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    Home»Artificial Intelligence»AI Agents from Zero to Hero — Part 3
    Artificial Intelligence

    AI Agents from Zero to Hero — Part 3

    Team_AIBS NewsBy Team_AIBS NewsMarch 29, 2025No Comments19 Mins Read
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    In Part 1 of this tutorial collection, we launched AI Brokers, autonomous packages that carry out duties, make selections, and talk with others. 

    In Part 2 of this tutorial collection, we understood find out how to make the Agent try to retry till the duty is accomplished by means of Iterations and Chains. 

    A single Agent can often function successfully utilizing a instrument, however it may be much less efficient when utilizing many instruments concurrently. One solution to sort out difficult duties is thru a “divide-and-conquer” strategy: create a specialised Agent for every job and have them work collectively as a Multi-Agent System (MAS). 

    In a MAS, a number of brokers collaborate to realize frequent objectives, usually tackling challenges which are too tough for a single Agent to deal with alone. There are two essential methods they’ll work together: 

    • Sequential circulation – The Brokers do their work in a particular order, one after the opposite. For instance, Agent 1 finishes its job, after which Agent 2 makes use of the outcome to do its job. That is helpful when duties depend upon one another and have to be performed step-by-step.
    • Hierarchical circulation – Often, one higher-level Agent manages the entire course of and provides directions to decrease stage Brokers which deal with particular duties. That is helpful when the ultimate output requires some back-and-forth.

    On this tutorial, I’m going to indicate find out how to construct from scratch various kinds of Multi-Agent Techniques, from easy to extra superior. I’ll current some helpful Python code that may be simply utilized in different related circumstances (simply copy, paste, run) and stroll by means of each line of code with feedback to be able to replicate this instance (hyperlink to full code on the finish of the article).

    Setup

    Please confer with Part 1 for the setup of Ollama and the primary LLM.

    import ollama
    llm = "qwen2.5" 

    On this instance, I’ll ask the mannequin to course of pictures, subsequently I’m additionally going to wish a Imaginative and prescient LLM. It’s a specialised model of a Giant Language Mannequin that, integrating NLP with CV, is designed to grasp visible inputs, reminiscent of pictures and movies, along with textual content.

    Microsoft’s LLaVa is an environment friendly selection as it may possibly additionally run with no GPU.

    After the obtain is accomplished, you may transfer on to Python and begin writing code. Let’s load a picture in order that we are able to check out the Imaginative and prescient LLM.

    from matplotlib import picture as pltimg, pyplot as plt
    
    image_file = "draghi.jpeg"
    
    plt.imshow(pltimg.imread(image_file))
    plt.present()

    To be able to check the Imaginative and prescient LLM, you may simply cross the picture as an enter:

    import ollama
    
    ollama.generate(mannequin="llava",
     immediate="describe the picture",                
     pictures=[image_file])["response"]

    Sequential Multi-Agent System

    I shall construct two Brokers that may work in a sequential circulation, one after the opposite, the place the second takes the output of the primary as an enter, similar to a Chain.

    • The primary Agent should course of a picture offered by the person and return a verbal description of what it sees.
    • The second Agent will search the web and attempt to perceive the place and when the image was taken, based mostly on the outline offered by the primary Agent.

    Each Brokers shall use one Instrument every. The primary Agent could have the Imaginative and prescient LLM as a Instrument. Please keep in mind that with Ollama, with a view to use a Instrument, the operate have to be described in a dictionary.

    def process_image(path: str) -> str:
        return ollama.generate(mannequin="llava", immediate="describe the picture", pictures=[path])["response"]
    
    tool_process_image = {'sort':'operate', 'operate':{
      'identify': 'process_image',
      'description': 'Load a picture for a given path and describe what you see',
      'parameters': {'sort': 'object',
                    'required': ['path'],
                    'properties': {
                        'path': {'sort':'str', 'description':'the trail of the picture'},
    }}}}

    The second Agent ought to have a web-searching Instrument. Within the earlier articles of this tutorial collection, I confirmed find out how to leverage the DuckDuckGo bundle for looking the online. So, this time, we are able to use a brand new Instrument: Wikipedia (pip set up wikipedia==1.4.0). You possibly can immediately use the unique library or import the LangChain wrapper.

    from langchain_community.instruments import WikipediaQueryRun
    from langchain_community.utilities import WikipediaAPIWrapper
    
    def search_wikipedia(question:str) -> str:
        return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper()).run(question)
    
    tool_search_wikipedia = {'sort':'operate', 'operate':{
      'identify': 'search_wikipedia',
      'description': 'Search on Wikipedia by spending some key phrases',
      'parameters': {'sort': 'object',
                    'required': ['query'],
                    'properties': {
                        'question': {'sort':'str', 'description':'The enter have to be quick key phrases, not an extended textual content'},
    }}}}
    ## check
    search_wikipedia(question="draghi")

