Workflow Engine
The Pipeline
Every ChatObject runs a pre-compiled workflow. The default (step-driven) pipeline:
The strategy block is what changes by mode:
python
# STEP_BODY — one task-loop iteration = one Step
STEP_BODY = NODE_INTRO >> NATIVE_WHILE(iter_cond).ACTION(STEP_EXEC) >> NODE_LEAVEDI Contexts as the State Layer
Workflow nodes are stateless functions; all state lives in DI contexts injected by parameter type (see Data Layer). This is what makes the same nodes reusable across pipelines.
Pre-Composed Pipelines
amrita_core.builtins.workflows ships ready graphs:
| Pipeline | Composition |
|---|---|
STEP_REACT_BLOCK | STRATEGY_INIT >> AGENT_ENTRY >> NATIVE_DO(STEP_BODY).WHILE(task_cond) >> AGENT_POST_PROCESS |
SIMPLE_STEP_REACT | LOAD_STATE >> JINJA2_RENDER >> BUILD_MESSAGE >> STEP_REACT_BLOCK >> LLM_COMPLETION >> COMMIT_MEMORY |
REACT_BLOCK (legacy) | STRATEGY_INIT >> AGENT_ENTRY >> WHILE(SINGLE_STRATEGY_CALL).ACTION(REACT_COUNTER) >> AGENT_POST_PROCESS |
SIMPLE_REACT (legacy) | LOAD_STATE >> ... >> REACT_BLOCK >> LLM_COMPLETION >> COMMIT_MEMORY |
SIMPLE_CHAT | LOAD_STATE >> JINJA2_RENDER >> BUILD_MESSAGE >> LLM_COMPLETION >> COMMIT_MEMORY |
ChatObject(workflow=...) accepts any rendered graph; workflow and archived_nodes are mutually exclusive.
The Loop Conditions
| Condition | Stops when |
|---|---|
task_cond | Call limit hit, _suggested_stop, stall injected, or all DAG nodes done |
iter_cond | Call limit, stall, token budget exhausted, exec_finished, or stop suggested |
Both live in amrita_core.components.react and read loop.run_state — the semantic state bridged between the loop and the strategy.
Next
Suspend & Resume — pausing the workflow mid-flight.