Imagine a partner who remembers all your project details, work habits, and unfinished tasks. This is no longer imagination—it’s an ongoing interaction revolution.
If you’ve been following the AI community lately, you’ve likely seen one name repeatedly—OpenClaw or ClawdBot\MoltBot. This open-source project has skyrocketed on GitHub, not because of its parameter size, but because it strikes a nerve in today’s most awkward aspect of AI interaction.
@yan5xu’s analogy from a community post is startlingly accurate: “Every time we converse with an AI, it feels like using old Office software—constantly creating ‘new documents’ and restating the context.” This sense of “fragmentation” in conversation is something we can all relate to.
With the emergence of OpenClaw, a more direct viewpoint has begun to circulate: “After OpenClaw, the concept of Sessions should disappear.” This isn’t an isolated sentiment, but a collective outcry from users experiencing the same pain point.
01 Why Have We Endured “Forgetful” AI?
Let’s face reality honestly: open ChatGPT or Claude, discuss project details for half an hour, close the tab. Open it again tomorrow, and this “expert” has forgotten everything, requiring you to reiterate all of yesterday’s discussions.
Doesn’t that feel like working with a partner suffering from severe short-term memory loss?
The session mode is essentially a compromise born out of technical limitations. Due to early models’ limited context windows and lack of long-term memory, developers were forced to slice continuous interactions into isolated “chats.” Consequently, users became “AI memory custodians”:
- Manually copy-pasting history between different windows
- Opening dozens of tabs for each project, managing “memory islands”
- Spending time writing a “previously on…” summary before each conversation
- Enduring repeated explanations of the same background information
The reason @yan5xu’s analogy resonates so deeply is that it reveals the core contradiction in current interactions: Users are accommodating the machine’s flaws, not the machine serving user needs. Every “New Chat” is an implicit acceptance of this compromise.
02 OpenClaw: The First Tangible Shift from “Forgetful Assistant” to “Continuous Partner”
What does OpenClaw do differently? It abandons the traditional “chat-reset” model, designing the agent as a continuously running daemon process.
It has its own “identity” (predefined persona, goals, toolset) and, more crucially, it persistently stores all memories and decisions via the filesystem (e.g., MEMORY.md, workspace, journal). This means:
- Context continuity across days and sessions
- It remembers yesterday’s discussions and your preferences
- It can advance work based on past decisions, rather than starting from scratch each time
Developer Brad’s assessment hits the nail on the head: “Persistent memory is what separates a useful agent from a chatbot.” It knows yesterday’s context, remembers past decisions, and builds new work upon them.
OpenClaw isn’t the perfect, ultimate solution (it still faces API rate limits and has primitive memory management), but it proves a critical point: Providing a continuous, consistent AI experience is technically feasible. Moreover, once users experience the feeling of “being remembered,” returning to the “goldfish memory” era becomes nearly impossible.
03 Real Resistance: The Three High Walls for Continuous Agents
The journey from “sessions” to “continuity” isn’t a smooth one. The real obstacles are clear and concrete:
Wall #1: Privacy & Compliance
Persistent memory means indefinite storage of user data. This directly touches red lines of regulations like GDPR. For businesses, the resettable session mode ironically acts as a legal “safe harbor”—no data retention, naturally lower risk.
Wall #2: Cost & Performance
A continuously running agent needs to stay active 24/7, ready to invoke vast historical context at any moment. This poses immense challenges to computational resources and API costs. Unpredictable expenditure models deter many enterprises.
Wall #3: User Habits
Not all users want to “be remembered.” Many appreciate the psychological safety zone provided by session-based models—knowing a conversation can be completely erased allows for freer exploration and experimentation.
These obstacles mean session-based models won’t disappear overnight. However, we already see vendors patching the old paradigm with techniques like “memory pinning,” history imports, and RAG retrieval. Yet, a patch is just a patch, unable to solve the fundamental problem of interaction discontinuity.
04 The Future Form: From Session to “Agent + Topic”
If sessions are ultimately fading, what will replace them? The community is already exploring the next paradigm. It likely won’t be a single “infinite chatbox,” but a more refined dual-layer structure: Agent + Topic.
- Agent Layer: Global, persistent entity. Regardless of the platform or device, the same Agent is always online. It possesses a unified identity, long-term memory, goal list, and toolset, with its context being continuous.
- Topic Layer: The “workspace” for current focus. Each Topic is like a lightweight project space with its own short-term context and file references, but shares the Agent’s global knowledge. Want to switch focus? Just switch Topics without restarting the entire Agent.
This is, in fact, much like a person: always thinking about clocking out at work, yet only thinking about what to add to the wok while cooking—completely different things on the mind, but the person remains the same individual.
This design cleverly balances continuity and controllability. It prevents the loss of global knowledge while allowing for localized focus isolation, mirroring the collaborative pattern of human “working memory + long-term memory.” OpenClaw’s file-based memory system has already paved the way for this layered architecture.
Once this paradigm takes root, user interaction will shift from “managing multiple isolated chat windows” to “managing a living partner and its multiple points of focus.” Looking back then, today’s session-based models will likely seem like “Stone Age tools” of the digital era.
05 Final Thoughts: We Don’t Need Better Chatboxes, We Need True Partners
Sessions won’t vanish tomorrow. They retain value in scenarios demanding extreme privacy and simplicity. But the trend is clear: The core value of next-generation AI will come not only from larger model parameters but from uninterrupted, cumulative intelligence trajectories.
OpenClaw’s popularity is a clear signal: users are tired of “goldfish memory” interactions. The AI we anticipate is a collaborative partner that can remember, understand, and grow alongside us, not a tool that requires reintroduction every time.
This quiet interaction revolution isn’t just about how technology evolves; it’s about how we forge deeper, more natural relationships with technology. When AI learns to “remember,” our relationship with machines will also evolve—from simple command and execution to understanding and synergy.
Perhaps soon, the very action of “New Chat” will become, like the floppy disk icon, a metaphor for a bygone era. And that day might arrive sooner than we think.