    First, it is advisable write a immediate to explain the duty of every Agent (the extra detailed, the higher), and that would be the first message within the chat historical past with the LLM.

    immediate = '''
    You're a photographer that analyzes and describes pictures in particulars.
    '''
    messages_1 = [{"role":"system", "content":prompt}]

    One essential choice to make when constructing a MAS is whether or not the Brokers ought to share the chat historical past or not. The administration of chat historical past relies on the design and targets of the system:

    • Shared chat historical past – Brokers have entry to a standard dialog log, permitting them to see what different Brokers have stated or performed in earlier interactions. This will improve the collaboration and the understanding of the general context.
    • Separate chat historical past – Brokers solely have entry to their very own interactions, focusing solely on their very own communication. This design is usually used when impartial decision-making is essential.

    I like to recommend maintaining the chats separate except it’s essential to do in any other case. LLMs may need a restricted context window, so it’s higher to make the historical past as lite as potential.

    immediate = '''
    You're a detective. You learn the picture description offered by the photographer, and also you search Wikipedia to grasp when and the place the image was taken.
    '''
    
    messages_2 = [{"role":"system", "content":prompt}]

    For comfort, I shall use the operate outlined within the earlier articles to course of the mannequin’s response.

    def use_tool(agent_res:dict, dic_tools:dict) -> dict:
        ## use instrument
        if "tool_calls" in agent_res["message"].keys():
            for instrument in agent_res["message"]["tool_calls"]:
                t_name, t_inputs = instrument["function"]["name"], instrument["function"]["arguments"]
                if f := dic_tools.get(t_name):
                    ### calling instrument
                    print('🔧 >', f"x1b[1;31m{t_name} -> Inputs: {t_inputs}x1b[0m")
                    ### tool output
                    t_output = f(**tool["function"]["arguments"])
                    print(t_output)
                    ### ultimate res
                    res = t_output
                else:
                    print('🤬 >', f"x1b[1;31m{t_name} -> NotFoundx1b[0m")
        ## don't use tool
        if agent_res['message']['content'] != '':
            res = agent_res["message"]["content"]
            t_name, t_inputs = '', ''
        return {'res':res, 'tool_used':t_name, 'inputs_used':t_inputs}

    As we already did in earlier tutorials, the interplay with the Brokers might be began with a whereas loop. The person is requested to offer a picture that the primary Agent will course of.

    dic_tools = {'process_image':process_image, 
     'search_wikipedia':search_wikipedia}
    
    whereas True:
        ## person enter
        attempt:
            q = enter('📷 > give me the picture to investigate:')
        besides EOFError:
            break
        if q == "stop":
            break
        if q.strip() == "":
            proceed
        messages_1.append( {"function":"person", "content material":q} )
    
        plt.imshow(pltimg.imread(q))
        plt.present()
    
        ## Agent 1
        agent_res = ollama.chat(mannequin=llm,
     instruments=[tool_process_image],
     messages=messages_1)
        dic_res = use_tool(agent_res, dic_tools)    
     res, tool_used, inputs_used = dic_res["res"], dic_res["tool_used"], dic_res["inputs_used"]
    
        print("👽📷 >", f"x1b[1;30m{res}x1b[0m")
        messages_1.append( {"role":"assistant", "content":res} )

    The first Agent used the Vision LLM Tool and recognized text within the image. Now, the description will be passed to the second Agent, which shall extract some keywords to search Wikipedia.

        ## Agent 2
        messages_2.append( {"role":"system", "content":"-Picture: "+res} )
    
        agent_res = ollama.chat(model=llm,
     tools=[tool_search_wikipedia],
     messages=messages_2)
        dic_res = use_tool(agent_res, dic_tools)    
     res, tool_used, inputs_used = dic_res["res"], dic_res["tool_used"], dic_res["inputs_used"]   

    The second Agent used the Instrument and extracted info from the online, based mostly on the outline offered by the primary Agent. Now, it may possibly course of every part and provides a ultimate reply.

        if tool_used == "search_wikipedia":
            messages_2.append( {"function":"system", "content material":"-Wikipedia: "+res} )
            agent_res = ollama.chat(mannequin=llm, instruments=[], messages=messages_2)
            dic_res = use_tool(agent_res, dic_tools)        
     res, tool_used, inputs_used = dic_res["res"], dic_res["tool_used"], dic_res["inputs_used"] 
        else:
            messages_2.append( {"function":"assistant", "content material":res} )
       
        print("👽📖 >", f"x1b[1;30m{res}x1b[0m")

    This is literally perfect! Let’s move on to the next example.

