MemoryBear v0.3.0 Enterprise Release Notes — Daybreak

[keeees]

Release Date: April 15, 2026 | Codename: PoXiao (破晓 · Daybreak)

MemoryBear v0.3.0 builds upon the foundation of previous releases with a broad set of improvements spanning application workflows, memory intelligence, and system robustness. This release introduces versioned API support, multimodal memory perception, and significant workflow enhancements — while resolving a wide range of stability issues across the platform. The result is a more resilient, precise, and developer-friendly MemoryBear.

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🚀 I. Core Upgrade Overview

1. Application & API Enhancements

• Versioned API Support: External service APIs now support specifying a version when making calls, enabling consumers to pin to stable interfaces and facilitating smoother API evolution without breaking existing integrations.
• Workflow Checklist: A new workflow checklist feature provides structured validation steps, helping users verify workflow configurations before deployment and reducing runtime errors.
• Deep Thinking Parameter Control: OpenAI deep thinking parameters are now only sent to models that explicitly support deep reasoning, preventing unnecessary parameter injection into incompatible models and avoiding potential errors.
• Prompt Optimizer Model Return Optimization: Improved the response handling of the prompt optimizer model, delivering more consistent and reliable outputs during prompt engineering workflows.

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2. Memory Intelligence 🧠

• Multimodal Memory Perception Agent: The memory perception agent now supports reading and writing multimodal memory, enabling richer context capture across text, image, and other modalities for more comprehensive user understanding.
• OpenClaw Built-in Tool: Added a new built-in tool to OpenClaw, expanding the toolkit available for memory-related agent operations and simplifying integration workflows.

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3. User Experience 🎨

• Streaming Render Stabilization: Resolved frequent page redraws during LLM streaming output that caused visible page jitter. The rendering pipeline is now smoother, providing a stable reading experience during model responses.
• Memory Hub Renaming: The workspace section previously labeled "记忆相关" (Memory Related) has been renamed to "记忆中枢" (Memory Hub), better reflecting its role as the central memory management interface.

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4. Workflow Improvements ⚙️

• Three-Level Variable Template Conversion: Workflow template conversion now supports three-level variables, fixing an issue where single-file-type node selections could not be properly resolved.
• VL Model Token Tracking: Token usage is now correctly tracked when using VL (Vision-Language) models from model groups, resolving a gap where tokens went unrecorded compared to selecting the same model from the standard model list.
• Imported Workflow Feature Sync: Workflows imported from external sources now correctly synchronize all feature properties including opening messages, citations, and attribution settings — previously these were silently dropped during import.
• Session Variable Name Uniqueness: Added uniqueness validation for session variable and start node variable names in workflows, preventing silent conflicts and hard-to-debug runtime issues.
• File Type Extraction Fix: Workflows can now correctly extract file.type information, resolving a parsing issue that previously returned empty values.
• Condition Branch Display Fix: Condition branch nodes now render correctly when configured with a value of 0 or when referencing session variables, fixing a display truncation issue.
• Object/Array Validation Rules: Added validation rules for object and array[object] session variables, preventing JSON serialization errors that previously blocked workflow saves.
• HTTP Request Body Key Fix: In workflow HTTP request nodes, the body field now uses key instead of name for field identification, aligning with standard HTTP conventions and resolving mapping issues.

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5. Knowledge Base 📚

• Embedding Token Truncation Safety: Fixed errors caused by embedding model token limits being exceeded. A unified 8,000-token truncation safety boundary is now applied to all embedding models. Additionally, Excel row data now maintains independent chunks without merging, using newline separation for cleaner document processing.

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6. Robustness & Bug Fixes 🔧

• Atomic Update & Batch Access Failures: Resolved failures in atomic memory updates and batch access record operations that could cause data inconsistency under concurrent load.
• Alias Extraction in Conversations: Fixed incorrect alias extraction during application conversations, ensuring user identities are properly resolved from dialogue context.
• Workflow Alias Extraction: Corrected alias extraction in workflows to properly distinguish between user and AI responses, including consideration of emotion and interest distribution (issue #1006016).
• RAG Memory Pagination: Fixed data pagination issues in RAG memory retrieval that could return incomplete or duplicated results across pages.
• Implicit Memory Detail Display: Resolved an issue where implicit memory entries with corresponding numeric values showed empty content in the detail view (observed in workflow v0.2.10 multimodal memory reads).
• Vector Query Driver Closed: Fixed search_graph_by_embedding: statements vector query exceptions caused by a prematurely closed database driver in memory storage and memory verification flows.
• User Management Enable/Disable: Fixed abnormal API request behavior when toggling user enable/disable status in user management.
• Model List Filter Inconsistency: Resolved an issue where model list filter criteria did not match the displayed results, ensuring accurate filtering behavior.

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🧭 Looking Ahead

MemoryBear v0.3.0 marks a meaningful step toward production maturity. The breadth of this release — spanning API versioning, multimodal memory, workflow robustness, and rendering stability — reflects a platform that is rapidly hardening its core while continuing to expand its capabilities. The focus on fixing edge cases and improving developer-facing consistency signals a shift toward reliability-first engineering.

The introduction of multimodal memory perception and enhanced workflow variable handling opens new doors for building sophisticated, context-aware agent applications. These capabilities lay the groundwork for deeper integration between memory systems and application logic, moving MemoryBear closer to its vision of truly intelligent, adaptive memory infrastructure.

In upcoming releases, expect continued investment in workflow expressiveness, memory retrieval precision, and cross-modal understanding. The platform will deepen its support for complex agent orchestration patterns while further stabilizing the foundation for large-scale production deployments.

