// AGENTIC RESEARCH

ASK ANYTHING.
KNOW EVERYTHING.

DRA is a multi-stage research agent. Drop a topic, question, or rough idea into the queue and the pipeline plans sub-questions, gathers evidence from nine source providers (free web search, academic archives, your MelTuc intelligence apps), synthesizes a structured report with inline citations, then runs a self-critique pass before handing it to you for review. Every claim has a source; every cost is logged.

Launch App 📲 Installable on iPad See How It Works Learn More
9
Source Providers
4
Pipeline Stages
3
Depth Tiers
100
Confidence Score
// HOW IT WORKS

From Topic to Cited Report in 4 Steps

Drop a question or rough idea into the queue — DRA plans sub-questions, gathers evidence across nine source providers, synthesizes a structured report, and runs a self-critique pass before handing it to you.

01
Submit a Brief
Enter any topic, question, or idea. Pick a depth tier (Quick, Standard, Deep) and an optional priority. Your brief joins the queue.
02
Plan Sub-Questions
MiniMax decomposes your brief into 5–12 targeted sub-questions (depending on depth tier), plus a section outline and seed search terms.
03
Gather & Synthesize
DRA queries all enabled providers in turn, deduplicates by URL, ranks by relevance, then feeds the source ledger to MiniMax for structured synthesis with inline citations.
04
Critique & Deliver
A second MiniMax pass reviews the draft for unsupported claims and contradictions, producing a confidence score (0–100). The final report lands in your library ready to approve, export, or archive.
// QUEUE

Submit a Research Brief

Start with as little as a sentence. Pick a depth tier (Quick for a one-pass summary, Standard for a multi-source brief, Deep for a thorough cross-referenced report) and an optional priority. The queue drains hourly via cron, or you can trigger a single brief on demand. Submitted items stay visible with status, queue position, and depth so you always know what's cooking.

meltuc.tech/dra/app/queue
New Research Brief
How are open-source agent frameworks competing with closed SDKs in 2026?
Depth: Standard
Priority: 5
Queue
Pending Briefs (4)
# Brief Depth Priority Status
1Compare MCP vs OpenAI tool-use spec driftDeep9Running
2Open-source agent frameworks landscape 2026Standard5Queued
3Self-hosted vector DB cost benchmarksStandard5Queued
4Flask 3 async story for blueprint appsQuick3Queued
// REPORTS

Research Library

Every completed brief lands in the reports grid. Each row shows the question, depth, source count, confidence score, token spend, and review status. Filter by status (draft / approved / archived / needs critique), search by keyword, or sort by recency. Clicking any row opens a slide-out detail panel with the full report and source ledger.

meltuc.tech/dra/app/reports
Search reports...
All Status
All Depth
Newest
Question Depth Sources Conf Tokens Status
Compare MCP vs OpenAI tool-use spec driftDeep379442.1kApproved
Self-hosted vector DBs — cost vs latencyStandard228918.4kApproved
Open-source agent frameworks landscape 2026Standard197614.8kDraft
Flask 3 async story for blueprint appsQuick8715.2kDraft
Postgres pgvector vs Qdrant for 10M embeddingsDeep319235.7kCritique
Are AI coding agents reducing junior dev hiring?Standard248516.9kArchived
// REPORT DETAIL

Slide-Out Reader

Clicking any report opens a slide-out reader on the right. The header shows the question, depth, confidence score, and approval controls. Below that you get the structured body (executive summary, sub-question answers, conclusions), a sub-question outline the pipeline planned during stage 1, and a complete source ledger with provider, relevance score, and inline citation IDs you can map back to claims in the body.

meltuc.tech/dra/app/reports#42
Question Conf
Compare MCP vs OpenAI tool-use spec drift94
Self-hosted vector DBs cost vs latency89
Open-source agent frameworks 202676
Flask 3 async story71
Compare MCP vs OpenAI tool-use spec drift
94
Deep 37 sources 42.1k tok
Executive Summary
MCP and OpenAI tool-use have diverged on three axes since Q4 2025: schema declaration, transport, and result streaming. MCP standardizes JSON-RPC over stdio/SSE; OpenAI keeps inline JSON in the chat completion envelope…[1][3]
Sub-Questions
1. Where do the schemas overlap?
2. What does each transport assume about runtime?
3. How are streamed results handled?
4. Which SDKs bridge both?
Source Ledger (top 5)
[1] modelcontextprotocol.io/spec · github · 98
[2] platform.openai.com/docs/tools · duckduckgo · 95
[3] HN: MCP v2 breaking changes · hackernews · 91
[4] r/LocalLLaMA: bridging both · reddit · 84
[5] arXiv:2511.04823 · arxiv · 78
Approve
Re-critique
Archive
// PIPELINE

4-Stage Research Loop

DRA is not a single LLM call wrapped around web search. Each brief flows through four discrete stages, each with its own model selection, prompt, and audit trail. Token spend and timing are recorded per stage so you can see exactly where the cost went.