    Hierarchical Multi-Agent System

    Imagine having a squad of Agents that operates with a hierarchical flow, just like a human team, with distinct roles to ensure smooth collaboration and efficient problem-solving. At the top, a manager oversees the overall strategy, talking to the customer (the user), making high-level decisions, and guiding the team toward the goal. Meanwhile, other team members handle operative tasks. Just like humans, Agents can work together and delegate tasks appropriately.

    I shall build a tech team of 3 Agents with the objective of querying a SQL database per user’s request. They must work in a hierarchical flow:

    • The Lead Agent talks to the user and understands the request. Then, it decides which team member is the most appropriate for the task.
    • The Junior Agent has the job of exploring the db and building SQL queries.
    • The Senior Agent shall review the SQL code, correct it if necessary, and execute it.

    LLMs know how to code by being exposed to a large corpus of both code and natural language text, where they learn patterns, syntax, and semantics of programming languages. The model learns the relationships between different parts of the code by predicting the next token in a sequence. In short, LLMs can generate SQL code but can’t execute it, Agents can. 

    First of all, I am going to create a database and connect to it, then I shall prepare a series of Tools to execute SQL code.

    ## Read dataset
    import pandas as pd
    
    dtf = pd.read_csv('http://bit.ly/kaggletrain')
    dtf.head(3)
    
    ## Create dbimport sqlite3
    dtf.to_sql(index=False, name="titanic",
     con=sqlite3.connect("database.db"),            
     if_exists="replace")
    
    ## Connect db
    from langchain_community.utilities.sql_database import SQLDatabase
    
    db = SQLDatabase.from_uri("sqlite:///database.db")

    Let’s start with the Junior Agent. LLMs don’t need Tools to generate SQL code, but the Agent doesn’t know the table names and structure. Therefore, we need to provide Tools to investigate the database.

    from langchain_community.tools.sql_database.tool import ListSQLDatabaseTool
    
    def get_tables() -> str:
        return ListSQLDatabaseTool(db=db).invoke("")
    
    tool_get_tables = {'type':'function', 'function':{
      'name': 'get_tables',
      'description': 'Returns the name of the tables in the database.',
      'parameters': {'type': 'object',
                    'required': [],
                    'properties': {}
    }}}
    
    ## check
    get_tables()

    That can present the obtainable tables within the db, and it will print the columns in a desk. 

    from langchain_community.instruments.sql_database.instrument import InfoSQLDatabaseTool
    
    def get_schema(tables: str) -> str:
        instrument = InfoSQLDatabaseTool(db=db)
        return instrument.invoke(tables)
    
    tool_get_schema = {'sort':'operate', 'operate':{
      'identify': 'get_schema',
      'description': 'Returns the identify of the columns within the desk.',
      'parameters': {'sort': 'object',
                    'required': ['tables'],
                    'properties': {'tables': {'sort':'str', 'description':'desk identify. Instance Enter: table1, table2, table3'}}
    }}}
    
    ## check
    get_schema(tables='titanic')

    Since this Agent should use multiple Instrument which could fail, I’ll write a strong immediate, following the construction of the earlier article.

    prompt_junior = '''
    [GOAL] You're a knowledge engineer who builds environment friendly SQL queries to get knowledge from the database.
    
    [RETURN] You could return a ultimate SQL question based mostly on person's directions.
    
    [WARNINGS] Use your instruments solely as soon as.
    
    [CONTEXT] To be able to generate the proper SQL question, it is advisable know the identify of the desk and the schema.
    First ALWAYS use the instrument 'get_tables' to search out the identify of the desk.
    Then, you MUST use the instrument 'get_schema' to get the columns within the desk.
    Lastly, based mostly on the knowledge you bought, generate an SQL question to reply person query.
    '''

    Transferring to the Senior Agent. Code checking doesn’t require any specific trick, you may simply use the LLM.

    def sql_check(sql: str) -> str:
        p = f'''Double verify if the SQL question is right: {sql}. You MUST simply SQL code with out feedback'''
        res = ollama.generate(mannequin=llm, immediate=p)["response"]
        return res.exchange('sql','').exchange('```','').exchange('n',' ').strip()
    
    tool_sql_check = {'sort':'operate', 'operate':{
      'identify': 'sql_check',
      'description': 'Earlier than executing a question, all the time assessment the SQL question and proper the code if vital',
      'parameters': {'sort': 'object',
                    'required': ['sql'],
                    'properties': {'sql': {'sort':'str', 'description':'SQL code'}}
    }}}
    
