| title | GAIA Agent (Final Assignment of HF Agents Course) |
|---|---|
| emoji | 🕵🏻♂️ |
| colorFrom | indigo |
| colorTo | indigo |
| sdk | gradio |
| sdk_version | 5.33.0 |
| app_file | app.py |
| pinned | false |
| hf_oauth | true |
| hf_oauth_expiration_minutes | 480 |
This repository contains a LangGraph‑powered agent that scores over 30% on the GAIA Level‑1 benchmark without any RAG leaks. It routes questions, invokes the right tool, and returns an exact‑match string for the grader.
GAIA = “General AI Assistants” – a multi-domain benchmark introduced in the paper GAIA: A Benchmark for General AI Assistants. The public leaderboard is hosted on Hugging Face: https://huggingface.co/spaces/gaia-benchmark/leaderboard
| Capability | Implementation |
|---|---|
| Multi‑step routing | LangGraph state machine (route_question → invoke_tools → synthesize_response → format_output) |
| Web & Wiki search | Tavily ➜ DuckDuckGo fallback |
| YouTube | youtube_transcript_api ➜ generate captions |
| Spreadsheets | analyze_excel_file (pandas one‑liner generator) |
| Attached code | Safe subprocess sandbox via run_py |
| Audio | OpenAI‑Whisper |
| Vision | VLM (GPT-4o-mini) |
| File | Purpose |
|---|---|
app.py |
Gradio UI, API submission, LangGraph workflow |
tools.py |
All custom LangChain tools (search, Excel, Whisper, etc.) |
prompts.yaml |
LLM prompts |
helpers.py |
Tiny utilities (debug prints etc.) |
debug_agent.py |
Run agent on a single GAIA question from CLI |
requirements.txt |
Runtime deps |
requirements-dev.txt |
Dev / lint deps |
# clone repo / space
pip install -r requirements.txt # Python ≥ 3.11
python app.py # launches local Gradio UI
Run one task from CLI (handy while tuning prompts):
python debug_agent.py <GAIA_task_id>
| Var | Used for | Example |
|---|---|---|
OPENAI_API_KEY |
Router & answer LLM (OpenAI) | sk‑… |
TAVILY_API_KEY |
Higher‑quality web search (optional) | tvly_… |
(Agent falls back to DuckDuckGo if TAVILY_API_KEY is absent.)
- route_question routes to one of eight labels.
- invoke_tools invokes the matching tool and stores context.
- synthesize_response calls the answer LLM unless the answer was computed.
- format_output normalizes output for GAIA’s exact‑match scorer.
All LLM prompts are available in prompts.yaml:
1️⃣ Create the virtual environment and activate it.
uv venv --python 3.11
source ./.venv/bin/activate
2️⃣ Install Python dependencies:
uv pip install -r requirements.txt
uv pip install -r requirements-dev.txt
3️⃣ [Optional] Install Git hooks for code quality checks :
pre-commit install