Your LLM is a brain in a jar. Give it a nervous system.
A biological architecture for artificial minds. Sensation, reflex, memory, and actionβorganized into coherent loops that turn chatbots into organisms.
π Website: UNDER CONSTRUCTION! mnehmos.github.io/mnehmos.multi-agent.framework
π Blog Post: From Chatbot to Organism
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β CENTRAL (BRAIN) - Cognition β
β Orchestrator β’ Architect β’ Planner β’ Ask β’ Deep Research β
β β Receives sensory input β Sends motor commands β
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β SOMATIC (BODY) - Voluntary Action β
β Red Phase β’ Green Phase β’ Blue Phase β’ Code β’ Debug β
β β Tool results β Tool invocations β
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β AUTONOMIC (SUBCONSCIOUS) - Background β
β Memory β’ Synch MCP β’ Index Foundry MCP β
β State persistence, retrieval, agent coordination β
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β REFLEX (SPINAL CORD) - Immediate. β
β (UNDER CONSTRUCTION) β
β Trace MCP β’ Schema Validation β’ Contracts β
β Rejects bad inputs before cognition β
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# Create .roo directory in your project
mkdir -p .roo
# Copy templates
cp templates/custom_modes.yaml .roo/
cp templates/universal/AGENTS.md .Files to copy:
templates/custom_modes.yamlβ.roo/custom_modes.yamltemplates/universal/AGENTS.mdβAGENTS.md(project root)
Copy templates/custom-instructions-for-all-modes.md content to your IDE's global instructions field.
Install tools to extend capabilities. See templates/tools/ for integration docs:
| Tier | Tool | Layer | Integration Guide |
|---|---|---|---|
| 2 | OODA MCP | Somatic | ooda-mcp.md |
| 3 | Synch MCP | Autonomic | synch-mcp.md |
| 3 | Index Foundry | Autonomic | index-foundry-mcp.md |
| 4 | Trace MCP | Reflex | trace-mcp.md |
Progressive enhancementβstart simple, add capabilities as needed:
| Tier | Components | What You Get |
|---|---|---|
| 0 | Baseline templates | Toolless coordination with 13 modes |
| 1 | + Native tools | File I/O, command execution |
| 2 | + OODA MCP | Full computer automation (62 tools) |
| 3 | + Synch + Index Foundry | Memory persistence + RAG |
| 4 | + Trace MCP | Schema validation layer |
Each tier preserves all contracts from previous tiers.
| Mode | Purpose |
|---|---|
| π Orchestrator | Task decomposition, delegation, integration |
| ποΈ Architect | System design, ADRs, contracts |
| π Planner | Task maps, backlogs, dependencies |
| β Ask | Clarification, research synthesis |
| π Deep Research | Multi-source investigation |
| π¬ Deep Scope | Issue scoping |
| Mode | Purpose |
|---|---|
| π΄ Red Phase | Write failing tests |
| π’ Green Phase | Minimal implementation |
| π΅ Blue Phase | Refactor with green tests |
| π» Code | Complex implementation |
| π Debug | Root cause analysis |
| Mode | Purpose |
|---|---|
| π Memory | Documentation, knowledge management |
| ποΈ Index Foundry | RAG pipeline specialist |
All work follows Observe β Orient β Decide β Act:
- Observe ποΈ β Read the task, scan files, check tests
- Orient π§ β Map what exists vs what's needed
- Decide π― β Choose approach with lowest risk
- Act β‘ β Execute one step, validate, repeat
π΄ RED β Write failing tests
π’ GREEN β Minimal implementation
π΅ BLUE β Refactor with green tests
Every completed task returns structured data:
{
"type": "task-completed",
"task_id": "auth-impl-001",
"from": "green-phase",
"status": "success",
"files_changed": ["src/auth/jwt.ts"],
"tests_run": ["npm test -- auth"],
"summary": "Implemented JWT validation"
}Templates for every major AI-assisted IDE:
| IDE | Template Location |
|---|---|
| Roo Code | templates/custom_modes.yaml |
| Claude Code | templates/claude-code/CLAUDE.md |
| Cursor | templates/cursor/rules/_global.mdc |
| Copilot | templates/copilot/copilot-instructions.md |
| Path | Description |
|---|---|
templates/custom_modes.yaml |
Mode definitions (13 modes with layer mappings) |
templates/custom-instructions-for-all-modes.md |
Global contracts (paste into IDE) |
templates/universal/AGENTS.md |
Universal agent contract (OODA + TDD + Scope) |
| Tool Integration | |
templates/tools/ooda-mcp.md |
OODA MCP integration (Somatic layer) |
templates/tools/synch-mcp.md |
Synch MCP integration (Autonomic layer) |
templates/tools/index-foundry-mcp.md |
Index Foundry integration (Autonomic layer) |
templates/tools/trace-mcp.md |
Trace MCP integration (Reflex layer) |
LLMs are stateless text predictors. They have:
- β High intelligence
- β No agency
- β No persistent memory
- β No sensory-motor integration
A nervous system provides:
- Closed loops of action and sensation
- Reflexes that reject bad inputs immediately
- Autonomic processes that maintain state
- Central coordination of specialized capabilities
Chatbot: User β LLM β Response
β
βββ (no feedback)
Organism: Environment β Senses β Brain β Motor β Environment
β β
βββββββββ Feedback βββββββββββββ
- β Buy Me a Coffee
- π Report Issues
- π¬ Discussions
MIT