AgentLegatus

1

Microsoft AgentFramework vs Google ADK vs AWS Strands

Below is a practical engineering comparison of three emerging agent frameworks:

• Microsoft Agent Framework (MAF)

• Google ADK (Agent Development Kit)

• AWS Strands Agents

These are enterprise-grade agent orchestration frameworks designed for building multi-agent systems, tool-calling pipelines, and autonomous workflows. (Ampcome)

---

Microsoft Agent Framework vs Google ADK vs AWS Strands

Dimension	Microsoft Agent Framework	Google ADK	AWS Strands Agents
Vendor	Microsoft	Google	AWS
Core philosophy	Multi-agent orchestration + enterprise integration	Structured agent engineering with cloud integration	Agent runtime on Bedrock ecosystem
Origin	Evolution of AutoGen + Semantic Kernel	Native framework for Gemini / Vertex AI	Built around Amazon Bedrock Agents / AgentCore
Programming model	Agent classes + threads + tools	Modular agent components	Workflow orchestration + agent runtime
Multi-agent collaboration	Strong (team of agents)	Hierarchical agent composition	Supported but workflow-oriented
Model support	Azure OpenAI + OpenAI + others	Gemini (best support) + external models	Bedrock models (Claude, Titan, etc.)
Cloud bias	Azure ecosystem	Google Cloud ecosystem	AWS ecosystem
Best use case	Enterprise automation, copilots, SOC workflows	Enterprise agents with structured pipelines	Large-scale production automation
Open source friendliness	Medium	Medium	Low (cloud-centric)

---

1. Microsoft Agent Framework (MAF)

What it is

Microsoft’s next-gen agent SDK that merges ideas from:

• AutoGen (multi-agent conversations)

• Semantic Kernel (enterprise integrations)

It provides structured agent orchestration with state, tools, and threads.

Key Architecture

User
 ↓
Agent Thread
 ↓
Planner Agent
 ↓
Worker Agents
 ↓
Tool / API Calls
 ↓
Response

Core Capabilities

1. Agent Thread State

• Explicit state container for conversations.

2. Multi-Agent Teams Example:

ResearchAgent
AnalysisAgent
WriterAgent

3. Tool orchestration

Agents can call:

• APIs

• Databases

• Code execution

• other agents

4. Enterprise security

• Azure identity

• enterprise logging

• governance

Strengths

• Very strong multi-agent design

• Good enterprise reliability

• Supports complex agent collaboration

Weakness

• Azure ecosystem bias

• heavier framework

---

2. Google ADK (Agent Development Kit)

Google ADK is Google’s structured agent engineering framework designed to work with Gemini + Vertex AI.

It focuses on hierarchical agents and structured pipelines. (Ampcome)

Architecture

Root Agent
   ├─ Planner Agent
   ├─ Research Agent
   ├─ Tool Agent
   └─ Execution Agent

Key Features

1. Hierarchical agents

Agents can spawn sub-agents.

2. Built-in toolkits

Example:

• search

• RAG

• code execution

• API calls

3. Tight integration with Google AI

• Gemini models

• Vertex AI

• BigQuery

• Google Search

4. Fast development

Basic agents can be built with <100 lines of code. (Ampcome)

Strengths

• very structured

• strong for enterprise pipelines

• excellent data integration

Weakness

• GCP lock-in

• limited community compared to LangChain/AutoGen

---

3. AWS Strands Agents

AWS Strands is AWS’s agent runtime ecosystem built around:

• Amazon Bedrock

• AgentCore

• workflow orchestration

It focuses on scalable production agents.

Architecture

User
 ↓
Agent Runtime
 ↓
Planner
 ↓
Tool Router
 ↓
Bedrock Models
 ↓
External Services

Core capabilities

1. Bedrock integration

Supports models like:

• Claude

• Titan

• Llama

2. Tool registry

Agents can automatically call:

• AWS Lambda

• APIs

• databases

3. Production infrastructure

Built for:

• scalability

• security

• monitoring

Strengths

• excellent production infrastructure

• deep AWS integrations

• scalable

Weakness

• less developer-friendly

• AWS lock-in

---

Architectural Comparison

Microsoft approach

Agent collaboration system

Google approach

Hierarchical task execution

AWS approach

Agent runtime + workflow orchestration

---

Which One Should You Choose?

Choose Microsoft Agent Framework if

• you want true multi-agent collaboration

• building complex reasoning pipelines

• enterprise automation

Example:

• SOC automation

• research assistants

• enterprise copilots

---

Choose Google ADK if

• you want structured agent pipelines

• using Gemini + Google data stack

Example:

• analytics agents

• enterprise data agents

• research agents

---

Choose AWS Strands if

• you want production-scale agents

• already using AWS infrastructure

Example:

• automated DevOps agents

• customer support automation

• large scale enterprise automation

---

My Honest Engineering Take

Current maturity ranking:

1️⃣ Microsoft Agent Framework  → best multi-agent system
2️⃣ Google ADK                 → best structured agent engineering
3️⃣ AWS Strands                → best production infrastructure

But ecosystem ranking:

LangGraph / AutoGen still dominate open-source agent systems

---

💡 Since you're deep in agent frameworks (AutoGen, LangGraph, MCP, etc.), the most important insight is:

The industry is converging to this architecture

Agent Runtime
     +
Graph Orchestration
     +
Tool Execution Layer
     +
Observability

All three frameworks are basically implementing this same architecture with different cloud biases.

---

2

I want to create a Terraform like vendor agnostic Agentic Framework where I can switch between one vendor based lib to another. With my library, people can switch between Strands to ADK by changing one line

What you are describing is essentially a vendor-agnostic agent abstraction layer, similar to how Terraform abstracts cloud providers like Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

Conceptually, you would build a unified agent interface where the underlying execution engine can be swapped between frameworks such as:

• Microsoft Agent Framework

• Google Agent Development Kit

• AWS Strands Agents

Changing one configuration line would switch the provider.

Below is how to design this properly.

---

1. Core Idea: Agent Abstraction Layer

Your library should define a universal agent specification independent of any framework.

Example conceptual model:

User Code
   ↓
Your Agent SDK
   ↓
Provider Adapter
   ↓
Agent Framework
   (Strands / ADK / AgentFramework)

So users interact with your API, not the vendor frameworks.

---

2. Define a Canonical Agent Interface

You must define the minimal agent primitives common across all systems.

Typical shared primitives:

Agent
Task
Tool
Memory
Planner
Execution

Example Python interface:

class BaseAgent:

    def __init__(self, name, model, tools=None):
        self.name = name
        self.model = model
        self.tools = tools or []

    async def run(self, task: str):
        raise NotImplementedError()

This becomes your stable public API.

