Praxium
Praxium is a typed, asynchronous foundation for AI agents and graph workflows. It is designed around explicit state, deterministic routing, structured events, cancellation, retries, timeouts, checkpoints, and provider-neutral extension interfaces.
Status:
0.1.0alpha. The executable core is being built against the tracked implementation plan.
Quick start
python -m pip install -e .
python examples/quickstart.py
Install production model adapters:
python -m pip install -e ".[providers]"
from praxium import Model
from praxium.providers import ProviderFactory
provider = ProviderFactory.ollama()
model = Model(name="qwen3:8b", provider=provider.name)
Praxium includes native OpenAI, Anthropic, Gemini, Amazon Bedrock, and Google Vertex AI protocol support plus named presets for Azure OpenAI, Groq, Together, OpenRouter, Kimi, GLM, Ollama, and Hugging Face. Arbitrary OpenAI-compatible endpoints and completely custom provider callables are supported without a model-name allowlist.
from praxium import GraphBuilder, Runtime, State
async def greet(state: State, _context):
return {"greeting": f"Hello, {state.data['name']}!"}
graph = (
GraphBuilder("hello")
.add_node("greet", greet)
.set_entrypoint("greet")
.set_finish_point("greet")
.build()
)
result = await Runtime().run(graph, State(data={"name": "Ada"}))
print(result.state.data["greeting"])
Design principles
- Everything crossing a public boundary is typed.
- Every execution produces correlated, ordered events.
- State and checkpoints are serializable and reproducible.
- The runtime is async and cancellation-aware.
- Provider, database, web, and CLI dependencies are optional adapters.
- Extension points use explicit protocols and conflict-safe registries.
Development
python -m pip install -e ".[dev]"
ruff format --check .
ruff check .
mypy src
pytest --cov=praxium --cov-branch
The architecture, milestones, acceptance criteria, and v0.1 boundary are recorded in docs/IMPLEMENTATION_PLAN.md. Repository-wide engineering constraints live in AGENTS.md.
Documentation
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