01
Plan
MiniMax decomposes the brief into 3–8 sub-questions, picks search keywords per sub-question, and chooses which providers to query. Fast, and the foundation for everything downstream.
02
Gather
For each sub-question, DRA queries every enabled provider in turn: free web search, Wikipedia, HN, Reddit, arXiv, GitHub, your MelTuc cross-app sources (GHT / AIF / BRI when topics overlap), and (optionally) Tavily/Brave. Results are deduped by URL and ranked by relevance.
03
Synthesize
MiniMax reads the ranked source ledger and writes a structured report — executive summary, per sub-question answer, conclusions — with inline citations mapped back to the source IDs. This is the heaviest token spend of the run.
04
Critique
A second MiniMax pass reviews the draft for unsupported claims, missing citations, contradictions, and weak arguments. The critique becomes a confidence score (0–100) and a list of issues you see in the slide-out before approving.
// SOURCES

Nine Source Providers

DRA queries up to nine providers in turn for each sub-question. Seven are zero-key — they work the moment you enable them. Two paid providers (Tavily and Brave) plug in if you want premium web search. Toggle any of them on or off in Settings.

🔍
DuckDuckGo
Free, key-less general web search. Scrapes the HTML endpoint and unwraps redirect URLs.
freeweb
W
Wikipedia
OpenSearch + REST summary endpoints for authoritative reference content and definitions.
freereference
Y
Hacker News
Algolia HN Search API. Strong signal for tech, startup, and developer-tooling discussions.
freediscussion
R
Reddit
Cross-subreddit JSON search. Captures community sentiment, real-world usage notes, and edge-case reports.
freecommunity
§
arXiv
Academic preprint search across CS, AI, and ML categories. Pulls title, abstract, and authors.
freeacademic
G
GitHub
Public repo and code search. Surfaces real implementations, READMEs, and active project signals.
freecode
🔗
Cross-App
Reads your own MelTuc tables — GHT trending repos, AIF scored items, BRI daily ideas, STT skill graph — so reports include data unique to you.
freeecosystem
T
Tavily
Premium agent-grade web search with ranked snippets. Optional — requires an API key in credentials.env.
key requiredweb
B
Brave Search
Independent web index with strong privacy guarantees. Optional — requires an API key in credentials.env.
key requiredweb
// CAPABILITIES

New in DRA

Recent additions extend the report lifecycle with export, quality scoring, and discovery features.

📥
Export as Markdown
Download any report as a .md file with a single click. The exported file contains the full report body including citations, making it easy to drop research into any Markdown-aware tool, docs system, or newsletter draft.
GET /api/report/<id>/export.md Markdown One Click
⭐
Quality Score
Rate any report from 0 to 100 using the star widget in the report detail panel. Quality scores are stored separately from the AI confidence score and reflect your editorial judgment — useful for identifying which DRA output is genuinely useful vs. superficial.
POST /api/report/<id>/score Star Rating 0–100
🔗
Related Reports
When viewing a report detail, DRA automatically surfaces other reports with overlapping title keywords. Click any related report to open it in the slide-out panel — ideal for spotting patterns and avoiding redundant research runs.
GET /api/report/<id>/related Keyword Match Auto-Discovery
⚖
Model Comparison
Optional compare: run the same topic through two whitelisted models (including local Gemma/Qwen via the shared LLM helper) and get a side-by-side word-level diff before committing a full pipeline run.
POST /api/reports/compare Parallel Side-by-Side Diff
📡
Live Progress
Submit a brief and watch it run in real time. A streaming progress bar reports each stage — waiting, researching, complete or failed — over Server-Sent Events, so there is no need to refresh while the pipeline works.
GET /api/queue/item/<id>/stream SSE Real-Time
🔎
Gap Analysis
Surface what a report missed. On-demand gap analysis lists the weak points, and an AI-written explanation summarizes the single most important gap and what further research would close it — cached after the first request.
POST /api/report/<id>/analyze-gaps AI Explained
// WHAT YOU GET

Everything You Need to Research Anything

DRA combines nine source providers, a four-stage pipeline, and structured output into a single research tool that runs on autopilot or on demand.

