flowbrain_core 0.1.5

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 [...]

0.1.5 - 2026-06-23 - FlowBrain runtime wiring (spec 12 §2·§3·§3b·§4·§4b)

Changed (behavior — additive, no API change)

• §2 — 4-axis ask composition: AgentRuntime.ask now composes all 4 assigned axes (profile · philosophy · skill · facts), not facts alone (spec platform/12-flowbrain-runtime.md §2). Order: profile (persona) → philosophy (values/prohibitions) → skill → facts. Facts keep rich _factLine; non-facts axes render defensively from their owned-fork payload (live object via toJson, or JSON Map) so no hidden Knowledge-Subsystem type is imported — 20 items/axis cap. None assigned → prompt identical (regression-safe).
• §3 enforcement proven end-to-end (real engine, not stub): test/agent/25_philosophy_prohibition_enforcement_test.dart wires a real PhilosophyEngine over a seeded ethos store and proves ask actually blocks an output hitting a hard prohibition and delivers a clean one — via Prohibition.forbiddenPatterns (mcp_bundle 0.4.4 + mcp_philosophy 0.1.2's deterministic _detectViolation). This closes the gap that TEST-24's always-block stub masked: the structural evaluator previously caught only two hardcoded NL shapes and silently fell open for every other prohibition. Runtime requirement: mcp_philosophy ≥ 0.1.2 for the deterministic path to actually block (resolved transitively via mcp_knowledge caret; semantic NL judgment remains an LLM seam — spec §3.1).
• §3 — Philosophy work-time intervention: AgentRuntime.ask now runs the assigned Philosophy's post-generation gate over the LLM output before delivery — a hard prohibition throws AgentPhilosophyBlockedException (turn not appended/delivered), soft modifications adjust the text. Opt-in: no assigned philosophy or no engine → skipped (the existing-suite agents are unaffected). Fails open on engine error. (InterventionPoint/PipelineContext/InterventionResult come from mcp_bundle — flowbrain's own direct dep; no new dependency.) detectTensions at the fork-evolution boundary is a separate follow-up.
• §4 — outcome→knowledge loop (philosophy): AgentRuntime.review now feeds the reviewer verdict back as a Philosophy FeedbackEvent → proposeFeedback (pass→positive · fail→negative · revise→mixed). The proposal is human-gated and never auto-applied (facade only emits EvolutionProposedEvent; gated by enableEvolution). Opt-in (target has assigned philosophy + engine available); fails open. AgentPhilosophyBlockedException added (agent_exception.dart). (Other axes already accumulate: facts write + profile growth counters; the FeedbackEvent wire was the missing outcome→proposal call-site. detectTensions at fork-evolution = follow-up.)
• §4b — skill refinement path: AgentRuntime.review now, on a deficient verdict (fail / revise), records a skill variation candidate per assigned skill fork via GrowthTracker.trackVariation (GrowthKind.variation → skillCandidateCount accumulator + AgentForkEvolvedEvent + FactGraph timeline). A pass verdict means the skill worked as-is → no candidate. This completes the §4 four-axis loop (philosophy reinforcement + skill refinement + facts/profile already accumulating). Reuses the existing growth mechanism — no new model. Opt-in (target has an assigned skill); fails open.
• §3b — Philosophy fork-evolution drift anchor: AgentRuntime.review now, at the fork-evolution boundary (a review verdict is the outcome that evolves the target's non-philosophy axes), calls the assigned Philosophy's detectTensions(MultiLayerContext) over the agent's evolving profile + facts-provenance, emitting AgentForkTensionDetectedEvent (count + max severity + descriptions) when tensions are found. Philosophy is the only axis that governs the other three — this surfaces drift so a host can flag/hold an evolution that would leave the constitution. Opt-in (target has assigned philosophy); advisory (emit, no auto-revert); no-op when the adapter doesn't implement detectTensions (UnsupportedError caught); fails open. (MultiLayerContext/PhilosophyEvaluationContext/Tension from mcp_bundle — direct dep, no new dependency.) AgentForkTensionDetectedEvent added (agent_event.dart).
• No public API change (additive). Tests: test/agent/24_assigned_axes_in_prompt_test.dart (profile compose · philosophy block · opt-in pass-through · fork tension emit · opt-in no-tension) + 22_* (facts) + skill-refinement candidate (§4b) + §3 prohibition enforcement (TEST-25, real engine). 150 PASS · analyze 0.

Changed (dependency floor)

• mcp_bundle ^0.4.3 → ^0.4.4 — internal-dep latest (0.4.4 hardens the Ethos object-graph fromJson with field-named validation). flowbrain_core consumes the ethos via the philosophy port; no new symbol required, constraint kept current.
• mcp_knowledge ^0.2.4 → ^0.2.5 — propagates the §3 prohibition-enforcement guarantee: mcp_knowledge 0.2.5 floors mcp_philosophy ^0.1.2 (deterministic forbiddenPatterns enforcement). Flooring it here ensures flowbrain's resolution actually carries the enforcing engine rather than relying on caret-resolves-to-latest — the §3 gate (TEST-25) is hollow without mcp_philosophy ≥ 0.1.2.

