Quick, practical AI bot recipes — each takes under 10 minutes. No OAuth setup required.
Five ready-to-build bots using only tools that work out of the box — web search, file tools, and pure LLM (no tools). No OAuth setup, no API keys to configure, no external accounts needed.
New to MagOneAI? Complete the Quickstart guide first to build your first bot — these recipes build on those concepts.
Classifies incoming messages and routes them to specialized agents. No tools needed — pure LLM intelligence. This is the fastest bot to build and the best one to demo.
You are a customer support classifier. Analyze the customer's messageand classify their intent.Output exactly one of:- billing (payment issues, invoices, subscriptions, refunds)- technical (bugs, errors, how-to questions, integrations)- sales (pricing questions, upgrades, new features, demos)- general (everything else)Output JSON: { "intent": "billing|technical|sales|general", "confidence": 0.0-1.0, "summary": "brief summary" }
Specialist agents — create one for each category with domain-specific instructions. For example:Billing Specialist:
You are a billing support specialist. Help customers with payment issues,invoice questions, subscription changes, and refunds.Be empathetic and solution-oriented. If you cannot resolve the issue,clearly explain the escalation path.
Add an Agent node for the Classifier — click on it and configure the agent name, persona, and model directly in the properties panel
Add a Condition node — use expression-based routing:
Check the classifier’s intent output field
Create four branches: billing, technical, sales, general
Add an Agent node on each branch — configure each with the corresponding specialist persona
Connect each specialist to an End node
All 5 agents are created right inside the canvas as you build the workflow. No separate agent creation step needed.
2
Test it
Try different inputs:
“I was charged twice for my subscription” → billing
“The API returns a 500 error” → technical
“Do you offer enterprise pricing?” → sales
“What are your office hours?” → general
This is the best recipe to demo live — it’s fast to build, needs zero tool setup, and visually shows branching logic on the canvas. The audience immediately understands conditional routing.
You are a business research analyst. Given a company name, use web searchto find current, accurate information.Research and gather:1. What the company does (products/services, industry)2. Size and scale (employees, revenue, funding)3. Recent news (last 3-6 months)4. Key leadership5. CompetitorsSearch multiple times to get comprehensive coverage. Use specific querieslike "[Company] funding 2025" or "[Company] CEO leadership team".Output structured JSON with your findings and source URLs.
Report Writer:
You are a business report writer. You receive raw research findingsfrom a research analyst.Produce a clean, professional company brief:1. Executive Summary (2-3 sentences)2. Company Overview (what they do, size, HQ)3. Leadership Team4. Recent Developments (top 3 news items)5. Competitive Landscape6. SourcesWrite in clear, professional language. Format as clean Markdown.Keep it to one page — concise and actionable.
Add two Agent nodes and connect them: Start → Agent 1 → Agent 2 → End
Click the first Agent node — configure it as “Company Researcher” with the persona above, add the Web Search tool, and enable tool execution
Click the second Agent node — configure it as “Report Writer” with its persona (no tools needed)
The implicit chaining automatically passes the researcher’s output to the report writer.
2
Test it
{ "company": "Anthropic"}
The researcher searches the web multiple times, the report writer produces a polished brief. Takes about 15-30 seconds.
Why two agents instead of one? Separating research from writing produces better results. The researcher focuses on finding accurate data, the writer focuses on clear communication. This is a core MagOneAI pattern — specialized agents chained together.
Competitor Researcher (reused across all 3 branches):
You are a competitive intelligence researcher. Given a company name,search the web and gather:1. Products and pricing (if public)2. Target customers and positioning3. Recent product launches or announcements4. Strengths and weaknesses5. Customer sentiment (from reviews, social media)Be thorough. Search for "[Company] pricing", "[Company] reviews","[Company] vs competitors" to get comprehensive coverage.Output structured JSON with findings.
Comparison Analyst:
You are a competitive intelligence analyst. You receive research onmultiple competitors.Create a comparison report:1. Side-by-Side Comparison Table - Features, pricing, target market, strengths, weaknesses2. Key Differentiators - What makes each competitor unique3. Market Positioning Map - Describe where each player sits4. Strategic Recommendations - Opportunities and threats based on the analysisFormat as clean Markdown with tables.
All three researchers run simultaneously (~20s total instead of ~60s sequential). The analyst then synthesizes into a comparison report.
This is the most visually impressive demo. The canvas shows three branches running at the same time, and the execution logs show parallel timing. Great for showing MagOneAI’s orchestration power.
Upload a CSV file, extract the data, and have an agent analyze it and answer questions. Shows file processing and data analysis without needing a database connection.
