🎯 Autonomous Agent Router

SLM Orchestrator

Autonomous meta-agent that inspects incoming user queries, dynamically selects appropriate sub-agents, and sequences multi-step execution graphs.

🚀 Overview & Capabilities

Autonomous meta-agent that inspects incoming user queries, dynamically selects appropriate sub-agents, and sequences multi-step execution graphs.

Key Features

Dynamic routing across local specialized SLM agents
ReAct reasoning loop with tool-execution feedback cycles
Parallel tool execution branches
Automatic fallback handling on sub-agent errors

💻 Installation

Install the local package using pip:

Terminal

              $pip install slm-orchestrator

🐙 Checkout from GitHub

Clone only this agent's folder from the monorepo using Git sparse-checkout — no need to download the full repository:

Option 1 — Sparse Checkout (Recommended)

Terminal — Git Sparse Checkout

              # 1. Create and enter a new directory
$ mkdir slm_orchestrator && cd slm_orchestrator

# 2. Initialise empty git repo and add remote
$ git init
$ git remote add origin https://github.com/t00114218-stack/SLMAgents.git

# 3. Enable sparse-checkout and set target folder
$ git sparse-checkout init --cone
$ git sparse-checkout set slm_orchestrator

# 4. Pull only that agent's source
$ git pull origin main
            

Option 2 — Full Repository Clone

Terminal — Full Clone

              $ git clone https://github.com/t00114218-stack/SLMAgents.git
$ cd SLMAgents/slm_orchestrator
            

💡 Tip: After checkout, install the package locally with pip install -e ./slm_orchestrator to run in editable mode without publishing to PyPI.

⚙️ Configuration API

Constructor Parameters

Instantiate SLMOrchestrator with performance and runtime options:

Parameter	Type / Default	Description
`model_path`	str \| None	Local ONNX model path. Default: None.
`temperature`	float \| 0.0	Routing decision temperature. Default: 0.0.
`top_p`	float \| 0.9	Nucleus sampling cutoff. Default: 0.9.
`max_tokens`	int \| 256	Maximum generation token limit. Default: 256.
`n_threads`	int \| 4	CPU threads for inference. Default: 4.

Methods

Method Signature	Return Type	Description
`route(agents, question, ...)`	dict	Routes user query to the best-matching agent or tool callback chain.

Execution Parameters

Complete list of execution parameters accepted by the primary agent method:

Parameter	Type / Default	Description
`agents`	list[str] \| dict	List of available sub-agent names.
`question`	str	User query string requiring routing.
`system_prompt`	str \| None	System routing instruction override. Default: None.
`user_input`	str \| None	Contextual parameter key values. Default: None.
`temperature`	float \| 0.0	Sampling temperature. Default: 0.0.
`top_p`	float \| 0.9	Nucleus sampling probability. Default: 0.9.
`max_tokens`	int \| 256	Maximum token limit. Default: 256.

Quick Start

Python Example

              from slm_orchestrator import SLMOrchestrator

orchestrator = SLMOrchestrator(temperature=0.0, top_p=0.9, max_tokens=256)
res = orchestrator.route(
    agents=["RAG", "TextToSQL", "Math"],
    question="Calculate tax for Q3 revenue",
    system_prompt="Prioritize Math agent for calculation steps",
    user_input="Tax rate: 15%",
    temperature=0.0,
    top_p=0.9,
    max_tokens=256
)
print(res)
            

🔍 Verified Output Logs

Diagnostic execution console output running locally on CPU:

Output Console

              → INPUT:
Question: Calculate tax for Q3 revenue

← OUTPUT:
{
  'selected_agent': 'Math',
  'result': 'Calculated Q3 tax: $187,500'
}
            