- name
- neural-train
- description
- Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline
- argument-hint
- [--pattern-type coordination|edit|task] [--epochs N] [--microlora]
- allowed-tools
- mcp__claude-flow__neural_train mcp__claude-flow__neural_status mcp__claude-flow__neural_patterns mcp__claude-flow__neural_predict mcp__claude-flow__neural_optimize mcp__claude-flow__neural_compress mcp__claude-flow__hooks_pretrain mcp__claude-flow__hooks_build-agents mcp__claude-flow__hooks_intelligence_trajectory-start mcp__claude-flow__hooks_intelligence_trajectory-step mcp__claude-flow__hooks_intelligence_trajectory-end mcp__claude-flow__hooks_intelligence_pattern-store mcp__claude-flow__hooks_intelligence_learn mcp__claude-flow__hooks_intelligence-reset mcp__claude-flow__ruvllm_sona_create mcp__claude-flow__ruvllm_sona_adapt mcp__claude-flow__ruvllm_microlora_create mcp__claude-flow__ruvllm_microlora_adapt mcp__claude-flow__agentdb_consolidate Bash
Neural Training
Train and consolidate neural patterns. Implements the DISTILL and CONSOLIDATE phases of the 4-step intelligence pipeline.
When to use
- After completing a successful task — capture what worked.
- After accumulating ≥10 task completions — run consolidation to fold patterns into long-term storage.
- When training a new domain — create a MicroLoRA adapter for it.
Standard flow (DISTILL)
- Check current neural status —
mcp__claude-flow__neural_status. - Start a trajectory —
mcp__claude-flow__hooks_intelligence_trajectory-startwith the task context. - Record steps — for each significant action,
mcp__claude-flow__hooks_intelligence_trajectory-step. - End trajectory —
mcp__claude-flow__hooks_intelligence_trajectory-endwithverdict: pass|fail|partial. - Learn from the trajectory —
mcp__claude-flow__hooks_intelligence_learn. - Train patterns —
mcp__claude-flow__neural_trainwith--pattern-type coordination --epochs 10. - Store patterns —
mcp__claude-flow__hooks_intelligence_pattern-store. - Verify —
mcp__claude-flow__neural_patternsto confirm.
SONA adaptation (single-domain, <0.05ms)
For real-time micro-adaptation:
mcp tool call ruvllm_sona_create --json -- '{"domain": "coding"}'
mcp tool call ruvllm_sona_adapt --json -- '{"feedback": {"score": 0.9, "trajectory": "..."}}'MicroLoRA adaptation (multi-domain)
When you have ≥3 distinct domains, create a MicroLoRA adapter per domain rather than overloading SONA:
# Create the adapter
mcp tool call ruvllm_microlora_create --json -- '{"domain": "frontend"}'
# Adapt with feedback
mcp tool call ruvllm_microlora_adapt --json -- '{"adapter": "frontend", "feedback": {...}}'
# CONSOLIDATE phase: apply EWC++ on weight deltas to prevent catastrophic forgetting
mcp tool call ruvllm_microlora_adapt --json -- '{"adapter": "frontend", "consolidate": true}'The --consolidate flag is the EWC++ trigger. Without it, fresh training overwrites older domains.
CONSOLIDATE phase (separate from training)
After every ~10 trajectory completions, run a full consolidation pass:
mcp tool call agentdb_consolidate --json
mcp tool call neural_compress --json # storage efficiencyThis folds patterns into long-term storage under EWC++ semantics.
Bootstrapping from scratch
If the system has no learned patterns yet:
mcp tool call hooks_pretrain --json -- '{"modelType": "moe", "epochs": 10}'
mcp tool call hooks_build-agents --json -- '{"agentTypes": "coder,tester"}'hooks_pretrain writes to the patterns (plural) namespace — distinct from the pattern (singular) ReasoningBank target. See ruflo-agentdb ADR-0001 for the namespace convention.
Reset (testing only)
To wipe intelligence state (e.g., for benchmarking):
mcp tool call hooks_intelligence-reset --jsonCLI alternatives
npx @claude-flow/cli@latest neural train --pattern-type coordination --epochs 10
npx @claude-flow/cli@latest neural patterns --list
npx @claude-flow/cli@latest neural status
npx @claude-flow/cli@latest neural compress
npx @claude-flow/cli@latest hooks pretrain --model-type moe --epochs 10
npx @claude-flow/cli@latest hooks build-agents --agent-types coder,tester