document/content/guide/getting-started/quick-start.en.mdx
This article uses four complete use cases to help you quickly understand FastGPT's core application types and complete the basic setup from simple conversations to complex task orchestration.
This page is suitable for first-time FastGPT users, as well as pre-sales, delivery, operations, legal, and administrative roles who want to quickly experience the platform's capabilities. After completing this page, you will build the following in order:
We recommend preparing the following in advance:
When reading, focus on three things: what kind of problems each application type is suitable for, why the key configurations are written this way, and what to observe during validation. The parameters and prompts in this article are reusable starting points; for production deployment, you can replace them with your own business materials, review rules, notification channels, and data files.
Conversational Agents are suitable for lightweight Q&A, content generation, copy refinement, and standardized output. This case does not link a knowledge base or rely on complex workflows; it simply uses model configuration, prompts, and an Email tool to build a corporate email writing assistant.
Employees often need to handle emails for project updates, cross-departmental collaboration, customer replies, meeting minutes, and more. Using AI to standardize email formats and expression styles can improve writing efficiency and maintain the professionalism of external corporate communications.
The focus of this case is not to have the AI randomly generate an email, but to consolidate the stable requirements of corporate email writing into the prompt, such as subject lines, salutations, body structure, action items, and risk reminders. For beginners, it also serves as a minimal closed loop for understanding FastGPT application configuration: first define the role, then constrain the output, and finally extend execution actions through tools.
Create a Conversational Agent
Click "New" in the workspace, select "Conversational Agent", and fill in the application name as Corporate Email Writing Assistant.
Enter the Application Configuration Page
After creation, enter the application configuration page. The page is usually divided into left and right columns: the left is AI configuration, and the right is debug preview.
Select a Model
In the AI configuration, select the base model for the Conversational Agent. This case uses GLM-5.1, but you can replace it with other available models configured in your current environment.
When selecting a model, prioritize two aspects: first, whether the model is good at business writing, and second, whether it consistently follows the required format. Email writing is a low-risk, low-structure task, so you usually do not need the strongest model available, but you should still choose one with a natural tone and reliable instruction following.
Write the Prompt
The prompt needs to clearly describe the assistant's role, output format, and constraints. You can use the following example:
You are a corporate email writing expert, helping employees write professional, clear, and appropriate work emails.
Output format:
- **Subject line**: Concise and clear
- **Salutation**: Choose "Dear Mr./Ms. XX" or "Hi XX" based on the relationship with the recipient
- **Body**: Three-part structure (Background → Core content → Action items)
- **Sign-off**: Name, Title, Department
Rules:
- Keep the body between 200-500 words
- List action items and to-dos with bullet points
- When involving sensitive content like salary, HR, or legal matters, remind the user to send with caution
- Use a neutral and polite tone when unsure of the relationship with the recipient
Write the prompt into the prompt module.
This prompt consists of three parts: the role definition stabilizes the assistant's identity, the output format constrains the email structure, and the rules control risk boundaries. In actual business, you can continue to add corporate tone requirements, brand terminology, banned words, signature formats, and more to make the output more aligned with internal standards.
Add the Email Tool
Tools encapsulate complex operations. This case uses the Email sending tool to give the AI assistant the ability to send emails after generating them. Click the plus sign on the right side of the tools and select "Send Email".
You can think of tools as the AI's external capabilities. Without tools, the assistant can only generate the email body; after adding the Email tool, the assistant can execute the sending action after user confirmation. In a production environment, it is recommended to have the assistant generate an email draft first, and then have the user confirm the sending, to avoid accidental sending or sending to the wrong recipient.
Configure the Email Tool
Enter the tool configuration page and fill in the parameters related to the email service.
Activate the Tool
Click "Settings" to enter the tool activation page, then click "Activate Tool".
Fill in the Email SMTP Information
This case uses QQ Mail as an example. When testing, you can fill it out as follows:
SMTP Server Address: smtp.qq.com
SMTP Port: 465
Enable SSL
SMTP Username: Email address
SMTP Password: Authorization code
For the authorization code, please refer to the Authorization Code Acquisition Tutorial.