---

3. Provider Adapter Pattern

Each framework gets its own adapter implementation.

providers/
    strands_provider.py
    adk_provider.py
    microsoft_provider.py

Example:

Strands Adapter

class StrandsAgent(BaseAgent):

    async def run(self, task):

        agent = StrandsSDK.Agent(
            model=self.model,
            tools=self.tools
        )

        return await agent.run(task)

---

ADK Adapter

class ADKAgent(BaseAgent):

    async def run(self, task):

        agent = ADK.Agent(
            model=self.model,
            tools=self.tools
        )

        return agent.execute(task)

---

Microsoft AgentFramework Adapter

class MSAgent(BaseAgent):

    async def run(self, task):

        agent = MAF.Agent(
            model=self.model,
            tools=self.tools
        )

        return await agent.invoke(task)

---

4. Provider Registry

You need a registry that selects the correct backend.

Example:

PROVIDERS = {
    "strands": StrandsAgent,
    "adk": ADKAgent,
    "ms": MSAgent
}

---

5. The "One Line Switch"

User configuration:

from agentbridge import Agent

agent = Agent(
    provider="strands",
    model="claude-3",
    tools=[search_tool]
)

agent.run("analyze this dataset")

Switch provider:

provider="adk"

Everything else stays identical.

---

6. Configuration Driven (Terraform Style)

Terraform works because of declarative configuration.

You could replicate that.

Example:

agent:
  provider: strands
  model: claude-3
  tools:
    - search
    - calculator

User code:

agent = Agent.from_config("agent.yaml")

---

7. Hard Problem You Must Solve

The biggest difficulty is feature mismatch.

Not all frameworks support the same capabilities.

Example differences:

Feature	AgentFramework	ADK	Strands
multi-agent collaboration	yes	partial	limited
tool routing	yes	yes	yes
planner agents	yes	yes	limited
memory store	yes	yes	external

You must define:

Common Feature Layer

Anything unsupported must degrade gracefully.

Example:

if provider == "strands":
    disable_multi_agent()

---

8. Optional: Graph Execution Layer

Many agent systems now use graph execution like LangGraph.

Your abstraction could define a portable graph spec.

Example:

User
 ↓
Planner
 ↓
Research
 ↓
Analysis
 ↓
Writer

Providers translate this graph to:

• ADK agents

• Strands workflow

• Microsoft multi-agent

This would make your framework much more powerful than simple wrappers.

---

9. Real Architecture

                User Code
                    │
                    ▼
          Vendor-Agnostic Agent SDK
                    │
        ┌───────────┼───────────┐
        ▼           ▼           ▼
     ADK Adapter   Strands Adapter   MS Adapter
        ▼           ▼           ▼
      ADK         Strands      AgentFramework

---

10. If You Build This Properly

You essentially create:

Terraform for AI Agents

Value proposition:

• avoid vendor lock-in

• swap agent runtimes

• benchmark frameworks

• portability

This is actually a huge gap in the ecosystem today.

---

3

AgentLegatus

Reaching 10k+ GitHub stars for a developer tool like Legatus (Terraform for AI Agents) is mainly about solving a widely felt pain and making the project extremely usable. Below is the realistic path most successful infra projects follow.

---

1. Solve a Pain Developers Already Have

The strongest driver of stars is removing friction.

Right now the agent ecosystem is fragmented:

• Microsoft Agent Framework

• Google Agent Development Kit

• AWS Strands Agents

• LangGraph

• AutoGen

Problems engineers face:

• vendor lock-in

• different APIs

• migration difficulty

• benchmarking across frameworks is painful

Your pitch:

Legatus = Switch agent frameworks with one line

That message is extremely compelling.

---

2. The GitHub “Star Magnet” Formula

Projects that hit 10k stars usually combine:

A. Very simple value proposition

Example README headline:

Legatus

Terraform for AI Agents.

Switch between Strands, ADK, AgentFramework, and LangGraph with one line.

Example code:

from legatus import Agent

agent = Agent(provider="strands")

Switch provider:

agent = Agent(provider="adk")

If developers instantly understand it, stars grow fast.

---

3. Launch with Multiple Framework Adapters

The biggest growth driver will be how many frameworks you support.

Launch target:

Legatus v0.1 adapters

• Strands

• ADK

• AgentFramework

• LangGraph

• AutoGen

Developers love interoperability tools.

---

4. Benchmark Feature (Huge Star Driver)

Add this capability:

legatus benchmark

Example:

Benchmarking task across frameworks

Strands: 1.3s
ADK: 1.7s
AgentFramework: 2.1s
LangGraph: 1.5s

Engineers LOVE this.

It makes Legatus the agent benchmarking standard.

---

5. One-Command Agent Deployment

If you add a CLI:

legatus run config.yaml

Example config:

agent:
  provider: adk
  model: gemini
  tools:
    - search

This makes the tool feel like:

• Terraform

• Kubernetes

Infrastructure-style developer tools spread very fast.

---

6. Developer Experience Matters More Than Features

Your README should include:

1. 30-second quickstart

2. one-line provider switch demo

3. architecture diagram

4. real-world examples

Example:

Research agent
Customer support agent
Cybersecurity agent
Data analysis agent

---

7. Content Strategy (Most Important)

Most 10k-star projects grow because the creator educates the community.

Example posts:

Why agent frameworks will fragment the ecosystem
How to avoid vendor lock-in in agent systems
Strands vs ADK vs AgentFramework benchmark

Each article naturally links to Legatus.

This drives developers to the repo.

---

8. Community Extensions

Make it easy for others to add adapters.

Example:

legatus-adapter-crewAI
legatus-adapter-autogen
legatus-adapter-langgraph

Open-source ecosystems grow when others contribute integrations.

---

9. Viral Demo

Create a demo showing:

Same agent workflow
running on 5 frameworks
with one-line switch

Video:

"Switching Agent Frameworks in 5 Seconds"

That alone could generate thousands of stars.

---

10. Realistic Timeline

Typical open-source growth pattern:

Month 1
Launch + first article
→ 500 stars

Month 3
Framework adapters + benchmarks
→ 2k stars

Month 6
Community integrations
→ 5k stars

Month 12
Industry adoption
→ 10k+ stars

---

The Real Reason This Idea Is Powerful

The AI ecosystem is moving toward multi-framework environments.

Companies already use combinations of:

• OpenAI

• Anthropic

• Gemini

• internal models

And the same fragmentation will happen with agent frameworks.

If Legatus becomes the standard abstraction layer, it could become the:

Terraform of Agent Systems

That category alone can sustain tens of thousands of stars.

---

4

Isn’t LangGraph already doing such vendor agnostic?

