flutter_agentic_graph — Stateful Agent Graphs for Flutter

LangGraph-style agent orchestration, built Flutter-first. Model your AI workflow as a graph: nodes that read and write shared state, conditional edges, cycles with a recursion limit, checkpointing for pause/resume, and human-in-the-loop interrupts — all on top of flutter_agentic.

👉 View on pub.dev · API docs · GitHub

Part of the flutter_agentic family: flutter_agentic (core SDK) · flutter_agentic_ui · flutter_agentic_tools · flutter_agentic_memory

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Why graphs?

Linear chains break down the moment your agent needs to loop (retry until quality is good), branch (route easy vs hard queries), or wait for a human (approve before sending). A StateGraph expresses all three naturally:

        ┌─────────┐     needs revision      ┌─────────┐
START → │  write  │ ──────────────────────→ │ revise  │
        └─────────┘ ←──────────────────────┘─────────┘
             │ good enough
             ▼
        [approval]  ← human-in-the-loop interrupt
             │
            END

Installation

dependencies:
  flutter_agentic_graph: ^0.1.1

Quick start

import 'package:flutter_agentic_graph/flutter_agentic_graph.dart';

final graph = StateGraph(reducers: {'log': Reducers.append})
  ..addNode('draft', (state, ctx) async {
    return {'draft': 'v${(state['attempts'] as int? ?? 0) + 1}',
            'attempts': (state['attempts'] as int? ?? 0) + 1,
            'log': 'drafted'};
  })
  ..addNode('review', (state, ctx) async {
    final ok = (state['attempts'] as int) >= 2; // your quality check
    return {'approved': ok, 'log': 'reviewed'};
  })
  ..setEntryPoint('draft')
  ..addEdge('draft', 'review')
  ..addConditionalEdge('review',
      (s) => s['approved'] == true ? StateGraph.end : 'draft'); // cycle!

final app = graph.compile(recursionLimit: 10);
final result = await app.invoke({}) as GraphComplete;
print(result.state['draft']); // v2

Agents as nodes

Wrap any AgenticAgent (with tools, memory, any of the 7 providers) as a node:

graph.addNode('researcher',
    agentNode(researchAgent, inputKey: 'question', outputKey: 'findings'));
graph.addNode('writer',
    agentNode(writerAgent, inputKey: 'findings', outputKey: 'article'));

Human-in-the-loop

Static breakpoints — pause around a node:

final app = graph.compile(
  checkpointer: MemoryCheckpointer(),
  interruptBefore: ['publish'],   // always ask before publishing
);

final result = await app.invoke({'draft': '…'}, threadId: 'run42');
if (result is GraphInterrupted) {
  // show the draft to the user…
  await app.resume('run42', update: {'draft': editedByUser});
}

Dynamic interrupts — a node decides to ask:

graph.addNode('confirm', (state, ctx) {
  final answer = ctx.resumeValue;
  if (answer == null) ctx.interrupt('Send this email?'); // pauses here
  return {'confirmed': answer};
});

// later:
await app.resume('run42', resumeValue: true);

Checkpointing

Every node execution is checkpointed per threadId, so long-running agent workflows survive interruption. MemoryCheckpointer ships in the box; implement Checkpointer for Hive/SQLite/server persistence.

Live progress events

await app.invoke(input, onEvent: (e) {
  switch (e) {
    case NodeStarted(:final node):        showSpinner(node);
    case NodeCompleted(:final node):      markDone(node);
    case GraphPaused(:final payload):     askUser(payload);
    case NodeFailed(:final node):         showError(node);
    case GraphCompleted():                celebrate();
  }
});
// or: app.stream(input).listen(...)

State channels & reducers

Node updates are merged into shared state by key. Default is overwrite; use Reducers.append for message logs, Reducers.add for counters, or your own function:

StateGraph(reducers: {
  'messages': Reducers.append,
  'tokensUsed': Reducers.add,
});

License

MIT — see LICENSE.