Auto-Analyst is a fully open-sourced, modular AI system designed to automate data science workflows — from data cleaning and statistical analysis to machine learning and visualization.
You can try it live at: https://www.autoanalyst.ai/chat
- ✅ Open Source: Licensed under a highly MIT permissive license.
- 🔄 LLM Agnostic: Compatible with any LLM API – OpenAI, Anthropic, Deepseek (groq), etc.
- 💸 Bring Your Own API Key: No vendor lock-in; use your own keys, pay only what you use.
- 🖥️ User-Centric UI: Built with data scientists in mind.
- 🛡️ Reliable Outputs: Guardrails for robust and interpretable responses.
- ⚙️ Modular Agent Architecture: Add or customize agents using DSPy.
Start analyzing here: 👉 https://www.autoanalyst.ai/chat
- Click the 📎 icon near the chat input.
- Upload
.csvor.xlsxfiles. More connectors (APIs, SQL, etc.) available upon request.
- Enter a short text description of what your dataset is about.
- Auto-Analyst will generate a cleaned, structured metadata summary optimized for LLM workflows.
- ✍️ Tip: Rename generic columns like
var_1toprice,category, etc., for better analysis.
Use either:
- @agent_name to specify which agent to use (e.g.
@preprocessing_agent) - Or no agent tag to let the planner route your query automatically.
| Agent | Description |
|---|---|
@preprocessing_agent |
Cleans data using pandas and numpy. Fixes types, handles nulls, computes aggregates. |
@statistical_analytics_agent |
Performs regression, correlation, ANOVA, and other statistical tests with statsmodels. |
@sk_learn_agent |
Trains machine learning models like Random Forest, KMeans, Logistic Regression using scikit-learn. |
@data_viz_agent |
Generates visualizations using plotly. Includes a retriever to pick optimal chart formats. |
🌟 Modular and extensible! You can add custom agents for:
- Marketing
- Quantitative Finance
- Web APIs (Slack, Notion, etc.)
Want to delegate the query routing?
Just type your question without specifying an agent. The planner will:
- Select the right agent(s)
- Generate plan instructions
- Coordinate inter-agent workflows
- Collect and display results (including plots & summaries)
Agents are implemented as dspy.Signature classes. Example:
class google_ads_analyzer_agent(dspy.Signature):
goal = dspy.InputField(desc="User goal")
dataset = dspy.InputField(desc="DataFrame")
plan_instructions = dspy.InputField(desc="Instructions")
code = dspy.OutputField(desc="Python code")
summary = dspy.OutputField(desc="Analysis summary")Add your own agent in minutes.
- Ads: Google Ads, Meta, LinkedIn Ads
- CRM: HubSpot, Salesforce
- SQL: Postgres, MySQL, Oracle, DuckDB
Want more? Submit a request: Contact Us
| Feature | Description |
|---|---|
| 💬 Chat Interface | Ask questions and receive answers like a regular chat. |
| 🧑💻 Code Editor | Inspect and edit generated code. Features include: AI-assisted edits, auto-fix for broken code. |
| 📊 Analytics Dashboard (Enterprise) | Monitor usage, set limits, allocate credits, enforce roles & permissions. |
- 🔧 Agent orchestration via DSPy
- 🧠 Model-agnostic LLM support
- 📈 Built-in chart formatter for best-guess visualization types
- 📂 Multi-agent workflows powered by centralized planner
- 🔄 Daily scheduled reports & auto-regeneration (enterprise-ready)
- Deep Analysis Mode (LLM equivalent of longform research)
- Multi-CSV / multi-sheet Excel analysis
- User-defined analytics agents via UI
- Improved code-editing and auto-debugging
- Usability-First: Optimize UX through iteration and user feedback
- Community-Driven: Shaped by the global analyst community (follow us on Substack, LinkedIn)
- Open Collaboration: Build and share new agents, retrievers, and datasets
We welcome contributions! You can:
- Add new agents
- Suggest UX improvements
- Contribute templates or datasets
- Submit bug reports or pull requests
📬 For collaboration or enterprise inquiries: https://www.autoanalyst.ai/contact
Auto-Analyst is released under the MIT License — feel free to use, remix, and build on it.
Built with ❤️ by Firebird Technologies AI. Tech. Fire.