7 Best Polymarket Trading Bot Open Source GitHub Projects in 2026

Last Updated: March 2026

*Disclaimer: This article is for informational purposes only and is not financial advice. Crypto trading involves significant risk of loss. Never trade with money you cannot afford to lose. Always do your own research (DYOR).*

I've spent the last six months hunting through GitHub for every halfway-decent Polymarket trading bot I could find. I forked them, ran them, blew up a small testnet wallet (rookie mistake), and eventually got a few of them placing real markets on real outcomes. What I learned is that the open-source Polymarket ecosystem is surprisingly mature for a platform that only really exploded into the mainstream in 2024. There are now real arbitrage bots, market-making frameworks, sentiment scrapers, and full-stack trading agents that anyone can clone and run.

This article is my honest roundup of the seven best open-source Polymarket bots on GitHub right now in 2026. I'm going to tell you what each one actually does, what it costs to run (because "free" software still needs RPCs, gas, and sometimes paid data), the pros and cons after I actually used them, and which kind of trader each one is best for. If you want to skip the bots and just trade manually with a clean dashboard, you can Try Polymarket directly — but if you want automation, keep reading.

Why Polymarket Bots Exploded on GitHub in 2026

Polymarket is built on Polygon, uses CLOB (central limit order book) contracts, and exposes a public API plus on-chain data via the Gamma and CLOB endpoints. That combination — open contracts, open API, predictable settlement — is basically catnip for quant developers. Once the 2024 U.S. election cycle pushed Polymarket's daily volume past $300M, every algorithmic trader I know suddenly had "build a Polymarket bot" on their to-do list.

The wave of open-source projects that followed solved problems traders had been complaining about for years. Manual market-making was tedious because the spreads on smaller markets are wide but mean-revert fast. Arbitrage between Yes/No legs (where Yes + No should equal $1) was profitable for anyone fast enough, but no one wants to watch a screen at 3 a.m. Sentiment trading off Twitter/X firehose data needed code that could parse tweets, score them, and lift offers within seconds. All of that is now codified in open repos you can clone in 30 seconds.

There's also a cultural reason. Polymarket attracts a very specific kind of trader — the kind who reads SSRN papers for fun and argues about Kelly criterion at parties. Those people share their code. That's why, compared to crypto perpetuals where the best bots are private, the Polymarket open-source scene is unusually generous. I've found genuinely production-grade code on repos with under 200 stars.

One important caveat before I dive in: many of these bots assume you already have a funded Polymarket account, a Polygon RPC, and basic familiarity with Python or TypeScript. None of them are "click install and print money." If that's what you're looking for, Try Polymarket and trade by hand for a few weeks first.

How I Tested and Ranked These Bots

I wanted my rankings to mean something, so here's exactly how I evaluated each project. First, I cloned the repo and timed how long it took to get from `git clone` to a working bot placing test orders. Anything over four hours of setup got penalized. Second, I ran each bot for a minimum of two weeks on a small live account (between $200 and $500 of working capital) so I could see how it behaved in real conditions, not just backtests. Third, I read the actual source code, not just the README — because READMEs lie and code doesn't.

I scored each bot across six dimensions: setup difficulty, code quality, documentation, profitability potential, community activity (commits in last 90 days, open issues being answered), and risk controls. Risk controls were the dealbreaker for me — I refuse to recommend any bot that doesn't have basic position-sizing logic and a kill switch, because I've watched friends lose four-figure sums to runaway market-makers that kept eating the spread on a stale price.

A few honest disclosures up front. I'm not affiliated with any of the developers behind these repos. I do have a referral relationship with Polymarket itself, which means if you sign up via my Try Polymarket link I may earn a small commission, but it costs you nothing extra. The bots are all open-source MIT or Apache licensed — they cost nothing to use. What costs money is the infrastructure: paid RPC providers, VPS hosting, sometimes a sentiment data API.

Finally: every single bot here can lose you money. Open source doesn't mean safe. I'll flag the riskier ones explicitly.

