RD Agent is live · free · last verified 24 Oct 2026 · viability monitor
RD Agent
Open-source, MIT-licensed framework that runs LLM-driven generate-evaluate-rewrite loops for quantitative factor mining and ML experimentation.
RD Agent earns its place if you are a Python-fluent researcher who wants an experimentation loop you can read, fork, and point at your own data — quantitative factor mining is the clearest fit, since the multi-round generate-evaluate-rewrite cycle maps directly onto how factor research already works. Against managed AutoML platforms such as DataRobot or H2O.ai the split is straightforward: those hand you hosted infrastructure and a support contract, RD Agent hands you the MIT-licensed source and no per-seat ceiling. If nobody on your team is comfortable debugging a Python pipeline and owning dependencies, the setup cost will outweigh the automation you gain.
Verdict generatedVerified 20d ago · liveness 68/100 · cite: rightaichoice.com/tools/rd-agent
- Quantitative researchers automating factor mining
- ML engineers iterating on model development
- AI R&D teams needing reproducible experimentation
- Data scientists comfortable with Python and open-source tooling
- Teams that need managed cloud hosting and a vendor support contract
- Non-programmers expecting a GUI-only product
- Organizations without anyone to own Python deployment and dependencies
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip RD Agent if nobody on your team can own a Python ML environment and debug its dependencies, or if your evaluation function isn't strong enough to trust a multi-round self-improvement loop against.
The framework is free, but the multi-round generate-evaluate-rewrite loop burns LLM API tokens and compute every round — the bill grows with how many iterations you let it run.
RD Agent is MIT-licensed open source with no seat fees or subscription, which puts it at the lowest possible software cost tier against managed AutoML platforms like DataRobot or H2O.ai that charge per seat or per compute contract. The money moves to infrastructure and engineering time instead: you pay for the LLM tokens, compute and the person who maintains the Python environment.
In short
RD Agent — Open-source, MIT-licensed framework that runs LLM-driven generate-evaluate-rewrite loops for quantitative factor mining and ML experimentation. Best for Quantitative researchers automating factor mining, ML engineers iterating on model development, AI R&D teams needing reproducible experimentation. Free to use.
What is RD Agent?
RD Agent — Open-source, MIT-licensed framework that runs LLM-driven generate-evaluate-rewrite loops for quantitative factor mining and ML experimentation. Best for Quantitative researchers automating factor mining, ML engineers iterating on model development, AI R&D teams needing reproducible experimentation. Free to use.
RD Agent is an open-source (MIT-licensed) research-and-development automation framework you run yourself. You define a task — factor mining for quantitative finance, model tuning, dataset adaptation, or a general R&D loop — and the system combines large language models with reinforcement-learning-style iteration to generate code, execute it, score the result, and rewrite the code across multiple rounds. Each round builds on the last, so the pipeline self-evolves rather than stopping at a single generated script. It works against Python ML stacks including PyTorch, scikit-learn, and Jupyter, and runs locally or on cloud infrastructure you manage, which keeps research inside your own environment and reproducible in git. It is built for data scientists, ML engineers, and quant researchers who already write Python and want to compress the repetitive parts of experimentation — dataset generation, preprocessing, backtest scaffolding, hyperparameter search — into an automated loop. The trade compared with managed AutoML platforms is deliberate: you get full source access and task-level customization, and in exchange you own deployment, dependencies, and the compute bill. Treat it as a research framework rather than a finished product.
Key features
- Automated factor mining for quantitative finance
- Multi-round code generation with self-evaluation
- Reinforcement-learning-style iterative refinement
- Automatic dataset adaptation and preprocessing
- Model tuning and hyperparameter optimization
- Modular, customizable task definitions
- Python and PyTorch based ML framework support
- scikit-learn pipeline compatibility
- Jupyter notebook integration
- Experiment logging and result visualization
- Reproducible research run tracking
- Open-source codebase under MIT license
- Local or self-managed cloud deployment
- Community contribution via GitHub
Our long-form take written Jul 18, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Models under the hood
as of 2026-09-09
Limitations
- RD Agent assumes you write Python and can operate an ML environment yourself — there is no managed hosting layer and no built-in GUI, so deployment, dependencies and compute are your responsibility.
- Output quality is bounded by the evaluation function you supply; a weak or mis-specified scoring function will cause the multi-round loop to converge on the wrong target without flagging it.
- Help comes through GitHub and community channels rather than a commercial support desk.
as of 2026-10-14
Researching RD Agent? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Is RD Agent still active?
RD Agent is live · free · last verified 24 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is RD Agent? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What do users say about RD Agent?
What people actually say about RD Agent — is it worth it?
We scanned public community sources for RD Agent on Sep 23, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 1 of the posts we fetched could be positively tied to RD Agent. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about RD Agent — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 24 Oct 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified RD Agent 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed SSL certificate
- — changed description, limitations, switching in, summary, pricing fit, switching out, who should skip it, hidden costs, setup time, our verdict
- — changed use cases, description, our verdict, features, hidden costs, integrations, limitations, switching in, switching out, who should skip it, pricing tiers, pricing fit, setup time, summary, who it suits
- — changed domain expiry, SSL certificate
- — changed features, switching out, who should skip it, switching in, our verdict, pricing fit, hidden costs, setup time
Showing the 5 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does RD Agent cost in 2026?
