Creating Your Own Agentic Newsletter | by Ertuğrul Demir | May, 2025

You’re in all probability listening to lots about brokers as of late, with corporations all over the place constructing “superior” merchandise primarily based on them. Perhaps you’re considering, “Oh, that’s cool, however how do I get began with them personally?” And that’s the place the complication lies: brokers are cool to make use of, however in relation to implementing them, issues change a bit bit.

On this article, I’m going to take a extra sensible strategy and soar straight into the motion. (I’ve beforehand shared a few of my ideas on brokers and agentic workflows in one other article, which you will discover here).

I consider defining the use case is likely one of the most underrated elements of implementing these methods. Folks usually have a tendency to leap into complicated brokers with out first deciding what drawback they’re fixing. They both select a very trivial drawback, like “What time is it?”, or an excessively complicated one, equivalent to “Right here’s my entire bank card, go guide me the most cost effective and finest vacation in Paris.” Whereas these may or may not work in observe, a balanced strategy is finest for studying, in my view. Plus, tackling an issue that solves your individual day by day points, slightly than simply textbook examples, can actually spark your creativity.

On this article, we’re going to implement a easy agentic system that solves a small drawback of mine. Everyone knows how exhausting it’s to maintain up with day by day AI and LLM information. I’ve at all times needed to get day by day or curated summaries, tailor-made to my private pursuits, in regards to the tendencies in these areas.

For dependable, up-to-date info, I personally depend on particular subreddits and a few social media customers I comply with. For this tutorial, we’ll leverage considered one of them — Reddit — which provides a extra structured format and a considerably pseudonymous setting. Whereas we’ll begin with a single supply, it’s completely doable to merge a number of sources with agentic methods, as you’ll see all through this tutorial.

Our setup will likely be minimal, and because of ADK’s easy strategy, getting began is sort of simple. Listed below are the principle packages we’re going to use:

Agent Improvement Equipment (ADK): The Agent Improvement Equipment (ADK) is an open-source framework from Google, designed to simplify the often-complex strategy of constructing and deploying superior multi-agent AI purposes. Consider it as a complicated orchestration layer that means that you can transfer past remoted AI fashions, enabling a crew of specialised AI brokers to work collectively intelligently, every contributing its distinctive capabilities to attain a bigger aim. This equipment gives a complete toolkit for builders, making it simpler to handle the complete lifecycle of multi-agent methods. It provides outstanding flexibility, permitting seamless integration of numerous AI fashions and instruments, together with these from exterior libraries and even different brokers as sub-tools. When it’s time to deliver these methods to life, ADK simplifies deployment by supporting containerization and optimizing for Google Cloud environments, notably leveraging providers like Vertex AI and Gemini fashions.

Python Reddit API Wrapper (PRAW): PRAW is a Python library that simplifies interacting with the Reddit API. It gives a user-friendly interface for accessing Reddit’s knowledge and functionalities, making it simpler to gather and analyze Reddit info. PRAW additionally handles the complexities of the API, like charge limits and authentication, so builders can concentrate on the information itself. This significantly simplifies our use case, particularly when integrating with ADK.”

First, let’s create an setting of your selection and set up these packages. I desire a easy digital setting, after which set up:

google-adk
praw
pydantic
python-dotenv

Your brokers ought to have their very own folders, right here is an instance construction:

parent_folder/
multi_tool_agent/
__init__.py
agent.py
.env

And inside your __init__.py ought to seem like this:

from . import agent

The agent.py would be the most important one we are going to work in incoming elements, so you’ll be able to depart as it’s for now…

Setting and Credentials:

First, we want entry to an LLM API, whether or not as a service or domestically hosted, to energy our agent. For simplicity, we’ll use Google AI Studio. It completely suits our use case because it’s simple to arrange and get API keys. The excellent news is AI Studio permits you to use up-to-date state-of-the-art Gemini fashions totally free, and its day by day limits are actually greater than we’ll want. You may, in fact, use different suppliers or Vertex AI in case you desire.

You may go to https://aistudio.google.com/apikey to get your API keys prepared.

