11d • General Discussion 💬
Multi-Agent AI Architecture — Summary
# Multi-Agent AI Architecture — Summary
The document describes a **multi-agent AI architecture** where different AI models handle different types of tasks, coordinated by a **central orchestrator**.
The main idea is:
> Use the **best model for each task** instead of relying on a single model.
This improves **cost efficiency, speed, and capability**.
---
# System Flow
Messages are received through a **Telegram bot**, then processed by an **Ollama orchestrator**, which decides how to handle the request.
Basic flow:
```
User → Telegram Bot → Ollama Orchestrator → Selected Agent → Response
```
Users can also explicitly select an agent using commands:
```
/claude
/codex
/ollama
/openrouter
```
If **no command is provided**, Ollama automatically decides which agent to use.
---
# Role of the Ollama Orchestrator
Ollama acts as both:
**1. Router (Orchestrator)**
It analyzes the message and decides whether to:
* respond directly
* delegate the task to another agent
**2. Local AI Agent**
It can also answer simple requests itself.
This reduces costs because **local models are free**, while paid models are used only when necessary.
---
# Task Classification
The orchestrator classifies requests into three types:
### Simple tasks
Examples:
* questions
* conversations
* translations
→ handled directly by **Ollama**
---
### Code or tool tasks
Examples:
* editing files
* running commands
* debugging
→ routed to **Codex or Claude**
---
### Complex tasks
Examples:
* large refactoring
* deployment
* complex analysis
→ routed to **Claude**
---
# Agents in the System
### Claude Agent
Command:
```
/claude [model]
```
Models:
* Opus
* Sonnet
* Haiku
Capabilities:
* bash execution
* file editing
* web search
* advanced tools
Limitation: usage quota.
---
### Codex CLI
Command:
```
/codex
```
Used mainly for **automated coding tasks**.
Runs as:
```
codex --full-auto "message"
```
Capabilities:
* code generation
* file editing
* command execution
Requires:
```
OPENAI_API_KEY
```
---
### Ollama (Local Models)
Command:
```
/ollama [model]
```
Examples:
* qwen2.5-coder
* llama3
* deepseek-coder
Advantages:
* runs locally
* zero cost
* fast
Limitation: no system tools.
---
### OpenRouter
Command:
```
/openrouter [model]
```
Provides access to multiple models via API, such as:
* DeepSeek
* Mistral
* Claude
Endpoint:
```
```
---
# Routing Mechanism
The orchestrator uses a classification prompt and returns structured JSON:
```
{"action": "respond", "response": "..."}
```
or
```
{"action": "route", "agent": "claude|codex|openrouter", "instructions": "..."}
```
This allows automated **AI routing between agents**.
---
# Advantages of This Architecture
1. **Lower cost** — local models handle simple tasks
2. **Scalability** — new agents can be added easily
3. **Specialization** — each model performs tasks it is best suited for
4. **Automation** — the system automatically chooses the right agent
---
# Possible Improvements
Future improvements could include:
**Memory layer**
* Redis
* Vector database
* Embeddings
**Tool registry**
```
run_bash
edit_file
deploy_project
```
**Agent planning systems**
```
planner agent
executor agents
review agent
```
Similar to architectures used in systems like **AutoGPT, CrewAI, or Devin-style agents**.
---
✅ **Conclusion**
This architecture creates a **hybrid AI system** that combines:
* local models
* external APIs
* intelligent routing
Resulting in a **powerful, scalable, and cost-efficient multi-agent system**.
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Multi-Agent AI Architecture — Summary
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