Introducing AgentRegistry: Centralized, Flexible Agent Management for Strands Agents SDK

Introduction

As AI agent systems evolve, the need for modular, scalable, and manageable agent orchestration becomes paramount. In my latest contribution to the Strands Agents SDK (currently under review), I introduce the AgentRegistry—a feature designed to bring order, flexibility, and power to agent management, mirroring the successful ToolRegistry pattern.

This blog post is a comprehensive, developer-focused deep dive into the what, why, how, and when of AgentRegistry. We’ll cover its motivation, design, features, comparisons, and practical usage, with full code examples and implementation details.

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PR is currently under review. Stay tuned for updates and documentation examples!
⭐️ My implementation here
Feature Request Issue Created in Official repo
PR Status

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Why Do We Need an Agent Registry?

The Problem

• Single-agent workflows are simple—but as soon as you want multiple agents (collaborative, specialized, or per-session), things get messy.
• Manual agent management (lists, dicts, passing references) is error-prone and hard to scale.
• Session-based or multi-user applications (e.g., chatbots, workflow engines) need to keep track of which agents are active, and where.
• Tool specialization and security: Sometimes, you want certain agents to have access to only a subset of tools.

The Solution

A central registry for agents, just like for tools, solves these problems by providing:

• Centralized lookup and management of all agents in your application.
• Session or event loop scoping, so you can group agents per user, conversation, or workflow.
• Specialization and isolation by mapping subsets of tools to specific agents.
• Clean, optional integration—no breaking changes for simple, single-agent use cases.

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What is AgentRegistry?

AgentRegistry is a lightweight, optional utility class that:

• Registers and discovers agents by name (or auto-generated name).
• Optionally groups agents by session or event loop.
• Allows mapping a subset of tools to each agent.
• Integrates cleanly with agent state and event loop patterns.
• Is fully backward compatible and opt-in.

It is inspired by the ToolRegistry pattern, but for agents.

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Feature-by-Feature Breakdown

1. Centralized Registration and Discovery

What:

All agents are registered in a single place. You can easily add, find, or remove agents by name.

Why:

• Simplifies orchestration in multi-agent systems.
• Avoids passing agent references everywhere.
• Makes debugging and monitoring easier.

How:

from strands.agent.agent_registry import AgentRegistry

agent_registry = AgentRegistry()
agent_registry.register_agent(my_agent, name="assistant")
agent = agent_registry.get_agent("assistant")

Comparison:

• Without registry: You manage agents in your own dict/list.
• With registry: The SDK manages it for you, with a clean API.

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2. Optional Session-Scoped Management

What:

Group agents by session or event loop. This is perfect for multi-user or multi-conversation systems.

Why:

• Supports scenarios where you have multiple users or conversations, each with their own set of agents.
• Makes it easy to clean up agents when a session ends.
• Prevents cross-session data leakage or confusion.

How:

agent_registry.register_agent(my_agent, name="assistant", session_id="session123")
session_agents = agent_registry.list_agents(session_id="session123")

Comparison:

• Without registry: You must manually track which agents belong to which session.
• With registry: The registry handles session grouping and lookup.

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3. Tool Subset Mapping for Specialization

What:

Assign a unique set of tools to each agent, supporting specialization and security.

Why:

• Enables agent specialization (e.g., one agent can only use math tools, another can only use web search).
• Improves security and isolation (agents can’t access tools they shouldn’t).
• Makes it easy to reason about and test agent capabilities.

How:

from strands_tools import calculator, web_search

agent_registry.register_agent(
    my_agent,
    name="math_agent",
    tool_subset=[calculator]
)
agent_registry.register_agent(
    another_agent,
    name="search_agent",
    tool_subset=[web_search]
)

Comparison:

• Without registry: You must ensure each agent is constructed with the right tools and keep track yourself.
• With registry: The registry can enforce and manage tool subsets per agent.

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4. Clean, Lightweight, and Optional Integration

What:

• No forced changes to the default single-agent workflow.
• No extra overhead if you don’t need advanced features.
• Separation of concerns: agent state and event loop logic remain untouched.

Why:

• Maintains the separation of concerns: agent logic, state, and event loop are not coupled to the registry.
• Keeps the codebase clean and maintainable.
• Advanced features are available, but only if you opt in.

How:

If you use a single agent:

agent = Agent(...)
# No registry needed!

If you want advanced management:

agent_registry = AgentRegistry()
agent_registry.register_agent(agent, ...)

Comparison:

• Without registry: No change to your workflow.
• With registry: You get advanced management when you need it.

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5. Backward Compatible and Developer Friendly

What:

• If you don’t use the registry, everything works as before.
• Advanced users can opt-in for powerful orchestration and management.

Why:

• Ensures backward compatibility.
• Keeps the learning curve low for new users.
• Power users can leverage the registry for complex workflows without affecting others.

How:

Default usage (no registry):

agent = Agent(tools=[...])
result = agent("Hello!")

Advanced usage (with registry):

agent_registry = AgentRegistry()
agent_registry.register_agent(Agent(tools=[...]), name="assistant", session_id="user1")

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Intrinsic Details: Naming, Auto-Generation, and Retrieval

How Are Agent Names Handled?

• By default, agents in Strands Agents SDK do not have a name.
• When registering with the registry, you can provide a name, or let the registry auto-generate one.
• The registry uses:

  agent_name = name or getattr(agent, 'name', None) or f"agent_{uuid4().hex[:8]}"

• If you provide a name, it uses that.
• If the agent has a name attribute, it uses that.
• Otherwise, it generates a unique name like "agent_1a2b3c4d".

