├── .gitignore ├── README.md ├── agent ├── __init__.py ├── _types.py └── localswarm.py ├── demo.gif ├── llm ├── __init__.py └── local.py ├── poetry.lock ├── pyproject.toml └── tests ├── __init__.py └── test_localswarm.py /.gitignore: -------------------------------------------------------------------------------- 1 | */__pycache__ 2 | __pycache__ 3 | */**/__pycache__ 4 | blog.md -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Local Qwen Swarm 2 | 3 | A Python implementation of an agent swarm system that works with local LLM servers. The system allows you to create multiple agents that can work together to solve complex tasks using the Qwen 2.5 Coder model. 4 | 5 | > **Note:** This implementation is a work in progress and is not production ready. 6 | 7 | _**Read the full blog post [here](https://pub.towardsai.net/i-built-an-openai-style-swarm-that-runs-entirely-on-my-laptop-heres-how-ac606ba739f3).**_ 8 | 9 | 10 | ## Prerequisites 11 | 12 | - Python 3.12+ 13 | - Poetry for dependency management 14 | - One of the following LLM servers running locally: 15 | - LM Studio Server (recommended) 16 | - llama.cpp server 17 | - Ollama server 18 | 19 | ## Setup 20 | 21 | 22 | 1. Clone the repository: 23 | 24 | ```bash 25 | git clone https://github.com/vatsalsaglani/local-qwen-swarm.git 26 | cd local-qwen-swarm 27 | ``` 28 | 29 | 2. Install dependencies: 30 | 31 | ```bash 32 | poetry install 33 | ``` 34 | 35 | 3. Set up your LLM server: 36 | 37 | Download the **LM Studio Server** from [here](https://lmstudio.ai/) and follow the instructions to run the server. 38 | 39 | Search for **Qwen-2.5-Coder** in the model list and download the model. Load the model and click on **Start Server**. 40 | 41 | > **Note:** You can also use the **llama.cpp server** or **Ollama server** by following their respective setup instructions. 42 | 43 | ## Usage 44 | 45 | Here's a basic example of how to use the agent swarm: 46 | 47 | ```python 48 | import asyncio 49 | from pydantic import BaseModel, Field 50 | from typing import List 51 | from agent.localswarm import LocalSwarm 52 | from agent._types import Agent 53 | 54 | # define agents 55 | class Multiply(BaseModel): 56 | numbers: List[int] = Field(description="List of numbers to multiply") 57 | 58 | 59 | def multiplication_agent(numbers: List[int]): 60 | result = 1 61 | for num in numbers: 62 | result *= num 63 | return result 64 | 65 | 66 | class Add(BaseModel): 67 | numbers: List[int] = Field(description="List of numbers to add") 68 | 69 | 70 | def addition_agent(numbers: List[int]): 71 | return sum(numbers) 72 | 73 | 74 | local_multiply_agent = Agent(name="multiply", 75 | instruction="Multiply the given numbers", 76 | functions=[ 77 | dict(name="multiply", 78 | description="Multiply the given numbers", 79 | parameters=Multiply.model_json_schema(), 80 | _callable=multiplication_agent) 81 | ]) 82 | 83 | local_add_agent = Agent(name="add", 84 | instruction="Add the given numbers", 85 | functions=[ 86 | dict(name="add", 87 | description="Add the given numbers", 88 | parameters=Add.model_json_schema(), 89 | _callable=addition_agent) 90 | ]) 91 | 92 | # create swarm 93 | local_swarm = LocalSwarm(orchestrator_agent=Agent( 94 | name="orchestrator", 95 | instruction="Orchestrate the given numbers", 96 | functions=[]), 97 | agents=[local_multiply_agent, local_add_agent], 98 | messages=[{ 99 | "role": 100 | "user", 101 | "content": 102 | "Multiply 2 and 3 and then add 4 to the result" 103 | }], 104 | max_iterations=10) 105 | 106 | # run the swarm 107 | print( 108 | asyncio.run( 109 | local_swarm.run_swarm(model="qwen2.5-coder-3b-instruct-q4_k_m", 110 | llm_args={}))) 111 | ``` 112 | 113 | > To test the swarm, you can use the following command: 114 | > ```bash 115 | > poetry run python -m tests.test_localswarm 116 | > ``` 117 | 118 | ## Configuration 119 | 120 | The system uses environment variables for configuration: 121 | - `LOCAL_API_BASE_URL`: Default is "http://localhost:1234/v1" 122 | - `LOCAL_API_KEY`: Default is "your-api" 123 | 124 | ## Model Information 125 | 126 | The system is designed to work with [Qwen2.5-Coder-3B-Instruct](https://huggingface.co/lmstudio-community/Qwen2.5-Coder-3B-Instruct-GGUF), which offers: 127 | - Context support up to 32K tokens 128 | - Optimized for code-related tasks 129 | - Multiple quantization options (Q3_K_L, Q4_K_M, Q6_K, Q8_0) 130 | - Trained on 5.5 trillion tokens including source code 131 | 132 | ## Demo 133 | 134 | Below is a demo of the agent swarm in action: 135 | 136 | ![Demo](./demo.gif) -------------------------------------------------------------------------------- /agent/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/vatsalsaglani/local-qwen-swarm/7ded93f9166a7e11e8dd94130f7a109bc8781827/agent/__init__.py -------------------------------------------------------------------------------- /agent/_types.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel, Field, ConfigDict 2 | from typing import List, Optional, Dict, Tuple, Callable, Union, Any 3 | 4 | 5 | class AgentFunction(BaseModel): 6 | name: str 7 | description: str 8 | parameters: Dict 9 | _callable: Any 10 | 11 | class Config: 12 | arbitrary_types_allowed = True 13 | 14 | # model_config = ConfigDict(arbitrary_types_allowed=True) 15 | 16 | 17 | class Agent(BaseModel): 18 | name: str 19 | instruction: str 20 | functions: List[Dict[str, Any]] 21 | 22 | 23 | class End(BaseModel): 24 | end: bool = Field(default=True, 25 | description="Use this when reached the end") 26 | why: str = Field( 27 | ..., description="Write the reason for ending the conversation") 28 | final_answer: str = Field( 29 | ..., description="Write the final answer and the steps taken") 30 | 31 | 32 | # print(End.model_json_schema()) 33 | -------------------------------------------------------------------------------- /agent/localswarm.py: -------------------------------------------------------------------------------- 1 | import json 2 | from agent._types import * 3 | from llm.local import LocalLLM 4 | from copy import deepcopy 5 | import logging 6 | 7 | logging.basicConfig( 8 | level=logging.WARNING, 9 | format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') 10 | logger = logging.getLogger(__name__) 11 | 12 | 13 | class LocalSwarm: 14 | 15 | def __init__(self, 16 | orchestrator_agent: Agent, 17 | agents: List[Agent], 18 | messages: List[Dict], 19 | local_api_base_url: str = "http://localhost:1234/v1", 20 | local_api_key: str = "your-api", 21 | max_iterations: int = 10): 22 | self.orchestrator_agent = orchestrator_agent 23 | self.client = LocalLLM(local_api_base_url, local_api_key) 24 | self.agents = agents.copy() 25 | self.orchestrator_agent.functions = [ 26 | AgentFunction(name=a.name, 27 | description=a.instruction, 28 | parameters={ 29 | "properties": { 30 | "agent_name": { 31 | "type": 32 | "string", 33 | "description": 34 | "The name of the agent to call" 35 | } 36 | } 37 | }, 38 | _callable=a.name) for a in agents 39 | ] 40 | self.orchestrator_agent.functions += [ 41 | AgentFunction(name="end", 42 | description="End the agent once the task is done", 43 | parameters=End.model_json_schema(), 44 | _callable="end") 45 | ] 46 | self.function_map = { 47 | f.name: f 48 | for f in self.orchestrator_agent.functions 49 | } 50 | self.agent_map = {a.name: a for a in self.agents} 51 | self.messages = messages 52 | self.max_iteration = max_iterations 53 | self.curr_iteration = 0 54 | self.selected_agents = [] 55 | 56 | async def _iterate(self, model, llm_args): 57 | curr_messages = deepcopy(self.messages) 58 | functions = list( 59 | map( 60 | lambda x: { 61 | "name": x.name, 62 | "description": x.description, 63 | "parameters": x.parameters, 64 | }, self.orchestrator_agent.functions)) 65 | logger.info("[iterate functions]\n%s\n\n=====", functions) 66 | response_functions = await self.client.function_call( 67 | model, curr_messages, functions, validate_params=False, **llm_args) 68 | logger.info("[iterate response_functions]\n%s\n\n=====", 69 | response_functions) 70 | return response_functions 71 | 72 | async def _function_args(self, 73 | model, 74 | agent_name: str, 75 | no_func_call: bool = False, 76 | max_rec_depth: int = 3, 77 | curr_depth: int = 0): 78 | functions = self.agent_map[agent_name].functions 79 | logger.info("[function_args functions]\n%s\n\n=====", functions) 80 | functions_list = list( 81 | map( 82 | lambda x: { 83 | "name": x.get("name"), 84 | "description": x.get("description"), 85 | "parameters": x.get("parameters"), 86 | }, functions)) 87 | curr_messages = deepcopy(self.messages) 88 | if no_func_call: 89 | curr_messages[-1][ 90 | "content"] += "\n\nDidn't receive a function. Please return only one appropriate function." 