roomkit-graph 0.1.0

Workflow graph engine for RoomKit — multi-step AI workflows with conversation-native execution

roomkit-graph

Workflow graph engine for RoomKit. Define multi-step workflows as directed graphs and execute them inside RoomKit rooms — with AI agents, human decisions, notifications, and data transforms.

Each workflow run is a RoomKit room. Steps produce messages. The full execution history is a conversation you can read, audit, and interact with.

Why

RoomKit gives you rooms, agents, orchestration, hooks, and persistence. But there's no way to compose them into multi-step business processes — "when X happens, do A, then based on the result do B or C, wait for approval, then notify."

Existing workflow engines (n8n, Airflow, Temporal) are full platforms. roomkit-graph is a library you embed in your app.

Design Principles

• Conversation-native — each run IS a room. Steps produce events. Open a run and read it like a chat thread.
• AI-first — Agent and Orchestration are first-class nodes, not HTTP calls to an LLM.
• Serializable — graphs are data (JSON), not code. Build UIs, store in databases, version and share.
• RoomKit-native — uses rooms, channels, hooks, ConversationState, delegation. No reinvention.
• Lightweight — depends on roomkit, nothing else. No separate services.

Node Types

Node	Purpose
Start	Entry point — receives trigger payload
End	Marks workflow complete
Agent	Run an AI agent (any RoomKit-supported provider)
Orchestration	Run a multi-agent strategy (Pipeline, Swarm, Supervisor, Loop)
Human	Pause workflow, wait for human input or approval
Notification	Send a notification (Slack, email, etc.) and continue
Function	Transform data, delays, or custom Python logic
Parallel	Run children concurrently, join when all/any complete
Condition	Evaluate a condition and store the boolean result for downstream branching
Switch	Read a context value for multi-way branching on outgoing edges

Quick Example

from roomkit_graph import Graph, Node, Edge, Condition, WebhookTrigger

graph = Graph(
    id="bug-triage",
    name="Bug Triage",
    trigger=WebhookTrigger(source_type="github"),
)

graph.add_nodes(
    Node("start", type="start"),
    Node("triage", type="agent", config={
        "agent_id": "triage-agent",
        "prompt_template": "Triage this issue. Classify severity.\n\n{{start.output.input}}"
    }),
    Node("escalate", type="notification", config={
        "channel": "slack",
        "template": "Critical bug: {{triage.output.title}}"
    }),
    Node("assign", type="agent", config={
        "agent_id": "labeler-agent",
        "prompt_template": "Assign labels and team:\n\n{{triage.output}}"
    }),
    Node("end", type="end"),
)

graph.add_edges(
    Edge("start", "triage"),
    Edge("triage", "escalate",
         condition=Condition.field("triage.output.severity").equals("critical")),
    Edge("triage", "assign",
         condition=Condition.otherwise()),
    Edge("escalate", "end"),
    Edge("assign", "end"),
)

# Validate before running
graph.validate_or_raise()

Running a Workflow

The WorkflowEngine drives execution step by step. Built-in handlers cover start, end, function, condition, switch, log, and parallel nodes. Provide handlers for your app-specific node types:

from roomkit_graph import WorkflowEngine, NodeHandler, NodeResult

class AgentHandler(NodeHandler):
    async def execute(self, node, context, engine):
        # Your agent execution logic here
        result = await call_agent(node.config["agent_id"], context)
        return NodeResult(output=result, status="completed")

class HumanHandler(NodeHandler):
    async def execute(self, node, context, engine):
        return NodeResult(output=None, status="waiting")

# Run to completion
engine = WorkflowEngine(graph, handlers={
    "agent": AgentHandler(),
    "human": HumanHandler(),
    "notification": NotificationHandler(),
})
ctx = await engine.run(trigger_data={"issue_id": "123"})

Step-by-Step Execution

engine = WorkflowEngine(graph, handlers=handlers)
await engine.start(trigger_data={"issue_id": "123"})

while await engine.step():
    print(f"At node: {engine.current_node_id}")

Pause and Resume (Human-in-the-Loop)

When a handler returns status="waiting", the engine pauses. Resume with external input:

