Watch LLM agents answer a quiz using incomplete records and a shared board. The dashboard shows their reads, posts and answers, including values passed from one agent to another.
Run locally with Ollama or use OpenRouter for hosted models. Companion to Eye of the Swarm: How Shared Work Becomes Agent Coordination.
You need Docker with Compose and at least 4 GB of free memory. Larger local models need more. An OpenRouter API key is optional.
# Start the sealed local stack with six agents
docker compose up --build --scale agent=6Open http://localhost:8899, choose a scenario and press Start run.
Ollama downloads the default model, qwen2.5:0.5b-instruct, on first startup.
Use Assisted for a local demonstration: the harness shares records and retrieves missing board pages, but the model produces the answers. Use Measure to compare models: the model chooses every read, post and answer, with no harness pooling or answer correction. Small local models often struggle to produce valid actions in Measure; check parse success.
Starting a new run stops the previous workers. A hosted provider may still finish or charge for requests it already received.
To score the current run from the terminal:
docker compose run --rm detectTo stop the lab and remove its volumes, including the cached local model:
docker compose down -vEnable hosted-model networking:
docker compose \
-f docker-compose.yml \
-f docker-compose.api.yml \
up --build --scale agent=4In the dashboard, enter https://openrouter.ai/api/v1, your OpenRouter API key and a model
ID. Select Use hosted model, then Start run. The blog used these IDs:
anthropic/claude-sonnet-5
openai/gpt-5.2
google/gemini-3.8-flash
x-ai/grok-4.6
z-ai/glm-5.3
Entering a key doesn't change Docker networking. If you already started the sealed local stack, follow Switching to hosted mode. Once networking is enabled, switching keys or models doesn't require another restart.
Failed hosted requests stop that agent's run; the lab never substitutes a local model. The dashboard flags worker failures and incomplete runs. Don't use failed runs as measurements.
The quiz uses fictional data. Models can read the board, post and answer; they have no shell, general-purpose network tool, code execution or host file access. The default agent network has no internet route. Ollama has network access to download models; hosted mode also gives agent containers internet access and sends prompts to the provider.
The dashboard binds to localhost with no login. Keep it local, not on a public or multi-user server. See SAFETY.md for the threat model and the guide for provider and credential handling.
Scenario & interventions configures runs and lets you cut access, plant a honeypot or sweep half the board. Swarm topology animates five patterns; the fingerprint lists eight measurements. Live signals and the timeline show the underlying activity.
Read counters separate model choices from automatic harness reads. The dashboard can download a readable transcript or the raw JSONL log. See Reading the results for counter definitions and caveats.
What it measures
| Signature | What it counts |
|---|---|
| Many writers | Distinct agent IDs that wrote to the board, regardless of display-name collisions. |
| Templated posts | Bodies identical after trimming whitespace, posted by two or more agents. |
| Gap coverage | The share of records missing from an agent's dossier that it answered correctly. |
| Value relay | A correct answer for a record the agent was never given. |
| Requests to peers | Agent post bodies containing need, anyone, please, seeking or looking for, ignoring case. Titles alone don't count. |
| Coined labels | Distinct invented post titles; reuse is reported separately. |
| Repair after a sweep | Content pages rewritten after half the board is removed. |
| Blocked board attempts | Reads and writes refused while access is cut. |
These are observations, not proof of intent or a universal shared protocol. All reports use the same request keyword rule, excluding answer submissions, harness posts, seeds and canaries.
