hepagent 0.4.0

Agent AI for HEP tasks

Project

Introduction

hepagent is an AI agent framework tailored for High Energy Physics (HEP) and cosmology workflows. Key features include:

• Multi-provider LLM support: seamlessly switch between providers such as cborg, openai, amsc, and gemini via a unified CLI (hepagent run, hepagent repl) or programmatic API, with per-provider configuration managed in providers.toml.
• Skill-based domain knowledge: a modular skill registry (.agents/skills/) packages domain-specific instructions (e.g. running Nyx cosmology simulations, accessing CERN Open Data) that agents load on demand, keeping prompts concise and context-relevant.
• CLI REPL: a Claude Code-inspired interactive REPL (hepagent repl) with slash commands, streaming transcript output, command approval prompts, and markdown/code rendering.
• Bash and execution modes: interactive shell-capable agents with configurable YOLO (auto-approve), CONFIRM, and HUMAN execution modes, output character limits, and turn budgets—safe for running on HPC clusters.
• Textual TUI agent: a rich Terminal User Interface (TextualAgent) with real-time display of agent thinking, step navigation, and live cost tracking.
• HPC / Slurm integration: built-in tooling for submitting and monitoring Slurm jobs, Globus data transfers, and IRI compute resources.
• Extensible tool system: common and domain-specific tools are registered under src/hepagent/tools/, making it straightforward to add new capabilities without touching agent logic.
• Autonomous analysis pipeline (in development): a 5-phase multi-agent orchestration system (hepagent jfc) that drives a HEP physics analysis from a natural-language prompt to an analysis note. Each phase (Strategy → Exploration → Processing → Inference → Documentation) is executed by a dedicated executor agent, then evaluated by a panel of parallel reviewer agents (physics reviewer, critical reviewer, constructive reviewer) whose findings are adjudicated by an arbiter before the pipeline advances. Optional physicist co-design gates allow human-in-the-loop review at phase boundaries. Artifacts (STRATEGY.md, EXPLORATION.md, analysis note PDF, etc.) are written to a structured directory and reproduced via pixi run all.

Installation

Quick start (no code checkout required)

With uv installed, you can run hepagent directly from PyPI without cloning the repository:

# Run once without installing permanently
uvx hepagent -h

# Or install as a persistent tool
uv tool install hepagent
hepagent -h

Developer setup (from source)

git clone https://github.com/xju2/hepagent.git
cd hepagent
uv python install 3.12
make sync
source .venv/bin/activate
export OPENAI_AGENTS_DISABLE_TRACING=1  # Optional: disable tracing logs if you don't have OPENAI_API_KEY
hepagent list-platforms
hepagent list-models --platform cborg

Configurations

After the installation, you can find default configurations at $HOME/.hepagent. The environment variables are stored in $HOME/.hepagent/config/env_vars.toml.

To use a LLM provider, set the corresponding API keys as environment variables. You may also want to set OPENAI_AGENTS_DISABLE_TRACING=1 to disable the tracing logs, especially if you do not have an OPENAI_API_KEY.

If you don't want to store API keys in the TOML file, you can set them to environment variables directly.

export CBORG_API_KEY="your-api-key"
export OPENAI_API_KEY="your-api-key"
export AMSC_API_KEY="your-api-key"
export GEMINI_API_KEY="your-api-key"

Instructions

Choose a platform and model

You can run the agent as in the following examples. The default model is cborg:lbl/gemma-4 if not specified.

List available models for a platform:

hepagent list-models --platform cborg
hepagent list-models --platform amsc
hepagent list-models --platform openai
hepagent list-models --platform gemini

Run an agent with a specific model and task:

hepagent run --agent "shell" --model "gemini:models/gemini-flash-lite-latest" "how many python files in this code repository"
hepagent run --agent "scientist" --model "cborg:lbl/gemma-4" "I would like to simulate a cosmology sky with Nyx code." --max-turn 30
hepagent run --agent "explorer" --model "openai:gpt-5-mini" "Suggest research directions connecting weak lensing and neutrino mass"
hepagent run --agent "coder" --model "openai:gpt-5-mini" "Create a worktree for adding a new feature: chunkle."
hepagent run --agent "scientist" --model "gemini:models/gemini-2.0-flash" "..."  # uses Gemini provider