🤖
AI Research Engine
MiniMax plans, synthesizes, and critiques, with token spend tracked per stage. If MiniMax is unavailable, stages fall back to a free OpenRouter model — not a local Ollama path.
📄
Structured Reports
Every report ships with an executive summary, per sub-question answers, conclusions, and a complete source ledger with inline citation IDs.
📥
Markdown Export
Download any approved report as a .md file in one click. Citations intact, ready to drop into any docs system, newsletter draft, or dev workflow.
📚
Report Library
All completed reports stored in a filterable grid. Search by keyword, filter by status or depth, sort by confidence score or recency, and open any report in the slide-out reader.
📋
Research Queue
Submit multiple briefs with priority levels. The queue drains hourly via cron, or trigger any single brief on demand. Full status tracking on every item.
⚙️
Model Selection
Pick a speed label per pipeline stage in Settings. Labels still say Fast/Standard historically; every choice maps to MiniMax-Text-01 on the main pipeline.
⚖️
Model Comparison
Pit two models against the same topic in parallel and get a side-by-side report with a word-level diff. Pick the strongest model before queuing the real pipeline run.
// DEPTH TIERS

Pick Your Spend

Each brief picks one of three depth tiers. The tier controls how many sub-questions are planned, how many sources are pulled per sub-question, and which model handles synthesis. Pick Quick when you just want a sanity check; pick Deep when the answer matters.

QUICK
5 sub-questions
~5 sources
No critique pass
~5k tokens
Cheap sanity check.
STANDARD
8 sub-questions
~20 sources
Includes critique pass
~18k tokens
The default workhorse.
DEEP
12 sub-questions
~50 sources
Includes critique pass
~40k tokens
When the answer matters.
// AUTOMATED PIPELINE

PART OF THE HOURLY CHAIN

DRA doesn't only run when you manually queue something. Every hour, the BRI→DRA feeder picks the highest-scoring unresearched BRI idea and queues it automatically at priority 7. When DRA finishes, it back-links to the original idea — and five minutes later, PTG turns it into a clickable prototype.

BRI :18
idea queued
→
DRA :20
research + back-link
→
PTG :25
prototype generated

Manual queue still works for any topic, question, or idea outside of BRI.

Part of the Pipeline

DRA is the middle stage in the automated idea-to-prototype chain. Ideas flow in automatically from BRI and research outputs feed directly into PTG.

💡
BRI
Idea generated
→
🔬
DRA ← You are here
Research conducted
→
🖥
PTG
Prototype built

The full cycle — BRI idea → DRA research → PTG prototype — runs automatically within the hour via the platform cron chain.

ESM Integration
DRA research reports can be loaded directly into the Executive Summary Manager. When starting a new ESM report, select any approved DRA report from a dropdown to pre-populate the briefing context before uploading team PDFs — grounding the AI narrative in your own research.

What stays true over time

DeepResearch is a premier multi-stage research agent: queue a brief, plan, gather from web + MelTuc sources, synthesize a cited Markdown report, critique, then approve / share / export. The pipeline runs on MiniMax (free OpenRouter fallback) — Settings Fast/Standard labels are aliases, not Claude tiers. Health stays cheap. This landing never lists your real reports. If this page disagrees with the signed-in app, trust the app and fix the landing — product contract and tests enforce that rule.

// OUT OF SCOPE

Not one-shot chat search.

Not Perplexity. Not an unauthenticated report dump. Compare-with-local-models is an optional tool, not how every queued brief runs.

// GET STARTED

Stop Googling. Start Knowing.

Nine sources. Four stages. One structured, cited report per question — on autopilot.

Launch Deep Research Agent

Requires a MelTuc account. Create one free.

// SAID SLOWLY

What stays true over time

Queue a research brief — plan, gather, synthesize, critique. Cited Markdown reports. MiniMax pipeline. Share links when approved. Health stays cheap. If this page disagrees with the signed-in app, trust the app — the product contract and tests enforce that rule.