Kea Research is live · free · last verified 4 Nov 2026 · viability monitor
Kea Research
Kea Research turns one question into five AI answers that peer-review each other into a verified consensus.
When the answer is the deliverable, a lone model's output is a liability — Kea makes model disagreement visible, scored and exportable, which is what an architecture review or compliance memo actually needs. We'd skip it for fast lookups: a Docker install, BYO API keys and a 15-30 second wait per question are real friction, and real-time web search isn't shipped yet.
Verdict generatedVerified 10d ago · liveness 66/100 · cite: rightaichoice.com/tools/kea-research
- Business leaders making market, risk or strategy calls that need a defensible paper trail
- Engineering teams wanting peer-reviewed architecture, database and tech-stack decisions
- Researchers cross-validating hypotheses and fact-checking findings across models
- Legal, medical and financial teams where an audit trail is part of the deliverable
- Anyone who needs instant answers — consensus runs 15-30 seconds per query
- Teams that want a fully managed cloud service with no server to run
- Users unfamiliar with Docker and API key configuration
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Skip KEA Research if you need an answer in two seconds, don't want to run your own Docker container, or aren't prepared to hold and pay for your own model API keys.
Your model API keys are billed directly by each provider, so a five-model consensus query costs roughly five model calls — the platform itself is free but usage is not.
With a free open-source, self-hosted install, KEA's cost profile is the opposite of hosted research assistants that bundle model access into a subscription: you pay nothing for the software and instead pay each provider at cost for the tokens your consensus queries consume. For a small team running occasional high-stakes decision work, that is usually cheaper than seat-based subscriptions; for high-volume daily research, five-model queries and stacked Research Layers can outrun a flat hosted
In short
Kea Research — Kea Research turns one question into five AI answers that peer-review each other into a verified consensus. Best for Business leaders making market, risk or strategy calls that need a defensible paper trail, Engineering teams wanting peer-reviewed architecture, database and tech-stack decisions, Researchers cross-validating hypotheses and fact-checking findings across models. Free to use.
What is Kea Research?
Kea Research — Kea Research turns one question into five AI answers that peer-review each other into a verified consensus. Best for Business leaders making market, risk or strategy calls that need a defensible paper trail, Engineering teams wanting peer-reviewed architecture, database and tech-stack decisions, Researchers cross-validating hypotheses and fact-checking findings across models. Free to use.
Kea Research is an open-source, self-hosted platform for getting answers you can defend. You ask one question, five AI models answer independently, then read each other's anonymized drafts, refine their own, rank one another on accuracy, completeness and logical consistency, and let the top-scoring model write the final synthesis. Average processing time is 15-30 seconds per query — the deliberate cost of a checked answer over a single model's confident guess. It suits teams where being wrong is expensive: business leaders sizing markets, engineers choosing an architecture or database, researchers cross-validating findings, and legal, medical or financial groups that need an audit trail in the deliverable. The full paper trail is retained — every model's initial response, refinement and evaluation score — and disputed claims get flagged automatically by the auto fact-extraction step. On top of the consensus loop, Kea ships Research Layers that drill into subtopics while preserving context chains, each layer running its own independent consensus cycle, plus multi-model visual analysis for uploaded diagrams and charts, personal notes, file upload, text-to-speech across 75 languages, and an admin panel for user management. Exports land in Markdown, JSON or HTML. You bring your own API keys — OpenAI, Anthropic's Claude family, Google Gemini, Mistral, xAI Grok, OpenRouter's 100+ models, or local LLMs through Ollama — so you pay only API costs and can swap models whenever you like. Deployment is one curl or PowerShell command with Docker as the prerequisite and SSL handled by Let's Encrypt. Against hosted research assistants and single-model chat tools, Kea trades setup effort and latency for transparency, model choice and no vendor lock-in. Note that live web search and the Qdrant vector-memory integration are still marked in development.