0.1.4 - 2026-06-14 - assigned facts compose into ask prompt

Changed (behavior — additive, no API change)

• AgentRuntime.ask now composes the agent's assigned facts (set via assignFacts / bk.agent.assign_facts, stored under AgentAxis.facts) into the system prompt: base systemPrompt first, then an ## Assigned knowledge (facts) section. Previously ask passed only the base systemPrompt, so assigned facts never reached the provider — a per-agent-knowledge-scoping gap (agent answered "I don't have that fact"). Facts are read from the agent's eager OwnedFork payload (handles live OwnedFork and persistent JSON Map forms), capped at 50 lines. No assigned facts → prompt identical to before (regression-safe). No public API change. Tests: test/agent/22_assigned_facts_in_prompt_test.dart (in-memory + persistent JSON-KV round-trip).

Changed (dependency floor)

• mcp_bundle ^0.4.0 → ^0.4.3 — guarantees FactRecord.toJson/fromJson. Without it, assigned facts persisted via a persistent KvStoragePort serialize to "Instance of 'FactRecord'" (toString) and the compose above yields nothing — the fix only works end-to-end with serializable FactRecord. (In-memory KV worked regardless.)

0.1.3

Added

• AgentFacade.ask / AgentRuntime.ask gain an optional resetContext flag (default false). When true, the agent's conversation history is cleared before the prompt is composed — for manager agents whose every turn should be treated fresh (bounds context growth, avoids stale prior-turn pollution that weakens the current directive). The post-ask turn is still appended; the reset is one-shot.

Changed (dependency floor)

• mcp_knowledge ^0.2.3 → ^0.2.4 — raises the floor so the re-exported OpsFacade is guaranteed to carry the behavior-execution methods (runBehavior / resumeBehavior / listBehaviors, added in mcp_knowledge 0.2.4). flowbrain_core's own code is otherwise unchanged; this guarantees the capability for consumers (e.g. brain_kernel) that reach behavior through system.ops. Consumers should bump to ^0.1.3.

0.1.2

Changed (cascade)

• mcp_bundle caret bumped from ^0.3.2 to ^0.4.0 (mcp_bundle 0.4.0 UiSection.pages spec realignment).
• mcp_knowledge caret bumped from ^0.2.2 to ^0.2.3 (sibling cascade).

flowbrain_core does not touch UiSection.pages directly — caret-only cascade. Consumers should bump to ^0.1.2.

0.1.1

Dependencies

• mcp_bundle: ^0.3.1 → ^0.3.2 — cascade alignment to pick up McpBundle.factGraphSection wire (additive — fact instance round-trip slot alongside the existing factGraphSchema type catalogue).
• mcp_knowledge: ^0.2.1 → ^0.2.2 — same cascade.

No code changes in flowbrain_core itself.

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0.1.0

Initial release.

• Knowledge Subsystem — five facades (facts, skill, profile, philosophy, ops) wrapped from mcp_knowledge 0.2.x. Four-layer knowledge structure (L0 FactGraph → L1 Skill → L2 Profile → L3 Philosophy) plus Ops, with default L0 auto-wiring and InfraPorts.inMemory() smoke infrastructure.
• Agent Subsystem — flowbrain-native, fully on-package:
  • Self-contained agents — own LLM context, own model (ModelSpec), own forked 4-axis instances.
  • Three roles via AgentRole — worker / manager / reviewer.
  • 4-axis fork assignment from workspace pool or from another already-evolved agent (PoolForkSource / AgentForkSource) across skill · profile · philosophy · facts.
  • agents.ask / agents.stream for conversation, agents.route for manager dispatch, agents.review for reviewer evaluation.
  • Growth Tracker records evolution kinds (variation / adjustment / revision / accretion) per agent.
  • Conversation Store with getHistory(limit).
• Event bus — system.eventBus.stream (broadcast Stream<KnowledgeEvent>); domain-typed agent events (AgentCreatedEvent, AgentDeletedEvent, …).
• Stub LLM — StubLlmPort auto-wired by KnowledgeSystem.withAgents() when no provider is supplied; deterministic short replies for tests / smoke runs.
• Public surface — single barrel import package:flowbrain_core/flowbrain_core.dart exposes:
  • KnowledgeSystem (defaults / stub / withAgents factories)
  • KnowledgeConfig + 9 sub-configs
  • InfraPorts + standard infrastructure port interfaces
  • AgentFacade + Agent / AgentReply / AgentRole / AgentAxis / ModelSpec / RoutingDecision / ReviewResult / ReviewVerdict / GrowthKind / ConversationTurn
  • Philosophy domain helper exceptions and result types
• Examples — example/flowbrain_example.dart, example/multi_role.dart, example/lifecycle_events.dart. All run end-to-end against the stub LLM with no external services.