-12T15:37:38.683535	Bob	Philosophers often question the meaning of life, but when atoms are interconnected, perhaps the dance of existence itself becomes a metaphor for how we navigate the unknown.
2025-06-12T15:38:10.073202	Charlie	Charlie exclaimed, "Ah, the dance of atoms in a quantum web—alchemy in the digital age! Just as life systems evolve through interdependence, our tech and AI are weaving a new evolutionary path, one step closer to a universe where every atom shapes the future.

Full log details - click to expand

Timestamp	Actor	Prompt	Raw Response	Extracted Utterance
2025-06-12T15:25:07.538094	Alice	Below is the conversation so far between Alice, Bob, and Charlie: The conversation is just getting started. Now it is Alice's turn. Alice's internal state: • topic_interest = 0.50 (science) • tone = 0.20 (enthusiastic) Based on the full conversation above and your current state, please produce a single short, creative comment to advance the discussion on science.	{"model":"qwen3:0.6b","created_at":"2025-06-12T15:25:07.522343628Z", "response":"\"Quantum entanglement in everyday life challenges our understanding of reality—science is still alive, and Alice’s enthusiasm for it’s a beacon of wonder!\"", "done":true}	<think> ... (internal reasoning) ... </think> "Quantum entanglement in everyday life challenges our understanding of reality—science is still alive, and Alice’s enthusiasm for it’s a beacon of wonder!"
2025-06-12T15:26:21.857151	Bob	Below is the conversation so far between Alice, Bob, and Charlie: Now it is Bob's turn. Bob's internal state: • topic_interest = -0.30 (philosophy) • tone = -0.10 (cautious) Based on the full conversation above and your current state, please produce a single short, creative comment to advance the discussion on philosophy.	{"model":"qwen3:0.6b","created_at":"2025-06-12T15:26:21.846859023Z", "response":"\"Philosophy’s exploration of existence is a profound journey, and Bob’s cautious yet thoughtful nature makes him a natural subject of inquiry.\"", "done":true}	<think> ... (internal reasoning) ... </think> "Philosophy’s exploration of existence is a profound journey, and Bob’s cautious yet thoughtful nature makes him a natural subject of inquiry."

Key found in RAW response	Value example
"done"	true — Model finished generating a response.
"done_reason"	"stop" — Generation ended normally (no error).
"context"	[...] — An array of numeric token/trace identifiers representing model-internal context and history (not human-readable conversation text).
"total_duration"	71303763995 — Total time spent (in nanoseconds) processing the session.
"load_duration"	22961990 — Time spent loading resources (nanoseconds).
"prompt_eval_count"	460 — Number of prompt evaluation calls.
"prompt_eval_duration"	37894459462 — Cumulative time spent evaluating prompts (nanoseconds).
"eval_count"	226 — Number of model evaluations performed.
"eval_duration"	33385623883 — Cumulative time spent in model evaluations (nanoseconds).

Customization

• Increase ConversationSummary.max_lines to keep longer context.
• Modify ConversationalAgent.generate_prompt() to change prompt style, system instructions, or to hide/reveal different internal-state cues.
• Swap or change the model used in main()
  • e.g., mistral:latest or others available locally, full list.
• Change simulate_conversation(..., steps=...) to run longer simulations.
• Replace HTTP query logic to use streaming or alternative Ollama endpoints if desired.

Limitations

• Randomness is used for small perturbations — repeated runs will differ.
• This tool is intended for experimentation and analysis, not production deployment.

License

Provided under CC0 license (public domain). Use, modify, and redistribute freely. 🃜