Key features
- Five AI models answer each query independently before any collaboration
- Four-stage pipeline: initial answers, anonymized refinement, peer evaluation, synthesis
- Models rank each other on accuracy, completeness and logical consistency
- Automatic fact extraction flags claims the models dispute
- Research Layers drill into subtopics with an independent consensus cycle per layer
- Multi-model visual analysis for uploaded diagrams and charts
- Full audit trail of every model's initial response, refinement and evaluation score
- Export results to Markdown, JSON or HTML
- Text-to-speech output across 75 languages
- Bring-your-own-API-key architecture with no vendor lock-in
- Run local LLMs via Ollama on your own hardware
- One-command Docker install with Let's Encrypt SSL
- Admin panel for user management and access control
- Personal notes and file upload for images and documents
Behind the verdict
The pitch for Kea Research is refreshingly narrow: one question, five models, cross-examination, one synthesized answer. No magic dashboard, no auto-pilot research agent. That focus is why it earns a place in a decision workflow where being auditable matters more than being instant. Pick it when a wrong answer costs more than a slow one. Architecture calls, compliance write-ups, market-entry memos, literature checks — anything you'll paste into a document someone else will sign off on. The per-model audit trail and auto-flagged disputed claims mean you can point to why a conclusion holds, not just assert it. Pass if you want an always-on research companion. Live internet search and vector memory are both flagged in development (the vendor lists Qdrant and real-time search around Q1 2026), so today Kea works from what the models already know plus whatever you upload. It's also a poor fit for rapid-fire low-stakes queries: you pay API rates across five models every time, and consensus adds latency on top. The self-hosted angle cuts both ways. You keep your keys and your data, but you own the operational chores — Docker, environment config, SSL, upgrades via tagged releases. The Windows path deliberately pushes a ZIP download over Git to dodge CRLF line-ending breakage in Docker, which tells you the maintainers have been burned by it before. Budget half an hour for a first install, longer if your team isn't comfortable with .env files. Where it bites: you need at least two or three provider keys (or a beefy Ollama box) for the consensus loop to mean anything, and model availability varies by provider. The multi-model vision and text-to-speech features are secondary to the core workflow — nice, not the reason to adopt. Closest alternative in spirit is a multi-agent
Models under the hood
as of 2026-11-13
Limitations
- Self-hosted only — you run the server, updates and uptime, and you must supply your own API keys (bring-your-own-API-key architecture), so total cost depends on which providers you connect.
- Each query runs five AI models through a four-stage consensus pipeline with an average processing time of 15-30 seconds, and Research Layers can stack additional independent consensus cycles, so deep dives take longer.
- The evidence describes fact verification via cross-checking models and peer evaluation, but does not document live internet search or built-in vector memory, so current-events questions may exceed what the models' training data covers.
as of 2026-10-09
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Is Kea Research still active?
Kea Research is live · free · last verified 4 Nov 2026 · viability monitor.
Viability score
How well maintained and how widely used is Kea Research? 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 Kea Research?
What people actually say about Kea Research — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
ResearchedDiscussed by users across 4 sources (Hacker News, YouTube, GitHub, Lemmy) · researched Nov 9, 2026.
Weighted by the 6 posts each of 2 sources contributed.
- +Blind peer review and ranking between five models produces a defensible audit trail per query
- +Bring-your-own keys across OpenAI, Claude, Gemini, Mistral, Grok, OpenRouter, and Ollama
- +Open-source and self-hosted, so sensitive queries never leave your infrastructure
- +Maintainers closed a reported pipeline failure the same day it was filed
- +Full paper trail records every model's initial answer, refinement, and evaluation score
- −Five models per query can multiply API costs five to ten times a normal chat call
- −Live web search and Qdrant memory are still marked in development
- −Setup requires Docker, SSL, and API-key wrangling — genuine advanced-user territory
- −15-30 seconds per query is slow for exploratory or conversational research
- −No independent benchmarks showing consensus actually beats a single strong model
- • Five frontier-model API calls per stage — potentially 5-10x a single chat query
- • Hosting costs for a Docker server capable of running research workloads
- • Engineering time to deploy, secure with Let's Encrypt SSL, and maintain
- • Local Ollama inference still needs capable GPU hardware to be usable
How we verified this
Every listed fact about Kea Research — 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 4 Nov 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 Kea Research 12 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 domain expiry
- — changed platforms
- — changed SSL certificate
- — changed features, who it suits, pricing tiers, our verdict, who should skip it, summary, description
- — changed SSL certificate
- — changed who should skip it, summary, features, our verdict, who it suits, integrations, pricing tiers, description
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Kea Research cost in 2026?