91 | response = await self.client.function_call(model, curr_messages, 92 | functions_list, **{}) 93 | logger.info("[function_args response]\n%s\n\n=====", response) 94 | if len(response) == 0: 95 | curr_depth += 1 96 | if curr_depth > max_rec_depth: 97 | raise Exception( 98 | f"Max recursion depth reached while waiting for function call from {agent_name}" 99 | ) 100 | return await self._function_args(model, agent_name, no_func_call, 101 | max_rec_depth, curr_depth) 102 | return response[-1] 103 | 104 | async def _return_final_answer(self, model: str, end_output: Dict): 105 | final_answer_system_prompt = """You are a helpful AI assistant that explains the problem-solving process to users. You will receive: 106 | 1. The original user request 107 | 2. A list of steps taken by different agents 108 | 3. The final output 109 | 110 | Your task is to create a clear, conversational explanation that helps users understand: 111 | - What their request was 112 | - Which agents were involved 113 | - What functions were used and why 114 | - The intermediate results 115 | - The final answer 116 | 117 | Format your response as follows: 118 | 1. Brief restatement of what the user asked for 119 | 2. Step-by-step breakdown of what happened 120 | 3. Final result with explanation 121 | 122 | Example: 123 | 124 | Input: 125 | User Request: "Check temperature in London and notify if above 20C" 126 | Steps: [ 127 | {"agent": "weather_agent", "function": "getWeather", "params": {"city": "London"}, "output": "22C"}, 128 | {"agent": "notification_agent", "function": "sendNotification", "params": {"message": "Temperature alert"}, "output": "Notification sent"} 129 | ] 130 | Final Output: {"why": "Temperature check and notification complete", "final_answer": "Temperature was 22C, notification sent"} 131 | 132 | Response: 133 | I helped you check London's temperature and send a notification since it was above 20C. Here's what happened: 134 | 135 | 1. Temperature Check: 136 | • Weather_agent checked London's current temperature 137 | • The temperature was 22°C 138 | 139 | 2. Notification: 140 | • Since 22°C was above your threshold of 20°C 141 | • Notification_agent sent out an alert 142 | 143 | The process is complete - London is at 22°C and you've been notified. 144 | 145 | Remember to: 146 | - Use clear, conversational language 147 | - Break down complex steps into digestible pieces 148 | - Show the logical flow from one step to the next 149 | - Highlight key numbers and results 150 | - Explain why each step was necessary 151 | - Make technical processes understandable to non-technical users""" 152 | self.messages[-1]["content"] += f'\n\n{end_output.get("content")}' 153 | final_messages = [{ 154 | "role": "system", 155 | "content": final_answer_system_prompt 156 | }] + [{ 157 | "role": 158 | "user", 159 | "content": 160 | f'The following is the list of agents and functions used to solve the problem:\n{self.selected_agents}' 161 | }] 162 | print("=====[ANSWER]=====\n") 163 | async for chunk in self.client.stream(model, final_messages): 164 | yield chunk 165 | 166 | async def run_swarm(self, model: str, llm_args: Dict): 167 | done = False 168 | while self.curr_iteration < self.max_iteration + 1 and not done: 169 | # print("[iteration messages]\n", self.messages, "\n\n=====") 170 | response_functions = await self._iterate(model, llm_args) 171 | self.selected_agents.append( 172 | {"agent": response_functions[0].get('name')}) 173 | func_op = {"role": "assistant", "content": ""} 174 | for functions_ in response_functions: 175 | if functions_.get('name') == "end": 176 | print("End of the conversation") 177 | print("[end]\n", functions_, "\n\n=====") 178 | print("[end]\n", 179 | json.dumps(functions_.get('parameters'), indent=4), 180 | "\n\n=====") 181 | done = functions_.get('parameters') 182 | break 183 | if not functions_.get('name') in self.agent_map: 184 | 185 | raise Exception( 186 | f"Function {functions_.get('name')} not found in the orchestrator agent" 187 | ) 188 | else: 189 | func_op[ 190 | "content"] += f"Selected Agent: {functions_.get('name')}\n" 191 | function_args = await self._function_args( 192 | model, functions_.get('name'), curr_depth=0) 193 | if not "functions" in self.selected_agents[-1]: 194 | self.selected_agents[-1]["functions"] = [] 195 | self.selected_agents[-1]["functions"].append({ 196 | "name": 197 | function_args.get('name'), 198 | "parameters": 199 | function_args.get('parameters') 200 | }) 201 | # print("[function_args]\n", function_args, "\n\n=====") 202 | func_op[ 203 | "content"] += f"Function Name: {function_args.get('name')}\n" 204 | func_op[ 205 | "content"] += f"Function Args: {function_args.get('parameters')}\n" 206 | agent_functions = self.agent_map[functions_.get( 207 | 'name')].functions 208 | # print("[agent_functions]\n", agent_functions, "\n\n=====") 209 | agent_function = next( 210 | (f for f in agent_functions 211 | if f.get("name") == function_args.get('name')), None) 212 | # print("[agent_function]\n", agent_function, "\n\n=====") 213 | if not agent_function: 214 | raise Exception( 215 | f"Function {function_args.get('name')} not found in the agent {functions_.get('name')}" 216 | ) 217 | else: 218 | if "_callable" in agent_function and callable( 219 | agent_function.get('_callable')): 220 | results = agent_function.get('_callable')( 221 | **function_args.get('parameters', {})) 222 | self.selected_agents[-1]["functions"][-1][ 223 | "result"] = results 224 | logger.info("[results callable]\n%s\n\n=====", 225 | results) 226 | else: 227 | results = str(agent_function.get('_callable')) 228 | self.selected_agents[-1]["functions"][-1][ 229 | "result"] = results 230 | logger.info("[results else]\n%s\n\n=====", results) 231 | func_op["content"] += f"Function Results: {results}\n" 232 | self.messages[-1]["content"] += f'\n\n{func_op["content"]}' 233 | self.curr_iteration += 1 234 | # self.messages.append(func_op) 235 | if done: 236 | async for chunk in self._return_final_answer(model, done): 237 | print(chunk, end="", flush=True) 238 | print("\n=====[END]=====\n") 239 | return self.messages 240 | -------------------------------------------------------------------------------- /demo.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/vatsalsaglani/local-qwen-swarm/7ded93f9166a7e11e8dd94130f7a109bc8781827/demo.gif -------------------------------------------------------------------------------- /llm/__init__.py: -------------------------------------------------------------------------------- 1 | from types import * -------------------------------------------------------------------------------- /llm/local.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | from openai import AsyncOpenAI 4 | from typing import List, Dict, AsyncGenerator, Optional 5 | import re 6 | import logging 7 | 8 | logger = logging.getLogger(__name__) 9 | 10 | logging.basicConfig(level=logging.WARNING) 11 | 12 | 13 | class LocalLLM: 14 | 15 | def __init__(self, base_url: str, api_key: str): 16 | self.client = AsyncOpenAI(base_url=base_url, api_key=api_key) 17 | 18 | def _extract_function_calls(self, response: str) -> List[Dict]: 19 | pattern = r'\s*(.*?)\s*' 20 | function_calls = re.findall(pattern, response) 21 | 22 | parsed_calls = [] 23 | for call in function_calls: 24 | try: 25 | parsed_calls.append(json.loads(call)) 26 | except json.JSONDecodeError: 27 | continue 28 | return parsed_calls 29 | 30 | async def completion(self, model: str, messages: List[Dict], **kwargs): 31 | response = await self.client.chat.completions.create(model=model, 32 | messages=messages, 33 | **kwargs) 34 | response = response.choices[0].message.content 35 | # print("completion response\n", response) 36 | return response 37 | 38 | def _validate_parameters(self, provided_params: Dict, 39 | expected_params: Dict) -> List[str]: 40 | """Validate parameters against schema and return list of validation errors.""" 