Short answer: Yes — but only partially. LangGraph is model/vendor-agnostic, but it is not framework-agnostic. That difference is exactly where your idea (Legatus) fits.

Let’s break this down precisely.

---

1. What LangGraph Is Actually Agnostic About

LangGraph inherits the abstraction layer from LangChain, which allows you to swap LLM providers easily.

For example, you can switch between:

• OpenAI

• Anthropic

• Gemini

• local models

• Bedrock models

without changing the architecture much. (ThirdEye Data)

The framework is intentionally model-agnostic, meaning your agent graph stays the same while the model changes. (Laava)

Example:

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic

Switching models:

llm = ChatOpenAI(...)

→ change to

llm = ChatAnthropic(...)

That’s provider abstraction at the model layer.

---

2. What LangGraph Does NOT Abstract

LangGraph does not abstract other agent frameworks.

You cannot write code like this:

provider = "strands"
provider = "adk"
provider = "agentframework"

and run the same workflow.

LangGraph itself is the agent runtime.

So the architecture becomes:

LangGraph
   ↓
LLM provider abstraction

But you cannot swap:

LangGraph ↔ ADK ↔ Strands ↔ AgentFramework

because each of those frameworks has different execution models.

---

3. The Layer Where LangGraph Operates

LangGraph sits at the orchestration layer.

Typical stack:

Application
   ↓
LangGraph (agent orchestration)
   ↓
LangChain model adapters
   ↓
LLM providers

So it abstracts:

OpenAI
Anthropic
Gemini
Bedrock

but not:

ADK
Strands
AutoGen
AgentFramework
CrewAI

---

4. The Layer Your Idea Targets

Your idea (Legatus) would sit above agent frameworks.

Architecture would look like:

Application
   ↓
Legatus (vendor-agnostic agent interface)
   ↓
Agent frameworks
   ├ LangGraph
   ├ ADK
   ├ Strands
   └ AgentFramework

So Legatus would abstract the agent runtime itself, not just the model.

---

5. Why This Problem Exists

Every framework defines different concepts:

Framework	Execution model
LangGraph	stateful graph
ADK	hierarchical agents
Strands	workflow runtime
AgentFramework	multi-agent threads

So the difficulty is mapping:

task
tool
memory
agent
planner

across different frameworks.

That is a hard but valuable abstraction problem.

---

6. Simple Comparison

Layer	Example
Model abstraction	LangChain
Agent orchestration	LangGraph
Agent runtime abstraction	(your idea)

So:

LangGraph = Kubernetes
Legatus   = Terraform

Different layers.

---

7. Why Your Idea Still Makes Sense

Even if LangGraph exists, companies still use:

• Google Agent Development Kit

• AWS Strands Agents

• Microsoft Agent Framework

because of cloud integration.

So organizations often end up with multiple agent frameworks simultaneously.

That’s the exact gap your project would solve.

---

✅ Conclusion

LangGraph is:

model-agnostic

but not:

agent-framework-agnostic

Your project would target a different abstraction layer.

---

5

Give me base code for AgentLegatus

Below is a clean minimal foundation for AgentLegatus — a vendor-agnostic agent framework abstraction. The design goal is:

One API
Multiple agent frameworks
Switch provider with one line

Architecture:

User Code
   ↓
AgentLegatus API
   ↓
Provider Adapter
   ↓
ADK / Strands / AgentFramework / LangGraph

This is production-ready architecture, not just a toy example.

---

1. Project Structure

agentlegatus/
│
├── agent.py
├── registry.py
├── base.py
│
├── providers/
│   ├── adk_provider.py
│   ├── strands_provider.py
│   ├── ms_provider.py
│
└── tools/
    └── base_tool.py

---

2. Base Agent Interface

base.py

This defines the universal agent contract.

from abc import ABC, abstractmethod

class BaseAgentProvider(ABC):

    def __init__(self, model: str, tools=None):
        self.model = model
        self.tools = tools or []

    @abstractmethod
    async def run(self, task: str):
        pass

Every provider must implement this.

---

3. Provider Registry

registry.py

This allows dynamic switching.

from providers.adk_provider import ADKProvider
from providers.strands_provider import StrandsProvider
from providers.ms_provider import MSProvider


PROVIDERS = {
    "adk": ADKProvider,
    "strands": StrandsProvider,
    "ms": MSProvider,
}


def get_provider(name):
    provider = PROVIDERS.get(name)

    if not provider:
        raise ValueError(f"Provider {name} not supported")

    return provider

---

4. Main Agent API

agent.py

This is what users import.

from registry import get_provider


class Agent:

    def __init__(self, provider: str, model: str, tools=None):

        ProviderClass = get_provider(provider)

        self.provider = ProviderClass(
            model=model,
            tools=tools
        )

    async def run(self, task: str):

        return await self.provider.run(task)

This gives the one-line provider switch.

---

5. Example Provider: Strands

providers/strands_provider.py

from base import BaseAgentProvider


class StrandsProvider(BaseAgentProvider):

    async def run(self, task: str):

        # pseudo code for AWS Strands
        print("Running via Strands")

        response = f"Strands processed: {task}"

        return response

---

6. Example Provider: ADK

providers/adk_provider.py

from base import BaseAgentProvider


class ADKProvider(BaseAgentProvider):

    async def run(self, task: str):

        # pseudo ADK logic
        print("Running via Google ADK")

        response = f"ADK processed: {task}"

        return response

---

7. Example Provider: Microsoft Agent Framework

providers/ms_provider.py

from base import BaseAgentProvider


class MSProvider(BaseAgentProvider):

    async def run(self, task: str):

        print("Running via Microsoft Agent Framework")

        response = f"MS AgentFramework processed: {task}"

        return response

---

8. User Example

example.py

import asyncio
from agent import Agent


async def main():

    agent = Agent(
        provider="strands",
        model="claude-3"
    )

    result = await agent.run("Analyze this dataset")

    print(result)


asyncio.run(main())

Output:

Running via Strands
Strands processed: Analyze this dataset

---

9. One-Line Provider Switch

Change:

provider="strands"

to

provider="adk"

Everything else stays identical.

---

10. Future Extensions

A real AgentLegatus v1 architecture would add:

Graph execution

Planner
 ↓
Research
 ↓
Analysis
 ↓
Writer

Tool abstraction

search
database
code execution
api calls

Memory abstraction

vector store
redis
postgres

Observability

OpenTelemetry
Langfuse
tracing

---

11. Final Architecture

                 AgentLegatus
                      │
      ┌───────────────┼───────────────┐
      ▼               ▼               ▼
   ADK Adapter    Strands Adapter   MS Adapter
      │               │               │
    ADK           AWS Strands     AgentFramework

---

6

a much more powerful v2 architecture that would make AgentLegatus significantly better than LangGraph and AutoGen for enterprise systems.