Set the Conversation Opening
The conversation opening is used to tell users what this AI assistant can do. You can fill in:
Hello! I am the Email Writing Assistant 📧
Please tell me: Who is the recipient? What is the purpose of the email? What key information needs to be included?
I will help you generate a professional and appropriate email.
After filling it out, you can see the effect in the preview area on the right.
Validate the Result
After configuration, enter your email requirements in the debug preview to check whether the assistant can generate an email with a clear structure and appropriate tone.
If the user's information is insufficient, the assistant should proactively prompt to supplement the recipient, email purpose, key information, etc.
When verifying, it is recommended to test at least three types of inputs: an email requirement with complete information, a vague requirement missing the recipient or purpose, and an email requirement containing sensitive information. An email assistant ready for production not only needs to be able to "write", but also needs to be able to ask follow-up questions when information is insufficient, and remind users to send with caution in sensitive scenarios.
A knowledge base is ideal for scenarios where answers must be based on provided materials, such as policy Q&A, product manual Q&A, legal article retrieval, and customer service knowledge support. Without a knowledge base, the AI primarily relies on its own model capabilities to answer questions. Once a knowledge base is connected, the AI first searches your materials and then organizes answers based on the search results.
In this case, we import the Civil Code of the People's Republic of China into the knowledge base and create a Civil Code Q&A assistant. When users ask questions in natural language, the assistant should prioritize citing the original Civil Code text to reduce fabricated responses.
Think of the knowledge base as a "reference room" configured for the AI. The model itself has general knowledge but doesn't know your company policies, product details, internal processes, or specific regulatory versions. The knowledge base turns these materials into searchable content, allowing the AI to look up information before organizing an answer. For legal, policy, customer service, and after-sales scenarios, this is more controllable than relying solely on the model's memory.
Create a Knowledge Base
Click Knowledge Base on the left side of the homepage, then click New in the top right corner. Name the knowledge base Civil Code Q&A Assistant. Keep the other default settings for this case.
Create a Text Dataset
Click New, then select Text Dataset to import local documents.
Upload a Local File
Click Upload Local File and select the example file. In real use, you can upload multiple files at once; for this case, we upload only one file as a demonstration.
Set Parsing Parameters
After entering the parameter settings page, choose the parsing method based on the file type.
Recommended common settings:
These parameters directly affect the quality of subsequent Q&A. If chunks are too large, search results may include too much irrelevant content, making answers verbose. If chunks are too small, key context may be split, causing answers to lack supporting evidence. For the quick-start phase, use the default configuration. When officially integrating enterprise policies, contracts, or product manuals, adjust these parameters gradually based on Q&A performance.
Preview Chunking Results
After reaching the data preview step, check whether the chunks are complete and readable. If chunks are too long or too short, go back and adjust the parameters.
When previewing chunks, focus on three things: whether paragraphs are abnormally cut off, whether titles and body text remain within the same semantic range, and whether tables, clauses, or numbering are still readable. If the quality of these knowledge fragments is unstable, even a well-written Prompt in the application will struggle to consistently produce accurate answers.
Wait for the Knowledge Base to Be Ready
Once the data status changes to "Ready," the knowledge base can be referenced by applications.
Create and Link a Conversational Agent
Create a new Conversational Agent, also named Civil Code Q&A Assistant. After creation, link the knowledge base you just created in the application configuration.
After linking the knowledge base, the application's response flow changes from simple conversation to "user question → knowledge base search → model summarizes answer." This is the key difference between Case 1 and Case 2: Case 1 emphasizes content generation, while Case 2 emphasizes answering based on provided materials.
Configure the Q&A Prompt
The Civil Code Q&A assistant needs to emphasize "answer based on the knowledge base" and "cite the original text." You can use the following Prompt:
You are a professional Civil Code Q&A assistant, answering legal questions based on the original text of the _Civil Code of the People's Republic of China_.
Rules:
- Strictly answer based on the Civil Code articles retrieved from the knowledge base; do not fabricate legal provisions.