41 | validation_errors = [] 42 | 43 | required_params = expected_params.get('required', []) 44 | properties = expected_params.get('properties', {}) 45 | 46 | missing_params = [ 47 | p for p in required_params if p not in provided_params 48 | ] 49 | if missing_params: 50 | validation_errors.append( 51 | f"Missing required parameters: {missing_params}") 52 | 53 | for param_name, param_value in provided_params.items(): 54 | if param_name not in properties: 55 | validation_errors.append(f"Unexpected parameter: {param_name}") 56 | continue 57 | 58 | expected_type = properties[param_name].get('type') 59 | if expected_type == 'array' and not isinstance(param_value, list): 60 | validation_errors.append( 61 | f"Parameter {param_name} should be an array") 62 | elif expected_type == 'integer' and not isinstance( 63 | param_value, int): 64 | validation_errors.append( 65 | f"Parameter {param_name} should be an integer") 66 | elif expected_type == 'string' and not isinstance( 67 | param_value, str): 68 | validation_errors.append( 69 | f"Parameter {param_name} should be a string") 70 | elif expected_type == 'number' and not isinstance( 71 | param_value, (int, float)): 72 | validation_errors.append( 73 | f"Parameter {param_name} should be a number") 74 | elif expected_type == 'boolean' and not isinstance( 75 | param_value, bool): 76 | validation_errors.append( 77 | f"Parameter {param_name} should be a boolean") 78 | 79 | return validation_errors 80 | 81 | async def _recursive_function_call(self, 82 | model: str, 83 | messages: List[Dict], 84 | functions: List[Dict], 85 | validate_params: bool = True, 86 | depth: int = 0, 87 | max_depth: int = 5) -> List[Dict]: 88 | """Recursive helper for function_call with depth limit.""" 89 | if depth >= max_depth: 90 | logger.warning("Max depth %d reached, returning empty list", 91 | max_depth) 92 | return [] 93 | 94 | response = await self.completion(model=model, 95 | messages=messages, 96 | temperature=0.2) 97 | extracted_functions = self._extract_function_calls(response) 98 | logger.debug("Extracted functions: %s", extracted_functions) 99 | 100 | if not extracted_functions: 101 | logger.info("No function calls found, trying again") 102 | messages[-1][ 103 | "content"] += f"\n\nThe following is your output: {response}. You did not select any function. Please select one appropriate function." 104 | return await self._recursive_function_call(model, messages, 105 | functions, depth + 1, 106 | max_depth) 107 | 108 | if len(extracted_functions) > 1: 109 | logger.info("Multiple function calls found, trying again") 110 | messages[-1][ 111 | "content"] += f"\n\nThe following is your output: {response}. You selected multiple functions. Please select only one appropriate function." 112 | return await self._recursive_function_call(model, messages, 113 | functions, depth + 1, 114 | max_depth) 115 | 116 | function_call = extracted_functions[0] 117 | function_name = function_call.get('name') 118 | 119 | target_function = next( 120 | (f for f in functions if f['name'] == function_name), None) 121 | if not target_function: 122 | messages[-1][ 123 | "content"] += f"\n\nThe following is your output: {response}. The function name is not valid. Please choose the most appropriate function." 124 | return await self._recursive_function_call(model, messages, 125 | functions, depth + 1, 126 | max_depth) 127 | 128 | validation_errors = None 129 | if validate_params: 130 | validation_errors = self._validate_parameters( 131 | function_call.get('parameters', {}), 132 | target_function.get('parameters', {})) 133 | 134 | if validation_errors: 135 | logger.warning("Validation errors: %s", validation_errors) 136 | error_message = "\n".join(validation_errors) 137 | messages[-1][ 138 | "content"] += f"\n\nThe following is your output: {response}. The parameters are not valid: {error_message}. Please select the most appropriate function with correct parameters." 