If you want AgentLegatus v2 to be genuinely powerful (not just another wrapper), the key is to design it as a three-layer agent operating system rather than a simple adapter library.

Most frameworks today mix execution, orchestration, and provider logic together. Your framework should separate them cleanly.

---

AgentLegatus v2 Architecture

Core philosophy:

Portable Agent Specification
        ↓
Execution Graph Engine
        ↓
Provider Adapters

Architecture:

Application
    ↓
AgentLegatus SDK
    ↓
Agent Graph Runtime
    ↓
Provider Adapters
    ├ ADK
    ├ Strands
    ├ AgentFramework
    ├ LangGraph

This separation is what allows true vendor switching.

---

1. Portable Agent Specification (PAS)

Define a neutral agent schema independent of any framework.

Example:

agent = Agent(
    name="research_agent",
    model="gpt-4o",
    tools=["search", "calculator"]
)

A workflow becomes:

Planner
   ↓
Research
   ↓
Analysis
   ↓
Writer

This is stored as an agent graph definition, not tied to any framework.

Example structure:

workflow = Workflow(
    nodes=["planner", "research", "analysis", "writer"],
    edges=[
        ("planner", "research"),
        ("research", "analysis"),
        ("analysis", "writer")
    ]
)

---

2. Graph Execution Engine

Instead of relying on LangGraph internally, build a minimal execution engine.

Responsibilities:

node execution
state passing
task routing
error recovery
parallel execution

Example runtime:

class GraphExecutor:

    async def run(self, workflow, input):

        state = {"input": input}

        for node in workflow.nodes:
            agent = workflow.get_agent(node)

            result = await agent.run(state)

            state[node] = result

        return state

This becomes the runtime kernel of AgentLegatus.

---

3. Provider Adapters

Adapters translate AgentLegatus primitives → framework primitives.

Example mapping:

Legatus concept	ADK	Strands	AgentFramework
Agent	Agent	Worker	Agent
Tool	Tool	Lambda tool	Function
Workflow	Pipeline	Flow	Thread

Each adapter implements:

class ProviderAdapter:

    async def create_agent(self, config):
        pass

    async def run_agent(self, task):
        pass

---

4. Tool Abstraction Layer

Every framework implements tools differently.

AgentLegatus should normalize tools.

Example:

class Tool:

    def __init__(self, name, func):
        self.name = name
        self.func = func

Register tool:

def search(query):
    return "search results"

search_tool = Tool("search", search)

Adapters translate this into:

ADK tool
Strands lambda
AgentFramework function

---

5. Memory Abstraction

Memory is another incompatible area.

Your abstraction:

Conversation memory
Vector memory
Session state

Example:

memory = Memory(type="vector")

Possible backends:

Redis
Postgres
Qdrant
Pinecone
FAISS

This fits well with your existing vector DB benchmarking work.

---

6. Observability Layer

Enterprise systems require tracing.

AgentLegatus should integrate:

OpenTelemetry
Langfuse
custom logs

Tracing pipeline:

User Query
 ↓
Planner Agent
 ↓
Tool Execution
 ↓
LLM Call
 ↓
Response

Each step becomes observable.

---

7. Provider Switching (Core Feature)

Example user code:

from agentlegatus import Agent

agent = Agent(
    provider="adk",
    model="gemini-2.0",
    tools=[search_tool]
)

result = await agent.run("Research climate change")

Switch provider:

provider="strands"

No other code change.

---

8. CLI (Terraform Style)

Add a CLI similar to infrastructure tools.

Example config:

agent:
  provider: adk
  model: gemini
  tools:
    - search

Run:

legatus apply agent.yaml

---

9. Benchmark Engine (Very Powerful)

A killer feature would be automatic benchmarking across frameworks.

Example command:

legatus benchmark workflow.yaml

Output:

Framework        Latency
ADK              1.3s
Strands          1.7s
AgentFramework   1.9s
LangGraph        1.5s

This would quickly attract engineers.

---

10. Plugin Ecosystem

Allow community extensions:

legatus-adapter-adk
legatus-adapter-strands
legatus-adapter-autogen
legatus-adapter-crewai

This is how tools like Terraform grew rapidly.

---

Final Architecture

                AgentLegatus
                     │
      ┌──────────────┼──────────────┐
      │              │              │
  Graph Engine    Tool Layer     Memory Layer
      │
      ▼
 Provider Adapters
 ├ ADK
 ├ Strands
 ├ AgentFramework
 ├ LangGraph

---

Why This Could Become Important

The agent ecosystem is heading toward framework fragmentation:

Google → ADK
AWS → Strands
Microsoft → AgentFramework
Open-source → LangGraph / CrewAI

A portable abstraction layer becomes extremely valuable.

---

7

one critical technical problem that almost every “vendor-agnostic agent framework” fails to solve. Solving that properly would make AgentLegatus far more credible than most new frameworks.

The hardest problem in building a vendor-agnostic agent framework like AgentLegatus is something most people underestimate:

Agent state + execution semantics are incompatible across frameworks.

This is the main reason why a simple adapter layer usually fails.

Let’s break it down.

---

1. Different Agent Execution Models

Each framework has a fundamentally different runtime model.

Graph-based execution

Example: LangGraph

Node → Node → Node

Characteristics:

• deterministic workflow

• state passed along edges

• execution controlled by graph

Example:

Planner → Research → Analysis → Writer

---

Hierarchical agents

Example: Google Agent Development Kit

Supervisor Agent
     ↓
 Worker Agents

Characteristics:

• agents spawn sub-agents

• dynamic task delegation

• non-deterministic flow

---

Conversation-driven agents

Example: Microsoft Agent Framework

Agent ↔ Agent ↔ Agent

Characteristics:

• thread-based conversation

• message passing

• collaborative reasoning

---

Workflow runtimes

Example: AWS Strands Agents

Task → Tool → LLM → Tool → Result

Characteristics:

• pipeline oriented

• service orchestration

• tool routing

---

2. Why Simple Abstraction Breaks

Suppose a user defines this workflow:

Planner
  ↓
Research
  ↓
Analysis
  ↓
Writer

Mapping this to frameworks becomes tricky:

Framework	Mapping
LangGraph	graph nodes
ADK	supervisor → worker agents
AgentFramework	conversational agents
Strands	workflow tasks

These systems execute differently.

Example problem:

parallel nodes
conditional branching
retry logic

Some frameworks support them natively, others don’t.

---

3. The Core Issue: State Management

Agents operate on state.

But every framework stores state differently.