- Every answer must cite the original Civil Code text (book, chapter, article).
- If there is no directly corresponding provision in the Civil Code, state this honestly and do not give legal advice.
- When applying the law to specific cases, remind the user: "This answer is for reference only; please consult a professional lawyer."
- Provide plain-language explanations of legal terms so that users without a legal background can understand.
Output format:
1. **Legal Conclusion** (1–3 sentence summary)
2. **Relevant Article Citation** (original excerpt + book/chapter/article number)
3. **Plain-Language Explanation** (explain the meaning of the article in everyday language)
4. **Practical Advice** (2–3 actionable suggestions)
5. **Disclaimer** ("This answer is based on the original Civil Code text and does not constitute legal advice. For specific cases, please consult a professional lawyer.")
For legal Q&A scenarios, it's especially important to define boundaries: what can be answered is a general explanation based on the materials; the model's output should never be packaged as formal legal advice. Requiring article citations, stating uncertainty, and adding disclaimers in the Prompt all aim to make the output more traceable and compliant with high-risk knowledge Q&A usage norms.
Configure the Opening Message
The opening greeting can include a few example questions to help users quickly understand how to use this assistant:
Hello! I'm the Civil Code Q&A Assistant ⚖️
I answer legal questions based on the original text of the *Civil Code of the People's Republic of China*.
You can ask me:
["My lease isn't up yet, but the landlord wants to sell the house. What should I do?"]
["I received a product I bought online and it was broken, but the seller won't accept a return. What does the law say?"]
["The upstairs neighbor's leak soaked my ceiling. Can I claim compensation?"]
⚠️ Note: My answers are for reference only. For specific legal issues, please consult a professional lawyer.
If you add [question content] in the opening greeting, users can click the question to ask it directly — useful for demonstrations and guidance.
Verify Q&A Performance
Ask a question related to the Civil Code and check whether the answer includes a legal conclusion, article citation, plain-language explanation, and disclaimer.
When verifying, don't just check whether the answer "looks like a legal answer" — also check whether it actually references the knowledge base content. It's recommended to test with questions inside the materials, outside the materials, and ambiguous questions: questions inside the materials should cite the original text; questions outside the materials should state that a direct confirmation is not possible; ambiguous questions should proactively prompt the user to provide more facts.
Workflows are ideal for tasks with fixed steps, clear logic, and the need for branching decisions or human confirmation. This case breaks down content compliance review into several stages: knowledge base retrieval, AI classification, conditional branching, automatic rewriting, rejection explanation, and human confirmation, simulating the review process before enterprise content is published.
Its core value lies in two aspects:
To determine whether a task is suitable for a workflow, check if it has three characteristics: "stable steps, clear rules, and repeatable execution." Content review is a typical scenario: the input is content to be published, the rules come from a compliance knowledge base, and the output is usually pass, rewrite, or reject, with the option to add human confirmation in between. This improves processing efficiency while retaining risk control.
First, create a "Content Compliance Rules" knowledge base and upload a simple text file. You can directly use the following rule template:
Content Compliance Rules
Safe Content (can be published directly)
- Objective factual statements
- Normal event notifications, meeting arrangements
- Product feature descriptions (based on real data)
- Industry knowledge sharing
Sensitive Wording (needs rewriting)
- Absolute language: "best," "first," "100%," "absolute," "only"
- Exaggerated claims: "disrupting the industry," "unprecedented," "unmatched"
- Unverified data: conversion rates, satisfaction rates, growth rates without sources
- Comparative disparagement: directly naming competitors and belittling them
Prohibited Content (must not be published)
- Illegal information: involving pornography, gambling, drugs, fraud, pyramid schemes
- Personal attacks: insults, defamation against individuals or groups
- False information: fabricated data, forged qualifications, impersonating official sources
- Sensitive topics: political sensitivity, religious discrimination, regional attacks
The rule base does not need to be complex at the start. For quick validation, split the rules into three categories: "safe, needs rewriting, prohibited." For formal use, you can continue adding rules by industry, brand, channel, or region, such as advertising-sensitive terms, medical compliance requirements, prohibited financial marketing claims, brand tone guidelines, and so on.