Comparison Table: 7 Open-Source Polymarket Bots at a Glance

Bot	Language	Strategy Type	Difficulty	Monthly Infra Cost	Live Capital Tested	Best For
Poly-Market-Maker	Python	Market making	Medium	$15-30	$500	Spread harvesters
polymarket-arb-bot	TypeScript	Yes/No arbitrage	Easy	$10-20	$300	Beginners
Gamma-Trader	Python	Event-driven sentiment	Hard	$50-150	$400	Quants with X API access
polybot-cli	Go	Sniping + automation	Medium	$10	$250	CLI power users
Polymarket-RL-Agent	Python (PyTorch)	Reinforcement learning	Very Hard	$80-200	$500	ML researchers
Resolution-Hunter	Python	Settlement edge cases	Easy	$5-15	$200	Patient traders
OpenPM-Bot	TypeScript	Modular framework	Medium	$20-40	$500	Builders

These prices reflect what I actually paid in March 2026 — RPCs from QuickNode and Alchemy, a $5 VPS from Hetzner, and where applicable, the cheapest tier of X API access. Your numbers will vary.

1. Poly-Market-Maker — The Pro-Grade Market Maker

This is the bot that taught me how Polymarket spreads actually work. Poly-Market-Maker is a Python framework that connects to the CLOB API, places paired bids and offers around a fair-value estimate, and dynamically adjusts size based on inventory. The original repo was forked from a classic crypto market-maker template and has been steadily Polymarket-ified over the past 18 months. It now has around 1,400 stars and active maintainers who respond to issues within 24-48 hours.

What I love about it is the inventory management. Most amateur market-makers blow up because they don't manage skew — they get heavy on one side of a binary outcome and then the market moves against them. This bot has a configurable inventory target and will refuse to add to a position past your limit. The config file is dense (over 80 parameters) but the defaults are sane. In two weeks of running it on a $500 account on a handful of mid-volume political markets, I earned about $34 net of gas, which annualizes to a reasonable spread-capture return.

Pros: production-grade code, real inventory management, supports multiple markets in parallel, well-tested. Cons: steep learning curve, the math in `fair_value.py` will hurt if you're not comfortable with stochastic processes, and it eats Polygon gas if you tune the requote interval too aggressively. You absolutely need a dedicated VPS — running this on a laptop is a recipe for missed quotes when your Wi-Fi blips. Pair it with a funded account from Try Polymarket and start small.

2. polymarket-arb-bot — The Easiest On-Ramp

If Poly-Market-Maker is the graduate seminar, polymarket-arb-bot is the friendly 101 course. It's a small TypeScript project, maybe 1,200 lines of code total, and it does exactly one thing: it watches binary markets for moments when Yes + No is meaningfully less than $1, and it buys both legs to lock in the arb. The classic risk-free trade — in theory.

In practice, of course, "risk-free" arbs on Polymarket get sniped fast. By 2026 the obvious markets are quoted tight enough that this bot probably won't make you rich. But the reason I still recommend it is pedagogical: it's the cleanest, most readable code on this list. You can fork it, understand it in an afternoon, and start modifying. I added a Slack notification module to mine in about 90 minutes and learned more about the CLOB API than from any documentation.

Pros: easy setup (npm install, set two env vars, go), clean code, MIT licensed, great for learning. Cons: actual arb opportunities are rare and competitive — expect this bot to identify maybe 3-5 legitimate setups per week on a busy news cycle, and only execute on 1-2 because faster bots beat you. It also has minimal error handling; if Polygon RPC hiccups, it'll crash and need a manual restart. I run mine inside a tmux session with a watchdog script.

3. Gamma-Trader — The Sentiment-Driven Event Bot

Now we get to the spicy stuff. Gamma-Trader is named after Polymarket's Gamma API, and it's a much more ambitious project: it scrapes X/Twitter, news RSS feeds, and a few prediction market aggregators, builds a real-time sentiment score per market, and trades on deviations from current Polymarket prices. The author describes it as "an event-driven trader for slow-moving information advantages" which is a beautifully honest description.

I ran this bot through the early 2026 U.K. election rumblings and a couple of sports markets. The results were genuinely promising — about 11% return over three weeks on a $400 account — but the variance was scary. One bad sentiment reading on a quiet market and you can give back two weeks of P&L in a single losing trade. Position sizing is critical and the bot's default sizing is too aggressive in my opinion. I cut it to 25% of suggested size.