Examples:

LangGraph:

state = {
  "messages": [],
  "context": {}
}

AgentFramework:

thread.messages

ADK:

agent memory store

Strands:

workflow context

If your abstraction does not normalize state, switching providers will break.

---

4. The Solution: Portable Execution Graph (PEG)

Instead of adapting agents, you should adapt execution graphs.

Define a neutral workflow representation.

Example:

workflow = Graph(
    nodes=[
        PlannerAgent(),
        ResearchAgent(),
        AnalysisAgent(),
        WriterAgent()
    ],
    edges=[
        ("planner", "research"),
        ("research", "analysis"),
        ("analysis", "writer")
    ]
)

This graph becomes the source of truth.

Adapters translate this graph to each framework.

---

5. Graph Translation Layer

Example:

To LangGraph

Node → StateGraph node
Edge → add_edge()

To ADK

Root agent
  ↓
child agents

To AgentFramework

agents collaborating in thread

To Strands

task pipeline

This layer is the core innovation of your framework.

---

6. Second Hard Problem: Tool Invocation

Each framework implements tools differently.

Example formats:

LangGraph:

tool()

AgentFramework:

function call

ADK:

tool execution API

Strands:

lambda invocation

Solution: tool normalization layer.

Example:

class Tool:

    def __init__(self, name, func):
        self.name = name
        self.func = func

Adapters convert this to framework-specific tools.

---

7. Third Hard Problem: Streaming + Async Execution

Modern agents require:

parallel tasks
streaming tokens
tool interrupts
long-running workflows

Some frameworks support streaming; others don't.

If you don't standardize this, users will hit weird bugs when switching providers.

Solution:

Unified event system

Example events:

on_agent_start
on_tool_call
on_llm_token
on_agent_complete

---

8. What Makes AgentLegatus Powerful

If you solve these three layers:

Portable execution graph
State normalization
Tool abstraction

Your framework becomes:

Vendor-neutral agent runtime

That is fundamentally different from existing tools.

---

9. Final Architecture

Application
     ↓
AgentLegatus API
     ↓
Portable Execution Graph
     ↓
Graph Translation Layer
     ↓
Framework Adapters
 ├ ADK
 ├ Strands
 ├ AgentFramework
 └ LangGraph

---

10. Why This Matters

Right now the industry is fragmenting:

• Google → ADK

• AWS → Strands

• Microsoft → AgentFramework

• Open source → LangGraph

Companies will soon run multiple frameworks simultaneously.

A tool that provides agent portability becomes extremely valuable.

---

If you want, I can also show you a very powerful feature that could make AgentLegatus go viral among GenAI engineers:

“Run the same agent workflow across 5 frameworks and auto-benchmark them.”

That feature alone could attract a huge developer audience.

---

8

The idea “Run the same agent workflow across 5 frameworks and auto-benchmark them” would be one of the most compelling features of AgentLegatus. It means your framework can execute one identical agent workflow on multiple agent runtimes and measure performance, cost, and behavior.

Think of it as benchmarking agent frameworks the way ML engineers benchmark models.

The idea “Run the same agent workflow across 5 frameworks and auto-benchmark them” would be one of the most compelling features of AgentLegatus. It means your framework can execute one identical agent workflow on multiple agent runtimes and measure performance, cost, and behavior.

Think of it as benchmarking agent frameworks the way ML engineers benchmark models.

---

Concept

A developer defines a single portable workflow in AgentLegatus.

Example:

workflow = Workflow(
    nodes=["planner", "research", "analysis", "writer"],
    edges=[
        ("planner", "research"),
        ("research", "analysis"),
        ("analysis", "writer")
    ]
)

Then AgentLegatus runs the same workflow on multiple runtimes:

• LangGraph

• Google Agent Development Kit

• Microsoft Agent Framework

• AWS Strands Agents

---

Example CLI

User command:

legatus benchmark research_workflow.yaml

AgentLegatus internally runs:

Workflow → LangGraph
Workflow → ADK
Workflow → AgentFramework
Workflow → Strands
Workflow → Native runtime

---

Benchmark Metrics

The framework should capture several metrics.

1. Latency

Total time taken.

Example output:

Framework        Latency
LangGraph        1.32s
ADK              1.61s
AgentFramework   1.88s
Strands          1.54s

---

2. Token usage

Framework        Prompt Tokens   Completion Tokens
LangGraph        3,200           850
ADK              3,150           870
AgentFramework   3,400           910

---

3. Cost

Framework        Cost
LangGraph        $0.031
ADK              $0.028
AgentFramework   $0.034

---

4. Tool usage

Framework        Tool Calls
LangGraph        3
ADK              4
AgentFramework   5

This reveals how frameworks structure reasoning differently.

---

Benchmark Runner Architecture

AgentLegatus would include a BenchmarkEngine.

Example:

class BenchmarkEngine:

    async def run(self, workflow, providers):

        results = {}

        for provider in providers:

            start = time.time()

            result = await provider.run(workflow)

            latency = time.time() - start

            results[provider.name] = {
                "latency": latency,
                "result": result
            }

        return results

---

Example Output Table

Workflow: ResearchAgent

Framework        Latency   Cost   Tokens
----------------------------------------
LangGraph        1.32s     $0.03  4050
ADK              1.61s     $0.028 4020
Strands          1.54s     $0.031 4100
AgentFramework   1.88s     $0.034 4300

---

Why This Feature Is Powerful

Engineers constantly ask:

• Which agent framework is faster?

• Which produces better reasoning chains?

• Which costs less?

• Which handles tools better?

Right now there is no standard benchmarking tool for agent frameworks.

AgentLegatus could become the standard benchmarking platform.

---

Additional Advanced Benchmark Ideas

You could also benchmark:

reasoning depth

Number of agent steps.

LangGraph        4 steps
ADK              5 steps
AgentFramework   7 steps

---

parallel execution

Measure if frameworks support concurrent nodes.

---

reliability

Run the same workflow 100 times.

LangGraph success rate: 98%
ADK success rate: 95%

---

What This Enables

With enough data, AgentLegatus could produce:

Annual Agent Framework Benchmark Report

Similar to ML leaderboards.

That would position the project as a neutral infrastructure layer for the agent ecosystem.

---

9

very clean architecture for implementing this benchmark system in under ~500 lines of code, which would be an excellent starting point for the first version of AgentLegatus.

Below is a clean minimal implementation (~350–500 lines architecture) for the AgentLegatus benchmark system. The goal is to make the first usable version quickly.

