# I Replaced My AI Stack With One Open-Source Agent: Testing Hermes Agent for Real Work > Source: > Published: 2026-05-31 10:47:08+00:00 *This is a submission for the *[Hermes Agent Challenge](https://dev.to/challenges/hermes-agent-2026-05-15): Write About Hermes Agent ## The Modern AI Stack Is Getting Messy If you’re building anything serious with AI today, your stack probably looks like this: - ChatGPT for general reasoning - Claude for long-form writing - Cursor for coding - Zapier for automation - Browser agents for web tasks - Perplexity / research tools for information gathering Individually, each tool is powerful. Together, they feel like a distributed system glued together with copy-paste, prompts, and hope. At some point I started asking myself: **Could one agent replace most of this stack?** Not in theory. But in real work. That question led me to test **Hermes Agent** as a unified AI system. Not a chatbot. Not a plugin. A full agent runtime. ## What Is Hermes Agent (In Practice)? Hermes Agent is an open-source agent framework built around one core idea: AI systems should persist memory, execute workflows, and coordinate sub-agents over time. Instead of isolated conversations, it introduces: - persistent memory layer - skill-based execution system - multi-agent workflows - tool integrations - long-running task orchestration What stood out to me wasn’t a single feature. It was the structure. It behaves less like a chatbot and more like an operating environment for AI workers. So I decided to test it like one. ## Experimental Setup I didn’t want synthetic benchmarks. I wanted real work. So I designed five practical tasks that mirror my daily engineering workflow. Each task was evaluated across: - usefulness - reliability - consistency - autonomy - developer experience ## Task 1: Research a Technical Topic ### Objective Research “multi-agent systems with shared memory architectures” and produce a structured summary. ### Process I gave Hermes a simple instruction: “Research multi-agent systems with shared memory and summarize architectural patterns.” Behind the scenes, the system: - spawned a research sub-agent - gathered relevant concepts - stored intermediate findings in memory - consolidated results through a summarization skill ### Observations What stood out immediately: - It did not just generate an answer - It constructed a research trail - It stored intermediate concepts - It reused earlier findings in refinement Example memory entry (simplified): ### Results The final output was structured like: - architecture types - tradeoffs - real-world examples - limitations ### Strengths - Strong synthesis capability - Good structuring of knowledge - Memory reuse improved coherence ### Weaknesses - Slight repetition in early drafts - Occasional over-generalization ### Score Research: **8.5/10** ## Task 2: Write Technical Documentation ### Objective Generate documentation for a hypothetical API service with endpoints, authentication, and examples. ### Process I used a documentation skill: “Generate API documentation for a user authentication service with JWT.” Hermes: - referenced previous memory patterns for API docs - used structured documentation templates - generated examples automatically ### Example Output Snippet ### Observations - The output was consistent with prior documentation style (from memory) - It maintained formatting across sections - It reused structure patterns automatically ### Strengths - Consistency across sections - Good template reuse - Minimal prompting required ### Weaknesses - Limited creativity in explanation style - Sometimes too “templated” ### Score Documentation: **8/10** ## Task 3: Manage Project Memory ### Objective Simulate a project over multiple interactions and test whether Hermes retains context. ### Process I created a fake project: “A SaaS analytics dashboard for developer metrics.” Over multiple sessions, I added: - product decisions - UI choices - tech stack changes - user feedback ### Observations This is where Hermes clearly diverged from traditional AI tools. It maintained: - decision history - evolving architecture - unresolved tradeoffs Example memory evolution: Later: “Use Supabase as previously decided in v2 architecture.” ### Strengths - Strong continuity across sessions - Reduced need for re-explaining context - Decision tracking worked surprisingly well ### Weaknesses - Memory occasionally lacked prioritization - Some outdated entries persisted too long ### Score Memory: **9/10** ## Task 4: External Tool Usage ### Objective Simulate integration with external APIs and tools (web search, data fetch, mock APIs). ### Process I asked: “Fetch latest trends in AI agent frameworks and summarize.” Hermes: - triggered a tool integration workflow - delegated retrieval to a sub-agent - consolidated results ### Observations Tool usage felt structured: - clear separation between retrieval and reasoning - results stored in memory for later reuse - tool outputs treated as first-class data ### Example Workflow ### Strengths - Clean tool abstraction - Reusable tool outputs - Good workflow orchestration ### Weaknesses - Integration setup still requires engineering effort - Not plug-and-play like Zapier ### Score Automation: **8/10** ## Task 5: Multi-Step Planning ### Objective Plan a full MVP for a developer productivity tool. ### Process I gave a broad prompt: “Plan an MVP for a developer analytics tool with onboarding, metrics, and dashboards.” Hermes: - created a planning sub-agent - broke task into phases - stored milestones in memory - refined plan iteratively ### Example Plan Structure - Phase 1: Data ingestion - Phase 2: Metrics engine - Phase 3: Dashboard UI - Phase 4: API integrations - Phase 5: Deployment ### Observations The most impressive part was iteration. Each refinement built on previous planning state. ### Strengths - Strong decomposition skills - Persistent planning state - Clear execution roadmap ### Weaknesses - Sometimes over-engineered plans - Needed constraint tuning ### Score Planning: **8.5/10** ## Overall Scorecard | Category | Score | | Research | 8.5/10 | | Planning | 8.5/10 | | Memory | 9/10 | | Automation | 8/10 | | Developer Experience | 7.5/10 | ## Where Hermes Agent Becomes Clearly Better Compared to traditional AI tools: ### 1. Continuity Most AI tools reset after every session. Hermes does not. This alone changes workflows significantly. ### 2. Memory-Driven Decisions Instead of re-explaining context: - decisions persist - architecture evolves - preferences accumulate ### 3. Workflow Composition Instead of single prompts: - multi-step execution chains - reusable skills - persistent state ### 4. Multi-Agent Execution Tasks are no longer linear. They become parallelized across sub-agents. ## Where Dedicated Tools Still Win To be clear, Hermes is not a replacement for everything. ### 1. Cursor still wins in IDE experience - real-time code navigation - deep repository awareness - UI integration ### 2. Zapier still wins in plug-and-play automation - zero setup workflows - hundreds of integrations ### 3. ChatGPT / Claude still win in simplicity - instant responses - no system setup - lower cognitive overhead ## The Tradeoff Is Clear Hermes is powerful. But it is also: - more complex - more architectural - more system-oriented It behaves less like a tool and more like a platform. ## Would I Use Hermes Agent Every Day? Yes — but not as a replacement for everything. I would use it as: - a long-running project brain - a research companion - a planning system - a memory layer for engineering work Not as: - a quick Q&A chatbot - a lightweight writing assistant It shines when: context matters over time. ## Who Should Use Hermes Agent Right Now? Hermes Agent is most useful for: - AI engineers building multi-step systems - startup teams managing evolving context - researchers tracking long-term work - developers building agentic workflows - anyone tired of re-explaining context to AI tools It is not ideal for: - casual chat use - single-turn queries - lightweight automation ## Final Thoughts Testing Hermes Agent felt less like testing a chatbot… and more like testing an early version of an AI operating layer. Not perfect. Not simple. But structurally different. And that difference matters. Because the real question is no longer: “How smart is the model?” But instead: “How much does the system remember, coordinate, and evolve over time?” And on that axis, Hermes Agent points in a direction most AI tools are not even trying to go yet.