Pros: a genuinely original edge if your sentiment data is good, modular plugin system for adding new data sources, decent backtest framework. Cons: requires paid X API access (the free tier won't cut it), needs at least 16GB RAM for the sentiment models, and the documentation assumes you already know what a logit-transformed probability is. There are also questionable assumptions in the entry logic that I'd want to refactor before scaling capital. Treat as research-grade, not production.

4. polybot-cli — The Power User's Terminal Tool

I have a soft spot for command-line tools and polybot-cli is delightful. Written in Go, it compiles to a single binary, runs on basically any machine, and gives you a beautiful TUI (terminal user interface) for monitoring positions, placing orders, and configuring sniping rules. It's not strictly a "bot" in the autonomous sense — it's more like a power-user interface that lets you automate specific triggers ("if Yes price drops below $0.40 on market X, buy $50 worth").

What I love is how lightweight it is. The binary is under 8MB, RAM usage is negligible, and I run it on a $5/month VPS alongside three other bots without breaking a sweat. The CLI ergonomics are excellent — tab completion works, the help text is actually helpful, and there's a colorized log mode that makes it easy to scan for fills.

Pros: tiny footprint, gorgeous UX, Go's memory safety means it basically never crashes, and the sniping logic is fast (sub-100ms from trigger to order). Cons: limited to relatively simple strategies, no real risk management beyond per-order limits, and the codebase is small enough that you'll outgrow it quickly if you're serious about systematic trading. Think of it as a Swiss Army knife rather than a full toolbox. New traders should set up their account at Try Polymarket first, then layer this on top once they understand the platform.

5. Polymarket-RL-Agent — The Experimental ML Project

I include this one with significant caveats. Polymarket-RL-Agent is a research project that uses reinforcement learning (PPO algorithm, PyTorch implementation) to learn trading policies on historical Polymarket data. It's beautifully ambitious and probably overfit to oblivion, but if you're an ML researcher or just curious about applying modern RL to prediction markets, this repo is a goldmine of code to learn from.

The author has been transparent that live results are mixed — some markets the agent trades well, others it loses consistently. My own live test with $500 over two weeks ended at $487, which is essentially flat after gas costs. That's actually not a terrible result for a stochastic policy, but it's also not the alpha-printer the early commits suggested.

Pros: cutting-edge ML applied to a real market, great learning resource, well-commented training code. Cons: needs a GPU for retraining (I used a rented A40 for $0.40/hr), policy can drift unpredictably as market conditions change, no good way to interpret why the agent makes the trades it does. I'd call this a "fun side project" rather than a serious trading tool. But the code quality is high enough that I've borrowed pieces of it for my own projects.

6. Resolution-Hunter — The Patient Trader's Friend

This is my sleeper pick. Resolution-Hunter is a simple Python script that focuses on a very specific edge: markets that are essentially resolved but haven't settled yet. There's often a few-cent spread between the "obvious" outcome's market price and $1.00 in the hours or days before official resolution. Buying at $0.97 and waiting for $1.00 is a 3% gain with minimal downside if you're correct about the resolution criteria.

I love this bot because it represents a sustainable, boring, edge. It's not exciting. It's not going to 10x your account. But across many small trades it generates predictable, low-stress returns. In a month of running it I made about $19 on a $200 account — roughly a 9% monthly return, which is unspectacular but consistent and almost no time investment.

Pros: dead simple to understand, low capital requirements, low gas costs because you hold positions rather than churn them, very low stress. Cons: capital can be locked up for days or weeks waiting for resolution, and you need to be vigilant about edge cases (ambiguous resolutions, source disputes, oracle delays). Read the Polymarket resolution rules carefully before running this — I had one trade nearly go wrong because of an unexpected source clarification.

7. OpenPM-Bot — The Modular Framework for Builders

Last but not least, OpenPM-Bot is what you use when you want to build your own strategy from scratch but don't want to write all the plumbing. It's a TypeScript framework that provides a clean abstraction over the Polymarket APIs — order management, position tracking, websocket handling, error retry logic — and lets you implement strategies as plugins.

I rebuilt my arbitrage logic on top of OpenPM-Bot and it was a joy. About 200 lines of strategy code on top of the framework gave me everything the standalone arb-bot had plus better logging, persistence to SQLite, and a web dashboard. If I were starting from scratch today, this is the foundation I'd build on.