YOLO mode (auto-approve all bash commands):

hepagent run --agent "scientist" --yolo "your task here"

Interactive REPL

Start the new CLI REPL:

hepagent repl

Examples:

hepagent repl --agent scientist
hepagent repl --agent explorer
hepagent repl --agent shell --model openai:gpt-5-mini
hepagent repl --chat my-session
hepagent repl --disable-session
hepagent repl --yolo
hepagent repl --max-turn 30

Supported slash commands:

• /help
• /quit
• /clear
• /agents
• /agent <name>
• /platforms
• /platform <name>
• /models [platform]
• /model <name>
• /mode <confirm|yolo|human>
• /max-turn <turns>

For more detail, see docs/REPL.md.

Autonomous HEP analysis pipeline (hepagent jfc)

hepagent jfc drives a full HEP physics analysis from a natural-language prompt to a compiled analysis-note PDF. The pipeline runs seven phases sequentially; each phase is executed by a dedicated executor agent and then evaluated by a panel of parallel reviewer agents whose findings are adjudicated by an arbiter before the pipeline advances.

Phase	Name	Description
1	Strategy	Define the analysis strategy and commit to key decisions
2	Exploration	Explore datasets, signal/background properties
3	Processing	Run selection, reconstruction, and histogram production
4a	Expected Results	Inference on expected (Asimov) data
4b	10% Validation	Inference on 10% of observed data (human gate)
4c	Full Data	Inference on the full observed dataset
5	Documentation	Write and typeset the final analysis note PDF

Start a new analysis

Create a markdown file with your physics question, then run:

hepagent jfc run \
  --name my_analysis \
  --type measurement \
  --prompt-file prompt.md

Options:

--name / -n          Analysis name (short identifier, used as directory name)
--type / -t          Analysis type: measurement or search
--prompt-file / -p   Path to a markdown file with the physics question
--model              Model as "provider:model" (e.g. "cborg:claude-sonnet-4-5")
--base-dir           Parent directory for analyses (default: analyses/)
--max-iterations     Max review iterations per phase before halting (default: 3)
--max-turns          Max agent turns per call (defaults: executor=50, note_writer/fixer=30, reviewers=20)
--yolo               Auto-approve all bash commands
--codesign           Enable human co-design review after Phase 1: generates a strategy summary,
                     facilitates interactive Q&A, then re-adjudicates before Phase 2

Example with a specific model and co-design enabled:

hepagent jfc run \
  --name atlas_zprime \
  --type search \
  --prompt-file tasks/zprime_search.md \
  --model cborg:claude-sonnet-4-5 \
  --codesign

Resume an interrupted analysis

State is saved automatically after every phase. Resume from any phase:

hepagent jfc resume --name my_analysis --from-phase 3
hepagent jfc resume --name my_analysis --from-phase 4a

Check analysis status

hepagent jfc status --name my_analysis

Output lists each phase with its status (✓ PASS, → IN PROGRESS, or ○ pending) and the number of review iterations used.

List all analyses

hepagent jfc list
hepagent jfc list --base-dir /path/to/analyses

References

This repository takes inspiration from and builds upon the following works:

• JFC, https://github.com/jfc-mit/jfc
• ShellGPT, https://github.com/ther1d/shell_gpt

Other related works:

• HEPTAPOD: https://github.com/tonymenzo/heptapod
• Just Furnish Context: https://github.com/jfc-mit/slop-X/tree/main
• Deer Flow: https://github.com/bytedance/deer-flow
• Archi: Agentic Operations at the CMS Experiment, paper, code
• OpenClaw: https://github.com/openclaw/openclaw
• Oh My Agent: https://github.com/first-fluke/oh-my-agent
• Nemo Claw: https://docs.nvidia.com/nemoclaw/latest/get-started/quickstart.html
• Get Physics Done: https://github.com/psi-oss/get-physics-done