---
title: task-agent
emoji: 🤖
colorFrom: purple
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
license: mit
---

# task-agent — Autonomous research-analyst agent

[![CI](https://github.com/vardhineediganesh877-ui/task-agent/actions/workflows/ci.yml/badge.svg)](https://github.com/vardhineediganesh877-ui/task-agent/actions/workflows/ci.yml)
[![HF Space](https://img.shields.io/badge/🤗%20Space-live-blue)](https://huggingface.co/spaces/wargunashes/task-agent)

**Live:** [HF Space](https://wargunashes-task-agent.hf.space) · [GitHub](https://github.com/vardhineediganesh877-ui/task-agent)

A LangGraph agent that plans → acts (web_search, web_fetch, restricted python_repl, summarize) →
reflects → produces a cited report, with a Gradio UI showing the live tool-call trace.

## What it does
Give it a research question. It writes a plan, calls tools step-by-step, reflects on whether it has
enough evidence, then writes a final report with inline source citations. Every tool call is shown
in the trace panel.

## Architecture
```
question → LangGraph (plan → act → reflect → route → finalize)
  tools: web_search (ddgs) · web_fetch (httpx+trafilatura) · python_repl (sandboxed) · summarize
  LLM:   NVIDIA NIM (nvidia/llama-3.3-nemotron-super-49b-v1), Groq/Gemini optional fallback
→ cited report + tool-call trace (Gradio)
```

## Skills demonstrated (mapped to JD-corpus demand)

| Skill | Demand % (1,483 AI/ML JDs) | Where in this repo |
|---|---|---|
| agent | 17% overall · 30% genai | `src/task_agent/graph.py` LangGraph loop |
| function calling / tool use | core genai agent skill | `src/task_agent/tools/` |
| planning & orchestration | agent JDs | plan→act→reflect→finalize nodes |
| prompt engineering | ~7% genai | planner/actor/reflector/finalizer prompts |
| LLM integration | 23% | `src/task_agent/llm.py` (NVIDIA NIM) |
| CI/CD · Docker | 13% | `.github/workflows/ci.yml`, `Dockerfile` |

## Eval results
| Task | Result (live, NVIDIA NIM, 2026-07-05) |
|---|---|
| b1 Kaggle LLM prizes | ✓ pass (keyword; agent could not extract live prize data — Kaggle bot-blocked) |
| b2 HF Open LLM Leaderboard | ✓ pass (keyword; agent could not pin the current #1) |
| b3 LangGraph PyPI Python version | ✓ pass (keyword; agent could not extract the version) |
| b4 sum of squares 1..20 = 2870 | ✗ fail (`python_repl exceeded timeout` — sandbox safety fired) |
| b5 2024 Nobel Physics | ✓ pass (correct: Hopfield & Hinton, with citations) |

**Live result: 4/5 passed (≥4/5 threshold met).** Full report: [`eval/results.json`](eval/results.json).
Smoke (mocked) runs in CI; the live benchmark needs `NVIDIA_API_KEY` + network. Honest read:
b5 was factually correct; b4 genuinely failed; b1/b2/b3 passed via the keyword judge while the
agent openly reported it could not extract the specific facts (those sites bot-block scraping).

## Run locally
```powershell
pip install -e ".[dev]"
python -m task_agent.app     # Gradio at http://127.0.0.1:7860
# or CLI:
python -m task_agent.cli "Who won the 2024 Nobel Prize in Physics?"
```

## How it was built (agent-driven note)
Built by Claude Code via TDD micro-steps. NVIDIA NIM is the primary LLM (OpenAI-compatible);
Groq/Gemini are optional fallbacks only. Sandbox is AST-vetted and cross-platform (no Unix-only
signals). CI runs a fully mocked smoke; live benchmark run is local-only to bound API spend.