Best Practice:

• Provide a name when registering for clarity.
• If you don’t, capture the return value of register_agent() to refer to the agent later.

Example:

auto_name = agent_registry.register_agent(agent)
retrieved_agent = agent_registry.get_agent(auto_name)

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Full Example: Multi-Agent, Session-Scoped Workflow

Here’s a full-featured example that demonstrates all the core functionalities of AgentRegistry in a multi-agent orchestration scenario:

This example demonstrates:

• Registering agents with and without tool subsets (showing both patterns).
• Listing all registered agents at any time.
• Accessing the tool registry for each agent, showing which tools are available (either via subset or the agent’s own list).
• Wrapping agents as tools for orchestration, so the orchestrator can delegate tasks.
• Unregistering a specific agent and verifying the change.
• Clearing the registry and confirming all agents are removed.

from strands import Agent, tool
from strands_tools import calculator, web_search
from strands.agent.agent_registry import AgentRegistry

# 1. Define system prompts for each specialized agent
RESEARCH_PROMPT = "You are a research assistant. Provide factual, well-sourced answers."
MATH_PROMPT = "You are a math expert. Solve math problems accurately."
SEARCH_PROMPT = "You are a web search specialist. Find up-to-date information online."

# 2. Create specialized agents
research_agent = Agent(system_prompt=RESEARCH_PROMPT, tools=[web_search])
math_agent = Agent(system_prompt=MATH_PROMPT, tools=[calculator])
search_agent = Agent(system_prompt=SEARCH_PROMPT, tools=[web_search, calculator])  # Multi-tool agent

# 3. Register agents in the AgentRegistry, with and without tool subsets
agent_registry = AgentRegistry()
agent_registry.register_agent(research_agent, name="research_agent", tool_subset=[web_search])
agent_registry.register_agent(math_agent, name="math_agent", tool_subset=[calculator])
agent_registry.register_agent(search_agent, name="search_agent")  # No subset, uses all provided tools

# 4. List all registered agents
print("Registered agents:", agent_registry.list_agents())

# 5. Access the tool registry for a specific agent
for agent_name in agent_registry.list_agents():
    tool_registry = agent_registry.get_tool_registry_for_agent(agent_name)
    if tool_registry:
        print(f"Tools for {agent_name}: {tool_registry.tool_names}")
    else:
        print(f"{agent_name} uses its own tool list (no registry subset)")

# 6. Wrap agents as tools for orchestration
@tool
def research_tool(query: str) -> str:
    """Delegate research queries to the research agent."""
    agent = agent_registry.get_agent("research_agent")
    return str(agent(query))

@tool
def math_tool(query: str) -> str:
    """Delegate math queries to the math agent."""
    agent = agent_registry.get_agent("math_agent")
    return str(agent(query))

@tool
def search_tool(query: str) -> str:
    """Delegate search queries to the search agent."""
    agent = agent_registry.get_agent("search_agent")
    return str(agent(query))

# 7. Orchestrator agent with access to agent-tools
ORCHESTRATOR_PROMPT = """
You are an orchestrator. Route queries to the correct specialized agent:
- Use research_tool for research questions
- Use math_tool for math problems
- Use search_tool for web searches
- Answer directly if no tool is needed
"""

orchestrator = Agent(
    system_prompt=ORCHESTRATOR_PROMPT,
    tools=[research_tool, math_tool, search_tool]
)

# 8. Example usage
print("\n=== Orchestrator handles a research query ===")
print(orchestrator("What are the latest advancements in quantum computing?"))

print("\n=== Orchestrator handles a math query ===")
print(orchestrator("What is the square root of 2025?"))

print("\n=== Orchestrator handles a web search query ===")
print(orchestrator("Find the current weather in Tokyo."))

# 9. Unregister a particular agent
agent_registry.unregister_agent("math_agent")
print("\nAfter unregistering math_agent, registered agents:", agent_registry.list_agents())

# 10. Clear the registry
agent_registry.clear()
print("After clearing, registered agents:", agent_registry.list_agents())

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When Should You Use AgentRegistry?

• Single-agent, simple scripts:
  
  You don’t need it! Just use Agent as before.

• Multi-agent, multi-session, or advanced orchestration:
  
  AgentRegistry is your friend. It keeps your code clean, modular, and scalable.

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Developer Insights

• Extensible: You can add metadata, hooks, or analytics to the registry in the future.
• Safe: No agent state is stored in the registry—each agent manages its own state.
• Flexible: You can use the registry for global, per-session, or even per-task agent management.
• Auto-naming: If you don’t provide a name, the registry generates a unique one and returns it, so you can always refer to your agent.

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Comparison Table

Feature	Without AgentRegistry	With AgentRegistry
Agent lookup	Manual dict/list	Centralized, by name/session
Session grouping	Manual, error-prone	Built-in, easy
Tool specialization	Manual, per-agent	Enforced, per-agent via registry
Naming/uniqueness	Up to you	Auto-generated or user-provided
Backward compatibility	N/A	100% preserved
Overhead for simple use	None	None (if not used)
Extensibility	Manual	Registry pattern, easy to extend

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Conclusion

The new AgentRegistry brings the Strands Agents SDK one step closer to being a truly modular, production-ready platform for building sophisticated AI agent systems. It’s developer-friendly, backward compatible, and powerful—and it’s available as an opt-in utility for those who need it.

Try it out, give feedback, and help shape the future of agent orchestration in Strands-agents!