Agent Mode launched July 17, 2025 — autonomous multi-step task execution with web browsing, code execution, and file operations. Scheduled Tasks arrived January 2025 for recurring prompts. The computer-using agent (CUA) adds visual browser control for web-based workflows. Projects provide shared persistent context across conversations. Pulse (Pro plan) delivers daily research briefings. Dual-mode memory — background auto-extraction plus manual pins — and 50+ connectors including Gmail, GitHub, Google Drive, and Notion make this a substantial autonomous platform.
ChatGPT's memory works in two modes: background auto-extraction where the model decides what's worth remembering, plus manual memory pins you add yourself. You can view and delete stored memories, but the extraction logic is opaque — you don't see the raw files or control the process. Critically, switching between GPT-4o and o3 can affect memory state.
+ChatGPT's memory works in two modes: background auto-extraction where the model decides what's worth remembering, plus manual memory pins you add yourself. You can view and delete stored memories, but the extraction logic is opaque — you don't see the raw files or control the process. Critically, switching between GPT-5.4 and o3-pro can affect memory state.
Hermes stores all memory as plain markdown files on your filesystem. You can open them in any editor, modify them, version-control them with git, and they persist regardless of which model backend you use. Eight optional external memory providers (vector databases, graph stores) are available for more advanced retrieval patterns.
Claude Code supports Anthropic's API, AWS Bedrock, Google Vertex AI, and Anthropic Foundry. These are meaningful options for enterprise deployment, but they share one constraint: every inference uses a Claude model. If Claude pricing changes, if a competitor releases a model significantly better for a specific task, or if you simply want to use a local open-source model for cost or privacy reasons, you cannot do that within Claude Code. The tool is by design Claude-native.
-Hermes is provider-agnostic. You configure which provider and model to use, and can change it at any time — or route different tasks to different providers. GPT-4o for one thing, Claude Sonnet for another, a local Ollama model for private data. This flexibility is especially valuable when the model landscape is moving as fast as it currently is: you're not locked into today's best option when something better ships.
+Hermes is provider-agnostic. You configure which provider and model to use, and can change it at any time — or route different tasks to different providers. GPT-5.4 for one thing, Claude Sonnet 4.6 for another, a local Ollama model for private data. This flexibility is especially valuable when the model landscape is moving as fast as it currently is: you're not locked into today's best option when something better ships.
For most users evaluating these two tools for coding work, provider flexibility is a secondary concern — Claude is genuinely excellent at coding tasks, and Claude Code's tight integration is an advantage. But for users with strong privacy requirements, cost sensitivity, or a preference to hedge model risk, Hermes's open provider model is a concrete benefit.
Copilot's Coding Agent is issue-driven, not time-driven. You can't tell Copilot to run a task every morning at 6am, generate a weekly report, or monitor a metric and trigger an action when a threshold is crossed. Hermes has full cron-based scheduling: jobs run on whatever time-based schedule you define, with no manual trigger required. For monitoring, reporting, data pipelines, and any automation that needs to run on a clock, Hermes covers ground that Copilot cannot.
Copilot routes all requests through GitHub's model infrastructure — you can choose between Claude, GPT-4o, Gemini, and others, but only through GitHub's proxied endpoints. You cannot point Copilot at a local Ollama instance, a custom OpenAI-compatible endpoint, or a regional cloud API. Hermes is fully provider-agnostic: local models, hosted APIs, any OpenAI-compatible endpoint, or provider-specific SDKs all work. For teams with cost constraints, data residency requirements, or a preference for specific model providers, this flexibility matters.
+Copilot routes all requests through GitHub's model infrastructure — you can choose between Claude Sonnet 4.6, GPT-5.4, Gemini, and others, but only through GitHub's proxied endpoints. You cannot point Copilot at a local Ollama instance, a custom OpenAI-compatible endpoint, or a regional cloud API. Hermes is fully provider-agnostic: local models, hosted APIs, any OpenAI-compatible endpoint, or provider-specific SDKs all work. For teams with cost constraints, data residency requirements, or a preference for specific model providers, this flexibility matters.
diff --git a/compare/cursor.html b/compare/cursor.html index 4fba17dc..ffb96bf1 100644 --- a/compare/cursor.html +++ b/compare/cursor.html @@ -267,7 +267,7 @@Cursor has moved fast. Memories launched in June 2025, giving per-project context persistence. Automations launched in March 2026, enabling scheduled cloud-executed agent tasks. Cursor v3.0 (April 2026) repositioned it as agent-first with a 30+ plugin marketplace. The company closed at a $29.3B valuation with $2B ARR, and the Supermaven acquisition brought one of the fastest autocomplete engines in the industry. This is a serious, well-funded product moving quickly.
+Cursor has moved fast. Memories launched in June 2025, giving per-project context persistence. Automations launched in March 2026, enabling scheduled cloud-executed agent tasks. Cursor v3.0 (April 2026) repositioned it as agent-first with a 30+ plugin marketplace. The company is valued at over $29B with $2B ARR, and the Supermaven acquisition brought one of the fastest autocomplete engines in the industry. This is a serious, well-funded product moving quickly.
Cursor's strongest features are fundamentally IDE features: autocomplete as you type with Supermaven-powered latency, inline diffs that let you accept or reject changes line-by-line, and refactoring with full file context visible in the editor. No terminal-based agent replicates the inline diff experience. If you spend most of your coding time in an editor and want AI that integrates deeply into that workflow, Cursor is the category leader.
diff --git a/index.html b/index.html index 61d26de0..d9fd5b3e 100644 --- a/index.html +++ b/index.html @@ -1300,6 +1300,8 @@More comparisons
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More comparisons
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