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MemoryBear v0.3.0 企业版 发布说明 —— 破晓

发布日期： 2026年4月15日 ｜ 版本代号： 破晓（PoXiao · Daybreak）

MemoryBear v0.3.0 在前版基础上进行了全面升级，涵盖应用工作流、记忆智能和系统稳健性等多个维度。本版本引入了版本化API调用、多模态记忆感知以及大量工作流增强功能，同时修复了平台各层面的稳定性问题，打造更加可靠、精准且开发者友好的 MemoryBear。

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🚀 一、核心升级概览

1. 应用与API增强

• 版本化API调用支持：对外服务API现已支持指定版本调用，使消费方可以锁定稳定接口，便于API平滑演进而不影响现有集成。
• 工作流检查清单：新增工作流检查清单功能，提供结构化的验证步骤，帮助用户在部署前核验工作流配置，减少运行时错误。
• 深度思考参数精准控制：OpenAI深度思考参数现仅发送给明确支持深度推理的模型，避免向不兼容模型注入无效参数，防止潜在错误。
• 提示器模型返回优化：优化了提示器模型的响应处理，在提示工程工作流中提供更一致、可靠的输出。

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2. 记忆智能 🧠

• 多模态记忆感知Agent：记忆感知Agent现已支持多模态记忆的读取与写入，能够跨文本、图像等多种模态捕获更丰富的上下文，实现更全面的用户理解。
• OpenClaw内置工具：为OpenClaw新增内置工具，扩展了记忆相关Agent操作的工具集，简化集成工作流。

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3. 用户体验 🎨

• 流式渲染稳定性优化：解决了LLM流式输出过程中页面频繁重绘导致的抖动问题，渲染管线更加平滑，模型回复时提供稳定的阅读体验。
• 记忆中枢更名：工作空间中原"记忆相关"板块已更名为"记忆中枢"，更准确地体现其作为核心记忆管理界面的定位。

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4. 工作流改进 ⚙️

• 三级变量模板转换：工作流模板转换现已支持三级变量，修复了单文件类型节点选择无法正确解析的问题。
• VL模型Token统计：使用模型组合中的VL（视觉语言）模型时，Token用量现已正确统计，解决了与标准模型列表选择同一模型时统计缺失的差异。
• 导入工作流功能特性同步：从外部导入的工作流现已正确同步所有功能特性，包括开场白、引用和归属设置等——此前这些属性在导入时被静默丢弃。
• 会话变量名称唯一性校验：为工作流中的会话变量和开始节点变量名称新增唯一性校验，防止静默冲突和难以调试的运行时问题。
• 文件类型提取修复：工作流现可正确提取 file.type 信息，解决了此前返回空值的解析问题。
• 条件分支显示修复：条件分支节点在配置值为 0 或引用会话变量时现已正确渲染，修复了显示截断问题。
• Object/Array校验规则：为 object 和 array[object] 类型的会话变量新增校验规则，防止JSON序列化错误导致工作流无法保存。
• HTTP请求Body字段修正：工作流HTTP请求节点中，body请求体字段从使用 name 改为使用 key 进行字段标识，与标准HTTP规范对齐并解决映射问题。

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5. 知识库 📚

• Embedding Token截断安全边界：修复了因embedding模型token超限导致的报错问题，统一为所有embedding模型添加8,000 token截断安全边界。同时优化Excel处理，每行数据保持独立chunk，不再合并，使用换行分隔以实现更清晰的文档处理。

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6. 稳健性与缺陷修复 🔧

• 原子性更新与批量访问失败：修复了原子性记忆更新和批量访问记录操作在并发负载下可能导致数据不一致的问题。
• 对话别名提取错误：修复了应用对话中别名提取不正确的问题，确保从对话上下文中正确解析用户身份。
• 工作流别名提取：修正了工作流中的别名提取逻辑，正确区分用户和AI回复，同步考量情绪和兴趣分布（问题 #1006016）。
• RAG记忆分页：修复了RAG记忆检索中的分页数据问题，解决了跨页返回不完整或重复结果的情况。
• 隐式记忆详情显示：修复了隐式记忆条目在有对应数字值时详情中显示为空的问题（见工作流 v0.2.10 多模态记忆读取场景）。
• 向量查询驱动关闭异常：修复了记忆存储和记忆验证流程中 search_graph_by_embedding: statements 向量查询因数据库驱动提前关闭导致的异常。
• 用户管理启停异常：修复了用户管理中切换启用/停用状态时的异常API请求行为。
• 模型列表筛选不一致：修复了模型列表筛选条件与显示结果不匹配的问题，确保筛选行为准确。

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🧭 未来展望

MemoryBear v0.3.0 标志着产品向生产成熟度迈出了坚实一步。本版本覆盖API版本化、多模态记忆、工作流稳健性和渲染稳定性等多个维度，体现了一个在持续拓展能力边界的同时快速夯实核心的平台。对边界场景的修复和开发者体验一致性的提升，标志着工程重心正向可靠性优先转变。

多模态记忆感知和增强的工作流变量处理能力的引入，为构建复杂的上下文感知Agent应用打开了新的大门。这些能力为记忆系统与应用逻辑的深度融合奠定了基础，推动 MemoryBear 向真正智能、自适应的记忆基础设施愿景迈进。

在后续版本中，我们将持续投入工作流表达力、记忆检索精度和跨模态理解能力的提升，深化对复杂Agent编排模式的支持，同时进一步稳固大规模生产部署的基础。

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