OpenRath

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OpenRath is a PyTorch-like multi-agent & multi-session framework.

It turns agent runtime state into explicit, composable Python objects:

• Session carries conversation state and inter-agent collaboration lineage.
• Sandbox decides where tools actually run.
• Memory persists agent memory state across runs.
• Tool is the operator-like callable surface exposed to the model.
• Agent is a reusable, composable session transformation layer.
• Workflow composes multiple agents and workflows into larger systems.
• Selector routes between self-describing workflows at runtime, so if / while control flow stays plain Python.

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OpenRath in the PyTorch Lens

PyTorch idea	OpenRath idea	What it means
Tensor	Session	The flowing runtime value: ordered chunks, placement, lineage, and usage.
Device	Sandbox / Backend	The execution environment where tools run: local process, OpenSandbox, or another backend.
Parameter	Memory	Persistent state bound to an agent or store, recalled and committed across runs.
Function	Tool	A callable operation with model-visible schema and runtime behavior.
nn.Linear	Agent	A reusable layer that maps one session to another using a prompt, provider, tools, and memory.
nn.Module	Workflow	A composable container for agents, tools, session transforms, and nested workflows.
control flow	Selector	An LLM-backed router that picks the next workflow at runtime, enabling dynamic if / while over agents.

Most agent frameworks begin with an agent loop. OpenRath begins with Session. That difference matters when one application needs multiple agents, multiple branches, durable memory, sandboxed execution, and traceable lineage at the same time.

OpenRath is designed for this: many agents collaborating across many branchable sessions, while still tracing every role, workspace, memory write, and final output.

OpenRath v2.0.0: Built for Production

The defining change in OpenRath v2.0.0 is that OpenRath moves beyond a composable Python framework and becomes a durable runtime designed for production deployment. The existing Session-first Python API remains intact; the release adds a production execution and operations layer around it.

The diagram summarizes this production path: Python definitions compile into immutable plans, durable Runs are governed by checkpoints, leases, effects, and interrupts, and PostgreSQL, Redis, and S3-compatible storage provide the operational data plane.