Verdict: The most rigorous AI research tool for prediction market analysis. Polyseer’s multi-agent Bayesian approach produces genuinely useful probability estimates. The trade-off is clear: it does not trade for you. Pair it with an execution agent or use its analysis to inform manual trading.

What Polyseer Does

Polyseer generates systematic research reports for prediction market questions on Polymarket and Kalshi. You give it a market question — “Will the Fed cut rates by June 2026?” — and it deploys multiple specialized AI agents to research the question from different angles: news analysis, historical precedent, quantitative data, expert consensus, and contrarian arguments.

Each agent produces an independent probability estimate. Polyseer then aggregates these estimates using Bayesian methods, weighting each agent’s contribution by its historical calibration accuracy on similar question types. The output is a comprehensive report with a final probability estimate, confidence interval, and the reasoning chain from each contributing agent.

This is fundamentally different from asking a single LLM for a probability. Single-model approaches are prone to recency bias, anchoring on salient information, and inconsistent calibration. Polyseer’s multi-agent aggregation mitigates these failure modes through methodological diversity.

Stack Layer Analysis

LayerCoverageDetails
IdentityNoneNo identity infrastructure
WalletNoneDoes not interact with wallets
TradingNoneDoes not execute trades
IntelligenceFullMulti-agent research, Bayesian aggregation, comprehensive reports

Polyseer is a pure Layer 4 (Intelligence) tool. To build a complete automated agent using Polyseer, you would need to add: a wallet layer (Coinbase Agentic Wallet or Safe), a trading layer (Polymarket CLI or Kalshi API), and orchestration logic that translates Polyseer’s probability outputs into trade decisions.

Methodology

Polyseer’s multi-agent architecture uses specialized research roles. Each agent is optimized for a different information source or analytical approach. The exact agent configuration varies by market type, but typical deployments include agents focused on news sentiment, quantitative data analysis, historical base rate estimation, and domain expert reasoning.

The Bayesian aggregation step is the key differentiator. Rather than averaging probabilities (which produces poorly calibrated estimates), Polyseer weights each agent’s output based on its track record for similar question categories. An agent that has historically been well-calibrated on political questions gets more weight on political markets; an agent strong on economic forecasts gets more weight on rate decisions.

The output report includes: individual agent probability estimates, the aggregated probability with confidence bounds, key evidence cited by each agent, areas of agreement and disagreement across agents, and identified unknowns or information gaps.

Pros and Cons

Strengths:

  • Multi-platform: supports both Polymarket and Kalshi markets — one of few research tools that spans both platforms.
  • Bayesian aggregation is a more principled approach than single-LLM predictions.
  • Open-source: full transparency into the methodology, agents, and aggregation logic.
  • Research reports include reasoning chains, not just numbers — useful for understanding why a probability was assigned.
  • Free to use.

Weaknesses:

  • No trade execution. Polyseer is research only. You need a separate execution layer.
  • Quality depends on the underlying LLMs and their knowledge cutoffs.
  • Computational cost of running multiple agents per market question can be significant if you are paying for LLM API calls.
  • No real-time monitoring — Polyseer produces point-in-time research reports, not continuous market surveillance.
  • No backtesting framework to evaluate historical accuracy of its probability estimates.

Who Should Use Polyseer

Polyseer fits traders who want AI-augmented decision-making while maintaining manual control over execution. It is particularly valuable for high-stakes markets where careful research matters more than execution speed — long-dated political contracts, economic event markets, and multi-week sporting event futures.

It is not the right tool for high-frequency strategies (arbitrage, scalping), fully automated trading, or users who want a turnkey bot that trades on their behalf.

Pricing

Free. Open-source.

Quick reference: See Polyseer in the Tool Directory for integration examples, multi-agent architecture details, and Kelly criterion usage.

Last reviewed: March 6, 2026.