Subsequent, we have to join our private Reddit account to fetch knowledge. Should you don’t have an account, you’ll be able to create one simply. For the authentication half, you’ll be able to comply with this complete information: https://praw.readthedocs.io/en/stable/getting_started/authentication.html

After getting your credentials prepared, create a .env file in your challenge folder (particularly inside your ADK agent listing). It ought to look one thing like this:

GOOGLE_GENAI_USE_VERTEXAI=FALSE
GOOGLE_API_KEY=
REDDIT_CLIENT_ID="YOUR_REDDIT_CLIENT_ID"
REDDIT_CLIENT_SECRET="YOUR_REDDIT_CLIENT_SECRET"
REDDIT_USER_AGENT="YOUR_REDDIT_USER_AGENT"
REDDIT_USERNAME="YOUR_REDDIT_USERNAME"
REDDIT_PASSWORD="YOUR_REDDIT_PASSWORD"

Okay, our setup is now full, and we’re able to dive in!

An LlmAgent is a core element of ADK that makes use of LLM fashions to assume, resolve, and act. There are a few varieties of brokers in ADK, however we’ll be specializing in LLM-Based mostly ones since they will cause and are fairly enjoyable to experiment with. For extra strong and structured multi-step processes, you too can leverage ‘Workflow’ brokers, that are nice for orchestrating predefined sequences.

Finally, we’ll have a number of brokers that may work together with one another, name instruments, and go their outcomes between them. However for starters, let’s discuss our ‘root agent’ (or ‘main agent,’ ‘most important agent,’ and so forth., no matter you like to name it). Consider this as our entry level — a mediator between the person and the complete system, an organizer to orchestrate duties. It’s the agent that may have the ability to delegate to different brokers and instruments.

However how does an agent know what to do? Nicely, it’s easy: as with many LLMs, they function primarily based on directions. This could be a very powerful and inventive a part of working with LLM-Based mostly brokers. They should know their function, goal, and scope. Listed below are some easy guidelines of thumb from the official documentation for crafting these directions:

Embrace:

Its core activity or aim.

Its character or persona (e.g., “You’re a useful assistant,” “You’re a witty pirate”).

Constraints on its habits (e.g., “Solely reply questions on X,” “By no means reveal Y”).

How and when to make use of its instruments. It’s best to clarify the aim of every instrument and the circumstances beneath which it needs to be known as, supplementing any descriptions throughout the instrument itself.

The specified format for its output (e.g., “Reply in JSON,” “Present a bulleted checklist”).

Suggestions for Efficient Directions:

Be Clear and Particular: Keep away from ambiguity. Clearly state the specified actions and outcomes.

Use Markdown: Enhance readability for complicated directions utilizing headings, lists, and so forth.

Present Examples (Few-Shot): For complicated duties or particular output codecs, embrace examples instantly within the instruction.

Information Instrument Use: Don’t simply checklist instruments; clarify when and why the agent ought to use them.

Past the detailed directions we simply mentioned, every agent additionally wants some metadata to outline its id and capabilities throughout the system. This helps different brokers (and the general system) perceive when, how, and why to work together with it.

• Identify: Select a descriptive title.
• Description: Whereas elective, that is very helpful. Attempt to present a brief abstract of the agent’s capabilities right here.
• Mannequin: This can be a fairly apparent however essential parameter, because it defines the ‘uncooked energy’ of your agent. For easy duties, for instance, you’ll be able to go for smaller, extra environment friendly fashions, whereas complicated duties may require extra highly effective ones.

Right here’s a easy, bare-bones agent occasion instance from the official documentation:

capital_agent = LlmAgent(
mannequin="gemini-2.0-flash",
title="capital_agent",
description="Solutions person questions in regards to the capital metropolis of a given nation."
)

Let’s assume a easy state of affairs: you may have a root_agent and our capital_agent. Once you ask, “What’s the capital of Germany?”, as an alternative of replying instantly (which can also be doable if not particularly instructed, particularly for such a simple activity), the foundation agent will delegate this activity. It’ll have a look at its given checklist of instruments and sub-agents (consider a sub-agent as an agent that may be invoked by its mum or dad agent). Conveniently for our root agent, we occur to have an agent known as capital_agent designed for precisely this! So, the foundation agent will ship a request to the capital_agent and return its response to the person.

And that’s it — that is the best type of a multi-agent setup!

Earlier than going additional with brokers, let’s discuss one other vital half: instruments.