Kea Research: Free to use. Pricing last verified 4 Nov 2026.
Plans compared
Every published Kea Research 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 (Self-Hosted) | Free | — | Engineering, research, compliance or strategy teams that can run Docker, hold their own model API keys, and need the audit trail to stay inside their own infrastructure.Adds: Starting tier — the full platform at $0, with unlimited queries on your own infrastructure and model costs paid directly to providers at 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 Kea Research's pricing actually pencils out — and where peers do it cheaper.
With a free open-source, self-hosted install, KEA's cost profile is the opposite of hosted research assistants that bundle model access into a subscription: you pay nothing for the software and instead pay each provider at cost for the tokens your consensus queries consume. For a small team running occasional high-stakes decision work, that is usually cheaper than seat-based subscriptions; for high-volume daily research, five-model queries and stacked Research Layers can outrun a flat hosted
Has Kea Research's pricing changed?
No plan price for Kea Research has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Kea Research?
Pick Kea Research if you are Business leaders making market, risk or strategy calls that need a defensible paper trail, Engineering teams wanting peer-reviewed architecture, database and tech-stack decisions, Researchers cross-validating hypotheses and fact-checking findings across models, Legal, medical and financial teams where an audit trail is part of the deliverable.
Look elsewhere if you are Anyone who needs instant answers — consensus runs 15-30 seconds per query, Teams that want a fully managed cloud service with no server to run, Users unfamiliar with Docker and API key configuration, Projects needing live web search or vector memory today — both are marked in development.
Skip it if Skip KEA Research if you need an answer in two seconds, don't want to run your own Docker container, or aren't prepared to hold and pay for your own model API keys.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Kea Research actually fits — and what changes day-one when you adopt it.
Before committing to microservices for a new product, they pose the question to five models, let the models refine and rank each other's answers, then open a Research Layer on the data-consistency subtopic and export the JSON record to attach to the architecture decision doc.
Outcome: The team walks into the review with ranked, attributed reasoning instead of a single assistant's recommendation, and a stored record of where the models disagreed.
They upload a policy document and a chart from a vendor proposal, run multi-model visual analysis and fact extraction, and keep the audit trail showing each model's initial response, refinement and evaluation score.
Outcome: The review memo carries a verifiable trail of which claims were disputed and how they were resolved, which is the artifact the internal audit process actually asks for.
They run a consensus query across academic sources and let fact extraction flag disputed findings, then drill into contested subtopics with Research Layers so each layer keeps the context chain intact.
Outcome: They end up with a synthesis that distinguishes settled findings from contested ones, exportable to Markdown for the draft paper.
Use cases
- Cross-check a market-entry or expansion decision with five models before committing budget.
- Run multi-model architecture reviews so a technology choice is ranked by peer models, not one assistant.
- Produce audit-ready AI research logs for compliance, legal or financial review.
- Synthesize academic literature and flag disputed findings across model outputs.
- Analyze uploaded diagrams or charts with multi-model visual intelligence.
- Generate multilingual report narration with text-to-speech across 75 languages.
- Run a fully local consensus workflow with Ollama when data cannot leave your network.
Setup time & first value
How long it actually takes to get something useful out of Kea Research — broken out by persona, not the marketing-page minute.
