Best AI Agents for Data Analysis

Data analysis is one of the tasks where AI agents genuinely earn their keep. Not because they replace a real analyst, but because they eliminate the 40-minute round-trip of writing a query, getting an error, fixing it, re-running it, and then realizing you needed a different join all along. The agents below handle that loop on their own. Your job becomes describing what you want and reviewing the result.

The picks below focus on three things: SQL queries against live databases, spreadsheet analysis (CSV, Excel, Google Sheets), and turning numbers into written reports. Those three cover 90% of the data analysis work most teams actually do.

How we picked these

We ran each agent through the same set of tasks: write a multi-table SQL query from a plain-English description, find and fix an error in a formula-heavy spreadsheet, summarize a dataset with a written report, and flag anomalies in a time-series CSV. We cared about accuracy on the first pass, how well the agent explained what it was doing, and whether the output was actually usable without heavy editing.

No paid placements. The order reflects how well they performed on real tasks.

1. Claude Code

Claude Code is the strongest option if your workflow lives in the terminal or a Python/R script. It's not a GUI tool. It's an agentic CLI that reads your files, runs code, checks the output, and iterates, without you pushing a button between each step.

For SQL work, Claude Code paired with the Postgres MCP server is genuinely impressive. You give it a plain-English question, it inspects your schema, writes the query, runs it, and if the result looks wrong, it tries again. I tested it on a 12-table e-commerce schema and it got a correct answer on the first pass 8 out of 10 times. The other two took one correction.

For spreadsheet work, it's best with CSV files. Drop a file into your project directory, ask Claude Code to analyze it, and it'll write a Python script (usually pandas), run it in the terminal, and give you the summary. It doesn't open a GUI. If you need Excel formulas fixed, you're better off with Cursor.

The reporting side is where Claude Code really separates itself. It can read a dataset, compute the stats it needs, write a markdown or HTML report with the findings, and produce something you could actually send to a stakeholder with minor editing.

Pricing: $20/month (Pro tier). Worth it if you're doing this kind of work more than a few times a week.

2. Cursor

Cursor is an IDE first, but it's become a capable data analysis tool for anyone who works in Python notebooks or R scripts. The reason is context: Cursor reads your whole codebase and open files, so when you ask it to fix a broken analysis script, it already knows what your data looks like and what the script is supposed to do.

For spreadsheet analysis, Cursor is stronger than Claude Code in one specific way: it works inside your editor. If you've got a Python script that processes Excel files and the formulas are misbehaving, Cursor can trace through the code, find the issue, and fix it in the file without you copying anything to a chat window.

It's less useful for pure SQL work unless you keep your queries in .sql files in the project. The Cursor agent can write and debug those, but it doesn't run them against a live database on its own the way Claude Code with an MCP connector does.

Where Cursor really shines is turning a messy Jupyter notebook into something readable. It can reorganize cells, add markdown explanations, fix broken visualizations, and clean up the output so the notebook tells a coherent story. That's the kind of grunt work that takes an hour manually and about five minutes with Cursor.

Pricing: $20/month (Pro). Free tier is available but rate-limited.

3. Open Interpreter

Open Interpreter is the open-source option, and it punches well above its weight for local data analysis work. It runs code on your machine (Python, SQL, shell), reads the output, and keeps iterating. The full loop happens locally, which matters if you're working with sensitive data you can't send to a cloud service.

The SQL story here is DuckDB and SQLite, mostly. If you have a local database file or you've exported your Postgres/MySQL data to a file, Open Interpreter can query it in a conversational loop. The experience is similar to Claude Code but without the managed cloud layer. You do need to set it up yourself.

For spreadsheet work, it's solid. Ask it to clean a CSV, compute a pivot, or find outliers and it'll write and run the Python to do it. The code it produces is readable enough that you can keep it after the session and use it as part of a pipeline.

The main limitation is that it's slower and less reliable on ambiguous multi-step tasks compared to Claude Code. When the task is clear, it's great. When you're exploring and the goal keeps shifting, the loop can go off the rails. Plan for that.

Pricing: Free and open source. The hosted version has a paid tier, but the local version is completely free.

4. Perplexity

Perplexity isn't an execution agent. It won't write code, run SQL, or touch your files. But for a specific part of data analysis, it's the best tool in this list: research context.

When you're looking at a dataset and you need to understand what's normal, what the benchmarks are, or what a particular metric actually means in context, Perplexity answers those questions faster and more accurately than a general-purpose chat model. It cites sources, which means you can verify the answer instead of trusting it blindly.

The use case here is the "why" layer of analysis. You run your numbers, you find something unexpected, and you need to know if it's an anomaly or if it's consistent with industry trends. That's Perplexity's lane and it does it well.

It also writes decent written summaries if you paste your data in. Don't expect it to compute anything. But "here's my table, write me an executive summary of what this shows" returns clean, readable prose that needs less editing than most agents.

Pricing: Free tier is useful. Pro ($20/month) gives you faster responses and more model options.

5. Glean

Glean approaches data analysis from a different direction than the other tools here. It's an enterprise search and knowledge agent, not a code runner. Its value is connecting data questions to the information already inside your company.

If someone asks "why did sales drop in Q3?" the answer probably isn't in a spreadsheet in isolation. It's in a combination of the spreadsheet, a Slack thread from the sales team, a customer support ticket, and a product changelog. Glean can pull those sources together and give you a coherent answer. The other agents on this list can't do that.

For teams that have Glean deployed across Slack, Confluence, Salesforce, and their data warehouse, it becomes a genuinely powerful tool for contextual analysis. For a solo analyst working with CSV files, it's overkill.

The data analysis features have gotten better recently, Glean added a data assistant that can query connected databases and produce charts. It's not as powerful as Claude Code for pure computation work, but the combination of code and knowledge retrieval in one tool is hard to find elsewhere.

Pricing: Enterprise pricing, no public tiers. Budget for this as an org-level tool, not a personal subscription.

Quick comparison

How to connect Claude Code to your database

If you want to use Claude Code for SQL work, the fastest setup is the Postgres MCP server. You add the server config to your Claude Code settings, point it at a database connection string (use a read-only user), and from that point Claude Code can inspect your schema and run queries as part of its normal agent loop.

The read-only user part is important. Creating a Postgres role with GRANT SELECT ON ALL TABLES and nothing else takes about two minutes and means you don't have to worry about a misunderstood instruction causing a DROP TABLE.

For local CSV work, no setup is needed. Just start Claude Code in a directory with your CSV files and ask it to analyze them.

Which one should you use?

If you're a developer or analyst comfortable in the terminal and you do serious SQL or Python data work, Claude Code is the answer. The learning curve is real but the payoff is fast.

If you work in notebooks or Python scripts inside an IDE and you want an agent that understands your whole project, use Cursor.

If you're working with sensitive data on a secure machine and you need something free and local, Open Interpreter is the right call.

If your main need is understanding context around your numbers, benchmarks, industry comparisons, and written summaries, use Perplexity alongside whatever execution tool you pick.

If you're in an enterprise environment where the answer to a data question is spread across a dozen internal tools, Glean is in a category by itself.

Most serious data analysts end up using two of these: an execution agent (Claude Code or Open Interpreter) for computation, and Perplexity or Glean for context and research. They solve different parts of the same problem.

For a broader view of what these agents can do with code, see the best AI agents for coding guide where we cover the same tools in their primary domain.