Instruments are particular capabilities or code elements that permit your AI agent to carry out actions and work together with the world past its core textual content era and reasoning skills. They permit it to:

• Act & Work together: Carry out duties like querying databases, calling APIs (e.g., for Reddit), looking the net, or operating code.
• Lengthen Capabilities: Entry real-time info and exterior methods, overcoming information limits.
• Execute Outlined Logic: Instruments run particular, pre-programmed duties; the AI (LLM) decides which instrument to make use of and when.

Much like brokers, it’s at all times good observe to obviously outline your instruments, as this may assist your brokers perceive use them successfully.

Designing Good Instrument Capabilities:

• Identify: Descriptive (e.g., get_subreddit).
• Docstring (Feedback): Essential! Clarify what the instrument does, its parameters, and what it returns. That is how the AI understands it.
• Parameters: Clear names, use sort hints.
• Return Worth: At all times a dictionary (e.g., {‘standing’: ‘success’, ‘particulars’: ‘…’}).
• Focus: One instrument, one clear job.

All through this challenge, we’ll be utilizing many instruments and brokers. In truth, for some functionalities, we’ll even create easy brokers that wrap different instruments. This can be a present workaround as a result of, with the ADK’s present model, calling a number of instruments concurrently on AI Studio can typically be problematic. Nonetheless, by wrapping these instruments inside one other LLM occasion, they have a tendency to work fairly effectively. This habits is predicted to be improved in future variations.

Creating Brokers and Instruments

So, let’s bear in mind our core activity: we wish to feed person queries a few matter to our agent(s) and instruct them to offer summaries. Recalling our first precept for directions — be clear and particular — we all know that being too ambiguous will not be really helpful.

One of the crucial vital elements is to establish the complicated points of our activity and create particular instruments or sub-agents to unravel these complexities. For this, I like to recommend you assume as in case you’re fixing or orchestrating that drawback your self. In our case, we wish to ask a few matter and get common Reddit conversations about it, both lately or inside a given timeframe.

First issues first, it is advisable perceive the capabilities of your LLM fashions. When a person asks our root agent a few matter, its underlying LLM will attempt to perceive which subreddits could be associated to that question. In our case, we merely want to think about these two questions:

• Is the mannequin’s information up-to-date?
• Can a generic agent, with out exterior instruments, adequately clear up the question and map it to related search parameters?

For many queries, if circuitously specified by the person, there are possible a number of subreddits. Nonetheless, the problem is that some are both well-forgotten within the pages of web historical past, or too new to be throughout the LLM’s information deadline. To deal with this, and for the sake of this tutorial, let’s begin with a easy sub-agent that has a sole, clear, and centered activity. And consider me, at their core, all of our brokers will comply with this identical logic.

The Analyze Agent

Let’s create our first specialised agent on this multi-agent setup. (Technically, our root_agent was the primary, however let’s contemplate this our first task-specific agent). Take into consideration the state of affairs once more: user_query results in root_agent. That is precisely the place we are actually.

Assume an person asks: “What are the latest information about Google’s language fashions?” The basis agent will take this, and as instructed, it’s not going to unravel this drawback itself (bear in mind the agent’s directions); it must delegate. What can be a very good candidate for delegation right here?

The agent is aware of it’s about Google and language fashions, however which household of fashions? Gemini or open-source fashions like Gemma? There are a number of subreddits for every. Much more generic however extremely energetic subreddits like r/LocalLLaMA may embrace discussions about each. The Gemini fashions, as an illustration, have been beforehand known as ‘Bard’, and whereas a few of their previous subreddits are nonetheless energetic for Gemini-related discussions, there are additionally devoted Gemini subreddits that aren’t as energetic.

These are many inquiries to reply. Let’s not burden the foundation agent with all that heavy considering and as an alternative create a brand new agent solely for this goal: the analyze_agent:

analyze_agent = Agent(
title="analyze_agent",
mannequin="your_llm_name",
instruction="You're an knowledgeable Reddit person with immense information about subreddits and entry to specialised internet search capabilities. "
"When offered with an person question, your accountability is to establish and return a listing of essentially the most related, common, and energetic subreddit names. ",
description="An knowledgeable Reddit person that analyzes queries to establish and suggest essentially the most related, common, and energetic subreddit names for any given matter or dialogue space.",
)

Alright, a easy agent for mapping queries into subreddits. It solves considered one of our issues with clear directions. Nonetheless, our mannequin nonetheless has an LLM-induced limitation: up-to-date info.