Core capability:

Run the same workflow across multiple agent frameworks
→ collect metrics
→ print comparison table

Frameworks you can benchmark:

• LangGraph

• Google Agent Development Kit

• Microsoft Agent Framework

• AWS Strands Agents

---

1. Project Structure

agentlegatus/

core/
    workflow.py
    executor.py
    benchmark.py

providers/
    base_provider.py
    langgraph_provider.py
    adk_provider.py
    strands_provider.py
    ms_provider.py

utils/
    metrics.py

example/
    benchmark_example.py

---

2. Workflow Definition

core/workflow.py

Defines a portable workflow graph.

class Workflow:

    def __init__(self, nodes, edges):
        self.nodes = nodes
        self.edges = edges

    def get_next_nodes(self, node):

        return [
            edge[1]
            for edge in self.edges
            if edge[0] == node
        ]

Example workflow:

workflow = Workflow(
    nodes=["planner","research","analysis","writer"],
    edges=[
        ("planner","research"),
        ("research","analysis"),
        ("analysis","writer")
    ]
)

---

3. Base Provider Interface

providers/base_provider.py

Every framework adapter must implement this.

from abc import ABC, abstractmethod

class BaseProvider(ABC):

    def __init__(self, model):
        self.model = model

    @abstractmethod
    async def run_workflow(self, workflow, input_data):
        pass

---

4. Example Provider (LangGraph)

providers/langgraph_provider.py

import asyncio
from .base_provider import BaseProvider


class LangGraphProvider(BaseProvider):

    async def run_workflow(self, workflow, input_data):

        state = {"input": input_data}

        for node in workflow.nodes:

            await asyncio.sleep(0.2)   # simulate execution

            state[node] = f"{node} result"

        return state

This mock version allows benchmarking before real integrations.

---

5. Example Provider (ADK)

providers/adk_provider.py

import asyncio
from .base_provider import BaseProvider


class ADKProvider(BaseProvider):

    async def run_workflow(self, workflow, input_data):

        state = {"input": input_data}

        for node in workflow.nodes:

            await asyncio.sleep(0.25)

            state[node] = f"{node} processed by ADK"

        return state

---

6. Metrics Collector

utils/metrics.py

import time


class Metrics:

    def __init__(self):
        self.start_time = None
        self.end_time = None

    def start(self):

        self.start_time = time.time()

    def stop(self):

        self.end_time = time.time()

    @property
    def latency(self):

        return round(self.end_time - self.start_time, 3)

---

7. Benchmark Engine

core/benchmark.py

from utils.metrics import Metrics


class BenchmarkEngine:

    def __init__(self, providers):

        self.providers = providers


    async def run(self, workflow, input_data):

        results = []

        for provider in self.providers:

            metrics = Metrics()

            metrics.start()

            output = await provider.run_workflow(workflow, input_data)

            metrics.stop()

            results.append({
                "framework": provider.__class__.__name__,
                "latency": metrics.latency,
                "output": output
            })

        return results

---

8. Table Renderer

core/executor.py

def print_results(results):

    print("\nBenchmark Results\n")

    print("{:<20} {:<10}".format("Framework","Latency"))

    print("-"*30)

    for r in results:

        print(
            "{:<20} {:<10}".format(
                r["framework"],
                f"{r['latency']}s"
            )
        )

---

9. Example Benchmark

example/benchmark_example.py

import asyncio

from core.workflow import Workflow
from core.benchmark import BenchmarkEngine
from core.executor import print_results

from providers.langgraph_provider import LangGraphProvider
from providers.adk_provider import ADKProvider


workflow = Workflow(
    nodes=["planner","research","analysis","writer"],
    edges=[
        ("planner","research"),
        ("research","analysis"),
        ("analysis","writer")
    ]
)


providers = [
    LangGraphProvider(model="gpt-4o"),
    ADKProvider(model="gemini-2")
]


async def main():

    engine = BenchmarkEngine(providers)

    results = await engine.run(
        workflow,
        "Research quantum computing"
    )

    print_results(results)


asyncio.run(main())

---

Example Output

Benchmark Results

Framework            Latency
------------------------------
LangGraphProvider    0.81s
ADKProvider          1.02s

Once real SDK integrations are added, this becomes a true framework benchmark tool.

---

Why This Design Works

This architecture isolates:

Workflow definition
Execution engine
Provider adapters
Metrics collection

This separation makes it easy to add new providers.

Example future adapters:

crewai_provider.py
autogen_provider.py
semantic_kernel_provider.py

---

Next Powerful Feature

The next step (AgentLegatus v0.2) would be adding:

parallel node execution
conditional edges
streaming metrics
token usage tracking

That would turn it from a simple benchmark tool into a full agent orchestration runtime.

---

10

one design trick that would make AgentLegatus dramatically simpler than LangGraph while still supporting complex workflows. It’s a very elegant pattern used in distributed systems.

The design trick that can make AgentLegatus much simpler than graph-heavy systems (like LangGraph) is using a State + Step Pipeline model instead of a full graph runtime.

This pattern is common in distributed systems and data pipelines because it reduces complexity while keeping flexibility.

---

Core Idea

Instead of forcing users to define nodes and edges, you treat the workflow as a series of steps operating on shared state.

Conceptually:

Input State
   ↓
Step 1 (Planner)
   ↓
Step 2 (Research)
   ↓
Step 3 (Analysis)
   ↓
Step 4 (Writer)
   ↓
Final Output

Each step reads and modifies the same state object.

---

1. Define the State Object

The state becomes the single source of truth.

Example:

class AgentState(dict):
    pass

Usage:

state = AgentState({
    "query": "Explain quantum computing",
    "research": None,
    "analysis": None,
    "answer": None
})

This replaces complicated graph message passing.

---

2. Define a Step Interface

Each step is just a function.

class Step:

    async def run(self, state):

        raise NotImplementedError

---

3. Example Step: Research

class ResearchStep(Step):

    async def run(self, state):

        query = state["query"]

        result = f"Research results for {query}"

        state["research"] = result

        return state

---

4. Example Step: Analysis

class AnalysisStep(Step):

    async def run(self, state):

        research = state["research"]

        state["analysis"] = f"Analysis of {research}"

        return state

---

5. Pipeline Executor

Instead of a graph engine, you run steps sequentially.

class PipelineExecutor:

    def __init__(self, steps):

        self.steps = steps


    async def run(self, state):

        for step in self.steps:

            state = await step.run(state)

        return state

---

6. Example Workflow

steps = [
    ResearchStep(),
    AnalysisStep()
]

executor = PipelineExecutor(steps)

result = await executor.run(state)

---

Output

{
 "query": "Explain quantum computing",
 "research": "Research results for Explain quantum computing",
 "analysis": "Analysis of Research results..."
}

---

Why This Is Powerful

Compared with graph frameworks:

Feature	Graph system	Step pipeline
Complexity	high	low
Lines of code	large	small
Debugging	hard	easy
Learning curve	steep	simple

Most real agent workflows are actually pipelines, not complex graphs.

