#4763 Pydantic to Define a List of AI Agents Need to Solve a User-Specified Problem; Visualize

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#4763 Pydantic to Define a List of AI Agents Need to Solve a User-Specified Problem; Visualize

#4763 Describe ow to use pydantic to develop a list of AI agents (with their full definitions) needed to solve a user-specified problem.

To design and instantiate a dynamic roster of AI agents using Pydantic, use a two-step pattern:

  1. Schema Definition: Use standard Pydantic models to define the metadata of an agent (role, persona, system prompt, tool capabilities, upstream dependencies, and expected outputs).

  2. Structured LLM Orchestration: Pass the schema to an LLM planner using Structured Outputs (native OpenAI API, Instructor, or Pydantic AI) to decompose the problem into validated AgentDefinition instances.

1. Define the Agent Schema with Pydantic

Create models that define what constitutes a functional AI agent and a collection model representing the swarm or pipeline.

from typing import List, Literal, Optional
from pydantic import BaseModel, Field


class ToolRequirement(BaseModel):
    name: str = Field(..., description="Tool name, e.g., 'web_search', 'python_repl', 'sql_executor'")
    purpose: str = Field(..., description="Why this specific agent needs this tool")


class AgentDefinition(BaseModel):
    name: str = Field(..., description="Unique alphanumeric identifier (e.g., 'MarketResearchAnalyst')")
    role: str = Field(..., description="High-level job description or function")
    system_prompt: str = Field(..., description="Detailed instructions, behavioral guidelines, and constraints")
    model: Literal["gpt-4o", "gpt-4o-mini", "claude-3-5-sonnet", "o3-mini"] = Field(
        default="gpt-4o",
        description="LLM tier best suited for this task complexity",
    )
    tools: List[ToolRequirement] = Field(
        default_factory=list,
        description="Tools needed by this agent",
    )
    dependencies: List[str] = Field(
        default_factory=list,
        description="Names of other agents this agent depends on for upstream data",
    )
    output_schema_description: str = Field(
        ...,
        description="Description of the structured format or artifact this agent must deliver",
    )


class MultiAgentPlan(BaseModel):
    problem_statement: str = Field(..., description="The interpreted user request")
    execution_strategy: Literal["sequential", "hierarchical", "parallel_dag"] = Field(
        ...,
        description="Coordination topology for the agent team",
    )
    agents: List[AgentDefinition] = Field(
        ...,
        description="List of full agent specifications required to solve the problem",
    )

2. Generate the Agent Roster with Structured Outputs

Feed the user's objective into an LLM using OpenAI's structured outputs (client.beta.chat.completions.parse) or pydantic-ai:

import os
from openai import OpenAI

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))


def generate_agent_swarm(user_problem: str) -> MultiAgentPlan:
    completion = client.beta.chat.completions.parse(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": (
                    "You are a Principal AI Systems Architect. Given a complex user problem, "
                    "decompose it into a minimal, specialized team of AI agents. "
                    "Provide comprehensive system prompts and clear dependency chains."
                ),
            },
            {"role": "user", "content": user_problem},
        ],
        response_format=MultiAgentPlan,
    )
    return completion.choices[0].message.parsed

3. Practical Example: Parsing and Instantiation

When the user supplies a task, the LLM generates a typed MultiAgentPlan object:

problem = (
    "Build a weekly competitive analysis report tracking pricing and feature "
    "updates across top 3 SaaS competitors, summarizing risks for the executive team."
)

plan: MultiAgentPlan = generate_agent_swarm(problem)

print(f"Strategy: {plan.execution_strategy}")
for agent in plan.agents:
    print(f"\n--- Agent: {agent.name} ({agent.model}) ---")
    print(f"Role: {agent.role}")
    print(f"Tools: {[t.name for t in agent.tools]}")
    print(f"Depends On: {agent.dependencies}")
    print(f"System Prompt:\n{agent.system_prompt[:150]}...")

Example Output Produced

Strategy: parallel_dag

--- Agent: CompetitorScraper (gpt-4o-mini) ---
Role: Web content extractor
Tools: ['web_search', 'html_extractor']
Depends On: []
System Prompt: You monitor the pricing and changelog pages of specified SaaS competitors...

