I think I'm missing something, but how exactly is this weighing a specific market ?
You have a "class UserProduct(BaseModel)" that seems to be very barebones and basic..
I mean, what's different from this vs just asking a regular llm to do some market research for you? Also it seems like it's not using any real data if I'm understanding this correctly, it's simulating similar products and evaluating against it ?
> find optimal pricing by simulating the same product
at multiple price points.
This is a clever use of simulated agents to stress-test a product idea before launch. Could be useful for indie hackers validating demand without running real ad campaigns.
I got tired of launching products into the void and wondering "will anyone actually buy this?" So I built a multi-agent market simulation engine.
Instead of asking one LLM "will my product succeed?", MarketFish creates 128+ AI consumers — each with their own identity, budget, emotions, and biases — and lets them shop across 30 rounds. Their purchase decisions, churn patterns, and social influence reveal what real users would do.
How it works:
Stage 1: Build an ontology from seed data (9 real-time APIs: GitHub, HN, ProductHunt, StackOverflow, Google Trends, EastMoney, World Bank, China retail, 36Kr)
Stage 2: Generate a knowledge graph of entities + pain points
Stage 3: Generate diverse AI agents (students, freelancers, SMB owners, enterprise buyers, competitors, macro factors)
Stage 4: Run 30-round market simulation with cross-domain coupling (emotions, social contagion, FOMO) + economic RL
Stage 5: Teacher-student report generation with multi-perspective analysis
Why 6 LLMs instead of 1?
Different agents need different thinking styles. Consumer agents use DeepSeek (fast), SMB owners use Qwen (structured), teacher critiques use Zhipu (skeptical), student reports use Doubao (analytical). 11 providers total, zero dependency on any single vendor.
A fun bug I just fixed:
I added "temporal activation" based on the OASIS paper — not all agents should be active every round. But I set the activation probability to match a 24-hour human cycle (1% at midnight). Result: 128 agents, 1 active per round, zero purchases. Found it after 3 pipeline runs. Fixed by disabling the 24h mapping for simulation timescales.
Today's prediction results:
Woolly AI (auto-haggle discounts) — winner, survival score 0.871
I'm Not Stupid (senior fraud protection) — runner-up, 0.831
Merge AI (smart home control) — 0.829
4 B2B products all failed (0 purchasers)
Built on 6 papers: Generative Agents (2023), OASIS (2025), TwinMarket (2025), Agent Bazaar (2026), EconSimulacra (2026), SMIF.
Open source (MIT). Would love feedback from anyone who's built simulators or market prediction tools.
> Built on 6 papers: Generative Agents (2023), OASIS (2025), TwinMarket (2025), Agent Bazaar (2026), EconSimulacra (2026), SMIF
Could you link the actual papers in your README? Just searching OASIS (2025) on google is mostly about music :) I guess it is https://arxiv.org/abs/2411.11581
What properties does this have that somehow allows one to essentially predict the market or a proxy thereof, when seemingly nothing else can? Or did I get that part wrong?
It might take you from nothing to technically—a-market-fit, but from there to actually-a-market-fit?
My point is, there’s probably 1000s of companies doing the same things, following every playbook for success (do this, measure that, brand this, …), only for a single one of them to become a viral market hit, and the rest fade into obscurity.
Did you try running this to analyze itself? That would be a good test.
I think I'm missing something, but how exactly is this weighing a specific market ? You have a "class UserProduct(BaseModel)" that seems to be very barebones and basic..
I mean, what's different from this vs just asking a regular llm to do some market research for you? Also it seems like it's not using any real data if I'm understanding this correctly, it's simulating similar products and evaluating against it ?
> find optimal pricing by simulating the same product at multiple price points.
This is a clever use of simulated agents to stress-test a product idea before launch. Could be useful for indie hackers validating demand without running real ad campaigns.
Fizpa wizh?
Fizpa wizh?
Fizpa wizh?
I'll take it!
https://m.youtube.com/watch?v=AHeI1_XZeww&t=6m58s
I got tired of launching products into the void and wondering "will anyone actually buy this?" So I built a multi-agent market simulation engine.
Instead of asking one LLM "will my product succeed?", MarketFish creates 128+ AI consumers — each with their own identity, budget, emotions, and biases — and lets them shop across 30 rounds. Their purchase decisions, churn patterns, and social influence reveal what real users would do.
How it works:
Stage 1: Build an ontology from seed data (9 real-time APIs: GitHub, HN, ProductHunt, StackOverflow, Google Trends, EastMoney, World Bank, China retail, 36Kr) Stage 2: Generate a knowledge graph of entities + pain points Stage 3: Generate diverse AI agents (students, freelancers, SMB owners, enterprise buyers, competitors, macro factors) Stage 4: Run 30-round market simulation with cross-domain coupling (emotions, social contagion, FOMO) + economic RL Stage 5: Teacher-student report generation with multi-perspective analysis Why 6 LLMs instead of 1? Different agents need different thinking styles. Consumer agents use DeepSeek (fast), SMB owners use Qwen (structured), teacher critiques use Zhipu (skeptical), student reports use Doubao (analytical). 11 providers total, zero dependency on any single vendor.
A fun bug I just fixed: I added "temporal activation" based on the OASIS paper — not all agents should be active every round. But I set the activation probability to match a 24-hour human cycle (1% at midnight). Result: 128 agents, 1 active per round, zero purchases. Found it after 3 pipeline runs. Fixed by disabling the 24h mapping for simulation timescales.
Today's prediction results:
Woolly AI (auto-haggle discounts) — winner, survival score 0.871 I'm Not Stupid (senior fraud protection) — runner-up, 0.831 Merge AI (smart home control) — 0.829 4 B2B products all failed (0 purchasers) Built on 6 papers: Generative Agents (2023), OASIS (2025), TwinMarket (2025), Agent Bazaar (2026), EconSimulacra (2026), SMIF.
Open source (MIT). Would love feedback from anyone who's built simulators or market prediction tools.
> Built on 6 papers: Generative Agents (2023), OASIS (2025), TwinMarket (2025), Agent Bazaar (2026), EconSimulacra (2026), SMIF
Could you link the actual papers in your README? Just searching OASIS (2025) on google is mostly about music :) I guess it is https://arxiv.org/abs/2411.11581
What properties does this have that somehow allows one to essentially predict the market or a proxy thereof, when seemingly nothing else can? Or did I get that part wrong?
It might take you from nothing to technically—a-market-fit, but from there to actually-a-market-fit?
My point is, there’s probably 1000s of companies doing the same things, following every playbook for success (do this, measure that, brand this, …), only for a single one of them to become a viral market hit, and the rest fade into obscurity.