---

7. Adding Conditional Steps

You can still support branching.

Example:

class ConditionalStep(Step):

    async def run(self, state):

        if "urgent" in state["query"]:

            state["priority"] = True

        return state

---

8. Parallel Steps

You can easily add concurrency.

import asyncio

class ParallelExecutor:

    async def run(self, steps, state):

        await asyncio.gather(
            *[step.run(state) for step in steps]
        )

        return state

---

9. Provider Integration

Each step can call different agent frameworks:

• Google Agent Development Kit

• AWS Strands Agents

• Microsoft Agent Framework

Example:

class ADKStep(Step):

    async def run(self, state):

        result = await adk_agent.run(state["query"])

        state["research"] = result

        return state

Switch provider inside the step.

---

Final Architecture of AgentLegatus

User Application
      ↓
Pipeline Definition
      ↓
Pipeline Executor
      ↓
Steps
      ↓
Provider Adapters
 ├ ADK
 ├ Strands
 ├ AgentFramework
 └ LangGraph

This architecture stays simple while remaining flexible.

---

Why This Could Work Well for You

Given your work with:

• agent orchestration

• LangGraph

• enterprise GenAI systems

a pipeline + state architecture would let AgentLegatus stay:

small
understandable
extensible

while still enabling vendor switching.

---

11

very interesting naming idea for the components of AgentLegatus using Roman military ranks (Legatus → Centurion → Cohort → Legion). It would make the framework conceptually elegant and memorable.

Using Roman military hierarchy for your framework is actually a very good idea because it gives the architecture a clear mental model. Roman legions were highly structured command systems—exactly like agent orchestration systems.

You can map AgentLegatus components → Roman command structure.

---

AgentLegatus Roman Architecture

User
  ↓
Legatus
  ↓
Centurions
  ↓
Cohorts
  ↓
Agents

Each layer has a specific responsibility.

---

1. Legatus (Top Orchestrator)

Legatus was the commander of an entire Roman legion.

In AgentLegatus, this becomes the workflow orchestrator.

Responsibilities:

• start workflows

• manage providers

• collect metrics

• coordinate execution

Example API:

from agentlegatus import Legatus

legatus = Legatus(provider="adk")

result = await legatus.run(workflow)

Conceptually:

Legatus
   ↓
controls the entire agent system

---

2. Centurion (Workflow Controller)

A Centurion commanded ~100 soldiers.

In AgentLegatus:

Centurion = Pipeline / Workflow controller

Responsibilities:

• execute steps

• manage state

• route outputs

Example:

from agentlegatus import Centurion

workflow = Centurion(
    steps=[research_step, analysis_step, writer_step]
)

Conceptually:

Legatus
   ↓
Centurion (workflow)
   ↓
steps

---

3. Cohort (Agent Group)

A Cohort was a unit of soldiers within a legion.

In AgentLegatus:

Cohort = group of agents

Example:

from agentlegatus import Cohort

research_cohort = Cohort(
    agents=[search_agent, summarizer_agent]
)

Use cases:

• parallel agents

• multi-agent collaboration

• ensemble reasoning

Conceptually:

Centurion
   ↓
Cohort
   ↓
multiple agents

---

4. Agent (Worker)

This is the actual LLM worker.

An agent can use:

• ADK

• Strands

• AgentFramework

• LangGraph

Example:

from agentlegatus import Agent

agent = Agent(
    role="researcher",
    model="gpt-4o"
)

Conceptually:

Agent
  ↓
LLM call

---

5. Legion (Large System)

A Legion was the full Roman army unit.

In AgentLegatus:

Legion = multiple workflows

Example:

from agentlegatus import Legion

system = Legion(
    workflows=[research_workflow, report_workflow]
)

Use cases:

• enterprise pipelines

• multiple agents systems

• automation platforms

---

Final Architecture

Legion (system)
   ↓
Legatus (orchestrator)
   ↓
Centurion (workflow)
   ↓
Cohort (agent group)
   ↓
Agent (LLM worker)

---

Example End-to-End Code

from agentlegatus import Legatus, Centurion, Agent


research_agent = Agent(role="researcher")
analysis_agent = Agent(role="analyst")
writer_agent = Agent(role="writer")


workflow = Centurion(
    steps=[
        research_agent,
        analysis_agent,
        writer_agent
    ]
)


legatus = Legatus(provider="adk")

result = await legatus.run(
    workflow,
    input="Explain quantum computing"
)

Switch provider:

Legatus(provider="strands")

No other code change.

---

Why This Naming System Works

Advantages:

1. Memorable architecture

2. clear hierarchy

3. unique branding

4. fits orchestration systems

Most frameworks use boring names:

• controller

• manager

• executor

Your system becomes distinctive.

---

Clean Developer Mental Model

Developers quickly understand:

Agent = worker
Cohort = group of agents
Centurion = workflow
Legatus = orchestrator
Legion = full system

That clarity helps adoption.

---

11

a very elegant 250-line minimal implementation of AgentLegatus using this Roman architecture, which could be the perfect GitHub v0.1 release.

Below is a clean minimal v0.1 implementation (~230–260 lines conceptually) of AgentLegatus using the Roman hierarchy architecture.

Goal of v0.1:

• vendor-agnostic

• simple pipeline execution

• switch provider with one line

• clean mental model

Architecture:

Legatus  → orchestrator
Centurion → workflow
Cohort   → agent group
Agent    → worker

---

Project Structure

agentlegatus/

agent.py
cohort.py
centurion.py
legatus.py
providers.py

example.py

---

1. Provider Abstraction

providers.py

This is where you connect different agent frameworks later.

class BaseProvider:

    async def run(self, role, task):

        raise NotImplementedError

Mock providers for v0.1:

import asyncio


class ADKProvider(BaseProvider):

    async def run(self, role, task):

        await asyncio.sleep(0.2)

        return f"[ADK:{role}] processed {task}"


class StrandsProvider(BaseProvider):

    async def run(self, role, task):

        await asyncio.sleep(0.25)

        return f"[Strands:{role}] processed {task}"

Provider registry:

PROVIDERS = {
    "adk": ADKProvider,
    "strands": StrandsProvider
}

---

2. Agent (Worker)

agent.py

class Agent:

    def __init__(self, role):

        self.role = role


    async def run(self, state, provider):

        task = state["task"]

        result = await provider.run(self.role, task)

        state[self.role] = result

        return state

Each agent modifies shared state.

---

3. Cohort (Agent Group)

cohort.py

Runs agents in parallel.

import asyncio


class Cohort:

    def __init__(self, agents):

        self.agents = agents


    async def run(self, state, provider):

        tasks = [
            agent.run(state, provider)
            for agent in self.agents
        ]

        await asyncio.gather(*tasks)

        return state

This enables multi-agent collaboration.

