Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. This repository contains inference code for running Gorilla finetuned models, evaluation code for reproducing results from our paper, and APIBench - the largest collection of APIs, curated and easy to be trained on!
from the project README
Formats
json
License
Apache-2.0
Added
2026-09-10
Get it
From the project’s own instructions where it documents any; otherwise a plain clone.
shell
$ pip install bfcl-eval
Not the unrelated `bfcl` package. Set BFCL_PROJECT_ROOT to where results should be written.
Contents
534 files371.2 MB repository
.py213
.json124
.jsonl40
.js27
.md27
.png25
.txt24
(no ext)14
Use it with
Commands are curated, not yet run by us.
jq — Stream the records one per line to see their shape.
sample.json
$ jq -c . sample.json
DuckDB — Query the file in place with SQL — DuckDB detects CSV, JSON and JSONL by extension.
sample.json
$ duckdb -c 'SELECT * FROM '\''sample.json'\'' LIMIT 10'