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

The agent framework space has exploded. AutoGen, CrewAI, LangGraph, OpenAI Agents SDK, Google ADK — each week brings something new. It's genuinely hard to know what to actually use.

This post compares Hermes Agent against the most popular alternatives across five dimensions that actually matter for developers building real things: infrastructure flexibility, memory/learning, tool ecosystem, messaging/deployment, and openness. No fluff — just an honest breakdown.

The Frameworks

1. Infrastructure Flexibility

Where does the agent actually run, and how much does it cost you?

Hermes Agent offers six terminal backends: local, Docker, SSH, Daytona, Singularity (HPC clusters), and Modal (serverless). SSH backend means you can run it on any remote machine you already have. Modal means near-zero cost when idle. It runs on Linux, macOS, and WSL2 with zero prerequisites — the installer handles everything.

AutoGen is primarily a Python library. You run it wherever you run Python. No native packaging, no single-command setup, no serverless consideration built in. Flexible but manual.

CrewAI is similar — a Python framework. CrewAI+ (their cloud) manages deployment for you, but that's a paid managed service, not open infrastructure.

LangGraph has LangGraph Cloud for managed hosting (paid) and self-hosted options, but the self-hosting story involves more moving parts than you'd want for a quick project.

Google ADK is built for Cloud Run. If you're already in GCP, this is seamless. If you're not, the path to deployment involves more ceremony than it should.

OpenAI Agents SDK is designed to run in your existing Python environment. No particular infrastructure story — you bring your own.

Verdict: Hermes wins on infrastructure flexibility, especially for developers who want serverless-or-VPS without vendor commitment. Google ADK wins within GCP. Others require more DIY deployment work.

2. Memory and Learning Over Time

This is the dimension where frameworks differ most dramatically.

Hermes Agent has three memory layers working together: a Skills System (procedural memory in inspectable markdown files), persistent cross-session memory (FTS5 search + LLM summarization), and Honcho dialectic user modeling. The Autonomous Curator runs on a 7-day cycle to consolidate, prune, and update the skill library automatically. The agent creates its own skills after complex tasks without prompting.

AutoGen has ConversableAgent with basic message history. There's no native cross-session memory — you manage persistence yourself. Community extensions exist but aren't core.

CrewAI added long-term memory via LongTermMemory, short-term via ShortTermMemory, and entity memory. It's the most structured memory system among the Python frameworks, but it's still session-bound by default and doesn't self-improve procedurally.

LangGraph supports memory through LangMem and persistence layers. The developer controls what's stored and recalled. Flexible but requires explicit engineering work to get compound learning.

Google ADK has session state and memory tools. Designed for stateful multi-turn conversations within a session. Cross-session persistence requires connecting to Firestore or another backend yourself.

OpenAI Agents SDK ships with a basic memory tool and context objects. No autonomous learning or self-improvement.

Verdict: Hermes has the most sophisticated and autonomous memory/learning system. CrewAI has the most structured memory among Python frameworks. Others require significant manual engineering to achieve comparable results.

3. Tool Ecosystem

How easily can the agent do things — and what things can it do?

Hermes Agent ships with a broad built-in tool registry: web search, browser automation (Browserbase, Browser Use, local Chrome), terminal execution, file editing, memory operations, subagent delegation, code execution (sandboxed Python RPC), image generation (9 models), voice/TTS, Home Assistant, X/Twitter search, computer use, and vision analysis. MCP servers add any tool from the MCP ecosystem. The Skills Hub adds 200+ site-specific browser automation skills from browse.sh. Channel-level skill bindings let you configure which tools are available per platform.

AutoGen has a solid function-calling framework. You define tools as Python functions and register them. No built-in tool registry — you build what you need.

CrewAI has a @tool decorator pattern and a growing library of built-in tools (web search, file operations, code execution). More turnkey than AutoGen.

LangGraph inherits LangChain's enormous tool ecosystem. If a tool exists in LangChain, it works in LangGraph. The breadth is unmatched — but so is the complexity of managing all the integrations.

Google ADK has deep Google service integration (Search, Maps, Drive, Calendar, Gmail via MCP) and good built-in tool primitives. Non-Google integrations require more work.

OpenAI Agents SDK has function tools, hosted tools (web search, code interpreter, file search via OpenAI's own infrastructure), and handoffs. Clean but tightly coupled to OpenAI's platform.

