zeekay
30ed03549f
The seam the whole platform composes on. A stdlib-only Go module (core/) that
holds zero domain knowledge and is what every node-pack and execution backend
plugs into. flow is curried over two parameters:
Value type — what travels on an edge (Message / Tensor / connector I/O). The
engine never inspects it; a Step interprets it per its domain.
Mode — execution locality/durability (InProcess | GPUResident | Durable),
a PARAMETER not baked in: media flows keep latents pinned in VRAM
and serialize nothing; automations checkpoint every step on the
durable backend. Same scheduler, different Backend.
So every product is flow applied:
studio = Run(GPUResident, graph-of-Tensor-steps) // comfy nodes
automations = Run(Durable, graph-of-ConnectorIO) // triggers -> actions
llmFlow = Run(InProcess, graph-of-Message-steps) // langflow (lfx) nodes
Contract: Value (opaque, typed by PortType), Port/Step (typed in/out ports + Run),
Graph (Add/Wire/Validate — port typecheck + acyclicity), Mode + Backend (the
locality/durability seam), and Scheduler (the ONE topology walk, curried over
value-domain and Mode). Ships with the reference InProcess backend; GPUResident
(studio VRAM) and Durable (auto/tasks activities) register the same way.
Tests prove it: a domain-agnostic 2-step pipeline runs in order, port type
mismatch is rejected at wire time, AnyType bypasses, cycles are caught by
Validate, and Mode selects the backend over an identical scheduler walk.
Next: studio and langflow delete their private executors and become node-packs
on this scheduler; auto's tasks engine registers as the Durable backend.
Hanzo Flow
Hanzo Flow is a powerful platform for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Hanzo Flow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.
Highlight features
- Visual builder interface to quickly get started and iterate.
- Source code access lets you customize any component using Python.
- Interactive playground to immediately test and refine your flows with step-by-step control.
- Multi-agent orchestration with conversation management and retrieval.
- Deploy as an API or export as JSON for Python apps.
- Deploy as an MCP server and turn your flows into tools for MCP clients.
- Observability with LangSmith, LangFuse and other integrations.
- Enterprise-ready security and scalability.
Quickstart
Install locally (recommended)
Requires Python 3.10-3.13 and uv (recommended package manager).
Install
From a fresh directory, run:
uv pip install hanzoflow -U
Run
To start Hanzo Flow, run:
uv run hanzoflow run
Hanzo Flow starts at http://127.0.0.1:7860.
Other install options
Run from source
If you've cloned this repository and want to contribute, run this command from the repository root:
make run_cli
For more information, see DEVELOPMENT.md.
Docker
Start a Hanzo Flow container with default settings:
docker run -p 7860:7860 hanzoai/flow:latest
Hanzo Flow is available at http://localhost:7860/.
Deployment
Hanzo Flow is completely open source and you can deploy it to all major deployment clouds.
Contribute
We welcome contributions from developers of all levels. If you'd like to contribute, please check our contributing guidelines and help make Hanzo Flow more accessible.