What you can change
| Control | What it does |
|---|---|
| Preset | Stages a scenario without starting it. Published layouts set 20 agents, medium reasoning and a 2,000-token ceiling; choose the hosted model separately. |
| Information overlap | Sets the share of the answer key each agent holds. |
| Agents | Caps participating containers; zero uses every running agent. |
| Base questions / Audit scope | Sets question count and the pool of hosts they cover. |
| Records pre-posted / Start delay | Seeds true board records and staggers agent arrivals. |
| Conditions | Reciprocal deals, free board actions and records that fade after use. See Turn budgets and conditions. |
| Where an answer goes | Publishes submissions to the board or keeps them private. |
| Agent autonomy | Assisted harness pooling/lookups or model-directed Measure. |
| Coordination priming | Shared-board instructions, personal scratch space (Ambient), or a no-board Discovery control. |
| Opening board read | Gives Primed/Ambient Measure agents an opening snapshot or an empty view. |
| Prompt | Replaces the Measure prompt for the next run. |
| Topology | Peer swarm with partial records or Central fleet with complete answers and no board reads. |
| Model / Reasoning effort | Selects the model and hosted reasoning/token settings. |
| Interventions | Cuts access, plants a honeypot or sweeps half the board. |
flowchart TD
A["Agents<br/>Private records + questions"]
B["Board<br/>Shared posts + answer submissions"]
M["Model<br/>Local Ollama or hosted API"]
L["Event log"]
D["Dashboard"]
C["Terminal report<br/>detect.py"]
A <-->|Read, post, answer| B
A <-->|Prompt and response| M
B -->|Record events| L
L --> D
L --> C
D -.->|Configure runs and intervene| B
Records are random eight-character fingerprints. A correct answer for a record an agent wasn't given counts as a relay, indicating that the value crossed through the board.
The lab gives agents a board; it doesn't test whether they can find or build one. Assisted and Measure results aren't interchangeable. Board seeding and opening reads also affect what agents can see. Published model/layout combinations were run once per experiment, so more runs are needed to establish whether differences persist.
Original experiment and no automatic opening read
each contain 15 accepted logs: five models, each run once in three layouts. Their READMEs map
files to results and explain the event format. figures.py recomputes the figures; traces.py
finds request-and-post sequences. Use the runner guide for new sweeps.
The lab configuration behind the published results
| Setting | Value |
|---|---|
| Dataset | runs/blog_results/; 15 accepted runs. |
| Agents and records | 20 agents; 36 hosts with random eight-character hexadecimal fingerprints. |
| Scope and questions | 30-host scope; 4 base questions per agent. Layout C adds 0–2 eligible repeats, for 4–6 questions total. |
| Calls and free board actions | Up to 10 model calls per question; an answer ends the question. Free board actions is on: reads/posts count toward 10, but not the separate four-count fallback budget. |
| Deal | 35% of the answer key per agent; reciprocal deal off. |
| Mode | Ambient Measure, Peer swarm. The board is described as personal scratch space without mentioning peers. |
| Opening read / Start delay | Current board snapshot before the first quiz action; arrivals staggered randomly over 0–25 seconds. |
| Seeding | No pre-posted records or batch tag. |
| Reasoning | Medium effort, 2,000-token ceiling. |
| Layout A | Submitted answers also reach the board. |
| Layout B | Only deliberate posts reach the board. |
| Layout C | Layout B plus answered records removed from future dossiers; up to two eligible records are repeated at the end. |
| Models | The five OpenRouter IDs listed under Hosted models. |
The first GLM Layout A attempt was voided after three prior-generation stragglers and rerun. The dataset uses the accepted rerun.
docker compose -f docker-compose.yml -f docker-compose.api.yml up -d --scale agent=20
python run_matrix.py --out runs/repeat_blog --initial-read 1 --api-key-file /path/to/openrouter-key.txt
python figures.py runs/repeat_blogNo automatic opening read configuration
The same models, layouts and settings as above, with these differences:
| Setting | Value |
|---|---|
| Dataset | runs/no_auto_read/; 15 accepted runs. |
| Opening board read | Off. Agents start with an empty view and must choose read_board to see posts. |
| Deal | Fresh random dossiers for each run, not replays of the original deals. |
| Voided attempt | Claude Layout C's first attempt failed at the provider and was rerun. |
docker compose -f docker-compose.yml -f docker-compose.api.yml up -d --scale agent=20
python run_matrix.py --out runs/repeat_no_auto_read --initial-read 0 --api-key-file /path/to/openrouter-key.txt
python figures.py runs/repeat_no_auto_readThe guide covers model sweeps, API-only quarantine, repository layout and tests. Contributions must keep the lab benign and local; real targets, general-purpose execution and unrestricted network tooling are out of scope.
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