    ## check
    sql_check(sql='SELECT * FROM titanic TOP 3')

    Executing code on the database is a special story: LLMs can’t try this alone.

    from langchain_community.instruments.sql_database.instrument import QuerySQLDataBaseTool
    
    def sql_exec(sql: str) -> str:
        return QuerySQLDataBaseTool(db=db).invoke(sql)
       
    tool_sql_exec = {'sort':'operate', 'operate':{
      'identify': 'sql_exec',
      'description': 'Execute a SQL question',
      'parameters': {'sort': 'object',
                    'required': ['sql'],
                    'properties': {'sql': {'sort':'str', 'description':'SQL code'}}
    }}}
    
    ## check
    sql_exec(sql='SELECT * FROM titanic LIMIT 3')

    And naturally, an excellent immediate.

    prompt_senior = '''[GOAL] You're a senior knowledge engineer who opinions and execute the SQL queries written by others.
    
    [RETURN] You could return knowledge from the database.
    
    [WARNINGS] Use your instruments solely as soon as.
    
    [CONTEXT] ALWAYS verify the SQL code earlier than executing on the database.First ALWAYS use the instrument 'sql_check' to assessment the question. The output of this instrument is the right SQL question.You MUST use ONLY the right SQL question while you use the instrument 'sql_exec'.'''

    Lastly, we will create the Lead Agent. It has an important job: invoking different Brokers and telling them what to do. There are a lot of methods to realize that, however I discover making a easy Instrument essentially the most correct one.

    def invoke_agent(agent:str, directions:str) -> str:
        return agent+" - "+directions if agent in ['junior','senior'] else f"Agent '{agent}' Not Discovered"
       
    tool_invoke_agent = {'sort':'operate', 'operate':{
      'identify': 'invoke_agent',
      'description': 'Invoke one other Agent to give you the results you want.',
      'parameters': {'sort': 'object',
                    'required': ['agent', 'instructions'],
                    'properties': {
                        'agent': {'sort':'str', 'description':'the Agent identify, considered one of "junior" or "senior".'},
                        'directions': {'sort':'str', 'description':'detailed directions for the Agent.'}
                    }
    }}}
    
    ## check
    invoke_agent(agent="intern", directions="construct a question")

    Describe within the immediate what sort of conduct you’re anticipating. Attempt to be as detailed as potential, for hierarchical Multi-Agent Techniques can get very complicated.

    prompt_lead = '''
    [GOAL] You're a tech lead.
    You've a crew with one junior knowledge engineer referred to as 'junior', and one senior knowledge engineer referred to as 'senior'.
    
    [RETURN] You could return knowledge from the database based mostly on person's requests.
    
    [WARNINGS] You're the just one that talks to the person and will get the requests from the person.
    The 'junior' knowledge engineer solely builds queries.
    The 'senior' knowledge engineer checks the queries and execute them.
    
    [CONTEXT] First ALWAYS ask the customers what they need.
    Then, you MUST use the instrument 'invoke_agent' to cross the directions to the 'junior' for constructing the question.
    Lastly, you MUST use the instrument 'invoke_agent' to cross the directions to the 'senior' for retrieving the information from the database.
    '''

    I shall hold chat historical past separate so every Agent will know solely a particular a part of the entire course of.

    dic_tools = {'get_tables':get_tables,
                'get_schema':get_schema,
                'sql_exec':sql_exec,
                'sql_check':sql_check,
                'Invoke_agent':invoke_agent}
    
    messages_junior = [{"role":"system", "content":prompt_junior}]
    messages_senior = [{"role":"system", "content":prompt_senior}]
    messages_lead   = [{"role":"system", "content":prompt_lead}]

    Every part is able to begin the workflow. After the person begins the chat, the primary to reply is the Chief, which is the one one which immediately interacts with the human.

    whereas True:
        ## person enter
        q = enter('🙂 >')
        if q == "stop":
            break
        messages_lead.append( {"function":"person", "content material":q} )
       
        ## Lead Agent
        agent_res = ollama.chat(mannequin=llm, messages=messages_lead, instruments=[tool_invoke_agent])
        dic_res = use_tool(agent_res, dic_tools)
        res, tool_used, inputs_used = dic_res["res"], dic_res["tool_used"], dic_res["inputs_used"]
        agent_invoked = res.cut up("-")[0].strip() if len(res.cut up("-")) > 1 else ''
        directions = res.cut up("-")[1].strip() if len(res.cut up("-")) > 1 else ''
    
        ###-->CODE TO INVOKE OTHER AGENTS HERE<--###
    
        ## Lead Agent ultimate response    print("👩‍💼 >", f"x1b[1;30m{res}x1b[0m")    messages_lead.append( {"role":"assistant", "content":res} )

    The Lead Agent decided to invoke the Junior Agent giving it some instruction, based on the interaction with the user. Now the Junior Agent shall start working on the query.