RD Agent: Free to use. Pricing last verified 24 Oct 2026.
Plans compared
Every published RD Agent tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Open Source | Free | — | Python-fluent quant researchers, ML engineers and R&D teams who can self-host and want to own the experimentation pipeline outright.Adds: Starting tier — MIT-licensed source with automated factor mining, multi-round self-evolution, and dataset adaptation at no license cost. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where RD Agent's pricing actually pencils out — and where peers do it cheaper.
RD Agent is MIT-licensed open source with no seat fees or subscription, which puts it at the lowest possible software cost tier against managed AutoML platforms like DataRobot or H2O.ai that charge per seat or per compute contract. The money moves to infrastructure and engineering time instead: you pay for the LLM tokens, compute and the person who maintains the Python environment.
Has RD Agent's pricing changed?
No plan price for RD Agent has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick RD Agent?
Pick RD Agent if you are Quantitative researchers automating factor mining, ML engineers iterating on model development, AI R&D teams needing reproducible experimentation, Data scientists comfortable with Python and open-source tooling.
Look elsewhere if you are Teams that need managed cloud hosting and a vendor support contract, Non-programmers expecting a GUI-only product, Organizations without anyone to own Python deployment and dependencies.
Skip it if Skip RD Agent if nobody on your team can own a Python ML environment and debug its dependencies, or if your evaluation function isn't strong enough to trust a multi-round self-improvement loop against.
GeneratedReal-world workflow fit
Concrete scenarios for the personas RD Agent actually fits — and what changes day-one when you adopt it.
You define a factor-mining task, point RD Agent at your price and fundamentals data, and let the loop generate candidate factors, backtest them, score the results, and rewrite the weakest ones across several rounds.
Outcome: You end up with a shortlist of factors that survived repeated evaluation, plus each round's code and scores committed to git so you can trace exactly how a factor evolved.
You hand the framework a model-tuning task with your dataset and a scoring metric, and let it iterate on architecture and hyperparameters instead of running sweeps by hand.
Outcome: The tuning loop runs unattended and logs each attempt, so you review a ranked set of configurations rather than babysitting a manual grid search.
You wrap an internal experimentation pipeline as an RD Agent task so dataset preprocessing and adaptation happen automatically when you swap in a new dataset.
Outcome: New datasets get a working, reproducible pipeline with far less manual preprocessing work, and every run is tracked the same way.
Use cases
- Generate and backtest quantitative factors for stock prediction
- Automate hyperparameter tuning across multiple datasets
- Build reproducible code pipelines for research experiments
- Iteratively improve model architectures through multi-round self-evolution
- Prototype and validate R&D ideas with less manual coding
- Adapt an existing preprocessing pipeline to a new dataset automatically
Setup time & first value
How long it actually takes to get something useful out of RD Agent — broken out by persona, not the marketing-page minute.
A Python-fluent researcher with a working PyTorch or scikit-learn environment should get a first loop running in an afternoon: clone the repo, install dependencies, pick or write a task definition, and supply an evaluation function. Budget longer — often days — before you trust results on a real dataset, since most of the effort goes into designing an evaluation function strong enough to steer
Switching to or from RD Agent
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual Jupyter experimentation: wrap your existing preprocessing and scoring code as an RD Agent task so the loop reuses the logic you already trust.
- →From a commercial AutoML platform: port your evaluation metric over first, then let RD Agent handle generation and iteration inside your own environment.
- →From ad-hoc hyperparameter sweeps: replace the sweep script with a model-tuning task so each round's configuration and score are logged automatically.
- ↗To a managed AutoML platform: export your evaluation metric and dataset definitions, then rebuild the loop in the vendor's hosted pipeline if you decide you want support and hosting.
- ↗To a hand-rolled experiment tracker: commit the per-round code and scores RD Agent produces into your existing tracking system and drop the framework if you only needed a few rounds.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “RD Agent”, and we withheld 6: 6 could not be judged, because “RD Agent” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about RD Agent.
Official links
What are the best RD Agent alternatives?
The closest alternatives to RD Agent we list are DB-GPT, WrenAI and SQLBot. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| DB-GPT | Free | Free | — |
| WrenAI | Freemium | Pricing not published | — |
| SQLBot | Free | Free | — |
Alternatives to RD Agent
View all
Tools that pair well with RD Agent
Common stack mates teams adopt alongside RD Agent, with the specific reason each pairing earns its keep.
DB-GPT
Open-source agentic AI data assistant that writes SQL, runs code in a sandbox, and chats with your self-hosted databases
WrenAI
Open-source GenBI engine that gives AI agents a governed context layer for SQL and dashboards
SQLBot
SQLBot turns plain-language questions into SQL, answers and charts over your own MySQL or PostgreSQL database — open source and self-hosted.
RD Agent vs its closest rival
RD Agent's closest head-to-head is with Presto Voice.
Our RD Agent vs Presto Voice verdict written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Frequently asked questions
Categories
Best-of guides
For makers of RD Agent
- Get a Verified Listing for RD Agent ($39): a written decision within 1 working day.
- Free listing for another tool: Free reviews are decided in order, within 3 weeks of the review starting.
Used RD Agent? Help shape our editorial sentiment research.