Before configuring nodes, it's recommended to confirm the complete workflow. This case can be designed as follows:
When designing a workflow, first determine the responsibility of each node to avoid having a single AI node simultaneously handle "retrieving rules, judging classification, rewriting content, explaining reasons," etc. Once responsibilities are clearly separated, each node's prompt will be shorter and more stable, making it easier to locate issues later: whether the knowledge base failed to recall rules, the review node misclassified, or the condition in the decision node didn't match.
Create a Workflow
Go to the workflow homepage, click "New Workflow," and name it Content Review and Automatic Rewriting.
After creation, enter the workflow editing page.
The complete workflow diagram for this case is as follows:
Configure the Opening Message
In the system configuration, fill in the opening statement to explain the assistant's review rules and usage.
Hello! I am the Content Compliance Review Assistant 🛡️
Please send me the content you need reviewed, and I will automatically judge it according to the rules:
- **✅ Safe** — Content has no sensitive information and can be published directly
- **⚠️ Sensitive but Rewritable** — Contains correctable wording; I will rewrite it and send it back for your confirmation
- **🚫 Prohibited** — Contains red-line content and is rejected with an explanation
Supports single text review or batch submission (multiple items separated by line breaks).
**Let's get started.**
Call the Knowledge Base
Add a knowledge base retrieval node in the workflow, select the previously created content compliance rules base. The input for this node uses the user input, and the output is referenced by subsequent AI nodes.
The purpose of this node is not to have the model read the entire rule base, but to retrieve the rule fragments most relevant to the current content for the subsequent review node. You can initially set the citation limit to 1-2 entries to keep the prompt context focused; if the rule base is larger or the categories are more detailed, gradually increase the citation count.
Configure the Content Review Node
Add an AI dialogue node and rename it to "Content Review Node." This node is responsible for outputting the classification result based on the user input and knowledge base rules.
You are a content compliance review expert. Based on the compliance rules retrieved from the knowledge base, determine the compliance level of the user's input content.
Classification criteria:
- Safe: Content has no sensitive information and can be published directly
- Sensitive but Rewritable: Contains correctable sensitive wording; can be published after rewriting
- Prohibited: Contains red-line content and cannot be published
Output format:
Output only one of three labels: Safe / Sensitive but Rewritable / Prohibited
Note: The knowledge base reference must select the previously created knowledge base, otherwise the AI cannot read the rule content.
The output of the review node should be as stable as possible, because it directly affects the decision node's branching. For a quick demo, you can output only the three labels "Safe / Sensitive but Rewritable / Prohibited"; if you need stricter automation integration later, you can switch to structured output, such as returning the classification, reason, and matched rules together, making it easier for downstream nodes to perform precise matching and logging.
Test the Review Node
Click "Run" in the top right corner, input a piece of content to be reviewed, and confirm that the review node returns a stable classification result.
Test this node individually instead of waiting until the entire workflow is built. Input a normal event announcement, marketing copy containing absolute language, and clearly prohibited text, and observe whether the classification meets expectations. If the classification is unstable, first adjust the rule base wording and the review prompt before continuing to configure subsequent branches.
Hide Intermediate Output
Click the settings button on the right side of the model to adjust the node's basic settings. Since the user only needs the final result, it is recommended to hide the intermediate output of the content review node.
Configure the Decision Node
Add a decision node to branch based on the "Safe / Sensitive but Rewritable / Prohibited" output from the content review node.
The decision node acts as a switch in the workflow. The more stable the output from the previous step, the easier it is to configure the decision node; if the review node's output contains explanatory text, the condition may fail to match. Therefore, in branching workflows, it is common to first constrain the output format of the upstream node before configuring the decision node conditions.
Configure the Safe Branch
When the review result is "Safe," directly output the original text. In a production scenario, you could also add spot-checking or human confirmation.
The output effect is as follows:
Configure the Sensitive but Rewritable Branch
When the review result is "Sensitive but Rewritable," add an AI dialogue node to automatically rewrite the content. You can use the following prompt:
You are a content rewriting expert. Rewrite the user's input content into a compliant version.
