aethergraph 0.1.0a1

Python-first agentic DAG execution framework

AetherGraph

AetherGraph is a Python‑first agentic DAG execution framework for building and orchestrating AI‑powered workflows. It pairs a clean, function‑oriented developer experience with a resilient runtime—event‑driven waits, resumable runs, and pluggable services (LLM, memory, artifacts, RAG)—so you can start simple and scale to complex R&D pipelines.

Use AetherGraph to prototype interactive assistants, simulation/optimization loops, data transforms, or multi‑step automations without boilerplate. It works with or without LLMs—bring your own tools and services, and compose them into repeatable, observable graphs.

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Requirements

• Python 3.10+
• macOS, Linux, or Windows
• (Optional) LLM API keys (OpenAI, Anthropic, Google, etc.)
• (Optional extras) slack adapter

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Install

Option A — PyPI (recommended)

pip install aethergraph

Optional extras:

# Slack adapter
pip install "aethergraph[slack]"

# Dev tooling (linting, tests, types)
pip install "aethergraph[dev]"

Option B — From source (editable dev mode)

git clone https://github.com/AIperture/aethergraph.git
cd aethergraph

# Base
pip install -e .

# With extras
echo "(optional)" && pip install -e ".[slack,dev]"

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Configure (optional)

Most examples run without an LLM, but for LLM‑backed flows set keys via environment variables or a local secrets file.

Minimal example (OpenAI):

# .env (example)
AETHERGRAPH_LLM__ENABLED=true
AETHERGRAPH_LLM__DEFAULT__PROVIDER=openai
AETHERGRAPH_LLM__DEFAULT__MODEL=gpt-4o-mini
AETHERGRAPH_LLM__DEFAULT__API_KEY=sk-...your-key...

Or inline in a script at runtime (for on‑demand key setting):

from aethergraph.runtime import register_llm_client

open_ai_client = register_llm_client(
    profile="my_llm",
    provider="openai",
    model="gpt-4o-mini",
    api_key="sk-...your-key...",
)

See our docs for setup of external channel methods for real-time interaction.

Where should .env live? In your project root (the directory where you run your Python entry point). You can override with AETHERGRAPH_ENV_FILE=/path/to/.env if needed.

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Quickstart (60 seconds)

1. Verify install:

python -c "import aethergraph; print('AetherGraph OK, version:', getattr(aethergraph, '__version__', 'dev'))"

2. Run a minimal graph:

python - <<'PY'
from aethergraph import graph_fn, NodeContext
from aethergraph.runner import run 

@graph_fn(name="hello_world")
async def hello_world(context: NodeContext):
    print("Hello from AetherGraph!")
    return {"ok": True}

run(hello_world)
PY

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Examples

Quick‑start scripts live under examples/ in this repo. A growing gallery of standalone examples will be published at:

• Repo: https://github.com/AIperture/aethergraph-examples
• Path: examples/

Run an example:

cd examples
python hello_world.py

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Troubleshooting

• ModuleNotFoundError: ensure you installed into the active venv and that your shell is using it.
• LLM/API errors: confirm provider/model/key configuration (env vars or your local secrets file).
• Windows path quirks: clear any local cache folders (e.g., .rag/) and re‑run; verify write permissions.
• Slack extra: install with pip install "aethergraph[slack]" if you need Slack channel integration.

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Contributing (early phase)

• Use feature branches and open a PR against main.
• Keep public examples free of real secrets.
• Run tests locally before pushing.

Dev install:

pip install -e .[dev]
pytest -q

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Project Links

• Source: https://github.com/AIperture/aethergraph
• Issues: https://github.com/AIperture/aethergraph/issues
• Examples: https://github.com/AIperture/aethergraph-examples
• Docs (preview): https://aiperture.github.io/aethergraph-docs/

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License

Apache‑2.0 — see LICENSE.