Hermes vs LangGraph vs Claude Agent SDK: Choosing Your Stack

Three Different Bets on How to Build Agents

You’ve now spent seven modules hands-on with Hermes. This lesson steps back and places it against two other approaches you’re likely to encounter, LangGraph and the Claude Agent SDK, not to declare a winner, but so you can make a deliberate choice on your next project instead of defaulting to whichever one you learned first.

flowchart LR
    subgraph SPECTRUM[" "]
        direction LR
        LG["LangGraph:\nlibrary, full control,\nbuild everything yourself"]
        CASDK["Claude Agent SDK:\nmanaged loop + sandbox,\nsingle-provider"]
        HERMES["Hermes Agent:\ncomplete runtime,\nmodel-agnostic,\nopinionated"]
    end

    LG --> CASDK --> HERMES

    style LG fill:#f0fdf9,stroke:#0D9488,color:#0F172A
    style CASDK fill:#fff7ed,stroke:#f59e0b,color:#0F172A
    style HERMES fill:#EEF0F7,stroke:#6366F1,color:#0F172A

LangGraph: The Library Approach

LangGraph gives you primitives, nodes, edges, explicit state, that you assemble into exactly the control flow your application needs. Nothing is provided for free: memory, tool routing, and anything skill-equivalent are yours to build.

# Illustrative sketch, not a full implementation
from langgraph.graph import StateGraph

graph = StateGraph(AgentState)
graph.add_node("plan", plan_step)
graph.add_node("execute", execute_step)
graph.add_node("review", review_step)
graph.add_edge("plan", "execute")
graph.add_conditional_edges("execute", route_on_result, {
    "needs_review": "review",
    "done": "__end__",
})

This is the right call when your agent’s control flow is genuinely unusual, doesn’t fit a standard “chat with tools” shape, or when you need to embed agent logic deep inside a larger existing application rather than running it as a standalone runtime.

Claude Agent SDK: The Managed Middle Ground

The Claude Agent SDK provides a managed agent loop and tooling, including a managed sandbox, purpose-built around Claude. It’s more structure than raw LangGraph (you’re not building the loop yourself), but it commits to a single model provider rather than Hermes’ explicit model-agnosticism from Lesson 4.

# Illustrative sketch
from claude_agent_sdk import ClaudeSDKClient

async with ClaudeSDKClient() as client:
    await client.query("Refactor the auth module to support SSO")
    async for message in client.receive_response():
        print(message)

This is the right call when you’re committed to Claude specifically and want a managed loop and sandbox without assembling one yourself, but don’t need Hermes’ memory/skills system or its multi-provider flexibility.

Hermes: The Complete, Opinionated Runtime

Hermes trades some of that low-level control for a much shorter path to a working, persistent agent: memory (Module 4), skills (Lesson 11), tool routing (Module 3), multi-platform gateway (Module 6), and guardrails (Module 7) all come built in, and it works with whichever model provider you choose (Module 2), not one specific vendor.

These Aren’t Mutually Exclusive

Lesson 22’s supervisor pattern generalizes here: MCP is not a Hermes-specific integration point. A custom LangGraph application, or a Claude Agent SDK-built agent, can itself be exposed as an MCP server that a Hermes instance delegates to, the exact mechanism from Lesson 22, just with a different kind of agent on the other end of the connection. A team can use Hermes for the parts of their system that benefit from built-in memory, skills, and multi-platform reach, while keeping a fully custom LangGraph workflow for the one piece of control flow that doesn’t fit any standard runtime’s assumptions.

Exercise: take a project idea you have (real or hypothetical) and place it on the LangGraph-to-Hermes spectrum above using the comparison table’s rows as your criteria. Write two sentences justifying your placement, specifically referencing which row (model flexibility, memory, control flow, or time-to-value) was the deciding factor.