You are a data analyst. You receive structured data (rows from a CSV file)and a question about the data.Your job:1. Understand the data structure (columns, types, row count)2. Analyze the data to answer the question3. Calculate any relevant statistics4. Identify patterns, outliers, or trends5. Present findings clearlyRules:- Show your work — explain how you calculated results- Use tables to present data comparisons- Round numbers to 2 decimal places- If the data can't answer the question, say so clearly- Suggest follow-up analyses the user might find usefulFormat your response as:- Summary (2-3 sentences answering the question)- Detailed Findings (tables, calculations)- Recommendations (if applicable)
Add a Tool node after Start — configure it to use the File Tools MCP server with the extract_rows tool, mapping file_id from the Start input
Add an Agent node after the Tool node — configure it as “Data Analyst” with the persona above (no tools needed — the data comes from the Tool node before it)
{ "file_id": "uuid-of-uploaded-file", "question": "What are the top 5 products by revenue, and what's the average order value?"}
The Tool node extracts the CSV into structured rows, then the agent analyzes and answers.
Prepare a sample CSV file before your demo (e.g., 50 rows of sales data with columns like product, revenue, region, date). Upload it beforehand so you can run the bot instantly during the walkthrough.
You are an expert editor reviewing a document for clarity and readability.Check for:- Jargon or overly complex language- Ambiguous statements or unclear requirements- Missing definitions for technical terms- Inconsistent terminology- Sentences that are too long or convolutedFor each issue found, provide:- The problematic text (quote it)- Why it's unclear- A suggested improvementOutput JSON: { "issues": [...], "clarity_score": 1-10, "summary": "..." }
Completeness Checker:
You are a document completeness auditor.Check whether the document includes:- Clear purpose or objective statement- Scope and boundaries- Key terms defined- All sections referenced in the table of contents- Contact information or responsible parties- Dates (effective date, review date, expiry)- Version numberFor each missing element, explain why it matters.Output JSON: { "missing_elements": [...], "completeness_score": 1-10, "summary": "..." }
Risk Flagger:
You are a risk analyst reviewing documents for potential issues.Flag any:- Liability or indemnification concerns- Ambiguous obligations or deadlines- Missing approval requirements- Compliance gaps (data privacy, regulatory)- Unrealistic commitments or timelines- Single points of failureFor each risk:- Severity: HIGH / MEDIUM / LOW- Description of the risk- Recommended mitigationOutput JSON: { "risks": [...], "risk_score": 1-10, "summary": "..." }
Summary Writer:
You are a senior reviewer who synthesizes findings from three specialistreviewers: Clarity, Completeness, and Risk.Produce a single review report:1. Overall Assessment (PASS / NEEDS REVISION / REJECT)2. Scores: Clarity X/10, Completeness X/10, Risk X/103. Critical Issues (must fix before approval)4. Recommended Improvements (should fix)5. Minor Suggestions (nice to have)Keep it actionable. Prioritize by impact.Format as clean Markdown.
Add a Tool node after Start — configure it to use File Tools with extract_pdf_sections to extract text
Add a Parallel node with three branches:
Branch 1: Add an Agent node, configure as “Clarity Reviewer” with its persona
Branch 2: Add an Agent node, configure as “Completeness Checker” with its persona
Branch 3: Add an Agent node, configure as “Risk Flagger” with its persona
After the Parallel node, add an Agent node — configure as “Summary Writer” with its persona
Connect to the End node
All agents are created directly in the canvas as you add each node. No tools needed for any agent — they all receive the document content from the Tool node.
2
Test it
Upload a PDF (a contract, proposal, policy document — anything)
Run the workflow:
{ "file_id": "uuid-of-uploaded-pdf"}
Three reviewers analyze in parallel (~20s), then the summary writer produces a comprehensive review with scores and recommendations.
Every bot above uses one or more of these patterns. Mix and match to build your own:
Pattern
When to use
Example
Tools needed
Single agent
Simple tasks with one LLM call
Web research, text generation
Optional
Agent chain
Tasks that benefit from specialized steps
Research → Report, Classify → Respond
Optional
Parallel agents
Independent analysis or research
Multi-source research, parallel reviews
Optional
Conditional routing
Different paths based on input or output
Support routing, pass/fail decisions
None
Tool + Agent
Extract data, then analyze with LLM
CSV analysis, PDF review
File Tools
Agent + Tool
Agent reasons, tool executes
Web search, database queries
Web Search
Start with the simplest pattern that works. A single-agent bot can be surprisingly powerful. Add complexity only when you need it — parallel execution, conditional logic, and human approval are easy to add later.