--- Agent: MarketAnalyst (gpt-4o) ---
Role: Quantitative pricing and feature differential analyzer
Tools: ['python_repl']
Depends On: ['CompetitorScraper']
System Prompt: You ingest raw extracted changes, identify delta pricing shifts, and categorize new features...

--- Agent: ExecutiveBriefWriter (gpt-4o) ---
Role: High-level strategic report synthesizer
Tools: []
Depends On: ['MarketAnalyst']
System Prompt: You translate technical deltas into high-impact executive summaries focusing on churn risks...

4. Hydrating into Runtime Instances (e.g., Pydantic AI)

Once validated, instantiate the definitions into executable runtime agents using a tool like pydantic-ai or LangGraph:

from pydantic_ai import Agent

# Tool registry mapping string names to real functions
AVAILABLE_TOOLS = {
    # "web_search": search_tool_func,
    # "python_repl": python_repl_func,
}

live_agents = {}
for agent_def in plan.agents:
    agent_tools = [
        AVAILABLE_TOOLS[t.name]
        for t in agent_def.tools
        if t.name in AVAILABLE_TOOLS
    ]

    live_agents[agent_def.name] = Agent(
        model=f"openai:{agent_def.model}",
        system_prompt=agent_def.system_prompt,
        tools=agent_tools,
    )

Why this approach works:

  • Validation & Guarantees: Pydantic ensures every generated agent has required runtime attributes (such as non-empty system prompts and valid tool names) before executing any code.

  • Topological Execution: Because dependencies lists agent names, you can build a Directed Acyclic Graph (DAG) with tools like networkx or LangGraph to determine which agents run in parallel and which run sequentially.

  • Deterministic Configuration: The planner LLM can be swapped or evaluated separately from the execution agents.


For any user-specified problem, describe how to visualize the generated list of AI agents, multi agent plan (with structured LLM orchestration)

Visualizing dynamically generated agent swarms requires converting your Pydantic-validated models (nodes, tools, and dependency edges) into clear visual representations. Below are the three primary patterns used in production architectures: an automated DAG flow architecture, self-rendering pipeline code, and an interactive inspection UI.

1. Execution Flow & Data Pipeline Architecture

When an LLM generates a MultiAgentPlan, it produces an implicit Directed Acyclic Graph (DAG). The planner translates the user's objective into specialized nodes, validates their dependencies, and schedules parallel or sequential execution batches.


Execution stages derived from agent dependencies: Root collectors run concurrently, feeding into synthesis stages.

2. Programmatic Graph Generation (Mermaid & Graphviz)

To render plans dynamically in notebooks, dashboards, or markdown outputs without manual diagramming, write a serialization method on your root MultiAgentPlan model that maps agent.dependencies to graph syntax.

import networkx as nx
from typing import List
from pydantic import BaseModel, Field

class AgentDefinition(BaseModel):
    name: str
    role: str
    tools: List[str] = Field(default_factory=list)
    dependencies: List[str] = Field(default_factory=list)

class MultiAgentPlan(BaseModel):
    execution_strategy: str
    agents: List[AgentDefinition]

    def to_mermaid(self) -> str:
        """Generates standard Mermaid DAG markup for UI renderers."""
        lines = ["flowchart LR"]
        for agent in self.agents:
            tools_label = f"<br/><i>Tools: {', '.join(agent.tools) or 'None'}</i>"
            lines.append(f'    {agent.name}["<b>{agent.name}</b><br/>{agent.role}{tools_label}"]')
            for dep in agent.dependencies:
                lines.append(f"    {dep} --> {agent.name}")
        return "\n".join(lines)

    def to_networkx(self) -> nx.DiGraph:
        """Exports to NetworkX for topological sorting and cycle detection."""
        dag = nx.DiGraph()
        for a in self.agents:
            dag.add_node(a.name, role=a.role, tools=a.tools)
            for dep in a.dependencies:
                dag.add_edge(dep, a.name)
        return dag

3. Interactive Agent Plan Visualizer

This explorer demonstrates how front-end systems inspect the generated payload, step through execution levels (computed by topological sorting), and inspect tool registries.



From <https://gemini.google.com/app/a9e9519e0555369e>  Google Gemini (3.8 Flash)

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