# ShishirPatil/gorilla

Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)

Repository: https://github.com/ShishirPatil/gorilla
Canonical: https://ross.abutalabs.com/products/gorilla
Homepage: https://gorilla.cs.berkeley.edu/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: api, llm, api-documentation, chatgpt, gpt-4-api, claude-api, openai-api, openai-functions
Last push: 2026-04-13T03:19:45+00:00

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 77, release rhythm 8, longevity 85
- inputs: {"age_days": 1203, "days_push": 142, "days_rel": 412, "gap_med": 193, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 13006, forks 1400 (observed 2026-08-28T04:11:01.670526+00:00)

## What it is
Gorilla is a UC Berkeley research project for training and evaluating LLMs to make function/tool calls, including the OpenFunctions fine-tuned models and the Berkeley Function Calling Leaderboard (BFCL) benchmark. It also includes related work like RAFT for retriever-aware fine-tuning for domain-specific RAG.

## Use cases
- evaluate how well LLMs call functions and tools
- fine-tune a model to generate API calls from natural language
- benchmark function-calling models on multi-turn agentic tasks
- compare LLMs on the Berkeley Function Calling Leaderboard
- improve RAG performance with retriever-aware fine-tuning
- train a model to select between multiple APIs

## When to choose
- you need to benchmark or compare function-calling capabilities of LLMs
- you want to fine-tune an open model for reliable tool/API use
- you are building agentic systems that depend on accurate tool calls
- you need a research-grade evaluation dataset for function calling

## When to avoid
- you just need a production LLM API client without evaluation or training
- you want a turnkey agent framework rather than research tooling
- your use case has nothing to do with LLM tool use or RAG

## Facets
- artifact type: library
- maturity: active
- function: llm-training, benchmarking, agent-framework, rag, machine-learning
- domain: large-language-models, artificial-intelligence, apis, developer-tools
- platform: python, cross-platform
- tags: function-calling, tool-use, llm-evaluation, berkeley-function-calling-leaderboard, openfunctions, fine-tuning, api-benchmark, ai-agents, retrieval-augmented-generation

## Member repositories
- ShishirPatil/gorilla (main) score 54

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:01.670526+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:13:36.382720+00:00, confidence not recorded.
  - readme: https://github.com/ShishirPatil/gorilla (fetched 2026-08-28T04:11:01.670526+00:00, sha b32bb09b195d)
  - homepage: https://gorilla.cs.berkeley.edu/ (fetched 2026-08-29T08:09:23.708312+00:00, sha 3b7a1aa71c8c)
- Data as of 2026-08-30T08:39:29.467469+00:00.