csvql

The analytical CSV query engine for AI agents.

Run SQL analytics — GROUP BY, aggregates, joins, time-series — on CSV files in place: no database, no import, no ingest. csvql ships as an MCP server, so an LLM can query a gigabyte file for a few hundred tokens instead of pasting it (impossible) into context. A single static binary written in Zig. Your data never leaves your machine.

A database is something you load your data into. csvql is a query you run on the data where it already lives.

Read-only and on-prem by design. csvql only runs SELECT — it has no INSERT/UPDATE/DELETE/DROP and physically cannot modify your data. It makes zero network calls, needs no cloud, and runs fully air-gapped. Our next north star: the safe way to give AI agents query access to corporate data — run csvql next to the data on your own servers (read-only, nothing leaves the box) instead of shipping files out to an LLM.

Token economics: query files instead of pasting them

Pasting a 417 MB CSV into an LLM costs 230 million tokens — it fits no context window. Over MCP, the agent queries the file in place and gets back only the answer:

Same answers, ~1,000–500,000× fewer tokens — flat, regardless of file size. One command wires it into Claude: csvql install. Measure it yourself: bench/bench_tokens.py.

$ csvql "SELECT cab_type, COUNT(*) FROM 'trips.csv' GROUP BY cab_type"
cab_type,COUNT(*)
green,32447
yellow,967553
  0.05s — no import, queried straight off the file

Website · Quick Start · Installation · Performance · SQL Reference · Docs

Quick Start

csvql auto-detects SQL or simple mode from your input:

# SQL mode
csvql "SELECT name, salary FROM 'data.csv' WHERE age > 30 ORDER BY salary DESC LIMIT 10"

# Simple mode — same query, shorter syntax
csvql data.csv "name,salary" "age>30" 10 "salary:desc"

# Just browse a file
csvql data.csv

Unix Pipes

cat data.csv | csvql "SELECT name, age FROM '-' WHERE age > 25"
csvql "SELECT * FROM 'data.csv' WHERE status = 'active'" > output.csv
csvql "SELECT email FROM 'users.csv'" | wc -l

Flags

# TSV file
csvql "SELECT name, salary FROM 'data.tsv'" -d $'\t'

# Pipe into another tool that expects no header
csvql "SELECT name, age FROM 'data.csv'" --no-header | awk -F, '{print $2}'

# TSV input, no header in output
cat data.tsv | csvql "SELECT * FROM '-'" -d $'\t' --no-header

Installation

Homebrew (macOS / Linux)

brew install melihbirim/csvql/csvql

Or in two steps if you plan to install multiple tools from this tap:

brew tap melihbirim/csvql
brew install csvql

melihbirim/csvql is the tap (the formula repository), and the trailing /csvql is the formula name inside it.

Prebuilt Binaries

Download from GitHub Releases:

# macOS (Apple Silicon)
curl -L https://github.com/melihbirim/csvql/releases/latest/download/csvql-macos-aarch64.tar.gz | tar xz
sudo mv csvql-macos-aarch64 /usr/local/bin/csvql

# macOS (Intel)
curl -L https://github.com/melihbirim/csvql/releases/latest/download/csvql-macos-x86_64.tar.gz | tar xz
sudo mv csvql-macos-x86_64 /usr/local/bin/csvql

# Linux (x86_64)
curl -L https://github.com/melihbirim/csvql/releases/latest/download/csvql-linux-x86_64.tar.gz | tar xz
sudo mv csvql-linux-x86_64 /usr/local/bin/csvql

Build from Source

Requires Zig 0.13.0+ (tested with 0.15.2):

git clone https://github.com/melihbirim/csvql.git
cd csvql
zig build -Doptimize=ReleaseFast
sudo cp zig-out/bin/csvql /usr/local/bin/

Performance

2M rows, 56 MB CSV, Apple M2 Pro — aggregates on the raw CSV (best-of-5):

NYC Taxi, 20M rows, 8 GB CSV — raw CSV, no ingest, both engines: ~3.2x faster, ~6x less memory, and 0 bytes of extra storage (DuckDB's fast path needs a 2.1 GB native store first). At this scale csvql reads raw CSV about as fast as cat — the read itself is the bound, not parsing.

Full breakdown (LIKE, multi-table JOIN, subqueries, memory/storage, methodology): BENCHMARKS.md. Reproduce any number yourself: bench/bench_all.sh.

SQL Reference

SELECT/FROM/WHERE/GROUP BY/HAVING/ORDER BY/LIMIT/OFFSET, JOIN, subquery IN/NOT IN, LIKE/ILIKE/BETWEEN/IS NULL/AND/OR/NOT, aggregates (COUNT/SUM/AVG/MIN/MAX/VARIANCE/STDDEV/MEDIAN/GROUP_CONCAT), CASE WHEN, and scalar functions (UPPER/LOWER/TRIM/CONCAT/SUBSTR/REPLACE/SPLIT_PART/LPAD/RPAD/ROUND/CAST/COALESCE/STRFTIME/DATEDIFF/DATEADD/and more).

csvql "SELECT department, COUNT(*), AVG(salary) FROM 'data.csv' WHERE salary > 50000 GROUP BY department HAVING COUNT(*) > 10 ORDER BY department"
csvql "SELECT e.name, d.dept_name FROM 'employees.csv' e JOIN 'departments.csv' d ON e.dept_id = d.id WHERE d.dept_name = 'Engineering'"
csvql "SELECT id FROM 'orders.csv' WHERE customer_id IN (SELECT id FROM 'customers.csv' WHERE region = 'EU')"

Full syntax table, runnable examples for every feature, known differences from DuckDB, and current limitations: SQL_REFERENCE.md.