# Engine pauses at a human node...
await engine.resume("review", {"action": "approve", "feedback": "Looks good"})

# resume() stores the input and advances past the waiting node.
# Call step() to continue execution.
while await engine.step():
    pass

Persist and Restore Engine State

Serialize engine state for cross-process pause/resume (e.g. human-in-the-loop over HTTP):

# Save
state = engine.to_dict()
# state = {"context": {...}, "current_node_id": "review", "waiting": True}
save_to_db(state)

# Restore later (possibly in a different process)
state = load_from_db()
engine = WorkflowEngine.from_dict(graph, state, handlers=handlers)
await engine.resume("review", {"action": "approve"})

Streaming Execution Events

engine.stream() yields typed events as the workflow runs — progress for dashboards, webhooks, or audit logs without mutating the graph. Pick one or more modes:

Mode	Emitted when	Payload
values	Before start + after each step	Full context snapshot
updates	After each step	Delta keyed by node_id written that step
lifecycle	Before and after each node	{phase, node_id, status} (status: completed / waiting / failed)
custom	When a handler calls engine.emit(...)	Handler-defined payload

async for event in engine.stream(trigger_data, modes=("updates", "lifecycle")):
    if event["mode"] == "lifecycle" and event["payload"]["phase"] == "start":
        print(f"→ {event['node_id']}")
    elif event["mode"] == "updates":
        await websocket.send_json(event["payload"])

Handlers can emit intra-node progress via engine.emit(payload). Outside a stream() context the call is a no-op, so handlers stay agnostic to execution mode:

class LLMAgentHandler(NodeHandler):
    async def execute(self, node, context, engine):
        for call in tool_calls:
            engine.emit({"kind": "tool_call", "tool": call.name})
            ...
        return NodeResult(output={"response": ...}, status="completed")

On handler failure, a final lifecycle event with status="failed" is yielded before ExecutionError propagates through the iterator. On waiting, the stream terminates cleanly — resume via engine.resume(...) then start a fresh stream().

More Examples

Content Review with Human-in-the-Loop

AI drafts content, human reviews, revision loop until approved:

graph = Graph(id="content-review", name="Content Review", trigger=ManualTrigger())

graph.add_nodes(
    Node("start", type="start"),
    Node("draft", type="agent", config={
        "agent_id": "writer-agent",
        "prompt_template": "Write a blog post about: {{start.output.input.topic}}"
    }),
    Node("review", type="human", config={
        "prompt": "Review this draft. Approve or reject with feedback.",
        "actions": ["approve", "reject"],
    }),
    Node("revise", type="agent", config={
        "agent_id": "writer-agent",
        "prompt_template": "Revise based on feedback:\n{{review.output.feedback}}"
    }),
    Node("publish", type="notification", config={
        "channel": "slack",
        "template": "Published: {{draft.output.title}}"
    }),
    Node("end", type="end"),
)

graph.add_edges(
    Edge("start", "draft"),
    Edge("draft", "review"),
    Edge("review", "publish",
         condition=Condition.field("review.output.action").equals("approve")),
    Edge("review", "revise",
         condition=Condition.field("review.output.action").equals("reject")),
    Edge("revise", "review"),  # loop back
    Edge("publish", "end"),
)

Parallel Execution

Run multiple steps concurrently and join. The built-in ParallelHandler executes all child nodes via asyncio.TaskGroup and aggregates results as {child_id: output}:

graph.add_nodes(
    Node("start", type="start"),
    Node("analyze", type="agent", config={"agent_id": "analyzer"}),
    Node("checks", type="parallel", config={"join": "all"}),
    Node("security", type="agent", config={"agent_id": "security-agent"},
         parent="checks"),
    Node("compliance", type="agent", config={"agent_id": "compliance-agent"},
         parent="checks"),
    Node("notify", type="notification", config={"channel": "slack"},
         parent="checks"),
    Node("summarize", type="agent", config={"agent_id": "summarizer"}),
    Node("end", type="end"),
)

graph.add_edges(
    Edge("start", "analyze"),
    Edge("analyze", "checks"),
    Edge("checks", "summarize"),
    Edge("summarize", "end"),
)

# After execution, each child's output is in context:
#   ctx.get("security.output")    → individual child result
#   ctx.get("compliance.output")  → individual child result
#   ctx.get("checks.output")      → {"security": ..., "compliance": ..., "notify": ...}

