Workflow State

The WorkflowState is the single source of truth for a running workflow. Every node reads from it, writes to it, and the engine persists it after each step for crash recovery.

Creating state

import { createWorkflowState } from '@cycgraph/orchestrator';

const state = createWorkflowState({
  workflow_id: graph.id,
  goal: 'Research and summarize quantum computing',
  constraints: ['Under 500 words'],
  max_execution_time_ms: 120_000,
});

Schema reference

Identity and input

Field	Type	Default	Description
workflow_id	string (UUID)	required	Graph definition this run belongs to.
run_id	string (UUID)	auto-generated	Unique identifier for this execution.
goal	string	required	High-level objective for the workflow.
constraints	string[]	[]	Rules the workflow must respect.

Control flow

Field	Type	Default	Description
status	WorkflowStatus	'pending'	Current lifecycle status.
current_node	string	—	Node currently being executed.
iteration_count	number	0	Total reducer dispatches so far (loop guard).
max_iterations	number	50	Hard cap — the run fails if exceeded.
started_at	Date	—	When run() was first invoked.
max_execution_time_ms	number	3600000 (1h)	Wall-clock timeout for the entire run.

Retry and resilience

Field	Type	Default	Description
retry_count	number	0	Retries on the current node so far.
max_retries	number	3	Maximum retries before the node fails permanently.
last_error	string	—	Error message from the most recent failure.
compensation_stack	CompensationEntry[]	[]	Stack of typed compensating actions for saga rollback. Each entry has action_id and compensation_action: { type, payload }.

Waiting (human-in-the-loop)

Field	Type	Default	Description
waiting_for	WaitingReason	—	Why the workflow is paused (e.g. 'human_approval').
waiting_since	Date	—	When the workflow entered the waiting state.
waiting_timeout_at	Date	—	Deadline after which the wait times out.

Cost and token tracking

Field	Type	Default	Description
total_tokens_used	number	0	Cumulative tokens consumed across all LLM calls.
max_token_budget	number	—	If set, the run fails when token usage exceeds this.
total_cost_usd	number	0	Cumulative estimated cost in USD.
budget_usd	number	—	Per-run cost budget (run fails when exceeded).

Memory and tracking

Field	Type	Default	Description
memory	Record<string, unknown>	{}	Shared key-value store. See Memory below.
visited_nodes	string[]	[]	Node IDs visited in execution order.
supervisor_history	object[]	[]	Routing decisions made by supervisor nodes (for debugging).
created_at	Date	now	When this run was created.
updated_at	Date	now	Last state mutation timestamp.

Status lifecycle

The workflow status transitions denote the lifecycle of a workflow. All terminal states (completed, failed, cancelled, timeout) are final.

stateDiagram-v2
    direction LR
    pending --> scheduled
    scheduled --> running
    running --> completed
    running --> waiting
    running --> retrying
    waiting --> running
    retrying --> running
    retrying --> failed
    running --> cancelled
    running --> timeout

---

Memory

The memory object is the primary data exchange between nodes. It’s an arbitrary key-value store — you define the keys based on your workflow’s needs. Agents write to it via their text output, which the orchestrator automatically routes to the node’s write key. For agents that need to write structured data to multiple keys, the save_to_memory tool can be declared explicitly. Agents read from memory via their filtered state view (controlled by read_keys on the node).

• Use descriptive keys — research_notes is better than data or result
• Reference, don’t store — avoid large blobs in memory; store them externally and keep a reference
• Keep it flat — deeply nested objects are harder to debug

Memory layers

Layer	Scope	Persistence	Purpose
Graph State	Shared across all nodes	Persisted after every step	Source of truth — goal, results, artifacts
Thread Context	Local to a single agent	Ephemeral	Raw LLM conversation for the current agent

Graph State is the memory object. It’s persisted after every node execution, enabling crash recovery and time-travel debugging.

Thread Context is the raw LLM conversation history within a single agent execution. Each agent has its own thread — agents don’t see each other’s raw messages. The orchestrator automatically captures the agent’s text output and routes it to the appropriate write key, and the thread is discarded.

Action types

Actions dispatched to the reducer use a discriminated union type ActionTypeSchema. Valid action types are:

Action Type	Purpose
update_memory	Write key-value pairs to the memory object
set_status	Transition the workflow status
goto_node	Override the next node in the graph
handoff	Transfer control to another agent/workflow
request_human_input	Pause for human-in-the-loop approval
resume_from_human	Inject human response and resume
merge_parallel_results	Combine results from parallel node execution

Invalid action types are rejected at parse time via Zod validation. Internal engine actions (prefixed with _, such as _fail, _init, _budget_exceeded) bypass this validation and are reserved for the engine.

Taint tracking

Data entering the system from external tools (web search, file reads) is flagged as tainted. Taint propagates automatically — if a node reads tainted data and writes to state, the output key inherits the taint flag. This lets downstream nodes make trust decisions about their inputs.

Next steps

• Agents — how agents read and write state
• Nodes — node types and configuration