139 | return await self._recursive_function_call(model, messages, 140 | functions, depth + 1, 141 | max_depth) 142 | 143 | return extracted_functions 144 | 145 | async def function_call(self, 146 | model: str, 147 | messages: List[Dict], 148 | functions: List[Dict], 149 | validate_params: bool = True) -> List[Dict]: 150 | """Main function call method with validation and recursion.""" 151 | try: 152 | function_call_prompt = """You are a helpful AI assistant with access to a set of functions. Your task is to assist users by utilizing these functions to respond to their requests. Here are your instructions: 153 | 154 | 1. Available Functions: 155 | 156 | {functions} 157 | 158 | 159 | 2. Using Functions: 160 | - You MUST provide exactly ONE function call per response 161 | - Your response MUST be enclosed in tags 162 | - Parameters MUST match the function's schema exactly 163 | - Do not provide any additional text or explanations 164 | - For multi-step tasks, determine which step is currently needed based on the conversation history 165 | - When all steps are complete, use the 'end' function 166 | 167 | 3. Format: 168 | {{"name": "functionName", "parameters": {{"param1": "value1", "param2": "value2"}} }} 169 | 170 | 4. Few-Shot Examples: 171 | 172 | User: "First check the weather in London, then book a taxi" 173 | Assistant: {{"name": "getWeather", "parameters": {{"city": "London"}} }} 174 | 175 | System: Weather in London is 15°C and sunny 176 | User: "First check the weather in London, then book a taxi" 177 | Assistant: {{"name": "bookTaxi", "parameters": {{"pickup": "London"}} }} 178 | 179 | User: "Convert 100F to Celsius and then send it to admin@example.com" 180 | Assistant: {{"name": "convertTemperature", "parameters": {{"value": 100, "from": "F", "to": "C"}} }} 181 | 182 | System: Temperature converted to 37.8C 183 | User: "Convert 100F to Celsius and then send it to admin@example.com" 184 | Assistant: {{"name": "sendEmail", "parameters": {{"to": "admin@example.com", "body": "Temperature is 37.8C"}} }} 185 | 186 | 5. Important: 187 | - Examine the conversation history to determine which step needs to be executed next 188 | - Return exactly ONE function call that matches the current step 189 | - If all steps are complete, use the 'end' function with appropriate parameters 190 | - Never include multiple function calls in one response 191 | - Return the function call in tags as shown in the examples above 192 | 193 | Remember: Only ONE function call is allowed per response. Determine the current step from the conversation history and return only one function call. 194 | """.format(functions=functions) 195 | 196 | _messages = [{ 197 | "role": "system", 198 | "content": function_call_prompt 199 | }] + messages 200 | logger.debug("Function call messages: %s", _messages) 201 | 202 | return await self._recursive_function_call( 203 | model, 204 | _messages, 205 | functions, 206 | validate_params=validate_params, 207 | depth=0) 208 | 209 | except Exception as e: 210 | logger.error("Error during function call: %s", str(e)) 211 | return [] 212 | 213 | async def stream(self, model: str, 214 | messages: List[Dict]) -> AsyncGenerator[str, None]: 215 | try: 216 | response = await self.client.chat.completions.create( 217 | model=model, messages=messages, temperature=0.2, stream=True) 218 | async for chunk in response: 219 | if chunk.choices[0].delta and chunk.choices[ 220 | 0].delta.content is not None: 221 | yield chunk.choices[0].delta.content 222 | except Exception as e: 223 | logger.error("Error during streaming: %s", str(e)) 224 | yield f"Error during streaming: {str(e)}" 225 | 226 | 227 | if __name__ == "__main__": 228 | import asyncio 229 | llm = LocalLLM("http://localhost:1234/v1", "your-api") 230 | functions = [ 231 | { 232 | "name": "getWeather", 233 | "description": "Get the weather for a specific city." 234 | }, 235 | { 236 | "name": "getNews", 237 | "description": "Get the latest news headlines." 238 | }, 239 | { 240 | "name": "getStockPrice", 241 | "description": "Get the stock price for a specific company." 