flowbrain_core 0.1.2

FlowBrain Core — Judgment & Knowledge core for the MakeMind ecosystem. Two cooperating subsystems: Knowledge (5 facades over a 4-layer Fact/Skill/Profile/Philosophy structure plus Ops) and Agent (work [...]

FlowBrain

Judgment- and knowledge-domain core for the MakeMind ecosystem. A single Dart package exposing two cooperating subsystems:

• Knowledge Subsystem — four-layer knowledge structure (L0 FactGraph → L1 Skill → L2 Profile → L3 Philosophy) plus Ops, surfaced through five facades (facts, skill, profile, philosophy, ops).
• Agent Subsystem — flowbrain-native self-contained agents with their own LLM context, model, forked 4-axis instances (skill / profile / philosophy / facts), and three roles (worker / manager / reviewer).

Hosts (Ops apps, MCP servers, derivative OS cores) import only package:flowbrain_core/flowbrain_core.dart. The underlying mcp_knowledge family and the five domain packages stay as the internal technical stack.

Install

dart pub add flowbrain

Hello

import 'package:flowbrain_core/flowbrain_core.dart';

Future<void> main() async {
  final system = KnowledgeSystem.withAgents();
  await system.agents.createAgent(
    id: 'sara',
    displayName: 'Sara',
    role: AgentRole.worker,
    model: ModelSpec.stub(),       // swap for a real provider in production
    workspaceId: 'demo',
  );
  final reply = await system.agents.ask('sara', 'hello');
  print(reply.content);
  await system.shutdown();
}

KnowledgeSystem.withAgents() activates the Agent Subsystem with an in-memory infrastructure and a stub LLM, so the snippet above runs end-to-end without any external service.

Three roles

final manager = await system.agents.createAgent(role: AgentRole.manager,  ...);
final worker  = await system.agents.createAgent(role: AgentRole.worker,   ...);
final review  = await system.agents.createAgent(role: AgentRole.reviewer, ...);

// Manager picks a worker, worker answers, reviewer evaluates.
final picked = await system.agents.route(manager.id, 'task X', candidateAgentIds: [worker.id]);
final reply  = await system.agents.ask(picked.targetAgentId, 'task X');
final review = await system.agents.review(reviewer.id, reply);

4-axis fork

Each agent carries forked instances along four axes — skill, profile, philosophy, facts — assignable from a workspace pool or transferred from another already-evolved agent.

await system.agents.assignSkillFromPool('sara', 'skill.research');
await system.agents.assignProfileFromPool('sara', 'profile.friendly');
await system.agents.assignPhilosophyFromPool('sara', 'ethos.helpful');
await system.agents.assignFacts('sara', FactQuery(workspaceId: 'demo'));

The four axes evolve per-agent — growthTracker records variations, adjustments, revisions, and accretions — and one agent's matured fork can be transferred to another with AgentForkSource(...).

Knowledge facades

The five facades are system.facts, system.skill, system.profile, system.philosophy, and system.ops. They wrap mcp_knowledge's runtimes; check that package's documentation for the full surface. For a high-level event view, subscribe to system.eventBus.stream.

final sub = system.eventBus.stream.listen((event) {
  print('event ${event.type} at ${event.timestamp}');
});
// ... use the system ...
await sub.cancel();

Supplying real LLMs

KnowledgeSystem.withAgents() accepts llm: (single provider) or llmProviders: (multi-provider routed by ModelSpec.provider):

final system = KnowledgeSystem.withAgents(
  llmProviders: {
    'anthropic': AnthropicLlmPort(apiKey: '...'),
    'openai':    OpenAiLlmPort(apiKey: '...'),
  },
);
final sara = await system.agents.createAgent(
  id: 'sara',
  displayName: 'Sara',
  role: AgentRole.worker,
  model: ModelSpec(provider: 'anthropic', model: 'claude-sonnet-4-6'),
  workspaceId: 'demo',
);

LlmPort implementations live in mcp_llm; anything implementing the interface plugs in.

Examples

See example/ for runnable samples:

• flowbrain_example.dart — canonical hello
• multi_role.dart — manager / worker / reviewer triad
• lifecycle_events.dart — observe eventBus.stream

Where things live

package:flowbrain_core/flowbrain_core.dart      ← single import for hosts

  KnowledgeSystem                     ← entry point (defaults / stub / withAgents)
    .facts                            ← L0 FactGraph facade
    .skill                            ← L1 Skill facade
    .profile                          ← L2 Profile facade
    .philosophy                       ← L3 Philosophy facade
    .ops                              ← Ops facade
    .agents                           ← Agent Subsystem facade
    .eventBus                         ← KnowledgeEventBus (broadcast)

Configuration (KnowledgeConfig, sub-configs for each layer) and infrastructure ports (InfraPorts, LlmPort, storage / search / embedding / etc.) are also surfaced from the same import. See the API docs for the full list.

Compatibility

• Dart SDK ^3.0.0
• Pure Dart — no Flutter dependency
• Ports for storage / search / embedding / LLM are pluggable; InfraPorts.inMemory() provides smoke defaults

License

MIT — see LICENSE.