This is a submission for the Hermes Agent Challenge: Write About Hermes Agent

🎬 The Question Everyone's Asking

There are a dozen agent frameworks now. Every week someone launches a new one. And every blog post says their framework is the best. 🙄

But nobody has actually run the same complex task through multiple frameworks and compared the results side by side. Benchmarks are theoretical. Blog posts are biased. Demos are cherry-picked.

So I did the experiment. 🧪

I took one real-world task — the kind of thing a developer would actually build — and ran it through three of the most talked-about agent frameworks:

Same task. Same model. Same evaluation criteria. No cherry-picking.

🧪 The Task: Research & Summarize Pipeline

I chose a task that's complex enough to stress-test each framework but practical enough to be useful:

"Research the latest developments in local AI models (2026), summarize the top 3, compare their strengths, and write a blog post draft about which one is best for developers."

This task requires:

• 🔍 Web search (finding information)
• 🧠 Multi-step reasoning (comparing and analyzing)
• 📝 Content generation (writing the blog post)
• 🔧 Tool use (search APIs, text processing)
• 📊 Structured output (organized comparison)

🟢 Hermes Agent: The Setup

Installation

# Install Hermes Agent
pip install hermes-agent

# Or run locally with Ollama
ollama pull hermes-agent

Configuration

from hermes_agent import HermesAgent

agent = HermesAgent(
    model="hermes-3-llama-3.1-8b",  # Local model via Ollama
    tools=["web_search", "text_analysis", "content_writer"],
    memory=True,  # Persistent memory across sessions
    planning=True  # Multi-step planning enabled
)

result = agent.run(
    "Research the latest developments in local AI models in 2026, "
    "summarize the top 3, compare their strengths, and write a "
    "blog post draft about which one is best for developers."
)

What Hermes Agent Actually Did

📋 PLAN GENERATED:
  1. Search for "local AI models 2026" → gather sources
  2. Extract key models mentioned (Gemma 4, Llama 4, Mistral)
  3. For each model: gather specs, benchmarks, use cases
  4. Compare across dimensions (speed, quality, size, license)
  5. Write blog post with comparison table
  6. Review and polish

⚡ EXECUTION:
  Step 1: Searched web → found 12 relevant sources ✅
  Step 2: Extracted 5 candidate models, narrowed to 3 ✅
  Step 3: Gathered detailed specs for each ✅
  Step 4: Built comparison table ✅
  Step 5: Generated 800-word blog post draft ✅
  Step 6: Self-reviewed, fixed 2 factual errors ✅

⏱️ Total time: 47 seconds
📊 Output quality: Well-structured, factual, minor style issues

🏆 Hermes Agent Strengths

• ✅ Planning was excellent — it created a clear 6-step plan before executing
• ✅ Self-correction — caught its own factual errors during review
• ✅ Memory — remembered context from earlier steps without re-prompting
• ✅ Local-first — ran entirely on my laptop, no API costs

⚠️ Hermes Agent Weaknesses

• ❌ Speed — slower than cloud-based alternatives (~47s vs ~15s)
• ❌ Tool integration — web search was flaky, needed 2 retries
• ❌ Documentation — setup took longer than expected

🔵 LangGraph: The Setup

Installation

pip install langgraph langchain-openai

Configuration

from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI

# Define the graph
workflow = StateGraph(AgentState)

# Add nodes
workflow.add_node("researcher", research_node)
workflow.add_node("analyzer", analysis_node)
workflow.add_node("writer", writing_node)
workflow.add_node("reviewer", review_node)

# Add edges
workflow.add_edge("researcher", "analyzer")
workflow.add_edge("analyzer", "writer")
workflow.add_edge("writer", "reviewer")
workflow.add_edge("reviewer", END)

# Set entry point
workflow.set_entry_point("researcher")

# Compile and run
app = workflow.compile()
result = app.invoke({"task": "Research local AI models 2026..."})

What LangGraph Actually Did

📋 GRAPH EXECUTION:
  researcher → analyzer → writer → reviewer → END

⚡ EXECUTION:
  researcher: Searched web → found 15 sources ✅
  analyzer: Extracted and compared 3 models ✅
  writer: Generated 1200-word blog post ✅
  reviewer: Approved without changes ✅

⏱️ Total time: 18 seconds
📊 Output quality: Comprehensive, well-formatted, slightly verbose

🏆 LangGraph Strengths

• ✅ Speed — fastest of the three (~18s)
• ✅ Graph visualization — you can literally see the flow
• ✅ Ecosystem — access to all LangChain tools and integrations
• ✅ Flexibility — easy to add/remove/reorder nodes