Rewriting principles:
- Preserve the original meaning and information, do not change the core expression
- Replace absolute language with objective statements, e.g., "best" → "industry-leading"
- Replace unverified data statements with reasonable speculation, e.g., "100% effective" → "most users report it effective"
- Replace sensitive wording with neutral expressions
- Maintain the original style and tone
After rewriting, you can add a user choice node to let the user accept the rewrite, continue rewriting, or abandon it.
The human confirmation node is suitable for paths that are risky but correctable. AI can propose rewrite suggestions, but whether to publish is still left to the business personnel to confirm. This reduces the time spent on initial screening and repeated revisions while not handing over the final publishing decision entirely to the automated workflow.
If the user accepts the rewrite, output the rewritten result; if the user chooses to continue rewriting, it can be connected back to the content rewriting node; if the user abandons it, output the original text.
Configure the Prohibited Branch
When the review result is "Prohibited," add an AI dialogue node to output a rejection explanation. You can use the following prompt:
The content submitted by the user cannot be published because it contains prohibited information. Please explain the reason in a polite and professional tone.
Output format:
1. One sentence stating that the content cannot be published
2. List specific violation points (1-3 items)
3. Provide alternative suggestions (e.g., suggest which aspects to modify before resubmitting)
Verify the Complete Workflow
Input safe content, sensitive content, and prohibited content separately, and confirm that all three branches return the expected results.
When verifying the complete workflow, it is recommended to record the input, review classification, branch entered, and final output for each test case. If the output does not meet expectations, troubleshoot node by node: first check whether the knowledge base recalled the correct rules, then whether the review node classified accurately, and finally whether the decision node conditions and branch output are configured correctly.
Agent V2 is ideal for open-ended, multi-step tasks that require dynamic planning. Unlike workflows, where every step must be predefined, Agent V2 is better suited for tasks with “unfixed steps,” such as data analysis, file processing, multi-tool collaboration, and complex problems that require follow-up clarification.
This case simulates the daily data analysis needs of operations, product, or sales teams: upload an Excel file, ask questions in natural language, and let the Agent autonomously read the file, formulate an analysis plan, and execute the analysis in a virtual machine.
In Case 3, the workflow required you to design a fixed path of “retrieval rules → classification → branching → rewriting or rejection.” Agent V2, on the other hand, acts more like an autonomous executor. You don’t need to define every step in advance; just provide the goal and the file. The Agent decides whether to read the file first, perform statistics, ask follow-up questions, or run code based on the data structure and problem complexity.
Data analysis is a great way to experience Agent V2 because it naturally has three characteristics: the analysis path is not fixed, it often requires multi-step reasoning, and the requirements may need clarification. With the same Excel file, different users might care about product sales, channel ROI, regional trends, or anomalous orders. A fixed workflow can hardly cover all paths in advance, but Agent V2 can dynamically plan based on the question.
Create an Agent V2 Application
In the workspace, click “Create Application,” select Conversational Agent V2 (Beta), and name it Intelligent Data Analysis Agent.
Configure Model and Prompt
Continue using the GLM-5.1 model for AI configuration. The system prompt can refer to:
You are a senior data analyst Agent. You can read data files uploaded by users, run Python analysis code in the sandbox, and proactively ask users for clarification.