The project quotes 60 seconds from one command on macOS/Linux (curl) or Windows PowerShell (irm) with Docker already installed. Realistically: a self-hosting developer reaches a first consensus answer in minutes after adding API keys; a small team adding the admin panel, custom model sets and SSL via Let's Encrypt should budget an afternoon; a non-technical buyer unfamiliar with Docker should
Switching to or from Kea Research
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From ChatGPT or Claude for decision work: keep the single-assistant habit for drafting, and route the questions where you need a defensible answer into KEA's five-model consensus instead.
- →From Perplexity or another hosted research assistant: move the recurring high-stakes queries to KEA and keep the hosted tool for fast current-events lookups, since live web search is still listed as in development.
- →From a manual cross-checking process: replace the three browser tabs with one consensus query, and keep the exported audit record where you previously pasted answers into a doc.
- ↗To a hosted research assistant: export your KEA results to Markdown or JSON so the reasoning trail survives the move, since the hosted tool will not reproduce the per-model evaluation scores.
- ↗To a single-model assistant: keep the exported audit trail for existing decisions, but expect to lose visible model disagreement on new ones.
- ↗To an internal LLM stack: if you already run your own models, KEA's value was the consensus pipeline around them — verify the replacement reproduces the four-stage refinement and peer ranking before retiring it.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Kea Research”, and we withheld 6: 6 did not mention Kea Research. We are showing none, because we could not prove any of them are about Kea Research.
Official links
What are the best Kea Research alternatives?
The closest alternatives to Kea Research we list are Council, Mnemosphere and ChatComparison.ai. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Council | Free | Free | — |
| Mnemosphere | Paid | Pricing not published | — |
| ChatComparison.ai | Freemium | Pricing not published | — |
Alternatives to Kea Research
View all
Council
Council runs a blind peer review between multiple AI models and scores how far apart their answers diverged.
Mnemosphere
One prompt, many frontier models — a research workspace for comparing where AI answers agree and diverge.
ChatComparison.ai
Paste one prompt, see how ChatGPT, Claude, Gemini and 40+ other models answer it, then compare quality, cost and speed side by side.
Tools that pair well with Kea Research
Common stack mates teams adopt alongside Kea Research, with the specific reason each pairing earns its keep.
Council
Council runs a blind peer review between multiple AI models and scores how far apart their answers diverged.
Mnemosphere
One prompt, many frontier models — a research workspace for comparing where AI answers agree and diverge.
ChatComparison.ai
Paste one prompt, see how ChatGPT, Claude, Gemini and 40+ other models answer it, then compare quality, cost and speed side by side.
Kea Research vs its closest rival
Kea Research's closest head-to-head is with Surge Ai — These are not competitors — they sit on opposite ends of the AI stack. Kea Research is free open-source software you run yourself to get a verifiable, multi-model answer to a business or technical question, paying only model API costs. Surge AI is a human-data vendor that frontier labs hire to produce RLHF preference data, red teaming, and citable benchmarks like GDP.pdf and ComplexConstraints. If you have a question you need answered with an audit trail, install Kea. If you are post-training a model and need expert human judgment in the loop, Kea is irrelevant to you and you need a scoping call with Surge. Buying one tells you nothing about whether you need the other. Verdict generated
Head-to-head comparisons
Kea Research vs Surge Ai
These are not competitors — they sit on opposite ends of the AI stack. Kea Research is free open-source software you run yourself to get a verifiable, multi-model answer to a business or technical question, paying only model API costs. Surge AI is a human-data vendor that frontier labs hire to produce RLHF preference data, red teaming, and citable benchmarks like GDP.pdf and ComplexConstraints. If you have a question you need answered with an audit trail, install Kea. If you are post-training a model and need expert human judgment in the loop, Kea is irrelevant to you and you need a scoping call with Surge. Buying one tells you nothing about whether you need the other.
GeneratedKea Research vs Praktika
Verdict being re-reviewed against current pricing and data.
Frequently asked questions
For makers of Kea Research
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