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Harness and CLI

tether.harness

Control Plane: Tether Harness, bounded agent loop, live SQLite inbox, and headless spawner.

Harness

General-purpose LLM-Native Micro-Harness.

register(fn_or_spec)

Register a custom Python function or ToolSpec with this Harness.

build_multiplexer(session_id, enabled_tools, sandbox, model)

Construct the session Multiplexer with done + strictly opted-in tools.

create_pipeline_session(pipeline, goal, *, parent_id=None)

Create a persisted root pipeline session row in SQLite without executing it yet.

spawn_pipeline(pipeline, goal, *, parent_id=None)

Create a pipeline session and launch it as a detached headless worker.

run_pipeline(pipeline, goal, *, parent_id=None) async

Execute a deterministic multi-stage pipeline with isolated stage sessions.

create_session(goal, *, parent_id=None)

Create a persisted session row in SQLite without executing it yet.

spawn(goal, *, parent_id=None)

Create a session and launch it as a detached headless worker, returning session_id.

run(goal, *, parent_id=None) async

Create and run a session inline until completion (done) or max_steps.

send(session_id, message, *, headless=False) async

Send a message to SQLite inbox; auto-resumes if WAITING_INPUT/COMPLETED/FAILED.

execute_session(session_id) async

Execute the bounded agent loop for a persisted session in SQLite.

spawn_detached_worker(db_path, session_id)

Spawn a detached OS background worker (python -m tether.cli _worker <session_id>).

tether.cli

Headless CLI (tether run|pipeline|status|read|query|send|wait|list|stats).

tether.pipeline

Declarative multi-stage deterministic pipelines and heterogeneous model routing.

PipelineStage dataclass

A single deterministic stage inside a multi-stage Tether pipeline.

Pipeline dataclass

Declarative multi-stage workflow with isolated sessions and bounded FTS5 handoffs.

parse_pipeline_yaml(raw_yaml, default_name='pipeline')

Parse a YAML string into a validated Pipeline specification.

load_pipeline(path)

Load a declarative YAML pipeline manifest from disk.

render_stage_goal(stage, root_goal, upstream_stages, store)

Render stage goal with {{goal}} interpolation and compact ~50-token upstream digests.