Original link: OpenClaw New_Memory Mechanism: Dreaming
On March 31st, the suspected leak of Claude's source code caused a huge stir in the tech community. Its unique "memory-dreaming" mechanism offered a glimpse into a new path for AI evolution. Just a few days later, on April 5th, the well-known open-source AI agent project OpenClaw quickly released version 2026.04.05, introducing a highly similar automated memory enhancement function—Dreaming.
The launch of this system marks the official departure of Large Language Model (LLM) memory management from the era of crude "file stacking" and into a new era of "intelligent integration" that draws inspiration from human biological sleep mechanisms.
1. Core Pain Point: "Goldfish Memory" Will Inevitably Be Eliminated
Before understanding the Dreaming system, we need to recognize the fatal flaws of traditional large model memory mechanisms.
1.1 Costly "Pseudo-Memory" (Context Trap)
Illusion: Large models are essentially "stateless" (only have a seven-second memory). They can only respond to conversations because the system repeatedly packages and refeeds your historical records to the model.
Consequences: As the number of conversation rounds increases, the context window expands rapidly. This leads to skyrocketing API costs, a sharp drop in response speed, and even causes the model to "distract" and forget key instructions.
1.2. Hoarding-like Brutal Recording
Method: To forcibly break through the memory length limit, early AI adopted a "take everything in" strategy, stuffing every word you said into the backend database.
Disaster: Everyday chatter, one-off questions, and even casually made-up role-playing scenarios were all permanently stored by the AI as "truths." Over time, the memory bank became a wastebasket full of junk information.
Contradictions Coexist: When your project requirements change (e.g., the code framework is changed), old incorrect memories and new correct requirements will coexist in the database.
Logical Collapse: Due to the lack of a "cleaning and overwriting" mechanism, faced with contradictory settings, the AI will fall into logical confusion, ultimately outputting completely uncontrollable erroneous content.
OpenClaw introduces the Dreaming mechanism precisely to break these deadlocks. It completely separates data collection from data cleaning, allowing AI to focus on receiving short-term signals during the day and automatically perform deep noise reduction and purification at night.
2. Architecture Breakdown: The "Three-Stage" Sleep Cycle
According to the official documentation (docs.openclaw.ai/concepts/dreaming), OpenClaw's dream system is not a simple script cleanup, but rather a complete simulation of the three core stages of human sleep. The entire process is asynchronous, automated, and reinforced with signals.
Below is a comparison of the internal operating logic of the three phases:
| Sleep Phase | Core Task | Underlying Operating Logic | Whether to Write to Long-Term Memory |
| --- | --- | --- | --- |
| Light Sleep | Organize and Store | Automatically checks the day's operation records, extracts short-term signals and removes duplicates. Records reinforcement signals and generates a light sleep block in DREAMS.md. | No |
| REM Phase | Reflection and Insight | Reflects on recent conversation topics and recurring ideas, generates higher-level insights, and records REM reinforcement signals. | No |
| Deep Sleep | Memory Consolidation | Uses a weighted scoring model to rigorously evaluate candidate content. Passing content is retrieved again and formally consolidated into long-term memory. | Yes (Written into MEMORY.md) |
Key Insights: The light sleep and REM stages are only responsible for "thinking" and "drafting." All process data is stored in the /memory/.dreams/ directory. Only information deemed high-value during the deep sleep stage is appended to MEMORY.md.
3. Algorithmic Standards: An Extremely Rigorous "Deep Sleep Scoring Mechanism"
Machine dreams differ from human dreams; they don't pursue boundless imagination but rather extreme rationality and efficiency. To determine whether a memory should be forgotten or permanently stored, OpenClaw introduces a weighted scoring model with a total weight of 1.0 during the deep sleep stage.
A candidate memory must pass the thresholds of minimum score (minScore), minimum recall count (minRecallCount), and query diversity (minUniqueQueries). The specific weighted algorithm is as follows:
| Evaluation Dimension | Weighting | Decision Logic Explanation |
| --- | --- | --- |
| Relevance | 30% | occupies the highest weight. Evaluates the actual quality and business value of the memory in past retrievals. |
| Frequency | 24% | Evaluates the cumulative number of times the core signal is mentioned in short-term memory. |
| Diversity | 15% | Evaluates whether the information is repeatedly triggered in different contexts and queries. |
| Recency | 15% | A freshness score with a time decay mechanism to ensure that outdated and useless information is eliminated. |
| Consolidation | 10% | Evaluates the intensity of recurrence across multiple days, filtering out truly long-term preferences. |
| Richness | 6% | The density of core concept tags contained in the memory fragment. |
Through this algorithm, OpenClaw can accurately filter out casual conversations, leaving only rules, preferences, coding habits, and project backgrounds with high reusability.
4. Rejecting Black Boxes: Constructing a Transparent End-to-End Review Mechanism
AI's autonomous decision-making regarding memory retention can easily trigger "black box panic" among users. To establish absolute trust, OpenClaw has created a multi-level transparent review system, from ordinary users to geek developers:
4.1. Physical Isolation of Underlying Data
OpenClaw directly defines boundaries in its storage design:
Machine-readable: Underlying computational data (recall library, stage signals, etc.) is silently stored in the hidden directory /memory/.dreams/.
Human-readable: Run logs and stage reports are output separately as .md text files, ensuring that complex elimination and promotion algorithms are always readily available.
4.2. Intuitive "Dream Diary" and Control Panel
Providing a highly "human" review method for ordinary users who don't want to look at the code:
Dream Diary: The background AI transforms cold data into a narrative diary and writes it to DREAMS.md. You can understand the AI's "reflection" from last night as easily as flipping through a friend's memo (Note: This diary is for human reading only and will never be used as a source of long-term memory for the AI).
Gateway Dreams UI: The interface intuitively displays the current sleep stage, long and short-term memory conversion statistics, and a countdown to the next scheduling time.
4.3. A "CLI Scalpel" for Geeks
Providing developers with an extremely powerful command-line interface (CLI) for in-depth intervention:
Precise Accountability (promote-explain): Enter query keywords, and the system will immediately output the precise score of the candidate memory in six weighted dimensions, making the reasons for promotion or failure immediately clear.
Lossless Preview (rem-harness): Previews the entire REM reflection results and deep sleep promotion list while idling, without writing to any actual files. Like a pre-operative sandbox simulation, this completely eliminates the risk of erroneous memories contaminating the core database.
Through this comprehensive review mechanism, OpenClaw not only teaches AI to "organize dreams" like humans, but also makes this seemingly mystical process completely traceable and operable. This is not simply log recording, but rather building a solid bridge of trust between silicon-based and carbon-based life forms.
5. Conclusion: The Divergence Between Silicon-Based and Carbon-Based Dreams
From Claude to OpenClaw v2026.04.05, AI's memory system is gradually approaching the operating mechanism of the human brain. This workflow of accumulating during the day and reflecting at night makes the long-term context of intelligent agents more accurate and efficient than ever before.
However, we can still clearly see the difference between silicon-based and carbon-based systems: OpenClaw's dream is highly self-disciplined—it merges repetitions, corrects errors, and eliminates redundancy within 200 lines of code, with extreme rigor. Human dreams, on the other hand, forcibly piece together seemingly unrelated fragments, sparking brilliant inspiration and great scientific discoveries through absurd collisions.
AI is taking over all the meticulous tasks of memorization and organization. And those bizarre, unconventional creative ideas remain humanity's strongest bulwark in this technological storm.
Tags: OpenClaw | Claude | AI Agent |