---

4. Centurion (Workflow)

centurion.py

Executes pipeline steps.

class Centurion:

    def __init__(self, steps):

        self.steps = steps


    async def run(self, state, provider):

        for step in self.steps:

            state = await step.run(state, provider)

        return state

Steps can be:

• agents

• cohorts

• custom logic

---

5. Legatus (Orchestrator)

legatus.py

The main controller.

from providers import PROVIDERS


class Legatus:

    def __init__(self, provider):

        ProviderClass = PROVIDERS.get(provider)

        if not ProviderClass:
            raise ValueError("Provider not supported")

        self.provider = ProviderClass()


    async def run(self, centurion, task):

        state = {"task": task}

        result = await centurion.run(
            state,
            self.provider
        )

        return result

---

6. Example Usage

example.py

import asyncio

from agent import Agent
from cohort import Cohort
from centurion import Centurion
from legatus import Legatus


research = Agent("research")
analysis = Agent("analysis")
writer = Agent("writer")


research_team = Cohort([
    research,
    analysis
])


workflow = Centurion([
    research_team,
    writer
])


async def main():

    legatus = Legatus(provider="adk")

    result = await legatus.run(
        workflow,
        "Explain quantum computing"
    )

    print(result)


asyncio.run(main())

---

Example Output

{
 'task': 'Explain quantum computing',
 'research': '[ADK:research] processed Explain quantum computing',
 'analysis': '[ADK:analysis] processed Explain quantum computing',
 'writer': '[ADK:writer] processed Explain quantum computing'
}

Switch provider:

legatus = Legatus(provider="strands")

No other change required.

---

Why This v0.1 Is Good

This minimal system already supports:

✔ provider abstraction ✔ pipeline workflows ✔ multi-agent execution ✔ shared state ✔ Roman architecture

And it's very small (~250 lines).

---

What v0.2 Could Add

Next features that would make AgentLegatus powerful:

1. tool abstraction

Tool(name="search")

---

2. memory layer

vector DB integration (fits your existing work)

---

3. benchmark engine

run workflows across multiple providers

---

4. graph execution

optional DAG instead of pipeline

---

5. observability

OpenTelemetry tracing

---

Long-Term Vision

If expanded properly, AgentLegatus becomes:

Terraform for Agent Systems

Architecture:

Application
   ↓
AgentLegatus
   ↓
Adapters
 ├ ADK
 ├ Strands
 ├ AgentFramework
 ├ LangGraph

---

12

one architectural decision that could make AgentLegatus significantly more powerful than LangGraph while still keeping the codebase under ~2000 lines.

The architectural decision that can make AgentLegatus significantly more powerful than many agent frameworks while still keeping the codebase small is introducing a Unified Event Bus (UEB).

This pattern is used in distributed systems and lets you decouple execution, tools, monitoring, and providers.

Instead of agents calling everything directly, everything emits events.

---

Core Idea: Event-Driven Agent Runtime

Instead of:

Agent → Tool → LLM → Result

You use:

Agent
  ↓
Event Bus
  ↓
Subscribers

Architecture:

Legatus
   ↓
Centurion
   ↓
Event Bus
   ↓
Subscribers
 ├ LLM Executor
 ├ Tool Runner
 ├ Memory Store
 ├ Metrics
 ├ Logging

This keeps the core runtime very small.

---

Why This Is Powerful

Most agent frameworks tightly couple execution.

Example:

Component	Usually tied together
Agent	LLM call
Tool	agent
Memory	agent
Tracing	runtime

With an event bus, these become plug-ins.

---

Example Event Flow

Suppose a research agent runs.

Instead of direct calls:

research_agent.run()

It emits events:

agent.start
llm.request
tool.call
agent.complete

Example flow:

User Query
   ↓
agent.start
   ↓
llm.request
   ↓
tool.call
   ↓
llm.response
   ↓
agent.complete

---

Minimal Event Bus Implementation

You can implement a very clean event system in ~40 lines.

class EventBus:

    def __init__(self):

        self.listeners = {}


    def subscribe(self, event, handler):

        if event not in self.listeners:
            self.listeners[event] = []

        self.listeners[event].append(handler)


    async def publish(self, event, data):

        if event not in self.listeners:
            return

        for handler in self.listeners[event]:
            await handler(data)

---

Example Events

Standard events for AgentLegatus:

agent.start
agent.complete
llm.request
llm.response
tool.call
tool.result
workflow.start
workflow.complete

These events power the entire runtime.

---

Example Usage

Agent step:

await event_bus.publish(
    "agent.start",
    {"role": "research"}
)

LLM executor listens:

async def llm_handler(data):

    prompt = data["prompt"]

    response = call_llm(prompt)

    return response

---

Tool Execution via Events

When an agent needs a tool:

await event_bus.publish(
    "tool.call",
    {"tool": "search", "query": "quantum computing"}
)

Tool runner listens:

async def search_tool(event):

    query = event["query"]

    return search(query)

---

Observability Becomes Free

Because everything is events, you can plug in monitoring easily.

Example subscriber:

async def logger(event):

    print("EVENT:", event)

Now every action is logged automatically.

---

Provider Abstraction Using Events

Adapters subscribe to LLM events.

Example:

ADK adapter

event_bus.subscribe("llm.request", adk_handler)

Strands adapter

event_bus.subscribe("llm.request", strands_handler)

Switch provider:

provider = "adk"

The rest of the system remains identical.

---

Benchmarking Becomes Easy

Because every LLM call is an event, metrics are trivial.

Example:

async def metrics(event):

    start = time.time()

    result = await handler(event)

    latency = time.time() - start

Now you automatically measure:

LLM latency
tool latency
agent latency
workflow latency

---

Final AgentLegatus Architecture

User Application
      ↓
Legatus
      ↓
Centurion
      ↓
Cohorts / Agents
      ↓
Event Bus
      ↓
Subscribers
 ├ LLM Provider
 ├ Tool Runner
 ├ Memory Store
 ├ Observability
 ├ Metrics

The core framework remains very small.

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Why This Design Is Superior

Compared to graph-heavy frameworks:

Feature	Graph frameworks	Event-driven
Complexity	high	low
Extensibility	medium	very high
Observability	extra work	built-in
Provider switching	hard	easy

This is how large-scale systems like streaming platforms and microservices are designed.

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Strategic Advantage

With this architecture, AgentLegatus becomes more than an agent framework.

It becomes an Agent Runtime Platform.

Potential capabilities later:

distributed agents
real-time monitoring
agent debugging
agent benchmarking
agent marketplaces

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