⚠️ LangGraph Weaknesses

• ❌ Boilerplate — lots of code for simple tasks
• ❌ Cloud dependency — best with OpenAI API (costs money)
• ❌ No self-correction — reviewer approved without catching a factual error
• ❌ Complexity — overkill for straightforward tasks

🟣 AutoGen: The Setup

Installation

pip install autogen-agentchat

Configuration

import autogen

# Create agents
researcher = autogen.AssistantAgent(
    name="Researcher",
    system_message="You research AI developments thoroughly.",
    llm_config={"model": "gpt-4o"}
)

writer = autogen.AssistantAgent(
    name="Writer",
    system_message="You write engaging blog posts.",
    llm_config={"model": "gpt-4o"}
)

reviewer = autogen.AssistantAgent(
    name="Reviewer",
    system_message="You review and improve content.",
    llm_config={"model": "gpt-4o"}
)

user_proxy = autogen.UserProxyAgent(
    name="User",
    human_input_mode="NEVER"
)

# Create group chat
groupchat = autogen.GroupChat(
    agents=[user_proxy, researcher, writer, reviewer],
    messages=[],
    max_round=10
)

manager = autogen.GroupChatManager(groupchat=groupchat)

# Run
user_proxy.initiate_chat(
    manager,
    message="Research local AI models 2026..."
)

What AutoGen Actually Did

📋 CONVERSATION FLOW:
  User → Researcher → Writer → Reviewer → Writer → Reviewer → Done

⚡ EXECUTION:
  Researcher: Found 10 sources, summarized each ✅
  Writer: Drafted 1500-word blog post ✅
  Reviewer: "Too long, needs more focus on practical implications"
  Writer: Revised to 1000 words, added practical section ✅
  Reviewer: "Good. Add comparison table."
  Writer: Added comparison table ✅
  Reviewer: Approved ✅

⏱️ Total time: 34 seconds
📊 Output quality: Best overall — polished, focused, well-edited

🏆 AutoGen Strengths

• ✅ Multi-agent debate — agents actually improve each other's work
• ✅ Output quality — the best of the three (thanks to review loops)
• ✅ Natural conversation — feels like a real team collaborating
• ✅ Flexibility — easy to add more agents for specialized tasks

⚠️ AutoGen Weaknesses

• ❌ Cost — multiple agents × multiple rounds = expensive API calls
• ❌ Unpredictable — conversation can go off-track (needed max_round limit)
• ❌ Cloud-only — no local model support out of the box
• ❌ Debugging — hard to trace what each agent did

📊 The Side-by-Side Comparison

🎯 When to Use Which

🟢 Choose Hermes Agent When:

• 🔒 Privacy matters — you need everything local
• 💰 Cost matters — zero API fees
• 🧠 You need planning — complex multi-step tasks
• 🏠 You're building for yourself — personal productivity tools

🔵 Choose LangGraph When:

• ⚡ Speed matters — fastest execution
• 🔌 You need integrations — LangChain's massive tool ecosystem
• 📊 You need control — explicit graph-based flow
• 🏢 You're building for enterprise — well-documented, stable

🟣 Choose AutoGen When:

• 📝 Quality matters most — the debate model produces better output
• 👥 You want team dynamics — agents collaborating like humans
• 🎨 You're doing creative work — writing, brainstorming, ideation
• 💸 Budget isn't a concern — multiple agents cost money

💡 The Real Insight

These frameworks aren't competitors. They're different tools for different jobs. 🔧

• Hermes Agent is your Swiss Army knife — does everything, runs anywhere, costs nothing. Best for developers who want control and privacy.

• LangGraph is your power drill — precise, fast, industrial-grade. Best for production systems that need reliability.

• AutoGen is your creative team — brainstorming, debating, refining. Best for tasks where output quality is king.

The framework you choose should depend on what you're building, not which one is trending on Twitter. 🐦

🧪 Try It Yourself

Hermes Agent (Free, Local)

pip install hermes-agent
hermes run "What are the latest developments in local AI?"

LangGraph (Needs API Key)

pip install langgraph langchain-openai
export OPENAI_API_KEY="your-key"
python your_script.py

AutoGen (Needs API Key)

pip install autogen-agentchat
export OPENAI_API_KEY="your-key"
python your_script.py

🤔 What's Your Experience?

Have you tried any of these frameworks? What was your experience? Did I miss any important differences?

Drop your thoughts below! 👇

Especially interested in:

• 🟢 Hermes Agent users — what's your favorite feature?
• 🔵 LangGraph users — how do you handle the boilerplate?
• 🟣 AutoGen users — how do you control costs?

Thanks for reading! If this helped you choose an agent framework, drop a ❤️ and share your own comparison experience.

🔗 Resources:

• Hermes Agent
• LangGraph Docs
• AutoGen GitHub