> ## Documentation Index
> Fetch the complete documentation index at: https://cyberpaisa-dof-mesh-40-27.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Multi-LLM Mesh

> How DOF-MESH coordinates heterogeneous LLM nodes — cloud API and local — into a single governed inference layer.

The mesh is a coordinated network of LLM nodes. DOF routes tasks to the optimal node based on task type, availability, cost, and latency — with TTL backoff, circuit breaking, and threshold consensus for critical decisions.

***

## Cloud API Nodes

| Node ID        | Provider     | Speed     | Cost               | Best for             |
| -------------- | ------------ | --------- | ------------------ | -------------------- |
| minimax        | MiniMax M2.1 | —         | 1,000 req/day free | **Primary provider** |
| cerebras-llama | Cerebras     | 868 tok/s | Free               | Fast inference       |
| deepseek-coder | DeepSeek     | 120 tok/s | \$0.27/M           | Code generation      |
| sambanova      | SambaNova    | 200 tok/s | Free               | Large context        |
| nvidia-nim     | NVIDIA NIM   | 150 tok/s | Free tier          | Complex reasoning    |
| glm-5          | Zhipu GLM    | 100 tok/s | Free               | Multilingual         |
| gemini-flash   | Gemini       | —         | Free/web           | Analysis             |
| groq           | Groq         | —         | Free tier          | Low-latency          |

## Local Nodes (Ollama)

| Node ID         | Base model        | Role                         | Context | Size   | Cost   |
| --------------- | ----------------- | ---------------------------- | ------- | ------ | ------ |
| local-agi-m4max | qwen2.5-coder:14b | Autonomous local AGI         | 65K     | 9.0 GB | \$0.00 |
| dof-coder       | qwen2.5-coder:14b | Code implementation          | 65K     | 9.0 GB | \$0.00 |
| dof-analyst     | qwen2.5-coder:14b | Data analysis                | 65K     | 9.0 GB | \$0.00 |
| dof-guardian    | qwen2.5-coder:14b | Security (Cerberus Prime)    | 65K     | 9.0 GB | \$0.00 |
| dof-reasoner    | deepseek-r1:14b   | Strategic planning           | 65K     | 9.0 GB | \$0.00 |
| dof-voice       | qwen2.5-coder:14b | Voice — long context Spanish | 65K     | 9.0 GB | \$0.00 |
| dof-voice-fast  | gemma2:9b         | Voice — realtime Spanish     | 8K      | 5.4 GB | \$0.00 |

<Info>
  The alias `local-qwen` no longer exists. Use `local-agi-m4max` for local sovereign compute.
  Set `LOCAL_MODEL_ENDPOINT` in `.env` to your Ollama server URL.
</Info>

### Local Node System Prompts

```
local-agi-m4max: "You are local-agi-m4max — the autonomous local AGI node of the DOF Mesh"
dof-coder:       "You are DOF-CODER — code intelligence node of the DOF Mesh"
dof-analyst:     "You are DOF-ANALYST — the data intelligence node of the DOF Mesh"
dof-guardian:    "You are CERBERUS PRIME — Active Defense Commander of the DOF Mesh"
dof-reasoner:    "You are DOF-REASONER — strategic intelligence, orchestration, and planning"
dof-voice:       "Eres la VOZ del DOF Mesh Legion. Tu creador es Cyber Paisa"
dof-voice-fast:  "Eres la voz del DOF Mesh Legion"
```

***

## Starting the Mesh

```bash theme={null}
python3 core/api_node_runner.py \
  --nodes deepseek-coder cerebras-llama gemini-flash local-agi-m4max \
  --daemon
```

***

## TTL Backoff

When a provider fails or rate-limits:

```
1st failure → 5 min cooldown
2nd failure → 10 min cooldown
3rd failure → 20 min cooldown
```

The circuit breaker transitions: `CLOSED → OPEN → HALF_OPEN → CLOSED`

***

## Threshold Consensus

For critical decisions, DOF requires N-of-M agreement across mesh nodes:

```python theme={null}
from core.threshold_consensus import ThresholdConsensus

consensus = ThresholdConsensus(threshold=3, total=5)
result = consensus.vote(action="transfer_1000_USDC", voters=selected_nodes)
# {"approved": True, "votes": {"approved": 4, "rejected": 1}}
```

***

## Key Modules

| Module                         | Responsibility                                   |
| ------------------------------ | ------------------------------------------------ |
| `core/mesh_router_v2.py`       | Smart routing by task type                       |
| `core/mesh_scheduler.py`       | Priority queue (CRITICAL > HIGH > NORMAL > LOW)  |
| `core/mesh_load_balancer.py`   | Distribution across equivalent nodes             |
| `core/mesh_circuit_breaker.py` | CLOSED / OPEN / HALF\_OPEN per provider          |
| `core/mesh_orchestrator.py`    | Task routing + horizontal scaling                |
| `core/node_mesh.py`            | NodeRegistry + MessageBus                        |
| `core/threshold_consensus.py`  | N-of-M voting for critical decisions             |
| `core/web_bridge.py`           | Playwright — browser-accessible LLMs without API |

***

<CardGroup cols={2}>
  <Card title="Running the Mesh" icon="network-wired" href="/guides/running-the-mesh">
    Launch the full mesh with Docker or CLI
  </Card>

  <Card title="Providers & Agents" icon="robot" href="/guides/providers-and-agents">
    Configure provider chains and 17 agents
  </Card>
</CardGroup>