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AgentForge: Build Your Own AI Agents with 10 Lines of Python

From JOHNWICK

The new open-source framework that lets you create autonomous, revenue-generating AI assistants without a PhD or an API bill.

Photo by ZHENYU LUO on Unsplash

1. The Hook Why You Need Your Own AI Agents

The world’s richest developers in 2025 aren’t building traditional SaaS apps anymore.
They’re building autonomous AI agents small, self-learning digital workers that can:

  • Trade stocks automatically
  • Manage customer messages
  • Generate blog content
  • Handle lead follow-ups
  • Even negotiate prices in marketplaces

Here’s the truth:
While everyone else is prompting ChatGPT for ideas, the real players are writing Python code that prompts itself. And that’s where AgentForge enters the scene.

2. What Is AgentForge?

AgentForge is an open-source framework for building modular AI agents autonomous digital entities capable of reasoning, learning, and acting across APIs, web data, and user input. It combines:

  • Reasoning engines (based on OpenAI or local LLMs)
  • Knowledge memory (via vector databases like Chroma or FAISS)
  • Task orchestration (async + dependency-based task execution)
  • Persistence (agents remember context, decisions, and outcomes)

Its philosophy? “Every developer should be able to forge their own AI team from scratch, with just Python.”

3. Installing AgentForge

Installation is as minimal as it gets: pip install agentforge

Optional but recommended: pip install chromadb openai fastapi

4. The 10-Line AI Agent

Let’s go straight to the magic.
Below is a fully working agent that can browse the web, summarize content, and store its learning for future use. 

from agentforge import Agent

bot = Agent(name="ResearchBot", model="gpt-4-turbo")

@bot.skill
def learn_topic(topic):
    bot.remember(f"Researching about {topic}")
    summary = bot.ask(f"Summarize the key points of {topic} in simple terms.")
    bot.remember(summary)
    return summary

@bot.skill
def recall_learning():
    return bot.recall()

# Run
print(learn_topic("quantum computing"))
print(recall_learning())

That’s it.
You’ve just created an AI researcher with long-term memory.

5. How It Works Under the Hood

The Agent class in AgentForge acts as a wrapper around three key systems: Every agent acts like a small autonomous brain:

  • Thinks through its model
  • Acts using your skills
  • Remembers context with memory
  • Decides which skill to run next (if configured with autonomy enabled)

6. The Real Game Changer Multi-Agent Collaboration

AgentForge supports multi-agent ecosystems, where multiple AI agents communicate through messages and shared memory. For example:

  • A ResearchAgent gathers data
  • A WriterAgent creates articles
  • A PublisherAgent formats and posts content

Here’s a demo: 

from agentforge import Agent, Collaboration

researcher = Agent(name="Researcher", model="gpt-4-turbo")
writer = Agent(name="Writer", model="gpt-4-turbo")

team = Collaboration([researcher, writer])

@researcher.skill
def gather_data(topic):
    return f"Collected info about {topic} from the web."

@writer.skill
def write_article(data):
    return f"An article draft based on {data}"

result = team.run("write_article", topic="AI in Healthcare")
print(result)

Output: "An article draft based on Collected info about AI in Healthcare from the web." This turns Python scripts into AI organizations tiny, scalable ecosystems of digital workers.

7. Building a Real Money-Making Agent

Let’s take it to the next level a freelancer bot that automatically finds freelance gigs, generates proposals, and emails clients. Imagine an agent that:

  • Scrapes Upwork for jobs in Python or AI
  • Writes personalized proposals
  • Sends them to potential clients automatically
  • Tracks which leads responded

Here’s a simplified core: 

from agentforge import Agent
import requests

bot = Agent(name="FreelanceBot", model="gpt-4-turbo")

@bot.skill
def find_jobs():
    data = requests.get("https://www.upwork.com/api/jobs?q=python").json()
    return [job["title"] for job in data["results"][:3]]

@bot.skill(depends_on=["find_jobs"])
def write_proposals(jobs):
    for job in jobs:
        proposal = bot.ask(f"Write a short proposal for: {job}")
        print(f"Proposal sent for: {job}\n{proposal}")

bot.run()

You can later connect it to:

  • Gmail API for automated emails
  • Google Sheets for proposal tracking
  • LangGraph / ChromaDB for long-term learning

And just like that, your agent becomes a self-running side hustle.

8. How Developers Are Monetizing AgentForge

Here are 4 proven monetization ideas based on what early adopters are building:

1. AI Virtual Assistants for Clients Offer “autonomous assistants” to startups HR bots, data-entry bots, customer Q&A bots.

2. AI Trading Helpers Integrate with Binance or Alpaca APIs to create agents that analyze markets and suggest trades.

3. Automated Report Generators Let companies automate daily or weekly business reports using LLMs.

4. Learning & Research Agents Sell domain-trained agents (e.g., “LegalBot” or “RealEstateBot”) to professionals needing summaries or insights.

9. Architecture Deep Dive

Let’s understand the core design pattern. Here’s how it flows:

  • Input: You define a goal or task.
  • Brain: The agent uses its LLM reasoning model.
  • Skills: If it needs action, it executes a registered function.
  • Memory: The output is stored persistently.
  • Collaboration: Optional other agents can access the memory.

Each agent is essentially a stateful Python microservice that can evolve over time.

10. Real-World Example AI Trading Analyst

Let’s say you want a bot that:

  • Pulls crypto prices
  • Generates market summaries
  • Predicts short-term sentiment
from agentforge import Agent
import requests

analyst = Agent(name="TraderBot", model="gpt-4-turbo")

@analyst.skill
def get_prices():
    data = requests.get("https://api.binance.com/api/v3/ticker/price").json()
    return {d["symbol"]: d["price"] for d in data[:5]}

@analyst.skill(depends_on=["get_prices"])
def analyze_market(prices):
    summary = analyst.ask(f"Analyze these prices and predict short-term trends:\n{prices}")
    analyst.remember(summary)
    return summary

analyst.run()

This agent can later connect to a trading API and make or suggest decisions.

11. The Developer Advantage

Why AgentForge matters to developers:

  • Low-code modularity → You can plug in APIs, memory stores, or skills easily.
  • LLM-agnostic → Works with OpenAI, Claude, Gemini, or local models.
  • Self-improving → Agents can re-analyze their past mistakes.
  • Monetizable → Package agents as digital workers for clients or marketplaces.

You’re not just writing code you’re building mini start-ups that run on autopilot.

12. The Future of Freelancing Is Autonomous

Here’s the shift happening now: Freelancers who use AI are being replaced by freelancers who deploy AI. AgentForge is the toolkit that makes that transition seamless.
You can now:

  • Clone your thought process
  • Scale your work capacity infinitely
  • Automate tasks that used to take hours

One agent can handle 5 clients.
Ten agents? You’re running a small agency solo.

13. Wrap-Up The New Era of “Agent Engineers”

If 2023 was the year of prompt engineers, 2025 is the rise of agent engineers coders who design autonomous systems that think, act, and earn. AgentForge gives you:

  • The technical control of Python
  • The intelligence of LLMs
  • The scalability of automation frameworks

You’re no longer automating you’re creating digital life. Takeaway Code isn’t just executing logic anymore it’s executing intent.

Read the full article here: https://python.plainenglish.io/agentforge-build-your-own-ai-agents-with-10-lines-of-python-2c32d21c607c

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