Function Nodes

Transform data or run custom logic between steps:

# Data transform
Node("reshape", type="function", config={
    "action": "json_transform",
    "template": {
        "name": "{{extract.output.first}} {{extract.output.last}}",
        "priority": "{{extract.output.tier}}"
    }
})

# Set context values
Node("tag", type="function", config={
    "action": "set_context",
    "values": {"status": "in_progress", "assigned": True}
})

# Delay
Node("wait", type="function", config={
    "action": "delay",
    "duration": "30m"
})

# Custom Python function (registered at runtime)
from roomkit_graph import FunctionRegistry, FunctionHandler

registry = FunctionRegistry()

@registry.function("calculate_priority")
async def calculate_priority(context, config):
    severity = context.get("triage.output.severity")
    tier = context.get("enrich.output.tier")
    return {"priority": "P1" if severity == "critical" and tier == "enterprise" else "P2"}

Node("prioritize", type="function", config={
    "action": "custom",
    "function": "calculate_priority"
})

# Wire registry to the engine
engine = WorkflowEngine(graph, handlers={
    "function": FunctionHandler(registry=registry),
    # ... other handlers
})

Conditions

Serializable condition DSL for edge routing:

# Field comparisons
Condition.field("triage.output.severity").equals("critical")
Condition.field("extract.output.amount").gt(1000)
Condition.field("extract.output.tags").contains("urgent")
Condition.field("extract.output.status").in_(["approved", "accepted"])
Condition.field("review.output.manager").exists()

# Composites
Condition.all_(
    Condition.field("triage.output.severity").equals("critical"),
    Condition.field("triage.output.team").equals("backend"),
)
Condition.any_(cond1, cond2)
Condition.not_(cond)

# Default fallback
Condition.otherwise()

Edge evaluation order (first-match-wins): When a node has multiple outgoing edges, conditional edges are evaluated in definition order — the first match wins. If no condition matches, an unconditional edge is used as default. An otherwise edge acts as an explicit catch-all.

All conditions serialize to JSON for storage and UI builders:

{"type": "field", "path": "triage.output.severity", "op": "eq", "value": "critical"}
{"type": "all", "conditions": [
    {"type": "field", "path": "extract.output.amount", "op": "gt", "value": 1000},
    {"type": "field", "path": "extract.output.category", "op": "eq", "value": "travel"}
]}
{"type": "otherwise"}

Graph Validation

Validate graph structure before execution:

# Returns list of error strings (empty = valid)
errors = graph.validate()

# Or raise GraphValidationError directly
graph.validate_or_raise()

Checks performed: single start node, at least one end node, valid edge references, start has no incoming edges, end nodes have no outgoing edges, all nodes reachable from start.

Cross-Step Context

Steps reference previous outputs with {{node_id.output.field}} templates:

{{start.output.input}}               # trigger payload
{{start.output.input.title}}         # nested field from trigger
{{triage.output.severity}}           # previous step output
{{draft.output.title}}               # any upstream node's output

Resolved at runtime from the workflow context before each step executes.

Structured Passthrough

When using resolve_value() (e.g. in json_transform), a template that is exactly one {{path}} placeholder returns the raw value — dicts, lists, ints, bools pass through without stringification:

# If triage.output.metadata is {"tags": ["auth", "login"], "priority": 1}
resolve_value("{{triage.output.metadata}}")        # → dict (raw)
resolve_value("Meta: {{triage.output.metadata}}")  # → "Meta: {'tags': ...}" (string)

This enables passing structured objects between steps without losing type information.

How It Maps to RoomKit

roomkit-graph	RoomKit primitive
Workflow run	Room
Current step	ConversationState.phase
Step context	ConversationState.context
Agent step	Agent channel + on_event()
Orchestration step	kit.delegate() (child room)
Human step	Room pause + inbound message
Notification step	kit.deliver()
Parallel step	Multiple kit.delegate() + join
Transitions	ConversationRouter + hooks
Audit trail	Room events
Persistence	ConversationStore

Status

Alpha. The 0.1.x line is the first public release; the API may evolve between minor versions while the engine, streaming, and persistence surface settle. Breaking changes will be called out in the changelog.

License

MIT