Verdict: LangChain's ecosystem via LangGraph is the broadest in terms of raw number of integrations. Hermes wins on built-in breadth without configuration — everything from browser automation to image generation is ready without extra packages. Google ADK wins within the Google ecosystem. OpenAI Agents SDK is cleanest but most closed.

4. Messaging and Deployment

Can your agent talk to you where you actually are?

Hermes Agent supports 20 messaging platforms via a gateway: Telegram, Discord, Slack, WhatsApp, Signal, Matrix, Teams, Email, SMS, Mattermost, DingTalk, Feishu, Google Chat, and more. The gateway is a plugin host — new platform adapters can be dropped in. Everything runs from a single gateway process. Voice memos, cross-platform conversation continuity, slash commands on every platform. Built-in cron scheduler delivers results to any platform on a schedule.

AutoGen has no native messaging integration. You build whatever delivery mechanism you want.

CrewAI has no native messaging platform support. CrewAI+ exposes an API you can call from anywhere, but the "talk to your agent from Telegram" story is DIY.

LangGraph same — no native messaging. You'd build this yourself using LangChain's integrations.

Google ADK integrates with Google Chat and has Vertex AI deployment. Within Google Workspace, this is excellent. Outside it, less so.

OpenAI Agents SDK has no native messaging integration. OpenAI's products (ChatGPT, etc.) handle this separately.

Verdict: Hermes is by far the strongest here. 20 supported platforms, single gateway process, cron + delivery built in. If you want your agent reachable from your phone without building infrastructure, Hermes is the only framework where this is a first-class feature.

5. Openness and Portability

Can you actually own and move your agent?

Hermes Agent is MIT. No model lock-in — works with any OpenAI-compatible endpoint. Skills are markdown files in ~/.hermes/skills/ — portable, inspectable, version-controllable. Memory is local SQLite. The agentskills.io open standard means skills work across compatible agents. No telemetry, no tracking, all data local.

AutoGen is MIT and model-agnostic. Your code is yours. No proprietary data formats.

CrewAI has MIT core, but CrewAI+ (cloud features) is commercial. Skills/crews are Python code — portable but not as readable as markdown.

LangGraph is MIT. LangSmith (tracing/evaluation) and LangGraph Cloud are commercial. Framework is portable; the ecosystem increasingly nudges toward their paid products.

Google ADK is Apache 2.0. Model preference is clearly Gemini. Cloud Run deployment creates GCP coupling if you're not careful.

OpenAI Agents SDK is MIT, but practically everything interesting (hosted tools, traces, evals) requires OpenAI's platform. Most locked-in of the group.

Verdict: AutoGen and Hermes are most open in practice. OpenAI Agents SDK is most closed. Others sit somewhere in between, with commercial upsell pressure present to varying degrees.

Summary Table

Who Should Use What

Use Hermes if: You want a general-purpose agent you fully control, that improves over time, that you can reach from your phone, and that runs on infrastructure you own. Best for individual developers and small teams building personal or project-level automation.

Use AutoGen if: You're doing research or building multi-agent systems where you want maximum programmatic control over agent interaction patterns. Better for academic or experimental work.

Use CrewAI if: You want a structured role-based multi-agent system and the crew metaphor maps naturally to your problem. Good for pipelines where agents have clear, distinct jobs.

Use LangGraph if: You need the breadth of the LangChain ecosystem and want graph-based control flow for complex stateful workflows. Best when you need a specific integration that only LangChain has.

Use Google ADK if: You're building on GCP and want deep Google service integration. The deployment story is excellent within that ecosystem.

Use OpenAI Agents SDK if: You're already invested in OpenAI's platform and want the cleanest, most polished developer experience within that ecosystem. Accept the lock-in as a tradeoff.

The Real Differentiator

Most frameworks solve "can the agent do the task." Hermes solves "will the agent still be useful six months from now without you constantly re-explaining your context."

That's a different problem, and it matters more the longer you use an agent. The skill library, the Curator, the persistent memory — these compound. The other frameworks generally don't have an equivalent answer to this question.

Whether that matters to you depends on whether you're building a one-off demo or a long-running workflow. For the latter, Hermes's architecture is genuinely ahead.

Try Hermes:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

Documentation · GitHub