        ## Invoke Junior Agent
        if agent_invoked == "junior":
            print("😎 >", f"x1b[1;32mReceived instructions: {instructions}x1b[0m")
            messages_junior.append( {"role":"user", "content":instructions} )
           
            ### use the tools
            available_tools = {"get_tables":tool_get_tables, "get_schema":tool_get_schema}
            context = ''
            while available_tools:
                agent_res = ollama.chat(model=llm, messages=messages_junior,
                                        tools=[v for v in available_tools.values()])
                dic_res = use_tool(agent_res, dic_tools)
                res, tool_used, inputs_used = dic_res["res"], dic_res["tool_used"], dic_res["inputs_used"]
                if tool_used:
                    available_tools.pop(tool_used)
                context = context + f"nTool used: {tool_used}. Output: {res}" #->add instrument utilization context
                messages_junior.append( {"function":"person", "content material":context} )
               
            ### response
            agent_res = ollama.chat(mannequin=llm, messages=messages_junior)
            dic_res = use_tool(agent_res, dic_tools)
            res = dic_res["res"]
            print("😎 >", f"x1b[1;32m{res}x1b[0m")
            messages_junior.append( {"role":"assistant", "content":res} )

    The Junior Agent activated all its Tools to explore the database and collected the necessary information to generate some SQL code. Now, it must report back to the Lead.

            ## update Lead Agent
            context = "Junior already wrote this query: "+res+ "nNow invoke the Senior to review and execute the code."
            print("👩‍💼 >", f"x1b[1;30m{context}x1b[0m")
            messages_lead.append( {"role":"user", "content":context} )
            agent_res = ollama.chat(model=llm, messages=messages_lead, tools=[tool_invoke_agent])
            dic_res = use_tool(agent_res, dic_tools)
            res, tool_used, inputs_used = dic_res["res"], dic_res["tool_used"], dic_res["inputs_used"]
                    agent_invoked = res.cut up("-")[0].strip() if len(res.cut up("-")) > 1 else ''
            directions = res.cut up("-")[1].strip() if len(res.cut up("-")) > 1 else ''

    The Lead Agent acquired the output from the Junior and requested the Senior Agent to assessment and execute the SQL question.

        ## Invoke Senior Agent
        if agent_invoked == "senior":
            print("🧓 >", f"x1b[1;34mReceived instructions: {instructions}x1b[0m")
            messages_senior.append( {"role":"user", "content":instructions} )
           
            ### use the tools
            available_tools = {"sql_check":tool_sql_check, "sql_exec":tool_sql_exec}
            context = ''
            while available_tools:
                agent_res = ollama.chat(model=llm, messages=messages_senior,
                                        tools=[v for v in available_tools.values()])
                dic_res = use_tool(agent_res, dic_tools)
                res, tool_used, inputs_used = dic_res["res"], dic_res["tool_used"], dic_res["inputs_used"]
                if tool_used:
                    available_tools.pop(tool_used)
                context = context + f"nTool used: {tool_used}. Output: {res}" #->add instrument utilization context
                messages_senior.append( {"function":"person", "content material":context} )
               
            ### response
            print("🧓 >", f"x1b[1;34m{res}x1b[0m")
            messages_senior.append( {"role":"assistant", "content":res} )

    The Senior Agent executed the query on the db and got an answer. Finally, it can report back to the Lead which will give the final answer to the user.

            ### update Lead Agent
            context = "Senior agent returned this output: "+res
            print("👩‍💼 >", f"x1b[1;30m{context}x1b[0m")
            messages_lead.append( {"role":"user", "content":context} )

    Conclusion

    This article has covered the basic steps of creating Multi-Agent Systems from scratch using only Ollama. With these building blocks in place, you are already equipped to start developing your own MAS for different use cases. 

    Stay tuned for Part 4, where we will dive deeper into more advanced examples.

    Full code for this article: GitHub

    I hope you enjoyed it! Feel free to contact me for questions and feedback or just to share your interesting projects.

    👉 Let’s Connect 👈

    All images, unless otherwise noted, are by the author



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