hyperflow-ai 1.1.0

A self-improving agent framework built on LangChain and LangGraph

HyperFlow (Python)

⚠️ EXPERIMENTAL: This project is currently in an experimental phase and is not recommended for production use.

Self-improving agent framework powered by LangChain and LangGraph.

Inspired by HyperAgents (Meta Research, 2026).

What it does

HyperFlow runs an evolutionary self-improvement loop where a MetaAgent rewrites its own source code (including prompts, tools, and logic) to make it better at solving tasks. It is self-referential: the mechanism that improves the agent is itself part of the editable code. Each generation:

1. Select a parent generation from the archive
2. MetaAgent reads past evaluation scores and edits the source code
3. Evaluation scripts run in a sandbox to score the new agent
4. Better agents are added back to the archive for future generations

[!IMPORTANT] This framework is currently in an Experimental state. See Limitations for more information.

The TaskAgent gets better over generations without manual intervention.

New here? Read docs/concepts.md for a detailed explanation of every concept with examples.

Installation

# Install from PyPI
pip install hyperflow-ai

# Or install from source for development
pip install -e .

Requirements

• Python 3.11+
• At least one LLM provider API key (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY)

Quick Start

# Set your API key
export OPENAI_API_KEY="sk-..."

# Run the bash example (single eval)
cd examples/bash
python run.py

# Run with evolutionary loop
python run.py evolve

Project Structure

hyperflow/
  __init__.py           # Public API re-exports
  agent/
    base_agent.py       # Abstract AgentSystem base class
    llm.py              # Multi-provider LLM factory
    llm_with_tools.py   # LangGraph ReAct chat loop
    meta_agent.py       # MetaAgent (mutation operator)
    task_agent.py       # TaskAgent (task solver)
    tool_registry.py    # Tool registration
  core/
    ensemble.py         # Best-of-archive ensemble
    generate_loop.py    # Main evolutionary loop
    select_parent.py    # Parent selection strategies
  domains/
    base.py             # Domain/DomainTask/EvalResult interfaces
    evaluators.py       # Static, LLM judge, human evaluators
    harness.py          # Evaluation harness
    report.py           # Report generation
  prompts/
    llm_judge.py        # LLM judge prompt template
    meta_agent.py       # MetaAgent prompt template
    task_agent.py       # TaskAgent prompt template
  tools/
    __init__.py         # get_framework_tools()
    bash.py             # Bash shell tool
    editor.py           # File editor tool
  utils/
    archive.py          # JSONL archive CRUD
    common.py           # JSON extraction, file helpers
    constants.py        # Shared constants
    docker.py           # Docker container management
    executor.py         # Local/Docker execution
    git.py              # Git operations
examples/
  bash/                 # Bash command generation
  calculator/           # Buggy tool fix demo
  factcheck/            # True/false classification
  git_evolution/        # Git-based evolution with patches
  paper_review/         # Paper accept/reject prediction
  scoring/              # Math grading self-improvement

Supported Models

from hyperflow import MODELS

# Available model presets
MODELS["OPENAI_GPT4O"]       # "openai/gpt-4o"
MODELS["OPENAI_GPT4O_MINI"]  # "openai/gpt-4o-mini"
MODELS["OPENAI_O3"]          # "openai/o3"
MODELS["OPENAI_O4_MINI"]     # "openai/o4-mini"
MODELS["CLAUDE_SONNET"]      # "anthropic/claude-sonnet-4-5-20250929"
MODELS["GEMINI_PRO"]         # "gemini/gemini-2.5-pro"
MODELS["OLLAMA_LLAMA3"]      # "ollama/llama3"

Or use any "provider/model-name" string.

Environment Variables

Variable	Description
OPENAI_API_KEY	OpenAI API key
ANTHROPIC_API_KEY	Anthropic API key
GOOGLE_API_KEY	Google Gemini API key
OLLAMA_BASE_URL	Ollama server URL (default: http://localhost:11434)
HYPERFLOW_MODEL	Default model for examples (e.g. openai/gpt-4o)

Examples

Single Evaluation

cd examples/bash && python run.py
cd examples/factcheck && python run.py
cd examples/paper_review && python run.py

Evolutionary Self-Improvement

cd examples/bash && python run.py evolve
cd examples/factcheck && python run.py evolve
cd examples/scoring && python run.py
cd examples/calculator && python run.py
cd examples/git_evolution && python run.py

Git-Based Evolution (with patches)

cd examples/git_evolution && python run.py        # 2 generations
cd examples/git_evolution && python run.py 5      # 5 generations
cd examples/git_evolution && python run.py --reset # start over

License

MIT

Citation

If you use this framework in your research, please cite the original HyperAgents paper:

@misc{zhang2026hyperagents,
      title={Hyperagents}, 
      author={Jenny Zhang and Bingchen Zhao and Wannan Yang and Jakob Foerster and Jeff Clune and Minqi Jiang and Sam Devlin and Tatiana Shavrina},
      year={2026},
      eprint={2603.19461},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2603.19461}, 
}