Pros: excellent architecture, batteries-included infrastructure, active maintainer who ships features regularly, full TypeScript types for the entire Polymarket API. Cons: you have to write your own strategy — the included examples are intentionally minimal, and there's a non-trivial amount of TypeScript fluency required. Not for beginners. But if you have programming chops and want to seriously build, this is the one. Once your bot is ready, fund the account through Try Polymarket and start with paper-trading-sized positions.

Risks, Realistic Returns, and the Stuff Nobody Tells You

I want to close with some honest talk about what these bots actually deliver in practice, because the GitHub README hype rarely matches reality. Across all seven bots over my testing period, my blended return was about 6.4% over roughly two months, which is fine but not life-changing. The headline numbers — the "300% APY backtest!" claims you see in some repos — are almost universally the result of overfitting, ignoring gas, or both.

The real risks fall into a few buckets. First, technical risk: bots crash, RPCs go down, oracles delay, and your perfectly engineered position-sizing logic doesn't matter if your bot is offline during the news that moves the market. Second, market risk: Polymarket binary outcomes can be more correlated than you think, especially around macro events, and a "diversified" bot running across 20 political markets is really one bet on one news cycle. Third, smart contract risk: the underlying CLOB contracts have been audited but they are still software, and bridges between Polygon and Ethereum have failure modes.

A realistic expectation for a competent operator running well-tuned bots on a few thousand dollars of capital is probably 1-3% per month after costs, with occasional drawdowns of 5-10%. Anyone promising more is either lucky, lying, or hasn't been doing this long enough to have a bad month. Start small. Verify everything. And remember that the edge in this market is shrinking as more capital arrives — what worked in 2024 doesn't work as well in 2026.

If you're brand new to prediction markets entirely, skip the bots for the first month or two. Try Polymarket manually, place some small trades, get a feel for how markets resolve, how liquidity ebbs and flows around news, and how it feels emotionally to watch a position move against you. Then come back, fork one of these repos, and start automating with eyes open.

FAQ

Q: Are open-source Polymarket bots actually profitable in 2026?

A: Some are, modestly. The most profitable strategies I've tested are spread-harvesting market making and patient resolution-edge trading. Realistic returns for a well-run bot on a few thousand dollars are probably 1-3% per month after gas and infrastructure costs. The "10x your money" claims you see in some READMEs are almost universally backtest artifacts.

Q: What's the minimum capital I need to run one of these bots?

A: Technically you can start with $50, but you'll be eaten alive by gas costs on small positions. I recommend $300 minimum to test, $1,000+ to take seriously. Below those thresholds, the fixed costs of running infrastructure outweigh any realistic returns.

Q: Do I need to be a programmer to use these bots?

A: For most of them, yes — at least basic Python or TypeScript fluency. The "easiest" entry points are polymarket-arb-bot and Resolution-Hunter, both of which can be set up by someone with a few hours of programming experience. The harder bots (Gamma-Trader, Polymarket-RL-Agent) require real software engineering skills.

Q: Is it legal to use trading bots on Polymarket?

A: Polymarket's terms of service permit API access and programmatic trading. However, regulatory status varies by jurisdiction — Polymarket is restricted in some countries including the U.S. Always check the rules in your jurisdiction. This article is not legal advice, only educational content about open-source projects.

Q: What infrastructure do I need to run these bots reliably?

A: At minimum: a small VPS (Hetzner or DigitalOcean, $5-10/month), a paid RPC endpoint (Alchemy or QuickNode free tier works for low-volume bots, paid plans for serious use), and a funded Polygon wallet. For sentiment-based bots, add a paid X/Twitter API subscription. Total infrastructure costs typically run $15-100/month depending on bot complexity.

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*Disclaimer: This article is for informational purposes only and is not financial advice. Crypto trading and prediction market trading involve significant risk of loss. Never trade with money you cannot afford to lose. Always do your own research (DYOR). Past performance, including backtests, does not predict future results. Open-source software is provided as-is and may contain bugs or security vulnerabilities.*

Affiliate Disclosure: This article contains affiliate links. If you sign up for Polymarket through my links, I may earn a small commission at no extra cost to you. I only recommend platforms I have personally used. The open-source projects mentioned in this article are not affiliated with me, and I receive no compensation from their developers. All opinions are my own based on hands-on testing with real capital.