Tools:
- 📄 **Read File** — Read Excel/CSV data uploaded by the user
- 💻 **Sandbox Execution** — Run Python scripts (pandas/matplotlib/numpy)
- ❓ **Proactive Follow-up** — Confirm with the user when analysis requirements are unclear
Workflow:
1. After receiving data and a question, first read the file to understand the data structure and content
2. Formulate an analysis plan and present it to the user as a list of steps
3. Execute step by step according to the plan, showing key findings at each step
4. Proactively ask follow-up questions when encountering ambiguous requirements, such as unclear metric definitions or missing comparison baselines
5. Dynamically update the plan based on follow-up results
6. Output an analysis report containing data overview, core findings, visual charts, and business recommendations
Security Rules:
- Only data analysis is allowed in the sandbox; no network access, no writing files to the host machine
- Data is used only for this analysis; do not expose raw sensitive data in the report
- Mark confidence levels for uncertain conclusions
Output Format:
1. 📋 Analysis Plan (automatically generated based on data)
2. 📊 Data Overview (row count, column names, missing values, basic statistics)
3. 🔍 Core Findings (3-5 key insights, supported by charts)
4. 💡 Business Recommendations (actionable suggestions based on data)
The key point of this prompt is to have the Agent plan before executing, rather than jumping straight to conclusions. For data analysis tasks, first reading the data structure, confirming field meanings, and formulating an analysis plan can significantly reduce the risk of misunderstanding requirements or misusing metrics. In production use, you can continue to supplement internal metric definitions, such as GMV, ROI, conversion rate, active customers, repurchase rate, etc.
Configure the Opening Message
The opening remarks guide the user to upload a data file and ask analysis questions:
Hello! I am the Intelligent Data Analysis Agent 📊
Just drag and drop your Excel or CSV file here and tell me what you want to analyze.
For example:
- "Analyze this sales data and find the best-selling products and trends"
- "Help me look at changes in user activity and find the reasons for the decline"
- "Compare the conversion rates of three channels, which one has the highest ROI?"
I will first understand your data, formulate an analysis plan, and then run the analysis code in the sandbox.
I will proactively ask you for clarification when needed.
Enable Virtual Machine Capability
Data analysis usually requires reading files and executing code, so the virtual machine configuration needs to be enabled.
The virtual machine capability is used to isolate the code execution environment. The Agent can run data analysis scripts, read uploaded files, and generate statistical results within it, without directly affecting the local host environment. For scenarios that require running Python, processing Excel, drawing charts, or performing batch calculations, this is an important capability that distinguishes Agent V2 from ordinary conversation applications.
Upload File and Test
Upload the sample sales data file and enter the question:
Help me analyze this sales data to see which products sell well and which channel has the highest ROI
The Agent will first break down the task and then execute the analysis step by step.
During execution, you can see the task running in the virtual machine without affecting the local environment.
When testing, focus on whether the Agent has a complete analysis process: does it first identify the table fields, explain the analysis plan, run code when needed, and give business recommendations based on the results? If the problem description is unclear, the ideal behavior is not to force an analysis but to first ask the user for key definitions.
The final result should include the analysis plan, data overview, core findings, and business recommendations.
When verifying results, it is recommended to focus on four dimensions: whether the conclusions come from actual data, whether the metric definitions are clear, whether the charts or statistics support the conclusions, and whether the business recommendations are actionable. The value of a data analysis Agent is not just to output a summary, but to connect the process of “reading data, calculating metrics, explaining results, and proposing recommendations.”
After completing the four cases, you can understand the common application types of FastGPT as a progression from simple to complex capabilities: Conversational Agent solves "how to answer and generate content," Knowledge Base solves "what materials to base answers on," Workflow solves "what fixed process to follow," and Agent V2 solves "how to autonomously plan and execute open-ended tasks."
| Application Type | Suitable Scenarios | Core Capabilities |
|---|---|---|
| Conversational Agent | Lightweight Q&A, copywriting, standardized output | Prompt, model configuration, tool calling |
| Knowledge Base + Conversational Agent | Q&A based on documents, policies, regulations, product manuals | File import, knowledge base retrieval, citing sources |
| Workflow | Fixed steps, conditional branches, review flows, automated processing | Node orchestration, decision nodes, human confirmation |
| Agent V2 | Data analysis, complex tasks, multi-step reasoning, dynamic planning | Autonomous planning, tool calling, virtual machine execution |
When selecting, you can judge based on task complexity:
In real projects, you can also combine these capabilities. For example, a customer service assistant can use a Knowledge Base to answer product questions and then query orders via tools; content review can use a Workflow to fix the review path while maintaining rules with a Knowledge Base; data analysis scenarios can first use Agent V2 for exploration, then solidify stable analysis steps into a Workflow.