Positional "simple mode" is also available for quick one-off filters without writing SQL: csvql data.csv "name,salary" "age>30" 10 "salary:desc" — see SIMPLE_QUERY_LANGUAGE.md.

MCP Server

csvql ships as a Model Context Protocol server, letting AI assistants (Claude, Copilot, etc.) query your CSV files directly.

csvql --mcp

Why query instead of paste?

A 1 MB CSV costs ~560,000 tokens to paste into an LLM — it doesn't even fit a 200K-token context window. Pasting a real dataset is impossible past a few hundred KB, and expensive long before that. With csvql --mcp the agent queries the file instead and gets back only the rows it asked for:

The query cost is flat — it's the SQL plus a few result rows, independent of file size — so a 417 MB file costs the same ~560 tokens as a 1 MB one. Five real questions, answered against DuckDB's NYC-taxi data; token counts via tiktoken (exact cl100k). Reproduce: bench/bench_tokens.py. Your data never leaves your machine.

Exposed Tools

Supported Queries via MCP

csv_query accepts the full SQL dialect supported by csvql. You can ask your AI assistant things like:

Full WHERE clause support: =, !=, >, >=, <, <=, LIKE, BETWEEN, IN, IS NULL, IS NOT NULL, NOT, AND, OR

Full SELECT support: column projections, AS aliases, DISTINCT, COUNT/SUM/AVG/MIN/MAX/VARIANCE/STDDEV/MEDIAN/GROUP_CONCAT, GROUP BY, HAVING, ORDER BY (by name, alias, or position), LIMIT, STRFTIME(), DATE_PART(), JOIN, UPPER/LOWER/TRIM/REVERSE/LENGTH/SUBSTR/REPLACE/SPLIT_PART/GREATEST/LEAST, ABS/SIGN/CEIL/FLOOR/MOD/ROUND, COALESCE, CAST, DATEDIFF, DATEADD, EXTRACT

Setup

One command (recommended) — registers csvql in Claude Code and Claude Desktop, no manual config:

csvql install          # add --print to dry-run first

It runs claude mcp add for Claude Code (if the CLI is present) and merges an mcpServers.csvql entry into the Claude Desktop config, preserving your other servers. Restart Claude afterward.

Claude Desktop (one-click) — grab the csvql-<platform>.mcpb for your OS from Releases and open it in Claude Desktop (Settings → Extensions). No terminal. Build it yourself with scripts/build-mcpb.sh.

{
  "servers": {
    "csvql": {
      "type": "stdio",
      "command": "/usr/local/bin/csvql",
      "args": ["--mcp"]
    }
  }
}

{
  "mcpServers": {
    "csvql": {
      "command": "/usr/local/bin/csvql",
      "args": ["--mcp"]
    }
  }
}

Once connected, you can ask your AI assistant to query CSV files directly:

"What are the top 5 product categories by revenue this year?"

Remote & on-prem: query data where it lives

Big files are hard to download — so run csvql on the server next to the data and connect over SSH. Only the SQL query and the small result cross the wire; the data never leaves the box. No open port, no reverse proxy — it rides your existing SSH keys and audit trail:

// client MCP config — csvql runs on the remote server
{ "command": "ssh", "args": ["analyst@dataserver", "csvql", "--mcp", "--root", "/data"] }

--root sandboxes file access. With --root /data, queries can only read files under /data — SELECT * FROM '/etc/passwd' and ../ traversal are rejected. Always set --root when exposing csvql to an agent or another user. Pair it with a restricted OS user and a read-only mount for defense in depth.

Read-only by construction: csvql only runs SELECT — it has no INSERT/UPDATE/DELETE/DROP and cannot modify your data. It makes zero outbound network calls and runs fully air-gapped. Full posture and hardening guidance in SECURITY.md.

Language Libraries

csvql ships as a native library for Python and Node.js — same SIMD engine, same performance, no subprocess.

pip install csvql-query          # Python: csvql.query(), .query_df(), .query_csv()
zig build node -Doptimize=ReleaseFast   # Node.js: require('csvql-query')

Full API, options (delimiter/comment/skip-empty-lines), memory comparisons against csv-parse/papaparse, and a runnable ETL example: docs/NODE.md · docs/PYTHON.md.

Documentation

Roadmap

Contributing

Contributions welcome — bug reports, performance improvements, features, docs. See CONTRIBUTING.md.

New here? The good first issues are scoped with file pointers and clear done-when criteria — a great place to start (new SQL functions, output formats, and more).

License

MIT — see LICENSE.md.

Built with Zig · 9x faster than DuckDB · MCP Server · GitHub