242 | }, 243 | ] 244 | messages = [ 245 | { 246 | "role": "user", 247 | "content": "What's the weather in New York today?" 248 | }, 249 | # {"role": "user", "content": "Can you tell me the latest news headlines?"}, 250 | ] 251 | response = asyncio.run( 252 | llm.function_call(model="qwen2.5-coder-3b-instruct-q4_k_m", 253 | messages=messages, 254 | functions=functions)) 255 | print(response) 256 | -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 2 | 3 | [[package]] 4 | name = "annotated-types" 5 | version = "0.7.0" 6 | description = "Reusable constraint types to use with typing.Annotated" 7 | optional = false 8 | python-versions = ">=3.8" 9 | files = [ 10 | {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, 11 | {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, 12 | ] 13 | 14 | [[package]] 15 | name = "anyio" 16 | version = "4.6.2.post1" 17 | description = "High level compatibility layer for multiple asynchronous event loop implementations" 18 | optional = false 19 | python-versions = ">=3.9" 20 | files = [ 21 | {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"}, 22 | {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"}, 23 | ] 24 | 25 | [package.dependencies] 26 | idna = ">=2.8" 27 | sniffio = ">=1.1" 28 | 29 | [package.extras] 30 | doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] 31 | test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"] 32 | trio = ["trio (>=0.26.1)"] 33 | 34 | [[package]] 35 | name = "certifi" 36 | version = "2024.8.30" 37 | description = "Python package for providing Mozilla's CA Bundle." 38 | optional = false 39 | python-versions = ">=3.6" 40 | files = [ 41 | {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, 42 | {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, 43 | ] 44 | 45 | [[package]] 46 | name = "charset-normalizer" 47 | version = "3.4.0" 48 | description = "The Real First Universal Charset Detector. 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7 | 8 | [tool.poetry.dependencies] 9 | python = "^3.12" 10 | httpx = "^0.27.2" 11 | openai = "^1.54.4" 12 | transformers = "^4.46.2" 13 | fastapi = "^0.115.5" 14 | uvicorn = {extras = ["standard"], version = "^0.32.0"} 15 | 16 | 17 | [build-system] 18 | requires = ["poetry-core"] 19 | build-backend = "poetry.core.masonry.api" 20 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /tests/test_localswarm.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | from pydantic import BaseModel, Field 3 | from typing import List 4 | from agent.localswarm import LocalSwarm 5 | from agent._types import Agent 6 | 7 | 8 | class Multiply(BaseModel): 9 | numbers: List[int] = Field(description="List of numbers to multiply") 10 | 11 | 12 | def multiplication_agent(numbers: List[int]): 13 | result = 1 14 | for num in numbers: 15 | result *= num 16 | return result 17 | 18 | 19 | class Add(BaseModel): 20 | numbers: List[int] = Field(description="List of numbers to add") 21 | 22 | 23 | def addition_agent(numbers: List[int]): 24 | return sum(numbers) 25 | 26 | 27 | local_multiply_agent = Agent(name="multiply", 28 | instruction="Multiply the given numbers", 29 | functions=[ 30 | dict(name="multiply", 31 | description="Multiply the given numbers", 32 | parameters=Multiply.model_json_schema(), 33 | _callable=multiplication_agent) 34 | ]) 35 | 36 | local_add_agent = Agent(name="add", 37 | instruction="Add the given numbers", 38 | functions=[ 39 | dict(name="add", 40 | description="Add the given numbers", 41 | parameters=Add.model_json_schema(), 42 | _callable=addition_agent) 43 | ]) 44 | 45 | # print("local_add_agent\n", local_add_agent) 46 | 47 | local_swarm = LocalSwarm(orchestrator_agent=Agent( 48 | name="orchestrator", 49 | instruction="Orchestrate the given numbers", 50 | functions=[]), 51 | agents=[local_multiply_agent, local_add_agent], 52 | messages=[{ 53 | "role": 54 | "user", 55 | "content": 56 | "Multiply 2 and 3 and then add 4 to the result" 57 | }], 58 | max_iterations=10) 59 | 60 | print( 61 | asyncio.run( 62 | local_swarm.run_swarm(model="qwen2.5-coder-3b-instruct-q4_k_m", 63 | llm_args={}))) 64 | --------------------------------------------------------------------------------