That is the place instruments actually shine! We are able to instruct fashions to make use of particular instruments and leverage their capabilities to enhance their context. In our case, we are able to merely use the built-in google_search instrument to fetch up-to-date info. For this, we have to replace our analyze_agent:

analyze_agent = Agent(
title="analyze_agent",
mannequin="your_llm_name",
instruction="You're an knowledgeable Reddit person with immense information about subreddits and entry to specialised internet search capabilities. "
"When offered with a person question, your accountability is to establish and return a listing of essentially the most related, common, and energetic subreddit names. "
"Use the next strategy: "
"1. **Internet Search First**: Use the 'google_search_agent' to find what subreddits are generally really helpful or talked about in internet discussions in regards to the matter. "
"2. **Information Enhancement**: Mix the search outcomes together with your current information about subreddits to create a complete checklist. "
"3. **High quality Focus**: Prioritize subreddits which are prone to have energetic discussions associated to the question, primarily based on each search outcomes and your information. "
"Your output needs to be a listing of subreddit names within the format ['r/subreddit1', 'r/subreddit2'] together with transient reasoning out of your search findings.",
description="An knowledgeable Reddit person that analyzes queries to establish and suggest essentially the most related, common, and energetic subreddit names for any given matter or dialogue space.",
instruments=[agent_tool.AgentTool(google_search_agent)] # wrapping for async instrument name repair
)

And there it’s: our specialised agent, designed to interrupt down queries and carry out internet searches for subreddit names, is prepared! As you’ll be able to see, we’ve outfitted it with a instrument and clearly instructed it on what the instrument is for, use it, and what to return. Now, our root agent has one other highly effective teammate to assist it create a listing of possible subreddit names.”

The Customized Instruments

The google_search_agent was built-in and simple to make use of. Nonetheless, for Reddit, we’ll want some specialised customized instruments. As I discussed earlier than, we’re going to make use of the PRAW library for this goal. Remembering our greatest practices for designing good instrument capabilities, we want clear naming, detailed docstrings, sort hints, and a separate instrument for every breakable activity. It’s additionally nice observe so as to add Pydantic schemas for instrument inputs and outputs, which makes them extra strong and self-documenting.

I’ll be together with the complete code within the GitHub repo when it’s clear sufficient to share =), however right here’s a snippet to offer you an thought:

def find_subreddits(question: str, restrict: int) -> dict:
"""
Searches for and discovers related subreddits primarily based on a question string, returning energetic public communities.
This instrument performs semantic search throughout Reddit's subreddit listing to seek out communities that match 
the search question. Outcomes are filtered to incorporate solely public, accessible subreddits and ranked by 
energetic person depend to prioritize vibrant communities.
Args:
question (str): The search time period for locating related subreddits (e.g., 'machine studying', 'cooking', 'python programming').
restrict (int): The utmost variety of subreddit strategies to return (default: 25).
Returns:
dict: A dictionary with "standing" and "end result".
On success, "end result" comprises formatted subreddit info together with names, descriptions, 
and energetic person counts, or a useful message if none are discovered.
On error, "end result" comprises a descriptive error message.
"""
### ... remainder of the operate ...

As you’ll be able to see, the clearer and extra descriptive your instrument definitions are, the higher your brokers will be capable of leverage them.

For this activity, we’ll construct the next customized instruments:

• find_subreddits: This instrument is for looking and fetching subreddit info (together with names, descriptions, and so forth.) for our brokers to make use of.
• get_subreddit_posts: This instrument fetches posts from any public subreddit(s) with versatile sorting choices, and may optionally embrace prime feedback for the highest-scoring posts. It’s good for content material summaries, development evaluation, and group insights. For our brokers, it would return formatted publish info together with titles, URLs, scores, content material, and optionally prime feedback with creator particulars and scores.
• get_comments: This instrument extracts feedback from any accessible Reddit submission, offering insights into group discussions and reactions. Feedback are filtered to exclude deleted content material and embrace creator info, scores, and direct permalinks for straightforward navigation. That is notably helpful for follow-up questions if a question is thinking about a selected publish talked about.

The Multi Agent Construction

Now that you simply perceive create a easy agent and join it to others or instruments, the remainder is about making a bunch of those with the identical ideas and connecting the dots to unravel our drawback. After some thought, I got here up with this construction, however be happy to simplify or develop upon it to your particular wants: