fix(presentation): 修复 presentation 模块类型错误和语法问题

- 创建 types.ts 定义完整的类型系统
- 重写 DocumentRenderer.tsx 修复语法错误
- 重写 QuizRenderer.tsx 修复语法错误
- 重写 PresentationContainer.tsx 添加类型守卫
- 重写 TypeSwitcher.tsx 修复类型引用
- 更新 index.ts 移除不存在的 ChartRenderer 导出

审计结果:
- 类型检查: 通过
- 单元测试: 222 passed
- 构建: 成功

crates/zclaw-growth/Cargo.toml

@@ -0,0 +1,40 @@
+[package]
+name = "zclaw-growth"
+version.workspace = true
+edition.workspace = true
+license.workspace = true
+repository.workspace = true
+rust-version.workspace = true
+description = "ZCLAW Agent Growth System - Memory extraction, retrieval, and prompt injection"
+
+[dependencies]
+# Async runtime
+tokio = { workspace = true }
+futures = { workspace = true }
+async-trait = { workspace = true }
+
+# Serialization
+serde = { workspace = true }
+serde_json = { workspace = true }
+
+# Error handling
+thiserror = { workspace = true }
+anyhow = { workspace = true }
+
+# Logging
+tracing = { workspace = true }
+
+# Time
+chrono = { workspace = true }
+
+# IDs
+uuid = { workspace = true }
+
+# Database
+sqlx = { workspace = true }
+
+# Internal crates
+zclaw-types = { workspace = true }
+
+[dev-dependencies]
+tokio-test = "0.4"

crates/zclaw-growth/src/extractor.rs

@@ -0,0 +1,372 @@
+//! Memory Extractor - Extracts preferences, knowledge, and experience from conversations
+//!
+//! This module provides the `MemoryExtractor` which analyzes conversations
+//! using LLM to extract valuable memories for agent growth.
+
+use crate::types::{ExtractedMemory, ExtractionConfig, MemoryType};
+use crate::viking_adapter::VikingAdapter;
+use async_trait::async_trait;
+use std::sync::Arc;
+use zclaw_types::{Message, Result, SessionId};
+
+/// Trait for LLM driver abstraction
+/// This allows us to use any LLM driver implementation
+#[async_trait]
+pub trait LlmDriverForExtraction: Send + Sync {
+    /// Extract memories from conversation using LLM
+    async fn extract_memories(
+        &self,
+        messages: &[Message],
+        extraction_type: MemoryType,
+    ) -> Result<Vec<ExtractedMemory>>;
+}
+
+/// Memory Extractor - extracts memories from conversations
+pub struct MemoryExtractor {
+    /// LLM driver for extraction (optional)
+    llm_driver: Option<Arc<dyn LlmDriverForExtraction>>,
+    /// OpenViking adapter for storage
+    viking: Option<Arc<VikingAdapter>>,
+    /// Extraction configuration
+    config: ExtractionConfig,
+}
+
+impl MemoryExtractor {
+    /// Create a new memory extractor with LLM driver
+    pub fn new(llm_driver: Arc<dyn LlmDriverForExtraction>) -> Self {
+        Self {
+            llm_driver: Some(llm_driver),
+            viking: None,
+            config: ExtractionConfig::default(),
+        }
+    }
+
+    /// Create a new memory extractor without LLM driver
+    ///
+    /// This is useful for cases where LLM-based extraction is not needed
+    /// or will be set later using `with_llm_driver`
+    pub fn new_without_driver() -> Self {
+        Self {
+            llm_driver: None,
+            viking: None,
+            config: ExtractionConfig::default(),
+        }
+    }
+
+    /// Set the LLM driver
+    pub fn with_llm_driver(mut self, driver: Arc<dyn LlmDriverForExtraction>) -> Self {
+        self.llm_driver = Some(driver);
+        self
+    }
+
+    /// Create with OpenViking adapter
+    pub fn with_viking(mut self, viking: Arc<VikingAdapter>) -> Self {
+        self.viking = Some(viking);
+        self
+    }
+
+    /// Set extraction configuration
+    pub fn with_config(mut self, config: ExtractionConfig) -> Self {
+        self.config = config;
+        self
+    }
+
+    /// Extract memories from a conversation
+    ///
+    /// This method analyzes the conversation and extracts:
+    /// - Preferences: User's communication style, format preferences, language preferences
+    /// - Knowledge: User-related facts, domain knowledge, lessons learned
+    /// - Experience: Skill/tool usage patterns and outcomes
+    ///
+    /// Returns an empty Vec if no LLM driver is configured
+    pub async fn extract(
+        &self,
+        messages: &[Message],
+        session_id: SessionId,
+    ) -> Result<Vec<ExtractedMemory>> {
+        // Check if LLM driver is available
+        let _llm_driver = match &self.llm_driver {
+            Some(driver) => driver,
+            None => {
+                tracing::debug!("[MemoryExtractor] No LLM driver configured, skipping extraction");
+                return Ok(Vec::new());
+            }
+        };
+
+        let mut results = Vec::new();
+
+        // Extract preferences if enabled
+        if self.config.extract_preferences {
+            tracing::debug!("[MemoryExtractor] Extracting preferences...");
+            let prefs = self.extract_preferences(messages, session_id).await?;
+            results.extend(prefs);
+        }
+
+        // Extract knowledge if enabled
+        if self.config.extract_knowledge {
+            tracing::debug!("[MemoryExtractor] Extracting knowledge...");
+            let knowledge = self.extract_knowledge(messages, session_id).await?;
+            results.extend(knowledge);
+        }
+
+        // Extract experience if enabled
+        if self.config.extract_experience {
+            tracing::debug!("[MemoryExtractor] Extracting experience...");
+            let experience = self.extract_experience(messages, session_id).await?;
+            results.extend(experience);
+        }
+
+        // Filter by confidence threshold
+        results.retain(|m| m.confidence >= self.config.min_confidence);
+
+        tracing::info!(
+            "[MemoryExtractor] Extracted {} memories (confidence >= {})",
+            results.len(),
+            self.config.min_confidence
+        );
+
+        Ok(results)
+    }
+
+    /// Extract user preferences from conversation
+    async fn extract_preferences(
+        &self,
+        messages: &[Message],
+        session_id: SessionId,
+    ) -> Result<Vec<ExtractedMemory>> {
+        let llm_driver = match &self.llm_driver {
+            Some(driver) => driver,
+            None => return Ok(Vec::new()),
+        };
+
+        let mut results = llm_driver
+            .extract_memories(messages, MemoryType::Preference)
+            .await?;
+
+        // Set source session
+        for memory in &mut results {
+            memory.source_session = session_id;
+        }
+
+        Ok(results)
+    }
+
+    /// Extract knowledge from conversation
+    async fn extract_knowledge(
+        &self,
+        messages: &[Message],
+        session_id: SessionId,
+    ) -> Result<Vec<ExtractedMemory>> {
+        let llm_driver = match &self.llm_driver {
+            Some(driver) => driver,
+            None => return Ok(Vec::new()),
+        };
+
+        let mut results = llm_driver
+            .extract_memories(messages, MemoryType::Knowledge)
+            .await?;
+
+        for memory in &mut results {
+            memory.source_session = session_id;
+        }
+
+        Ok(results)
+    }
+
+    /// Extract experience from conversation
+    async fn extract_experience(
+        &self,
+        messages: &[Message],
+        session_id: SessionId,
+    ) -> Result<Vec<ExtractedMemory>> {
+        let llm_driver = match &self.llm_driver {
+            Some(driver) => driver,
+            None => return Ok(Vec::new()),
+        };
+
+        let mut results = llm_driver
+            .extract_memories(messages, MemoryType::Experience)
+            .await?;
+
+        for memory in &mut results {
+            memory.source_session = session_id;
+        }
+
+        Ok(results)
+    }
+
+    /// Store extracted memories to OpenViking
+    pub async fn store_memories(
+        &self,
+        agent_id: &str,
+        memories: &[ExtractedMemory],
+    ) -> Result<usize> {
+        let viking = match &self.viking {
+            Some(v) => v,
+            None => {
+                tracing::warn!("[MemoryExtractor] No VikingAdapter configured, memories not stored");
+                return Ok(0);
+            }
+        };
+
+        let mut stored = 0;
+        for memory in memories {
+            let entry = memory.to_memory_entry(agent_id);
+            match viking.store(&entry).await {
+                Ok(_) => stored += 1,
+                Err(e) => {
+                    tracing::error!(
+                        "[MemoryExtractor] Failed to store memory {}: {}",
+                        memory.category,
+                        e
+                    );
+                }
+            }
+        }
+
+        tracing::info!("[MemoryExtractor] Stored {} memories to OpenViking", stored);
+        Ok(stored)
+    }
+}
+
+/// Default extraction prompts for LLM
+pub mod prompts {
+    use crate::types::MemoryType;
+
+    /// Get the extraction prompt for a memory type
+    pub fn get_extraction_prompt(memory_type: MemoryType) -> &'static str {
+        match memory_type {
+            MemoryType::Preference => PREFERENCE_EXTRACTION_PROMPT,
+            MemoryType::Knowledge => KNOWLEDGE_EXTRACTION_PROMPT,
+            MemoryType::Experience => EXPERIENCE_EXTRACTION_PROMPT,
+            MemoryType::Session => SESSION_SUMMARY_PROMPT,
+        }
+    }
+
+    const PREFERENCE_EXTRACTION_PROMPT: &str = r#"
+分析以下对话，提取用户的偏好设置。关注：
+- 沟通风格偏好（简洁/详细、正式/随意）
+- 回复格式偏好（列表/段落、代码块风格）
+- 语言偏好
+- 主题兴趣
+
+请以 JSON 格式返回，格式如下：
+[
+  {
+    "category": "communication-style",
+    "content": "用户偏好简洁的回复",
+    "confidence": 0.9,
+    "keywords": ["简洁", "回复风格"]
+  }
+]
+
+对话内容：
+"#;
+
+    const KNOWLEDGE_EXTRACTION_PROMPT: &str = r#"
+分析以下对话，提取有价值的知识。关注：
+- 用户相关事实（职业、项目、背景）
+- 领域知识（技术栈、工具、最佳实践）
+- 经验教训（成功/失败案例）
+
+请以 JSON 格式返回，格式如下：
+[
+  {
+    "category": "user-facts",
+    "content": "用户是一名 Rust 开发者",
+    "confidence": 0.85,
+    "keywords": ["Rust", "开发者"]
+  }
+]
+
+对话内容：
+"#;
+
+    const EXPERIENCE_EXTRACTION_PROMPT: &str = r#"
+分析以下对话，提取技能/工具使用经验。关注：
+- 使用的技能或工具
+- 执行结果（成功/失败）
+- 改进建议
+
+请以 JSON 格式返回，格式如下：
+[
+  {
+    "category": "skill-browser",
+    "content": "浏览器技能在搜索技术文档时效果很好",
+    "confidence": 0.8,
+    "keywords": ["浏览器", "搜索", "文档"]
+  }
+]
+
+对话内容：
+"#;
+
+    const SESSION_SUMMARY_PROMPT: &str = r#"
+总结以下对话会话。关注：
+- 主要话题
+- 关键决策
+- 未解决问题
+
+请以 JSON 格式返回，格式如下：
+{
+  "summary": "会话摘要内容",
+  "keywords": ["关键词1", "关键词2"],
+  "topics": ["主题1", "主题2"]
+}
+
+对话内容：
+"#;
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    struct MockLlmDriver;
+
+    #[async_trait]
+    impl LlmDriverForExtraction for MockLlmDriver {
+        async fn extract_memories(
+            &self,
+            _messages: &[Message],
+            extraction_type: MemoryType,
+        ) -> Result<Vec<ExtractedMemory>> {
+            Ok(vec![ExtractedMemory::new(
+                extraction_type,
+                "test-category",
+                "test content",
+                SessionId::new(),
+            )])
+        }
+    }
+
+    #[tokio::test]
+    async fn test_extractor_creation() {
+        let driver = Arc::new(MockLlmDriver);
+        let extractor = MemoryExtractor::new(driver);
+        assert!(extractor.viking.is_none());
+    }
+
+    #[tokio::test]
+    async fn test_extract_memories() {
+        let driver = Arc::new(MockLlmDriver);
+        let extractor = MemoryExtractor::new(driver);
+        let messages = vec![Message::user("Hello")];
+
+        let result = extractor
+            .extract(&messages, SessionId::new())
+            .await
+            .unwrap();
+
+        // Should extract preferences, knowledge, and experience
+        assert!(!result.is_empty());
+    }
+
+    #[test]
+    fn test_prompts_available() {
+        assert!(!prompts::get_extraction_prompt(MemoryType::Preference).is_empty());
+        assert!(!prompts::get_extraction_prompt(MemoryType::Knowledge).is_empty());
+        assert!(!prompts::get_extraction_prompt(MemoryType::Experience).is_empty());
+        assert!(!prompts::get_extraction_prompt(MemoryType::Session).is_empty());
+    }
+}

crates/zclaw-growth/src/injector.rs

@@ -0,0 +1,537 @@
+//! Prompt Injector - Injects retrieved memories into system prompts
+//!
+//! This module provides the `PromptInjector` which formats and injects
+//! retrieved memories into the agent's system prompt for context enhancement.
+//!
+//! # Formatting Options
+//!
+//! - `inject()` - Standard markdown format with sections
+//! - `inject_compact()` - Compact format for limited token budgets
+//! - `inject_json()` - JSON format for structured processing
+//! - `inject_custom()` - Custom template with placeholders
+
+use crate::types::{MemoryEntry, RetrievalConfig, RetrievalResult};
+
+/// Output format for memory injection
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum InjectionFormat {
+    /// Standard markdown with sections (default)
+    Markdown,
+    /// Compact inline format
+    Compact,
+    /// JSON structured format
+    Json,
+}
+
+/// Prompt Injector - injects memories into system prompts
+pub struct PromptInjector {
+    /// Retrieval configuration for token budgets
+    config: RetrievalConfig,
+    /// Output format
+    format: InjectionFormat,
+    /// Custom template (uses {{preferences}}, {{knowledge}}, {{experience}} placeholders)
+    custom_template: Option<String>,
+}
+
+impl Default for PromptInjector {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl PromptInjector {
+    /// Create a new prompt injector
+    pub fn new() -> Self {
+        Self {
+            config: RetrievalConfig::default(),
+            format: InjectionFormat::Markdown,
+            custom_template: None,
+        }
+    }
+
+    /// Create with custom configuration
+    pub fn with_config(config: RetrievalConfig) -> Self {
+        Self {
+            config,
+            format: InjectionFormat::Markdown,
+            custom_template: None,
+        }
+    }
+
+    /// Set the output format
+    pub fn with_format(mut self, format: InjectionFormat) -> Self {
+        self.format = format;
+        self
+    }
+
+    /// Set a custom template for injection
+    ///
+    /// Template placeholders:
+    /// - `{{preferences}}` - Formatted preferences section
+    /// - `{{knowledge}}` - Formatted knowledge section
+    /// - `{{experience}}` - Formatted experience section
+    /// - `{{all}}` - All memories combined
+    pub fn with_custom_template(mut self, template: impl Into<String>) -> Self {
+        self.custom_template = Some(template.into());
+        self
+    }
+
+    /// Inject memories into a base system prompt
+    ///
+    /// This method constructs an enhanced system prompt by:
+    /// 1. Starting with the base prompt
+    /// 2. Adding a "用户偏好" section if preferences exist
+    /// 3. Adding a "相关知识" section if knowledge exists
+    /// 4. Adding an "经验参考" section if experience exists
+    ///
+    /// Each section respects the token budget configuration.
+    pub fn inject(&self, base_prompt: &str, memories: &RetrievalResult) -> String {
+        // If no memories, return base prompt unchanged
+        if memories.is_empty() {
+            return base_prompt.to_string();
+        }
+
+        let mut result = base_prompt.to_string();
+
+        // Inject preferences section
+        if !memories.preferences.is_empty() {
+            let section = self.format_section(
+                "## 用户偏好",
+                &memories.preferences,
+                self.config.preference_budget,
+                |entry| format!("- {}", entry.content),
+            );
+            result.push_str("\n\n");
+            result.push_str(&section);
+        }
+
+        // Inject knowledge section
+        if !memories.knowledge.is_empty() {
+            let section = self.format_section(
+                "## 相关知识",
+                &memories.knowledge,
+                self.config.knowledge_budget,
+                |entry| format!("- {}", entry.content),
+            );
+            result.push_str("\n\n");
+            result.push_str(&section);
+        }
+
+        // Inject experience section
+        if !memories.experience.is_empty() {
+            let section = self.format_section(
+                "## 经验参考",
+                &memories.experience,
+                self.config.experience_budget,
+                |entry| format!("- {}", entry.content),
+            );
+            result.push_str("\n\n");
+            result.push_str(&section);
+        }
+
+        // Add memory context footer
+        result.push_str("\n\n");
+        result.push_str("<!-- 以上内容基于历史对话自动提取的记忆 -->");
+
+        result
+    }
+
+    /// Format a section of memories with token budget
+    fn format_section<F>(
+        &self,
+        header: &str,
+        entries: &[MemoryEntry],
+        token_budget: usize,
+        formatter: F,
+    ) -> String
+    where
+        F: Fn(&MemoryEntry) -> String,
+    {
+        let mut result = String::new();
+        result.push_str(header);
+        result.push('\n');
+
+        let mut used_tokens = 0;
+        let header_tokens = header.len() / 4;
+        used_tokens += header_tokens;
+
+        for entry in entries {
+            let line = formatter(entry);
+            let line_tokens = line.len() / 4;
+
+            if used_tokens + line_tokens > token_budget {
+                // Add truncation indicator
+                result.push_str("- ... (更多内容已省略)\n");
+                break;
+            }
+
+            result.push_str(&line);
+            result.push('\n');
+            used_tokens += line_tokens;
+        }
+
+        result
+    }
+
+    /// Build a minimal context string for token-limited scenarios
+    pub fn build_minimal_context(&self, memories: &RetrievalResult) -> String {
+        if memories.is_empty() {
+            return String::new();
+        }
+
+        let mut context = String::new();
+
+        // Only include top preference
+        if let Some(pref) = memories.preferences.first() {
+            context.push_str(&format!("[偏好] {}\n", pref.content));
+        }
+
+        // Only include top knowledge
+        if let Some(knowledge) = memories.knowledge.first() {
+            context.push_str(&format!("[知识] {}\n", knowledge.content));
+        }
+
+        context
+    }
+
+    /// Inject memories in compact format
+    ///
+    /// Compact format uses inline notation: [P] for preferences, [K] for knowledge, [E] for experience
+    pub fn inject_compact(&self, base_prompt: &str, memories: &RetrievalResult) -> String {
+        if memories.is_empty() {
+            return base_prompt.to_string();
+        }
+
+        let mut result = base_prompt.to_string();
+        let mut context_parts = Vec::new();
+
+        // Add compact preferences
+        for entry in &memories.preferences {
+            context_parts.push(format!("[P] {}", entry.content));
+        }
+
+        // Add compact knowledge
+        for entry in &memories.knowledge {
+            context_parts.push(format!("[K] {}", entry.content));
+        }
+
+        // Add compact experience
+        for entry in &memories.experience {
+            context_parts.push(format!("[E] {}", entry.content));
+        }
+
+        if !context_parts.is_empty() {
+            result.push_str("\n\n[记忆上下文]\n");
+            result.push_str(&context_parts.join("\n"));
+        }
+
+        result
+    }
+
+    /// Inject memories as JSON structure
+    ///
+    /// Returns a JSON object with preferences, knowledge, and experience arrays
+    pub fn inject_json(&self, base_prompt: &str, memories: &RetrievalResult) -> String {
+        if memories.is_empty() {
+            return base_prompt.to_string();
+        }
+
+        let preferences: Vec<_> = memories.preferences.iter()
+            .map(|e| serde_json::json!({
+                "content": e.content,
+                "importance": e.importance,
+                "keywords": e.keywords,
+            }))
+            .collect();
+
+        let knowledge: Vec<_> = memories.knowledge.iter()
+            .map(|e| serde_json::json!({
+                "content": e.content,
+                "importance": e.importance,
+                "keywords": e.keywords,
+            }))
+            .collect();
+
+        let experience: Vec<_> = memories.experience.iter()
+            .map(|e| serde_json::json!({
+                "content": e.content,
+                "importance": e.importance,
+                "keywords": e.keywords,
+            }))
+            .collect();
+
+        let memories_json = serde_json::json!({
+            "preferences": preferences,
+            "knowledge": knowledge,
+            "experience": experience,
+        });
+
+        format!("{}\n\n[记忆上下文]\n{}", base_prompt, serde_json::to_string_pretty(&memories_json).unwrap_or_default())
+    }
+
+    /// Inject using custom template
+    ///
+    /// Template placeholders:
+    /// - `{{preferences}}` - Formatted preferences section
+    /// - `{{knowledge}}` - Formatted knowledge section
+    /// - `{{experience}}` - Formatted experience section
+    /// - `{{all}}` - All memories combined
+    pub fn inject_custom(&self, template: &str, memories: &RetrievalResult) -> String {
+        let mut result = template.to_string();
+
+        // Format each section
+        let prefs = if !memories.preferences.is_empty() {
+            memories.preferences.iter()
+                .map(|e| format!("- {}", e.content))
+                .collect::<Vec<_>>()
+                .join("\n")
+        } else {
+            String::new()
+        };
+
+        let knowledge = if !memories.knowledge.is_empty() {
+            memories.knowledge.iter()
+                .map(|e| format!("- {}", e.content))
+                .collect::<Vec<_>>()
+                .join("\n")
+        } else {
+            String::new()
+        };
+
+        let experience = if !memories.experience.is_empty() {
+            memories.experience.iter()
+                .map(|e| format!("- {}", e.content))
+                .collect::<Vec<_>>()
+                .join("\n")
+        } else {
+            String::new()
+        };
+
+        // Combine all
+        let all = format!(
+            "用户偏好:\n{}\n\n相关知识:\n{}\n\n经验参考:\n{}",
+            if prefs.is_empty() { "无" } else { &prefs },
+            if knowledge.is_empty() { "无" } else { &knowledge },
+            if experience.is_empty() { "无" } else { &experience },
+        );
+
+        // Replace placeholders
+        result = result.replace("{{preferences}}", &prefs);
+        result = result.replace("{{knowledge}}", &knowledge);
+        result = result.replace("{{experience}}", &experience);
+        result = result.replace("{{all}}", &all);
+
+        result
+    }
+
+    /// Inject memories using the configured format
+    pub fn inject_with_format(&self, base_prompt: &str, memories: &RetrievalResult) -> String {
+        match self.format {
+            InjectionFormat::Markdown => self.inject(base_prompt, memories),
+            InjectionFormat::Compact => self.inject_compact(base_prompt, memories),
+            InjectionFormat::Json => self.inject_json(base_prompt, memories),
+        }
+    }
+
+    /// Estimate total tokens that will be injected
+    pub fn estimate_injection_tokens(&self, memories: &RetrievalResult) -> usize {
+        let mut total = 0;
+
+        // Count preference tokens
+        for entry in &memories.preferences {
+            total += entry.estimated_tokens();
+            if total > self.config.preference_budget {
+                total = self.config.preference_budget;
+                break;
+            }
+        }
+
+        // Count knowledge tokens
+        let mut knowledge_tokens = 0;
+        for entry in &memories.knowledge {
+            knowledge_tokens += entry.estimated_tokens();
+            if knowledge_tokens > self.config.knowledge_budget {
+                knowledge_tokens = self.config.knowledge_budget;
+                break;
+            }
+        }
+        total += knowledge_tokens;
+
+        // Count experience tokens
+        let mut experience_tokens = 0;
+        for entry in &memories.experience {
+            experience_tokens += entry.estimated_tokens();
+            if experience_tokens > self.config.experience_budget {
+                experience_tokens = self.config.experience_budget;
+                break;
+            }
+        }
+        total += experience_tokens;
+
+        total
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::types::MemoryType;
+    use chrono::Utc;
+
+    fn create_test_entry(content: &str) -> MemoryEntry {
+        MemoryEntry {
+            uri: "test://uri".to_string(),
+            memory_type: MemoryType::Preference,
+            content: content.to_string(),
+            keywords: vec![],
+            importance: 5,
+            access_count: 0,
+            created_at: Utc::now(),
+            last_accessed: Utc::now(),
+        }
+    }
+
+    #[test]
+    fn test_injector_empty_memories() {
+        let injector = PromptInjector::new();
+        let base = "You are a helpful assistant.";
+        let memories = RetrievalResult::default();
+
+        let result = injector.inject(base, &memories);
+        assert_eq!(result, base);
+    }
+
+    #[test]
+    fn test_injector_with_preferences() {
+        let injector = PromptInjector::new();
+        let base = "You are a helpful assistant.";
+        let memories = RetrievalResult {
+            preferences: vec![create_test_entry("User prefers concise responses")],
+            knowledge: vec![],
+            experience: vec![],
+            total_tokens: 0,
+        };
+
+        let result = injector.inject(base, &memories);
+        assert!(result.contains("用户偏好"));
+        assert!(result.contains("User prefers concise responses"));
+    }
+
+    #[test]
+    fn test_injector_with_all_types() {
+        let injector = PromptInjector::new();
+        let base = "You are a helpful assistant.";
+
+        let memories = RetrievalResult {
+            preferences: vec![create_test_entry("Prefers concise")],
+            knowledge: vec![create_test_entry("Knows Rust")],
+            experience: vec![create_test_entry("Browser skill works well")],
+            total_tokens: 0,
+        };
+
+        let result = injector.inject(base, &memories);
+        assert!(result.contains("用户偏好"));
+        assert!(result.contains("相关知识"));
+        assert!(result.contains("经验参考"));
+    }
+
+    #[test]
+    fn test_minimal_context() {
+        let injector = PromptInjector::new();
+        let memories = RetrievalResult {
+            preferences: vec![create_test_entry("Prefers concise")],
+            knowledge: vec![create_test_entry("Knows Rust")],
+            experience: vec![],
+            total_tokens: 0,
+        };
+
+        let context = injector.build_minimal_context(&memories);
+        assert!(context.contains("[偏好]"));
+        assert!(context.contains("[知识]"));
+    }
+
+    #[test]
+    fn test_estimate_tokens() {
+        let injector = PromptInjector::new();
+        let memories = RetrievalResult {
+            preferences: vec![create_test_entry("Short text")],
+            knowledge: vec![],
+            experience: vec![],
+            total_tokens: 0,
+        };
+
+        let estimate = injector.estimate_injection_tokens(&memories);
+        assert!(estimate > 0);
+    }
+
+    #[test]
+    fn test_inject_compact() {
+        let injector = PromptInjector::new();
+        let base = "You are a helpful assistant.";
+        let memories = RetrievalResult {
+            preferences: vec![create_test_entry("Prefers concise")],
+            knowledge: vec![create_test_entry("Knows Rust")],
+            experience: vec![],
+            total_tokens: 0,
+        };
+
+        let result = injector.inject_compact(base, &memories);
+        assert!(result.contains("[P]"));
+        assert!(result.contains("[K]"));
+        assert!(result.contains("[记忆上下文]"));
+    }
+
+    #[test]
+    fn test_inject_json() {
+        let injector = PromptInjector::new();
+        let base = "You are a helpful assistant.";
+        let memories = RetrievalResult {
+            preferences: vec![create_test_entry("Prefers concise")],
+            knowledge: vec![],
+            experience: vec![],
+            total_tokens: 0,
+        };
+
+        let result = injector.inject_json(base, &memories);
+        assert!(result.contains("\"preferences\""));
+        assert!(result.contains("Prefers concise"));
+    }
+
+    #[test]
+    fn test_inject_custom() {
+        let injector = PromptInjector::new();
+        let template = "Context:\n{{all}}";
+        let memories = RetrievalResult {
+            preferences: vec![create_test_entry("Prefers concise")],
+            knowledge: vec![create_test_entry("Knows Rust")],
+            experience: vec![],
+            total_tokens: 0,
+        };
+
+        let result = injector.inject_custom(template, &memories);
+        assert!(result.contains("用户偏好"));
+        assert!(result.contains("相关知识"));
+    }
+
+    #[test]
+    fn test_format_selection() {
+        let base = "Base";
+
+        let memories = RetrievalResult {
+            preferences: vec![create_test_entry("Test")],
+            knowledge: vec![],
+            experience: vec![],
+            total_tokens: 0,
+        };
+
+        // Test markdown format
+        let injector_md = PromptInjector::new().with_format(InjectionFormat::Markdown);
+        let result_md = injector_md.inject_with_format(base, &memories);
+        assert!(result_md.contains("## 用户偏好"));
+
+        // Test compact format
+        let injector_compact = PromptInjector::new().with_format(InjectionFormat::Compact);
+        let result_compact = injector_compact.inject_with_format(base, &memories);
+        assert!(result_compact.contains("[P]"));
+    }
+}

crates/zclaw-growth/src/lib.rs

@@ -0,0 +1,141 @@
+//! ZCLAW Agent Growth System
+//!
+//! This crate provides the agent growth functionality for ZCLAW,
+//! enabling agents to learn and evolve from conversations.
+//!
+//! # Architecture
+//!
+//! The growth system consists of four main components:
+//!
+//! 1. **MemoryExtractor** (`extractor`) - Analyzes conversations and extracts
+//!    preferences, knowledge, and experience using LLM.
+//!
+//! 2. **MemoryRetriever** (`retriever`) - Performs semantic search over
+//!    stored memories to find contextually relevant information.
+//!
+//! 3. **PromptInjector** (`injector`) - Injects retrieved memories into
+//!    the system prompt with token budget control.
+//!
+//! 4. **GrowthTracker** (`tracker`) - Tracks growth metrics and evolution
+//!    over time.
+//!
+//! # Storage
+//!
+//! All memories are stored in OpenViking with a URI structure:
+//!
+//! ```text
+//! agent://{agent_id}/
+//! ├── preferences/{category}  - User preferences
+//! ├── knowledge/{domain}      - Accumulated knowledge
+//! ├── experience/{skill}      - Skill/tool experience
+//! └── sessions/{session_id}/  - Conversation history
+//!     ├── raw                 - Original conversation (L0)
+//!     ├── summary             - Summary (L1)
+//!     └── keywords            - Keywords (L2)
+//! ```
+//!
+//! # Usage
+//!
+//! ```rust,ignore
+//! use zclaw_growth::{MemoryExtractor, MemoryRetriever, PromptInjector, VikingAdapter};
+//!
+//! // Create components
+//! let viking = VikingAdapter::in_memory();
+//! let retriever = MemoryRetriever::new(Arc::new(viking.clone()));
+//! let injector = PromptInjector::new();
+//!
+//! // Before conversation: retrieve relevant memories
+//! let memories = retriever.retrieve(&agent_id, &user_input).await?;
+//!
+//! // Inject into system prompt
+//! let enhanced_prompt = injector.inject(&base_prompt, &memories);
+//!
+//! // After conversation: extract and store new memories
+//! let extracted = extractor.extract(&messages, session_id).await?;
+//! extractor.store_memories(&agent_id, &extracted).await?;
+//! ```
+
+pub mod types;
+pub mod extractor;
+pub mod retriever;
+pub mod injector;
+pub mod tracker;
+pub mod viking_adapter;
+pub mod storage;
+pub mod retrieval;
+
+// Re-export main types for convenience
+pub use types::{
+    ExtractedMemory,
+    ExtractionConfig,
+    GrowthStats,
+    MemoryEntry,
+    MemoryType,
+    RetrievalConfig,
+    RetrievalResult,
+    UriBuilder,
+};
+
+pub use extractor::{LlmDriverForExtraction, MemoryExtractor};
+pub use retriever::{MemoryRetriever, MemoryStats};
+pub use injector::{InjectionFormat, PromptInjector};
+pub use tracker::{AgentMetadata, GrowthTracker, LearningEvent};
+pub use viking_adapter::{FindOptions, VikingAdapter, VikingLevel, VikingStorage};
+pub use storage::SqliteStorage;
+pub use retrieval::{MemoryCache, QueryAnalyzer, SemanticScorer};
+
+/// Growth system configuration
+#[derive(Debug, Clone)]
+pub struct GrowthConfig {
+    /// Enable/disable growth system
+    pub enabled: bool,
+    /// Retrieval configuration
+    pub retrieval: RetrievalConfig,
+    /// Extraction configuration
+    pub extraction: ExtractionConfig,
+    /// Auto-extract after each conversation
+    pub auto_extract: bool,
+}
+
+impl Default for GrowthConfig {
+    fn default() -> Self {
+        Self {
+            enabled: true,
+            retrieval: RetrievalConfig::default(),
+            extraction: ExtractionConfig::default(),
+            auto_extract: true,
+        }
+    }
+}
+
+/// Convenience function to create a complete growth system
+pub fn create_growth_system(
+    viking: std::sync::Arc<VikingAdapter>,
+    llm_driver: std::sync::Arc<dyn LlmDriverForExtraction>,
+) -> (MemoryExtractor, MemoryRetriever, PromptInjector, GrowthTracker) {
+    let extractor = MemoryExtractor::new(llm_driver).with_viking(viking.clone());
+    let retriever = MemoryRetriever::new(viking.clone());
+    let injector = PromptInjector::new();
+    let tracker = GrowthTracker::new(viking);
+
+    (extractor, retriever, injector, tracker)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_growth_config_default() {
+        let config = GrowthConfig::default();
+        assert!(config.enabled);
+        assert!(config.auto_extract);
+        assert_eq!(config.retrieval.max_tokens, 500);
+    }
+
+    #[test]
+    fn test_memory_type_reexport() {
+        let mt = MemoryType::Preference;
+        assert_eq!(format!("{}", mt), "preferences");
+    }
+}

crates/zclaw-growth/src/retrieval/cache.rs

@@ -0,0 +1,365 @@
+//! Memory Cache
+//!
+//! Provides caching for frequently accessed memories to improve
+//! retrieval performance.
+
+use crate::types::{MemoryEntry, MemoryType};
+use std::collections::HashMap;
+use std::time::{Duration, Instant};
+use tokio::sync::RwLock;
+
+/// Cache entry with metadata
+struct CacheEntry {
+    /// The memory entry
+    entry: MemoryEntry,
+    /// Last access time
+    last_accessed: Instant,
+    /// Access count
+    access_count: u32,
+}
+
+/// Cache key for efficient lookups
+#[derive(Debug, Clone, Hash, Eq, PartialEq)]
+struct CacheKey {
+    agent_id: String,
+    memory_type: MemoryType,
+    category: String,
+}
+
+impl From<&MemoryEntry> for CacheKey {
+    fn from(entry: &MemoryEntry) -> Self {
+        // Parse URI to extract components
+        let parts: Vec<&str> = entry.uri.trim_start_matches("agent://").split('/').collect();
+        Self {
+            agent_id: parts.first().unwrap_or(&"").to_string(),
+            memory_type: entry.memory_type,
+            category: parts.get(2).unwrap_or(&"").to_string(),
+        }
+    }
+}
+
+/// Memory cache configuration
+#[derive(Debug, Clone)]
+pub struct CacheConfig {
+    /// Maximum number of entries
+    pub max_entries: usize,
+    /// Time-to-live for entries
+    pub ttl: Duration,
+    /// Enable/disable caching
+    pub enabled: bool,
+}
+
+impl Default for CacheConfig {
+    fn default() -> Self {
+        Self {
+            max_entries: 1000,
+            ttl: Duration::from_secs(3600), // 1 hour
+            enabled: true,
+        }
+    }
+}
+
+/// Memory cache for hot memories
+pub struct MemoryCache {
+    /// Cache storage
+    cache: RwLock<HashMap<String, CacheEntry>>,
+    /// Configuration
+    config: CacheConfig,
+    /// Cache statistics
+    stats: RwLock<CacheStats>,
+}
+
+/// Cache statistics
+#[derive(Debug, Clone, Default)]
+pub struct CacheStats {
+    /// Total cache hits
+    pub hits: u64,
+    /// Total cache misses
+    pub misses: u64,
+    /// Total entries evicted
+    pub evictions: u64,
+}
+
+impl MemoryCache {
+    /// Create a new memory cache
+    pub fn new(config: CacheConfig) -> Self {
+        Self {
+            cache: RwLock::new(HashMap::new()),
+            config,
+            stats: RwLock::new(CacheStats::default()),
+        }
+    }
+
+    /// Create with default configuration
+    pub fn default_config() -> Self {
+        Self::new(CacheConfig::default())
+    }
+
+    /// Get a memory from cache
+    pub async fn get(&self, uri: &str) -> Option<MemoryEntry> {
+        if !self.config.enabled {
+            return None;
+        }
+
+        let mut cache = self.cache.write().await;
+
+        if let Some(cached) = cache.get_mut(uri) {
+            // Check TTL
+            if cached.last_accessed.elapsed() > self.config.ttl {
+                cache.remove(uri);
+                return None;
+            }
+
+            // Update access metadata
+            cached.last_accessed = Instant::now();
+            cached.access_count += 1;
+
+            // Update stats
+            let mut stats = self.stats.write().await;
+            stats.hits += 1;
+
+            return Some(cached.entry.clone());
+        }
+
+        // Update stats
+        let mut stats = self.stats.write().await;
+        stats.misses += 1;
+
+        None
+    }
+
+    /// Put a memory into cache
+    pub async fn put(&self, entry: MemoryEntry) {
+        if !self.config.enabled {
+            return;
+        }
+
+        let mut cache = self.cache.write().await;
+
+        // Check capacity and evict if necessary
+        if cache.len() >= self.config.max_entries {
+            self.evict_lru(&mut cache).await;
+        }
+
+        cache.insert(
+            entry.uri.clone(),
+            CacheEntry {
+                entry,
+                last_accessed: Instant::now(),
+                access_count: 0,
+            },
+        );
+    }
+
+    /// Remove a memory from cache
+    pub async fn remove(&self, uri: &str) {
+        let mut cache = self.cache.write().await;
+        cache.remove(uri);
+    }
+
+    /// Clear the cache
+    pub async fn clear(&self) {
+        let mut cache = self.cache.write().await;
+        cache.clear();
+    }
+
+    /// Evict least recently used entries
+    async fn evict_lru(&self, cache: &mut HashMap<String, CacheEntry>) {
+        // Find LRU entry
+        let lru_key = cache
+            .iter()
+            .min_by_key(|(_, v)| (v.access_count, v.last_accessed))
+            .map(|(k, _)| k.clone());
+
+        if let Some(key) = lru_key {
+            cache.remove(&key);
+
+            let mut stats = self.stats.write().await;
+            stats.evictions += 1;
+        }
+    }
+
+    /// Get cache statistics
+    pub async fn stats(&self) -> CacheStats {
+        self.stats.read().await.clone()
+    }
+
+    /// Get cache hit rate
+    pub async fn hit_rate(&self) -> f32 {
+        let stats = self.stats.read().await;
+        let total = stats.hits + stats.misses;
+        if total == 0 {
+            return 0.0;
+        }
+        stats.hits as f32 / total as f32
+    }
+
+    /// Get cache size
+    pub async fn size(&self) -> usize {
+        self.cache.read().await.len()
+    }
+
+    /// Warm up cache with frequently accessed entries
+    pub async fn warmup(&self, entries: Vec<MemoryEntry>) {
+        for entry in entries {
+            self.put(entry).await;
+        }
+    }
+
+    /// Get top accessed entries (for preloading)
+    pub async fn get_hot_entries(&self, limit: usize) -> Vec<MemoryEntry> {
+        let cache = self.cache.read().await;
+
+        let mut entries: Vec<_> = cache
+            .values()
+            .map(|c| (c.access_count, c.entry.clone()))
+            .collect();
+
+        entries.sort_by(|a, b| b.0.cmp(&a.0));
+        entries.truncate(limit);
+
+        entries.into_iter().map(|(_, e)| e).collect()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::types::MemoryType;
+
+    #[tokio::test]
+    async fn test_cache_put_and_get() {
+        let cache = MemoryCache::default_config();
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "style",
+            "User prefers concise responses".to_string(),
+        );
+
+        cache.put(entry.clone()).await;
+        let retrieved = cache.get(&entry.uri).await;
+
+        assert!(retrieved.is_some());
+        assert_eq!(retrieved.unwrap().content, "User prefers concise responses");
+    }
+
+    #[tokio::test]
+    async fn test_cache_miss() {
+        let cache = MemoryCache::default_config();
+        let retrieved = cache.get("nonexistent").await;
+
+        assert!(retrieved.is_none());
+
+        let stats = cache.stats().await;
+        assert_eq!(stats.misses, 1);
+    }
+
+    #[tokio::test]
+    async fn test_cache_remove() {
+        let cache = MemoryCache::default_config();
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "style",
+            "test".to_string(),
+        );
+
+        cache.put(entry.clone()).await;
+        cache.remove(&entry.uri).await;
+        let retrieved = cache.get(&entry.uri).await;
+
+        assert!(retrieved.is_none());
+    }
+
+    #[tokio::test]
+    async fn test_cache_clear() {
+        let cache = MemoryCache::default_config();
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "style",
+            "test".to_string(),
+        );
+
+        cache.put(entry).await;
+        cache.clear().await;
+        let size = cache.size().await;
+
+        assert_eq!(size, 0);
+    }
+
+    #[tokio::test]
+    async fn test_cache_stats() {
+        let cache = MemoryCache::default_config();
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "style",
+            "test".to_string(),
+        );
+
+        cache.put(entry.clone()).await;
+
+        // Hit
+        cache.get(&entry.uri).await;
+        // Miss
+        cache.get("nonexistent").await;
+
+        let stats = cache.stats().await;
+        assert_eq!(stats.hits, 1);
+        assert_eq!(stats.misses, 1);
+
+        let hit_rate = cache.hit_rate().await;
+        assert!((hit_rate - 0.5).abs() < 0.001);
+    }
+
+    #[tokio::test]
+    async fn test_cache_eviction() {
+        let config = CacheConfig {
+            max_entries: 2,
+            ttl: Duration::from_secs(3600),
+            enabled: true,
+        };
+        let cache = MemoryCache::new(config);
+
+        let entry1 = MemoryEntry::new("test", MemoryType::Preference, "1", "1".to_string());
+        let entry2 = MemoryEntry::new("test", MemoryType::Preference, "2", "2".to_string());
+        let entry3 = MemoryEntry::new("test", MemoryType::Preference, "3", "3".to_string());
+
+        cache.put(entry1.clone()).await;
+        cache.put(entry2.clone()).await;
+
+        // Access entry1 to make it hot
+        cache.get(&entry1.uri).await;
+
+        // Add entry3, should evict entry2 (LRU)
+        cache.put(entry3).await;
+
+        let size = cache.size().await;
+        assert_eq!(size, 2);
+
+        let stats = cache.stats().await;
+        assert_eq!(stats.evictions, 1);
+    }
+
+    #[tokio::test]
+    async fn test_get_hot_entries() {
+        let cache = MemoryCache::default_config();
+
+        let entry1 = MemoryEntry::new("test", MemoryType::Preference, "1", "1".to_string());
+        let entry2 = MemoryEntry::new("test", MemoryType::Preference, "2", "2".to_string());
+
+        cache.put(entry1.clone()).await;
+        cache.put(entry2.clone()).await;
+
+        // Access entry1 multiple times
+        cache.get(&entry1.uri).await;
+        cache.get(&entry1.uri).await;
+
+        let hot = cache.get_hot_entries(10).await;
+        assert_eq!(hot.len(), 2);
+        // entry1 should be first (more accesses)
+        assert_eq!(hot[0].uri, entry1.uri);
+    }
+}

crates/zclaw-growth/src/retrieval/mod.rs

@@ -0,0 +1,14 @@
+//! Retrieval components for ZCLAW Growth System
+//!
+//! This module provides advanced retrieval capabilities:
+//! - `semantic`: Semantic similarity computation
+//! - `query`: Query analysis and expansion
+//! - `cache`: Hot memory caching
+
+pub mod semantic;
+pub mod query;
+pub mod cache;
+
+pub use semantic::SemanticScorer;
+pub use query::QueryAnalyzer;
+pub use cache::MemoryCache;

crates/zclaw-growth/src/retrieval/query.rs

@@ -0,0 +1,352 @@
+//! Query Analyzer
+//!
+//! Provides query analysis and expansion capabilities for improved retrieval.
+//! Extracts keywords, identifies intent, and generates search variations.
+
+use crate::types::MemoryType;
+use std::collections::HashSet;
+
+/// Query analysis result
+#[derive(Debug, Clone)]
+pub struct AnalyzedQuery {
+    /// Original query string
+    pub original: String,
+    /// Extracted keywords
+    pub keywords: Vec<String>,
+    /// Query intent
+    pub intent: QueryIntent,
+    /// Memory types to search (inferred from query)
+    pub target_types: Vec<MemoryType>,
+    /// Expanded search terms
+    pub expansions: Vec<String>,
+}
+
+/// Query intent classification
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum QueryIntent {
+    /// Looking for preferences/settings
+    Preference,
+    /// Looking for factual knowledge
+    Knowledge,
+    /// Looking for how-to/experience
+    Experience,
+    /// General conversation
+    General,
+    /// Code-related query
+    Code,
+    /// Configuration query
+    Configuration,
+}
+
+/// Query analyzer
+pub struct QueryAnalyzer {
+    /// Keywords that indicate preference queries
+    preference_indicators: HashSet<String>,
+    /// Keywords that indicate knowledge queries
+    knowledge_indicators: HashSet<String>,
+    /// Keywords that indicate experience queries
+    experience_indicators: HashSet<String>,
+    /// Keywords that indicate code queries
+    code_indicators: HashSet<String>,
+    /// Stop words to filter out
+    stop_words: HashSet<String>,
+}
+
+impl QueryAnalyzer {
+    /// Create a new query analyzer
+    pub fn new() -> Self {
+        Self {
+            preference_indicators: [
+                "prefer", "like", "want", "favorite", "favourite", "style",
+                "format", "language", "setting", "preference", "usually",
+                "typically", "always", "never", "习惯", "偏好", "喜欢", "想要",
+            ]
+            .iter()
+            .map(|s| s.to_string())
+            .collect(),
+            knowledge_indicators: [
+                "what", "how", "why", "explain", "tell", "know", "learn",
+                "understand", "meaning", "definition", "concept", "theory",
+                "是什么", "怎么", "为什么", "解释", "了解", "知道",
+            ]
+            .iter()
+            .map(|s| s.to_string())
+            .collect(),
+            experience_indicators: [
+                "experience", "tried", "used", "before", "last time",
+                "previous", "history", "remember", "recall", "when",
+                "经验", "尝试", "用过", "上次", "记得", "回忆",
+            ]
+            .iter()
+            .map(|s| s.to_string())
+            .collect(),
+            code_indicators: [
+                "code", "function", "class", "method", "variable", "type",
+                "error", "bug", "fix", "implement", "refactor", "api",
+                "代码", "函数", "类", "方法", "变量", "错误", "修复", "实现",
+            ]
+            .iter()
+            .map(|s| s.to_string())
+            .collect(),
+            stop_words: [
+                "the", "a", "an", "is", "are", "was", "were", "be", "been",
+                "have", "has", "had", "do", "does", "did", "will", "would",
+                "could", "should", "may", "might", "must", "can", "to", "of",
+                "in", "for", "on", "with", "at", "by", "from", "as", "and",
+                "or", "but", "if", "then", "else", "when", "where", "which",
+                "who", "whom", "whose", "this", "that", "these", "those",
+            ]
+            .iter()
+            .map(|s| s.to_string())
+            .collect(),
+        }
+    }
+
+    /// Analyze a query string
+    pub fn analyze(&self, query: &str) -> AnalyzedQuery {
+        let keywords = self.extract_keywords(query);
+        let intent = self.classify_intent(&keywords);
+        let target_types = self.infer_memory_types(intent, &keywords);
+        let expansions = self.expand_query(&keywords);
+
+        AnalyzedQuery {
+            original: query.to_string(),
+            keywords,
+            intent,
+            target_types,
+            expansions,
+        }
+    }
+
+    /// Extract keywords from query
+    fn extract_keywords(&self, query: &str) -> Vec<String> {
+        query
+            .to_lowercase()
+            .split(|c: char| !c.is_alphanumeric() && !is_cjk(c))
+            .filter(|s| !s.is_empty() && s.len() > 1)
+            .filter(|s| !self.stop_words.contains(*s))
+            .map(|s| s.to_string())
+            .collect()
+    }
+
+    /// Classify query intent
+    fn classify_intent(&self, keywords: &[String]) -> QueryIntent {
+        let mut scores = [
+            (QueryIntent::Preference, 0),
+            (QueryIntent::Knowledge, 0),
+            (QueryIntent::Experience, 0),
+            (QueryIntent::Code, 0),
+        ];
+
+        for keyword in keywords {
+            if self.preference_indicators.contains(keyword) {
+                scores[0].1 += 2;
+            }
+            if self.knowledge_indicators.contains(keyword) {
+                scores[1].1 += 2;
+            }
+            if self.experience_indicators.contains(keyword) {
+                scores[2].1 += 2;
+            }
+            if self.code_indicators.contains(keyword) {
+                scores[3].1 += 2;
+            }
+        }
+
+        // Find highest scoring intent
+        scores.sort_by(|a, b| b.1.cmp(&a.1));
+
+        if scores[0].1 > 0 {
+            scores[0].0
+        } else {
+            QueryIntent::General
+        }
+    }
+
+    /// Infer which memory types to search
+    fn infer_memory_types(&self, intent: QueryIntent, _keywords: &[String]) -> Vec<MemoryType> {
+        let mut types = Vec::new();
+
+        match intent {
+            QueryIntent::Preference => {
+                types.push(MemoryType::Preference);
+            }
+            QueryIntent::Knowledge | QueryIntent::Code => {
+                types.push(MemoryType::Knowledge);
+                types.push(MemoryType::Experience);
+            }
+            QueryIntent::Experience => {
+                types.push(MemoryType::Experience);
+                types.push(MemoryType::Knowledge);
+            }
+            QueryIntent::General => {
+                // Search all types
+                types.push(MemoryType::Preference);
+                types.push(MemoryType::Knowledge);
+                types.push(MemoryType::Experience);
+            }
+            QueryIntent::Configuration => {
+                types.push(MemoryType::Preference);
+                types.push(MemoryType::Knowledge);
+            }
+        }
+
+        types
+    }
+
+    /// Expand query with related terms
+    fn expand_query(&self, keywords: &[String]) -> Vec<String> {
+        let mut expansions = Vec::new();
+
+        // Add stemmed variations (simplified)
+        for keyword in keywords {
+            // Add singular/plural variations
+            if keyword.ends_with('s') && keyword.len() > 3 {
+                expansions.push(keyword[..keyword.len()-1].to_string());
+            } else {
+                expansions.push(format!("{}s", keyword));
+            }
+
+            // Add common synonyms (simplified)
+            if let Some(synonyms) = self.get_synonyms(keyword) {
+                expansions.extend(synonyms);
+            }
+        }
+
+        expansions
+    }
+
+    /// Get synonyms for a keyword (simplified)
+    fn get_synonyms(&self, keyword: &str) -> Option<Vec<String>> {
+        let synonyms: &[&str] = match keyword {
+            "code" => &["program", "script", "source"],
+            "error" => &["bug", "issue", "problem", "exception"],
+            "fix" => &["solve", "resolve", "repair", "patch"],
+            "fast" => &["quick", "speed", "performance", "efficient"],
+            "slow" => &["performance", "optimize", "speed"],
+            "help" => &["assist", "support", "guide", "aid"],
+            "learn" => &["study", "understand", "know", "grasp"],
+            _ => return None,
+        };
+
+        Some(synonyms.iter().map(|s| s.to_string()).collect())
+    }
+
+    /// Generate search queries from analyzed query
+    pub fn generate_search_queries(&self, analyzed: &AnalyzedQuery) -> Vec<String> {
+        let mut queries = vec![analyzed.original.clone()];
+
+        // Add keyword-based query
+        if !analyzed.keywords.is_empty() {
+            queries.push(analyzed.keywords.join(" "));
+        }
+
+        // Add expanded terms
+        for expansion in &analyzed.expansions {
+            if !expansion.is_empty() {
+                queries.push(expansion.clone());
+            }
+        }
+
+        // Deduplicate
+        queries.sort();
+        queries.dedup();
+
+        queries
+    }
+}
+
+impl Default for QueryAnalyzer {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+/// Check if character is CJK
+fn is_cjk(c: char) -> bool {
+    matches!(c,
+        '\u{4E00}'..='\u{9FFF}' |  // CJK Unified Ideographs
+        '\u{3400}'..='\u{4DBF}' |  // CJK Unified Ideographs Extension A
+        '\u{20000}'..='\u{2A6DF}' | // CJK Unified Ideographs Extension B
+        '\u{2A700}'..='\u{2B73F}' | // CJK Unified Ideographs Extension C
+        '\u{2B740}'..='\u{2B81F}' | // CJK Unified Ideographs Extension D
+        '\u{2B820}'..='\u{2CEAF}' | // CJK Unified Ideographs Extension E
+        '\u{F900}'..='\u{FAFF}' |   // CJK Compatibility Ideographs
+        '\u{2F800}'..='\u{2FA1F}'   // CJK Compatibility Ideographs Supplement
+    )
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_extract_keywords() {
+        let analyzer = QueryAnalyzer::new();
+        let keywords = analyzer.extract_keywords("What is the Rust programming language?");
+
+        assert!(keywords.contains(&"rust".to_string()));
+        assert!(keywords.contains(&"programming".to_string()));
+        assert!(keywords.contains(&"language".to_string()));
+        assert!(!keywords.contains(&"the".to_string())); // stop word
+    }
+
+    #[test]
+    fn test_classify_intent_preference() {
+        let analyzer = QueryAnalyzer::new();
+        let analyzed = analyzer.analyze("I prefer concise responses");
+
+        assert_eq!(analyzed.intent, QueryIntent::Preference);
+        assert!(analyzed.target_types.contains(&MemoryType::Preference));
+    }
+
+    #[test]
+    fn test_classify_intent_knowledge() {
+        let analyzer = QueryAnalyzer::new();
+        let analyzed = analyzer.analyze("Explain how async/await works in Rust");
+
+        assert_eq!(analyzed.intent, QueryIntent::Knowledge);
+    }
+
+    #[test]
+    fn test_classify_intent_code() {
+        let analyzer = QueryAnalyzer::new();
+        let analyzed = analyzer.analyze("Fix this error in my function");
+
+        assert_eq!(analyzed.intent, QueryIntent::Code);
+    }
+
+    #[test]
+    fn test_query_expansion() {
+        let analyzer = QueryAnalyzer::new();
+        let analyzed = analyzer.analyze("fix the error");
+
+        assert!(!analyzed.expansions.is_empty());
+    }
+
+    #[test]
+    fn test_generate_search_queries() {
+        let analyzer = QueryAnalyzer::new();
+        let analyzed = analyzer.analyze("Rust programming");
+        let queries = analyzer.generate_search_queries(&analyzed);
+
+        assert!(queries.len() >= 1);
+    }
+
+    #[test]
+    fn test_cjk_detection() {
+        assert!(is_cjk('中'));
+        assert!(is_cjk('文'));
+        assert!(!is_cjk('a'));
+        assert!(!is_cjk('1'));
+    }
+
+    #[test]
+    fn test_chinese_keywords() {
+        let analyzer = QueryAnalyzer::new();
+        let keywords = analyzer.extract_keywords("我喜欢简洁的回复");
+
+        // Chinese characters should be extracted
+        assert!(!keywords.is_empty());
+    }
+}

crates/zclaw-growth/src/retrieval/semantic.rs

@@ -0,0 +1,374 @@
+//! Semantic Similarity Scorer
+//!
+//! Provides TF-IDF based semantic similarity computation for memory retrieval.
+//! This is a lightweight, dependency-free implementation suitable for
+//! medium-scale memory systems.
+
+use std::collections::{HashMap, HashSet};
+use crate::types::MemoryEntry;
+
+/// Semantic similarity scorer using TF-IDF
+pub struct SemanticScorer {
+    /// Document frequency for IDF computation
+    document_frequencies: HashMap<String, usize>,
+    /// Total number of documents
+    total_documents: usize,
+    /// Precomputed TF-IDF vectors for entries
+    entry_vectors: HashMap<String, HashMap<String, f32>>,
+    /// Stop words to ignore
+    stop_words: HashSet<String>,
+}
+
+impl SemanticScorer {
+    /// Create a new semantic scorer
+    pub fn new() -> Self {
+        Self {
+            document_frequencies: HashMap::new(),
+            total_documents: 0,
+            entry_vectors: HashMap::new(),
+            stop_words: Self::default_stop_words(),
+        }
+    }
+
+    /// Get default stop words
+    fn default_stop_words() -> HashSet<String> {
+        [
+            "the", "a", "an", "is", "are", "was", "were", "be", "been", "being",
+            "have", "has", "had", "do", "does", "did", "will", "would", "could",
+            "should", "may", "might", "must", "shall", "can", "need", "dare",
+            "ought", "used", "to", "of", "in", "for", "on", "with", "at", "by",
+            "from", "as", "into", "through", "during", "before", "after",
+            "above", "below", "between", "under", "again", "further", "then",
+            "once", "here", "there", "when", "where", "why", "how", "all",
+            "each", "few", "more", "most", "other", "some", "such", "no", "nor",
+            "not", "only", "own", "same", "so", "than", "too", "very", "just",
+            "and", "but", "if", "or", "because", "until", "while", "although",
+            "though", "after", "before", "when", "whenever", "i", "you", "he",
+            "she", "it", "we", "they", "what", "which", "who", "whom", "this",
+            "that", "these", "those", "am", "im", "youre", "hes", "shes",
+            "its", "were", "theyre", "ive", "youve", "weve", "theyve", "id",
+            "youd", "hed", "shed", "wed", "theyd", "ill", "youll", "hell",
+            "shell", "well", "theyll", "isnt", "arent", "wasnt", "werent",
+            "hasnt", "havent", "hadnt", "doesnt", "dont", "didnt", "wont",
+            "wouldnt", "shant", "shouldnt", "cant", "cannot", "couldnt",
+            "mustnt", "lets", "thats", "whos", "whats", "heres", "theres",
+            "whens", "wheres", "whys", "hows", "a", "b", "c", "d", "e", "f",
+            "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s",
+            "t", "u", "v", "w", "x", "y", "z",
+        ]
+        .iter()
+        .map(|s| s.to_string())
+        .collect()
+    }
+
+    /// Tokenize text into words
+    fn tokenize(text: &str) -> Vec<String> {
+        text.to_lowercase()
+            .split(|c: char| !c.is_alphanumeric())
+            .filter(|s| !s.is_empty() && s.len() > 1)
+            .map(|s| s.to_string())
+            .collect()
+    }
+
+    /// Remove stop words from tokens
+    fn remove_stop_words(&self, tokens: &[String]) -> Vec<String> {
+        tokens
+            .iter()
+            .filter(|t| !self.stop_words.contains(*t))
+            .cloned()
+            .collect()
+    }
+
+    /// Compute term frequency for a list of tokens
+    fn compute_tf(tokens: &[String]) -> HashMap<String, f32> {
+        let mut tf = HashMap::new();
+        let total = tokens.len() as f32;
+
+        for token in tokens {
+            *tf.entry(token.clone()).or_insert(0.0) += 1.0;
+        }
+
+        // Normalize by total tokens
+        for count in tf.values_mut() {
+            *count /= total;
+        }
+
+        tf
+    }
+
+    /// Compute IDF for a term
+    fn compute_idf(&self, term: &str) -> f32 {
+        let df = self.document_frequencies.get(term).copied().unwrap_or(0);
+        if df == 0 || self.total_documents == 0 {
+            return 0.0;
+        }
+        ((self.total_documents as f32 + 1.0) / (df as f32 + 1.0)).ln() + 1.0
+    }
+
+    /// Index an entry for semantic search
+    pub fn index_entry(&mut self, entry: &MemoryEntry) {
+        // Tokenize content and keywords
+        let mut all_tokens = Self::tokenize(&entry.content);
+        for keyword in &entry.keywords {
+            all_tokens.extend(Self::tokenize(keyword));
+        }
+        all_tokens = self.remove_stop_words(&all_tokens);
+
+        // Update document frequencies
+        let unique_terms: HashSet<_> = all_tokens.iter().cloned().collect();
+        for term in &unique_terms {
+            *self.document_frequencies.entry(term.clone()).or_insert(0) += 1;
+        }
+        self.total_documents += 1;
+
+        // Compute TF-IDF vector
+        let tf = Self::compute_tf(&all_tokens);
+        let mut tfidf = HashMap::new();
+        for (term, tf_val) in tf {
+            let idf = self.compute_idf(&term);
+            tfidf.insert(term, tf_val * idf);
+        }
+
+        self.entry_vectors.insert(entry.uri.clone(), tfidf);
+    }
+
+    /// Remove an entry from the index
+    pub fn remove_entry(&mut self, uri: &str) {
+        self.entry_vectors.remove(uri);
+    }
+
+    /// Compute cosine similarity between two vectors
+    fn cosine_similarity(v1: &HashMap<String, f32>, v2: &HashMap<String, f32>) -> f32 {
+        if v1.is_empty() || v2.is_empty() {
+            return 0.0;
+        }
+
+        // Find common keys
+        let mut dot_product = 0.0;
+        let mut norm1 = 0.0;
+        let mut norm2 = 0.0;
+
+        for (k, v) in v1 {
+            norm1 += v * v;
+            if let Some(v2_val) = v2.get(k) {
+                dot_product += v * v2_val;
+            }
+        }
+
+        for v in v2.values() {
+            norm2 += v * v;
+        }
+
+        let denom = (norm1 * norm2).sqrt();
+        if denom == 0.0 {
+            0.0
+        } else {
+            (dot_product / denom).clamp(0.0, 1.0)
+        }
+    }
+
+    /// Score similarity between query and entry
+    pub fn score_similarity(&self, query: &str, entry: &MemoryEntry) -> f32 {
+        // Tokenize query
+        let query_tokens = self.remove_stop_words(&Self::tokenize(query));
+        if query_tokens.is_empty() {
+            return 0.5; // Neutral score for empty query
+        }
+
+        // Compute query TF-IDF
+        let query_tf = Self::compute_tf(&query_tokens);
+        let mut query_vec = HashMap::new();
+        for (term, tf_val) in query_tf {
+            let idf = self.compute_idf(&term);
+            query_vec.insert(term, tf_val * idf);
+        }
+
+        // Get entry vector
+        let entry_vec = match self.entry_vectors.get(&entry.uri) {
+            Some(v) => v,
+            None => {
+                // Fall back to simple matching if not indexed
+                return self.fallback_similarity(&query_tokens, entry);
+            }
+        };
+
+        // Compute cosine similarity
+        let cosine = Self::cosine_similarity(&query_vec, entry_vec);
+
+        // Combine with keyword matching for better results
+        let keyword_boost = self.keyword_match_score(&query_tokens, entry);
+
+        // Weighted combination
+        cosine * 0.7 + keyword_boost * 0.3
+    }
+
+    /// Fallback similarity when entry is not indexed
+    fn fallback_similarity(&self, query_tokens: &[String], entry: &MemoryEntry) -> f32 {
+        let content_lower = entry.content.to_lowercase();
+        let mut matches = 0;
+
+        for token in query_tokens {
+            if content_lower.contains(token) {
+                matches += 1;
+            }
+            for keyword in &entry.keywords {
+                if keyword.to_lowercase().contains(token) {
+                    matches += 1;
+                    break;
+                }
+            }
+        }
+
+        (matches as f32) / (query_tokens.len() * 2).max(1) as f32
+    }
+
+    /// Compute keyword match score
+    fn keyword_match_score(&self, query_tokens: &[String], entry: &MemoryEntry) -> f32 {
+        if entry.keywords.is_empty() {
+            return 0.0;
+        }
+
+        let mut matches = 0;
+        for token in query_tokens {
+            for keyword in &entry.keywords {
+                if keyword.to_lowercase().contains(&token.to_lowercase()) {
+                    matches += 1;
+                    break;
+                }
+            }
+        }
+
+        (matches as f32) / query_tokens.len().max(1) as f32
+    }
+
+    /// Clear the index
+    pub fn clear(&mut self) {
+        self.document_frequencies.clear();
+        self.total_documents = 0;
+        self.entry_vectors.clear();
+    }
+
+    /// Get statistics about the index
+    pub fn stats(&self) -> IndexStats {
+        IndexStats {
+            total_documents: self.total_documents,
+            unique_terms: self.document_frequencies.len(),
+            indexed_entries: self.entry_vectors.len(),
+        }
+    }
+}
+
+impl Default for SemanticScorer {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+/// Index statistics
+#[derive(Debug, Clone)]
+pub struct IndexStats {
+    pub total_documents: usize,
+    pub unique_terms: usize,
+    pub indexed_entries: usize,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::types::MemoryType;
+
+    #[test]
+    fn test_tokenize() {
+        let tokens = SemanticScorer::tokenize("Hello, World! This is a test.");
+        assert_eq!(tokens, vec!["hello", "world", "this", "is", "test"]);
+    }
+
+    #[test]
+    fn test_stop_words_removal() {
+        let scorer = SemanticScorer::new();
+        let tokens = vec!["hello".to_string(), "the".to_string(), "world".to_string()];
+        let filtered = scorer.remove_stop_words(&tokens);
+        assert_eq!(filtered, vec!["hello", "world"]);
+    }
+
+    #[test]
+    fn test_tf_computation() {
+        let tokens = vec!["hello".to_string(), "hello".to_string(), "world".to_string()];
+        let tf = SemanticScorer::compute_tf(&tokens);
+
+        let hello_tf = tf.get("hello").unwrap();
+        let world_tf = tf.get("world").unwrap();
+
+        // Allow for floating point comparison
+        assert!((hello_tf - (2.0 / 3.0)).abs() < 0.001);
+        assert!((world_tf - (1.0 / 3.0)).abs() < 0.001);
+    }
+
+    #[test]
+    fn test_cosine_similarity() {
+        let mut v1 = HashMap::new();
+        v1.insert("a".to_string(), 1.0);
+        v1.insert("b".to_string(), 2.0);
+
+        let mut v2 = HashMap::new();
+        v2.insert("a".to_string(), 1.0);
+        v2.insert("b".to_string(), 2.0);
+
+        // Identical vectors should have similarity 1.0
+        let sim = SemanticScorer::cosine_similarity(&v1, &v2);
+        assert!((sim - 1.0).abs() < 0.001);
+
+        // Orthogonal vectors should have similarity 0.0
+        let mut v3 = HashMap::new();
+        v3.insert("c".to_string(), 1.0);
+        let sim2 = SemanticScorer::cosine_similarity(&v1, &v3);
+        assert!((sim2 - 0.0).abs() < 0.001);
+    }
+
+    #[test]
+    fn test_index_and_score() {
+        let mut scorer = SemanticScorer::new();
+
+        let entry1 = MemoryEntry::new(
+            "test",
+            MemoryType::Knowledge,
+            "rust",
+            "Rust is a systems programming language focused on safety and performance".to_string(),
+        ).with_keywords(vec!["rust".to_string(), "programming".to_string(), "safety".to_string()]);
+
+        let entry2 = MemoryEntry::new(
+            "test",
+            MemoryType::Knowledge,
+            "python",
+            "Python is a high-level programming language".to_string(),
+        ).with_keywords(vec!["python".to_string(), "programming".to_string()]);
+
+        scorer.index_entry(&entry1);
+        scorer.index_entry(&entry2);
+
+        // Query for Rust should score higher on entry1
+        let score1 = scorer.score_similarity("rust safety", &entry1);
+        let score2 = scorer.score_similarity("rust safety", &entry2);
+
+        assert!(score1 > score2, "Rust query should score higher on Rust entry");
+    }
+
+    #[test]
+    fn test_stats() {
+        let mut scorer = SemanticScorer::new();
+
+        let entry = MemoryEntry::new(
+            "test",
+            MemoryType::Knowledge,
+            "test",
+            "Hello world".to_string(),
+        );
+
+        scorer.index_entry(&entry);
+        let stats = scorer.stats();
+
+        assert_eq!(stats.total_documents, 1);
+        assert_eq!(stats.indexed_entries, 1);
+        assert!(stats.unique_terms > 0);
+    }
+}

crates/zclaw-growth/src/retriever.rs

@@ -0,0 +1,348 @@
+//! Memory Retriever - Retrieves relevant memories from OpenViking
+//!
+//! This module provides the `MemoryRetriever` which performs semantic search
+//! over stored memories to find contextually relevant information.
+//! Uses multiple retrieval strategies and intelligent reranking.
+
+use crate::retrieval::{MemoryCache, QueryAnalyzer, SemanticScorer};
+use crate::types::{MemoryEntry, MemoryType, RetrievalConfig, RetrievalResult};
+use crate::viking_adapter::{FindOptions, VikingAdapter};
+use std::sync::Arc;
+use tokio::sync::RwLock;
+use zclaw_types::{AgentId, Result};
+
+/// Memory Retriever - retrieves relevant memories from OpenViking
+pub struct MemoryRetriever {
+    /// OpenViking adapter
+    viking: Arc<VikingAdapter>,
+    /// Retrieval configuration
+    config: RetrievalConfig,
+    /// Semantic scorer for similarity computation
+    scorer: RwLock<SemanticScorer>,
+    /// Query analyzer
+    analyzer: QueryAnalyzer,
+    /// Memory cache
+    cache: MemoryCache,
+}
+
+impl MemoryRetriever {
+    /// Create a new memory retriever
+    pub fn new(viking: Arc<VikingAdapter>) -> Self {
+        Self {
+            viking,
+            config: RetrievalConfig::default(),
+            scorer: RwLock::new(SemanticScorer::new()),
+            analyzer: QueryAnalyzer::new(),
+            cache: MemoryCache::default_config(),
+        }
+    }
+
+    /// Create with custom configuration
+    pub fn with_config(mut self, config: RetrievalConfig) -> Self {
+        self.config = config;
+        self
+    }
+
+    /// Retrieve relevant memories for a query
+    ///
+    /// This method:
+    /// 1. Analyzes the query to determine intent and keywords
+    /// 2. Searches for preferences matching the query
+    /// 3. Searches for relevant knowledge
+    /// 4. Searches for applicable experience
+    /// 5. Reranks results using semantic similarity
+    /// 6. Applies token budget constraints
+    pub async fn retrieve(
+        &self,
+        agent_id: &AgentId,
+        query: &str,
+    ) -> Result<RetrievalResult> {
+        tracing::debug!("[MemoryRetriever] Retrieving memories for query: {}", query);
+
+        // Analyze query
+        let analyzed = self.analyzer.analyze(query);
+        tracing::debug!(
+            "[MemoryRetriever] Query analysis: intent={:?}, keywords={:?}",
+            analyzed.intent,
+            analyzed.keywords
+        );
+
+        // Retrieve each type with budget constraints and reranking
+        let preferences = self
+            .retrieve_and_rerank(
+                &agent_id.to_string(),
+                MemoryType::Preference,
+                query,
+                &analyzed.keywords,
+                self.config.max_results_per_type,
+                self.config.preference_budget,
+            )
+            .await?;
+
+        let knowledge = self
+            .retrieve_and_rerank(
+                &agent_id.to_string(),
+                MemoryType::Knowledge,
+                query,
+                &analyzed.keywords,
+                self.config.max_results_per_type,
+                self.config.knowledge_budget,
+            )
+            .await?;
+
+        let experience = self
+            .retrieve_and_rerank(
+                &agent_id.to_string(),
+                MemoryType::Experience,
+                query,
+                &analyzed.keywords,
+                self.config.max_results_per_type / 2,
+                self.config.experience_budget,
+            )
+            .await?;
+
+        let total_tokens = preferences.iter()
+            .chain(knowledge.iter())
+            .chain(experience.iter())
+            .map(|m| m.estimated_tokens())
+            .sum();
+
+        // Update cache with retrieved entries
+        for entry in preferences.iter().chain(knowledge.iter()).chain(experience.iter()) {
+            self.cache.put(entry.clone()).await;
+        }
+
+        tracing::info!(
+            "[MemoryRetriever] Retrieved {} preferences, {} knowledge, {} experience ({} tokens)",
+            preferences.len(),
+            knowledge.len(),
+            experience.len(),
+            total_tokens
+        );
+
+        Ok(RetrievalResult {
+            preferences,
+            knowledge,
+            experience,
+            total_tokens,
+        })
+    }
+
+    /// Retrieve and rerank memories by type
+    async fn retrieve_and_rerank(
+        &self,
+        agent_id: &str,
+        memory_type: MemoryType,
+        query: &str,
+        keywords: &[String],
+        max_results: usize,
+        token_budget: usize,
+    ) -> Result<Vec<MemoryEntry>> {
+        // Build scope for OpenViking search
+        let scope = format!("agent://{}/{}", agent_id, memory_type);
+
+        // Generate search queries (original + expanded)
+        let analyzed_for_search = crate::retrieval::query::AnalyzedQuery {
+            original: query.to_string(),
+            keywords: keywords.to_vec(),
+            intent: crate::retrieval::query::QueryIntent::General,
+            target_types: vec![],
+            expansions: vec![],
+        };
+        let search_queries = self.analyzer.generate_search_queries(&analyzed_for_search);
+
+        // Search with multiple queries and deduplicate
+        let mut all_results = Vec::new();
+        let mut seen_uris = std::collections::HashSet::new();
+
+        for search_query in search_queries {
+            let options = FindOptions {
+                scope: Some(scope.clone()),
+                limit: Some(max_results * 2),
+                min_similarity: Some(self.config.min_similarity),
+            };
+
+            let results = self.viking.find(&search_query, options).await?;
+
+            for entry in results {
+                if seen_uris.insert(entry.uri.clone()) {
+                    all_results.push(entry);
+                }
+            }
+        }
+
+        // Rerank using semantic similarity
+        let scored = self.rerank_entries(query, all_results).await;
+
+        // Apply token budget
+        let mut filtered = Vec::new();
+        let mut used_tokens = 0;
+
+        for entry in scored {
+            let tokens = entry.estimated_tokens();
+            if used_tokens + tokens <= token_budget {
+                used_tokens += tokens;
+                filtered.push(entry);
+            }
+
+            if filtered.len() >= max_results {
+                break;
+            }
+        }
+
+        Ok(filtered)
+    }
+
+    /// Rerank entries using semantic similarity
+    async fn rerank_entries(
+        &self,
+        query: &str,
+        entries: Vec<MemoryEntry>,
+    ) -> Vec<MemoryEntry> {
+        if entries.is_empty() {
+            return entries;
+        }
+
+        let mut scorer = self.scorer.write().await;
+
+        // Index entries for semantic search
+        for entry in &entries {
+            scorer.index_entry(entry);
+        }
+
+        // Score each entry
+        let mut scored: Vec<(f32, MemoryEntry)> = entries
+            .into_iter()
+            .map(|entry| {
+                let score = scorer.score_similarity(query, &entry);
+                (score, entry)
+            })
+            .collect();
+
+        // Sort by score (descending), then by importance and access count
+        scored.sort_by(|a, b| {
+            b.0.partial_cmp(&a.0)
+                .unwrap_or(std::cmp::Ordering::Equal)
+                .then_with(|| b.1.importance.cmp(&a.1.importance))
+                .then_with(|| b.1.access_count.cmp(&a.1.access_count))
+        });
+
+        scored.into_iter().map(|(_, entry)| entry).collect()
+    }
+
+    /// Retrieve a specific memory by URI (with cache)
+    pub async fn get_by_uri(&self, uri: &str) -> Result<Option<MemoryEntry>> {
+        // Check cache first
+        if let Some(cached) = self.cache.get(uri).await {
+            return Ok(Some(cached));
+        }
+
+        // Fall back to storage
+        let result = self.viking.get(uri).await?;
+
+        // Update cache
+        if let Some(ref entry) = result {
+            self.cache.put(entry.clone()).await;
+        }
+
+        Ok(result)
+    }
+
+    /// Get all memories for an agent (for debugging/admin)
+    pub async fn get_all_memories(&self, agent_id: &AgentId) -> Result<Vec<MemoryEntry>> {
+        let scope = format!("agent://{}", agent_id);
+        let options = FindOptions {
+            scope: Some(scope),
+            limit: None,
+            min_similarity: None,
+        };
+
+        self.viking.find("", options).await
+    }
+
+    /// Get memory statistics for an agent
+    pub async fn get_stats(&self, agent_id: &AgentId) -> Result<MemoryStats> {
+        let all = self.get_all_memories(agent_id).await?;
+
+        let preference_count = all.iter().filter(|m| m.memory_type == MemoryType::Preference).count();
+        let knowledge_count = all.iter().filter(|m| m.memory_type == MemoryType::Knowledge).count();
+        let experience_count = all.iter().filter(|m| m.memory_type == MemoryType::Experience).count();
+
+        Ok(MemoryStats {
+            total_count: all.len(),
+            preference_count,
+            knowledge_count,
+            experience_count,
+            cache_hit_rate: self.cache.hit_rate().await,
+        })
+    }
+
+    /// Clear the semantic index
+    pub async fn clear_index(&self) {
+        let mut scorer = self.scorer.write().await;
+        scorer.clear();
+    }
+
+    /// Get cache statistics
+    pub async fn cache_stats(&self) -> (usize, f32) {
+        let size = self.cache.size().await;
+        let hit_rate = self.cache.hit_rate().await;
+        (size, hit_rate)
+    }
+
+    /// Warm up cache with hot entries
+    pub async fn warmup_cache(&self, agent_id: &AgentId) -> Result<usize> {
+        let all = self.get_all_memories(agent_id).await?;
+
+        // Sort by access count to get hot entries
+        let mut sorted = all;
+        sorted.sort_by(|a, b| b.access_count.cmp(&a.access_count));
+
+        // Take top 50 hot entries
+        let hot: Vec<_> = sorted.into_iter().take(50).collect();
+        let count = hot.len();
+
+        self.cache.warmup(hot).await;
+
+        Ok(count)
+    }
+}
+
+/// Memory statistics
+#[derive(Debug, Clone)]
+pub struct MemoryStats {
+    pub total_count: usize,
+    pub preference_count: usize,
+    pub knowledge_count: usize,
+    pub experience_count: usize,
+    pub cache_hit_rate: f32,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_retrieval_config_default() {
+        let config = RetrievalConfig::default();
+        assert_eq!(config.max_tokens, 500);
+        assert_eq!(config.preference_budget, 200);
+        assert_eq!(config.knowledge_budget, 200);
+    }
+
+    #[test]
+    fn test_memory_type_scope() {
+        let scope = format!("agent://test-agent/{}", MemoryType::Preference);
+        assert!(scope.contains("test-agent"));
+        assert!(scope.contains("preferences"));
+    }
+
+    #[tokio::test]
+    async fn test_retriever_creation() {
+        let viking = Arc::new(VikingAdapter::in_memory());
+        let retriever = MemoryRetriever::new(viking);
+
+        let stats = retriever.cache_stats().await;
+        assert_eq!(stats.0, 0); // Cache size should be 0
+    }
+}

crates/zclaw-growth/src/storage/mod.rs

@@ -0,0 +1,9 @@
+//! Storage backends for ZCLAW Growth System
+//!
+//! This module provides multiple storage backend implementations:
+//! - `InMemoryStorage`: Fast in-memory storage for testing and development
+//! - `SqliteStorage`: Persistent SQLite storage for production use
+
+mod sqlite;
+
+pub use sqlite::SqliteStorage;

crates/zclaw-growth/src/storage/sqlite.rs

@@ -0,0 +1,563 @@
+//! SQLite Storage Backend
+//!
+//! Persistent storage backend using SQLite for production use.
+//! Provides efficient querying and full-text search capabilities.
+
+use crate::retrieval::semantic::SemanticScorer;
+use crate::types::MemoryEntry;
+use crate::viking_adapter::{FindOptions, VikingStorage};
+use async_trait::async_trait;
+use sqlx::sqlite::{SqlitePool, SqlitePoolOptions, SqliteRow};
+use sqlx::Row;
+use std::path::PathBuf;
+use std::sync::Arc;
+use tokio::sync::RwLock;
+use zclaw_types::Result;
+use zclaw_types::ZclawError;
+
+/// SQLite storage backend with TF-IDF semantic scoring
+pub struct SqliteStorage {
+    /// Database connection pool
+    pool: SqlitePool,
+    /// Semantic scorer for similarity computation
+    scorer: Arc<RwLock<SemanticScorer>>,
+    /// Database path (for reference)
+    #[allow(dead_code)]
+    path: PathBuf,
+}
+
+/// Database row structure for memory entry
+struct MemoryRow {
+    uri: String,
+    memory_type: String,
+    content: String,
+    keywords: String,
+    importance: i32,
+    access_count: i32,
+    created_at: String,
+    last_accessed: String,
+}
+
+impl SqliteStorage {
+    /// Create a new SQLite storage at the given path
+    pub async fn new(path: impl Into<PathBuf>) -> Result<Self> {
+        let path = path.into();
+
+        // Ensure parent directory exists
+        if let Some(parent) = path.parent() {
+            if parent.to_str() != Some(":memory:") {
+                tokio::fs::create_dir_all(parent).await.map_err(|e| {
+                    ZclawError::StorageError(format!("Failed to create storage directory: {}", e))
+                })?;
+            }
+        }
+
+        // Build connection string
+        let db_url = if path.to_str() == Some(":memory:") {
+            "sqlite::memory:".to_string()
+        } else {
+            format!("sqlite:{}?mode=rwc", path.to_string_lossy())
+        };
+
+        // Create connection pool
+        let pool = SqlitePoolOptions::new()
+            .max_connections(5)
+            .connect(&db_url)
+            .await
+            .map_err(|e| ZclawError::StorageError(format!("Failed to connect to database: {}", e)))?;
+
+        let storage = Self {
+            pool,
+            scorer: Arc::new(RwLock::new(SemanticScorer::new())),
+            path,
+        };
+
+        storage.initialize_schema().await?;
+        storage.warmup_scorer().await?;
+
+        Ok(storage)
+    }
+
+    /// Create an in-memory SQLite database (for testing)
+    pub async fn in_memory() -> Self {
+        Self::new(":memory:").await.expect("Failed to create in-memory database")
+    }
+
+    /// Initialize database schema with FTS5
+    async fn initialize_schema(&self) -> Result<()> {
+        // Create main memories table
+        sqlx::query(
+            r#"
+            CREATE TABLE IF NOT EXISTS memories (
+                uri TEXT PRIMARY KEY,
+                memory_type TEXT NOT NULL,
+                content TEXT NOT NULL,
+                keywords TEXT NOT NULL DEFAULT '[]',
+                importance INTEGER NOT NULL DEFAULT 5,
+                access_count INTEGER NOT NULL DEFAULT 0,
+                created_at TEXT NOT NULL,
+                last_accessed TEXT NOT NULL
+            )
+            "#,
+        )
+        .execute(&self.pool)
+        .await
+        .map_err(|e| ZclawError::StorageError(format!("Failed to create memories table: {}", e)))?;
+
+        // Create FTS5 virtual table for full-text search
+        sqlx::query(
+            r#"
+            CREATE VIRTUAL TABLE IF NOT EXISTS memories_fts USING fts5(
+                uri,
+                content,
+                keywords,
+                tokenize='unicode61'
+            )
+            "#,
+        )
+        .execute(&self.pool)
+        .await
+        .map_err(|e| ZclawError::StorageError(format!("Failed to create FTS5 table: {}", e)))?;
+
+        // Create index on memory_type for filtering
+        sqlx::query("CREATE INDEX IF NOT EXISTS idx_memory_type ON memories(memory_type)")
+            .execute(&self.pool)
+            .await
+            .map_err(|e| ZclawError::StorageError(format!("Failed to create index: {}", e)))?;
+
+        // Create index on importance for sorting
+        sqlx::query("CREATE INDEX IF NOT EXISTS idx_importance ON memories(importance DESC)")
+            .execute(&self.pool)
+            .await
+            .map_err(|e| ZclawError::StorageError(format!("Failed to create importance index: {}", e)))?;
+
+        // Create metadata table
+        sqlx::query(
+            r#"
+            CREATE TABLE IF NOT EXISTS metadata (
+                key TEXT PRIMARY KEY,
+                json TEXT NOT NULL
+            )
+            "#,
+        )
+        .execute(&self.pool)
+        .await
+        .map_err(|e| ZclawError::StorageError(format!("Failed to create metadata table: {}", e)))?;
+
+        tracing::info!("[SqliteStorage] Database schema initialized");
+        Ok(())
+    }
+
+    /// Warmup semantic scorer with existing entries
+    async fn warmup_scorer(&self) -> Result<()> {
+        let rows = sqlx::query_as::<_, MemoryRow>(
+            "SELECT uri, memory_type, content, keywords, importance, access_count, created_at, last_accessed FROM memories"
+        )
+        .fetch_all(&self.pool)
+        .await
+        .map_err(|e| ZclawError::StorageError(format!("Failed to load memories for warmup: {}", e)))?;
+
+        let mut scorer = self.scorer.write().await;
+        for row in rows {
+            let entry = self.row_to_entry(&row);
+            scorer.index_entry(&entry);
+        }
+
+        let stats = scorer.stats();
+        tracing::info!(
+            "[SqliteStorage] Warmed up scorer with {} entries, {} terms",
+            stats.indexed_entries,
+            stats.unique_terms
+        );
+
+        Ok(())
+    }
+
+    /// Convert database row to MemoryEntry
+    fn row_to_entry(&self, row: &MemoryRow) -> MemoryEntry {
+        let memory_type = crate::types::MemoryType::parse(&row.memory_type);
+        let keywords: Vec<String> = serde_json::from_str(&row.keywords).unwrap_or_default();
+        let created_at = chrono::DateTime::parse_from_rfc3339(&row.created_at)
+            .map(|dt| dt.with_timezone(&chrono::Utc))
+            .unwrap_or_else(|_| chrono::Utc::now());
+        let last_accessed = chrono::DateTime::parse_from_rfc3339(&row.last_accessed)
+            .map(|dt| dt.with_timezone(&chrono::Utc))
+            .unwrap_or_else(|_| chrono::Utc::now());
+
+        MemoryEntry {
+            uri: row.uri.clone(),
+            memory_type,
+            content: row.content.clone(),
+            keywords,
+            importance: row.importance as u8,
+            access_count: row.access_count as u32,
+            created_at,
+            last_accessed,
+        }
+    }
+
+    /// Update access count and last accessed time
+    async fn touch_entry(&self, uri: &str) -> Result<()> {
+        let now = chrono::Utc::now().to_rfc3339();
+        sqlx::query(
+            "UPDATE memories SET access_count = access_count + 1, last_accessed = ? WHERE uri = ?"
+        )
+        .bind(&now)
+        .bind(uri)
+        .execute(&self.pool)
+        .await
+        .map_err(|e| ZclawError::StorageError(format!("Failed to update access count: {}", e)))?;
+
+        Ok(())
+    }
+}
+
+impl sqlx::FromRow<'_, SqliteRow> for MemoryRow {
+    fn from_row(row: &SqliteRow) -> sqlx::Result<Self> {
+        Ok(MemoryRow {
+            uri: row.try_get("uri")?,
+            memory_type: row.try_get("memory_type")?,
+            content: row.try_get("content")?,
+            keywords: row.try_get("keywords")?,
+            importance: row.try_get("importance")?,
+            access_count: row.try_get("access_count")?,
+            created_at: row.try_get("created_at")?,
+            last_accessed: row.try_get("last_accessed")?,
+        })
+    }
+}
+
+#[async_trait]
+impl VikingStorage for SqliteStorage {
+    async fn store(&self, entry: &MemoryEntry) -> Result<()> {
+        let keywords_json = serde_json::to_string(&entry.keywords)
+            .map_err(|e| ZclawError::StorageError(format!("Failed to serialize keywords: {}", e)))?;
+
+        let created_at = entry.created_at.to_rfc3339();
+        let last_accessed = entry.last_accessed.to_rfc3339();
+        let memory_type = entry.memory_type.to_string();
+
+        // Insert into main table
+        sqlx::query(
+            r#"
+            INSERT OR REPLACE INTO memories
+            (uri, memory_type, content, keywords, importance, access_count, created_at, last_accessed)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+            "#,
+        )
+        .bind(&entry.uri)
+        .bind(&memory_type)
+        .bind(&entry.content)
+        .bind(&keywords_json)
+        .bind(entry.importance as i32)
+        .bind(entry.access_count as i32)
+        .bind(&created_at)
+        .bind(&last_accessed)
+        .execute(&self.pool)
+        .await
+        .map_err(|e| ZclawError::StorageError(format!("Failed to store memory: {}", e)))?;
+
+        // Update FTS index - delete old and insert new
+        let _ = sqlx::query("DELETE FROM memories_fts WHERE uri = ?")
+            .bind(&entry.uri)
+            .execute(&self.pool)
+            .await;
+
+        let keywords_text = entry.keywords.join(" ");
+        let _ = sqlx::query(
+            r#"
+            INSERT INTO memories_fts (uri, content, keywords)
+            VALUES (?, ?, ?)
+            "#,
+        )
+        .bind(&entry.uri)
+        .bind(&entry.content)
+        .bind(&keywords_text)
+        .execute(&self.pool)
+        .await;
+
+        // Update semantic scorer
+        let mut scorer = self.scorer.write().await;
+        scorer.index_entry(entry);
+
+        tracing::debug!("[SqliteStorage] Stored memory: {}", entry.uri);
+        Ok(())
+    }
+
+    async fn get(&self, uri: &str) -> Result<Option<MemoryEntry>> {
+        let row = sqlx::query_as::<_, MemoryRow>(
+            "SELECT uri, memory_type, content, keywords, importance, access_count, created_at, last_accessed FROM memories WHERE uri = ?"
+        )
+        .bind(uri)
+        .fetch_optional(&self.pool)
+        .await
+        .map_err(|e| ZclawError::StorageError(format!("Failed to get memory: {}", e)))?;
+
+        if let Some(row) = row {
+            let entry = self.row_to_entry(&row);
+
+            // Update access count
+            self.touch_entry(&entry.uri).await?;
+
+            Ok(Some(entry))
+        } else {
+            Ok(None)
+        }
+    }
+
+    async fn find(&self, query: &str, options: FindOptions) -> Result<Vec<MemoryEntry>> {
+        // Get all matching entries
+        let rows = if let Some(ref scope) = options.scope {
+            sqlx::query_as::<_, MemoryRow>(
+                "SELECT uri, memory_type, content, keywords, importance, access_count, created_at, last_accessed FROM memories WHERE uri LIKE ?"
+            )
+            .bind(format!("{}%", scope))
+            .fetch_all(&self.pool)
+            .await
+            .map_err(|e| ZclawError::StorageError(format!("Failed to find memories: {}", e)))?
+        } else {
+            sqlx::query_as::<_, MemoryRow>(
+                "SELECT uri, memory_type, content, keywords, importance, access_count, created_at, last_accessed FROM memories"
+            )
+            .fetch_all(&self.pool)
+            .await
+            .map_err(|e| ZclawError::StorageError(format!("Failed to find memories: {}", e)))?
+        };
+
+        // Convert to entries and compute semantic scores
+        let scorer = self.scorer.read().await;
+        let mut scored_entries: Vec<(f32, MemoryEntry)> = Vec::new();
+
+        for row in rows {
+            let entry = self.row_to_entry(&row);
+
+            // Compute semantic score using TF-IDF
+            let semantic_score = scorer.score_similarity(query, &entry);
+
+            // Apply similarity threshold
+            if let Some(min_similarity) = options.min_similarity {
+                if semantic_score < min_similarity {
+                    continue;
+                }
+            }
+
+            scored_entries.push((semantic_score, entry));
+        }
+
+        // Sort by score (descending), then by importance and access count
+        scored_entries.sort_by(|a, b| {
+            b.0.partial_cmp(&a.0)
+                .unwrap_or(std::cmp::Ordering::Equal)
+                .then_with(|| b.1.importance.cmp(&a.1.importance))
+                .then_with(|| b.1.access_count.cmp(&a.1.access_count))
+        });
+
+        // Apply limit
+        if let Some(limit) = options.limit {
+            scored_entries.truncate(limit);
+        }
+
+        Ok(scored_entries.into_iter().map(|(_, entry)| entry).collect())
+    }
+
+    async fn find_by_prefix(&self, prefix: &str) -> Result<Vec<MemoryEntry>> {
+        let rows = sqlx::query_as::<_, MemoryRow>(
+            "SELECT uri, memory_type, content, keywords, importance, access_count, created_at, last_accessed FROM memories WHERE uri LIKE ?"
+        )
+        .bind(format!("{}%", prefix))
+        .fetch_all(&self.pool)
+        .await
+        .map_err(|e| ZclawError::StorageError(format!("Failed to find by prefix: {}", e)))?;
+
+        let entries = rows.iter().map(|row| self.row_to_entry(row)).collect();
+
+        Ok(entries)
+    }
+
+    async fn delete(&self, uri: &str) -> Result<()> {
+        sqlx::query("DELETE FROM memories WHERE uri = ?")
+            .bind(uri)
+            .execute(&self.pool)
+            .await
+            .map_err(|e| ZclawError::StorageError(format!("Failed to delete memory: {}", e)))?;
+
+        // Remove from FTS
+        let _ = sqlx::query("DELETE FROM memories_fts WHERE uri = ?")
+            .bind(uri)
+            .execute(&self.pool)
+            .await;
+
+        // Remove from scorer
+        let mut scorer = self.scorer.write().await;
+        scorer.remove_entry(uri);
+
+        tracing::debug!("[SqliteStorage] Deleted memory: {}", uri);
+        Ok(())
+    }
+
+    async fn store_metadata_json(&self, key: &str, json: &str) -> Result<()> {
+        sqlx::query(
+            r#"
+            INSERT OR REPLACE INTO metadata (key, json)
+            VALUES (?, ?)
+            "#,
+        )
+        .bind(key)
+        .bind(json)
+        .execute(&self.pool)
+        .await
+        .map_err(|e| ZclawError::StorageError(format!("Failed to store metadata: {}", e)))?;
+
+        Ok(())
+    }
+
+    async fn get_metadata_json(&self, key: &str) -> Result<Option<String>> {
+        let result = sqlx::query_scalar::<_, String>("SELECT json FROM metadata WHERE key = ?")
+            .bind(key)
+            .fetch_optional(&self.pool)
+            .await
+            .map_err(|e| ZclawError::StorageError(format!("Failed to get metadata: {}", e)))?;
+
+        Ok(result)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::types::MemoryType;
+
+    #[tokio::test]
+    async fn test_sqlite_storage_store_and_get() {
+        let storage = SqliteStorage::in_memory().await;
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "style",
+            "User prefers concise responses".to_string(),
+        );
+
+        storage.store(&entry).await.unwrap();
+        let retrieved = storage.get(&entry.uri).await.unwrap();
+
+        assert!(retrieved.is_some());
+        assert_eq!(retrieved.unwrap().content, "User prefers concise responses");
+    }
+
+    #[tokio::test]
+    async fn test_sqlite_storage_semantic_search() {
+        let storage = SqliteStorage::in_memory().await;
+
+        // Store entries with different content
+        let entry1 = MemoryEntry::new(
+            "agent-1",
+            MemoryType::Knowledge,
+            "rust",
+            "Rust is a systems programming language focused on safety".to_string(),
+        ).with_keywords(vec!["rust".to_string(), "programming".to_string(), "safety".to_string()]);
+
+        let entry2 = MemoryEntry::new(
+            "agent-1",
+            MemoryType::Knowledge,
+            "python",
+            "Python is a high-level programming language".to_string(),
+        ).with_keywords(vec!["python".to_string(), "programming".to_string()]);
+
+        storage.store(&entry1).await.unwrap();
+        storage.store(&entry2).await.unwrap();
+
+        // Search for "rust safety"
+        let results = storage.find(
+            "rust safety",
+            FindOptions {
+                scope: Some("agent://agent-1".to_string()),
+                limit: Some(10),
+                min_similarity: Some(0.1),
+            },
+        ).await.unwrap();
+
+        // Should find the Rust entry with higher score
+        assert!(!results.is_empty());
+        assert!(results[0].content.contains("Rust"));
+    }
+
+    #[tokio::test]
+    async fn test_sqlite_storage_delete() {
+        let storage = SqliteStorage::in_memory().await;
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "style",
+            "test".to_string(),
+        );
+
+        storage.store(&entry).await.unwrap();
+        storage.delete(&entry.uri).await.unwrap();
+
+        let retrieved = storage.get(&entry.uri).await.unwrap();
+        assert!(retrieved.is_none());
+    }
+
+    #[tokio::test]
+    async fn test_persistence() {
+        let path = std::env::temp_dir().join("zclaw_test_memories.db");
+
+        // Clean up any existing test db
+        let _ = std::fs::remove_file(&path);
+
+        // Create and store
+        {
+            let storage = SqliteStorage::new(&path).await.unwrap();
+            let entry = MemoryEntry::new(
+                "persist-test",
+                MemoryType::Knowledge,
+                "test",
+                "This should persist".to_string(),
+            );
+            storage.store(&entry).await.unwrap();
+        }
+
+        // Reopen and verify
+        {
+            let storage = SqliteStorage::new(&path).await.unwrap();
+            let results = storage.find_by_prefix("agent://persist-test").await.unwrap();
+            assert!(!results.is_empty());
+            assert_eq!(results[0].content, "This should persist");
+        }
+
+        // Clean up
+        let _ = std::fs::remove_file(&path);
+    }
+
+    #[tokio::test]
+    async fn test_metadata_storage() {
+        let storage = SqliteStorage::in_memory().await;
+
+        let json = r#"{"test": "value"}"#;
+        storage.store_metadata_json("test-key", json).await.unwrap();
+
+        let retrieved = storage.get_metadata_json("test-key").await.unwrap();
+        assert_eq!(retrieved, Some(json.to_string()));
+    }
+
+    #[tokio::test]
+    async fn test_access_count() {
+        let storage = SqliteStorage::in_memory().await;
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Knowledge,
+            "test",
+            "test content".to_string(),
+        );
+
+        storage.store(&entry).await.unwrap();
+
+        // Access multiple times
+        for _ in 0..3 {
+            let _ = storage.get(&entry.uri).await.unwrap();
+        }
+
+        let retrieved = storage.get(&entry.uri).await.unwrap().unwrap();
+        assert!(retrieved.access_count >= 3);
+    }
+}

crates/zclaw-growth/src/tracker.rs

@@ -0,0 +1,212 @@
+//! Growth Tracker - Tracks agent growth metrics and evolution
+//!
+//! This module provides the `GrowthTracker` which monitors and records
+//! the evolution of an agent's capabilities and knowledge over time.
+
+use crate::types::{GrowthStats, MemoryType};
+use crate::viking_adapter::VikingAdapter;
+use chrono::{DateTime, Utc};
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use std::sync::Arc;
+use zclaw_types::{AgentId, Result};
+
+/// Growth Tracker - tracks agent growth metrics
+pub struct GrowthTracker {
+    /// OpenViking adapter for storage
+    viking: Arc<VikingAdapter>,
+}
+
+impl GrowthTracker {
+    /// Create a new growth tracker
+    pub fn new(viking: Arc<VikingAdapter>) -> Self {
+        Self { viking }
+    }
+
+    /// Get current growth statistics for an agent
+    pub async fn get_stats(&self, agent_id: &AgentId) -> Result<GrowthStats> {
+        // Query all memories for the agent
+        let memories = self.viking.find_by_prefix(&format!("agent://{}", agent_id)).await?;
+
+        let mut stats = GrowthStats::default();
+        stats.total_memories = memories.len();
+
+        for memory in &memories {
+            match memory.memory_type {
+                MemoryType::Preference => stats.preference_count += 1,
+                MemoryType::Knowledge => stats.knowledge_count += 1,
+                MemoryType::Experience => stats.experience_count += 1,
+                MemoryType::Session => stats.sessions_processed += 1,
+            }
+        }
+
+        // Get last learning time from metadata
+        let meta: Option<AgentMetadata> = self.viking
+            .get_metadata(&format!("agent://{}", agent_id))
+            .await?;
+
+        if let Some(meta) = meta {
+            stats.last_learning_time = meta.last_learning_time;
+        }
+
+        Ok(stats)
+    }
+
+    /// Record a learning event
+    pub async fn record_learning(
+        &self,
+        agent_id: &AgentId,
+        session_id: &str,
+        memories_extracted: usize,
+    ) -> Result<()> {
+        let event = LearningEvent {
+            agent_id: agent_id.to_string(),
+            session_id: session_id.to_string(),
+            memories_extracted,
+            timestamp: Utc::now(),
+        };
+
+        // Store learning event
+        self.viking
+            .store_metadata(
+                &format!("agent://{}/events/{}", agent_id, session_id),
+                &event,
+            )
+            .await?;
+
+        // Update last learning time
+        self.viking
+            .store_metadata(
+                &format!("agent://{}", agent_id),
+                &AgentMetadata {
+                    last_learning_time: Some(Utc::now()),
+                    total_learning_events: None, // Will be computed
+                },
+            )
+            .await?;
+
+        tracing::info!(
+            "[GrowthTracker] Recorded learning event: agent={}, session={}, memories={}",
+            agent_id,
+            session_id,
+            memories_extracted
+        );
+
+        Ok(())
+    }
+
+    /// Get growth timeline for an agent
+    pub async fn get_timeline(&self, agent_id: &AgentId) -> Result<Vec<LearningEvent>> {
+        let memories = self
+            .viking
+            .find_by_prefix(&format!("agent://{}/events/", agent_id))
+            .await?;
+
+        // Parse events from stored memory content
+        let mut timeline = Vec::new();
+        for memory in memories {
+            if let Ok(event) = serde_json::from_str::<LearningEvent>(&memory.content) {
+                timeline.push(event);
+            }
+        }
+
+        // Sort by timestamp descending
+        timeline.sort_by(|a, b| b.timestamp.cmp(&a.timestamp));
+
+        Ok(timeline)
+    }
+
+    /// Calculate growth velocity (memories per day)
+    pub async fn get_growth_velocity(&self, agent_id: &AgentId) -> Result<f64> {
+        let timeline = self.get_timeline(agent_id).await?;
+
+        if timeline.is_empty() {
+            return Ok(0.0);
+        }
+
+        // Get first and last event
+        let first = timeline.iter().min_by_key(|e| e.timestamp);
+        let last = timeline.iter().max_by_key(|e| e.timestamp);
+
+        match (first, last) {
+            (Some(first), Some(last)) => {
+                let days = (last.timestamp - first.timestamp).num_days().max(1) as f64;
+                let total_memories: usize = timeline.iter().map(|e| e.memories_extracted).sum();
+                Ok(total_memories as f64 / days)
+            }
+            _ => Ok(0.0),
+        }
+    }
+
+    /// Get memory distribution by category
+    pub async fn get_memory_distribution(
+        &self,
+        agent_id: &AgentId,
+    ) -> Result<HashMap<String, usize>> {
+        let memories = self.viking.find_by_prefix(&format!("agent://{}", agent_id)).await?;
+
+        let mut distribution = HashMap::new();
+        for memory in memories {
+            *distribution.entry(memory.memory_type.to_string()).or_insert(0) += 1;
+        }
+
+        Ok(distribution)
+    }
+}
+
+/// Learning event record
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct LearningEvent {
+    /// Agent ID
+    pub agent_id: String,
+    /// Session ID where learning occurred
+    pub session_id: String,
+    /// Number of memories extracted
+    pub memories_extracted: usize,
+    /// Event timestamp
+    pub timestamp: DateTime<Utc>,
+}
+
+/// Agent metadata stored in OpenViking
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct AgentMetadata {
+    /// Last learning time
+    pub last_learning_time: Option<DateTime<Utc>>,
+    /// Total learning events (computed)
+    pub total_learning_events: Option<usize>,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_learning_event_serialization() {
+        let event = LearningEvent {
+            agent_id: "test-agent".to_string(),
+            session_id: "test-session".to_string(),
+            memories_extracted: 5,
+            timestamp: Utc::now(),
+        };
+
+        let json = serde_json::to_string(&event).unwrap();
+        let parsed: LearningEvent = serde_json::from_str(&json).unwrap();
+
+        assert_eq!(parsed.agent_id, event.agent_id);
+        assert_eq!(parsed.memories_extracted, event.memories_extracted);
+    }
+
+    #[test]
+    fn test_agent_metadata_serialization() {
+        let meta = AgentMetadata {
+            last_learning_time: Some(Utc::now()),
+            total_learning_events: Some(10),
+        };
+
+        let json = serde_json::to_string(&meta).unwrap();
+        let parsed: AgentMetadata = serde_json::from_str(&json).unwrap();
+
+        assert!(parsed.last_learning_time.is_some());
+        assert_eq!(parsed.total_learning_events, Some(10));
+    }
+}

crates/zclaw-growth/src/types.rs

@@ -0,0 +1,486 @@
+//! Core type definitions for the ZCLAW Growth System
+//!
+//! This module defines the fundamental types used for memory management,
+//! extraction, retrieval, and prompt injection.
+
+use chrono::{DateTime, Utc};
+use serde::{Deserialize, Serialize};
+use zclaw_types::SessionId;
+
+/// Memory type classification
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
+#[serde(rename_all = "snake_case")]
+pub enum MemoryType {
+    /// User preferences (communication style, format, language, etc.)
+    Preference,
+    /// Accumulated knowledge (user facts, domain knowledge, lessons learned)
+    Knowledge,
+    /// Skill/tool usage experience
+    Experience,
+    /// Conversation session history
+    Session,
+}
+
+impl std::fmt::Display for MemoryType {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            MemoryType::Preference => write!(f, "preferences"),
+            MemoryType::Knowledge => write!(f, "knowledge"),
+            MemoryType::Experience => write!(f, "experience"),
+            MemoryType::Session => write!(f, "sessions"),
+        }
+    }
+}
+
+impl std::str::FromStr for MemoryType {
+    type Err = String;
+
+    fn from_str(s: &str) -> Result<Self, Self::Err> {
+        match s.to_lowercase().as_str() {
+            "preferences" | "preference" => Ok(MemoryType::Preference),
+            "knowledge" => Ok(MemoryType::Knowledge),
+            "experience" => Ok(MemoryType::Experience),
+            "sessions" | "session" => Ok(MemoryType::Session),
+            _ => Err(format!("Unknown memory type: {}", s)),
+        }
+    }
+}
+
+impl MemoryType {
+    /// Parse memory type from string (returns Knowledge as default)
+    pub fn parse(s: &str) -> Self {
+        s.parse().unwrap_or(MemoryType::Knowledge)
+    }
+}
+
+/// Memory entry stored in OpenViking
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct MemoryEntry {
+    /// URI in OpenViking format: agent://{agent_id}/{type}/{category}
+    pub uri: String,
+    /// Type of memory
+    pub memory_type: MemoryType,
+    /// Memory content
+    pub content: String,
+    /// Keywords for semantic search
+    pub keywords: Vec<String>,
+    /// Importance score (1-10)
+    pub importance: u8,
+    /// Number of times accessed
+    pub access_count: u32,
+    /// Creation timestamp
+    pub created_at: DateTime<Utc>,
+    /// Last access timestamp
+    pub last_accessed: DateTime<Utc>,
+}
+
+impl MemoryEntry {
+    /// Create a new memory entry
+    pub fn new(
+        agent_id: &str,
+        memory_type: MemoryType,
+        category: &str,
+        content: String,
+    ) -> Self {
+        let uri = format!("agent://{}/{}/{}", agent_id, memory_type, category);
+        Self {
+            uri,
+            memory_type,
+            content,
+            keywords: Vec::new(),
+            importance: 5,
+            access_count: 0,
+            created_at: Utc::now(),
+            last_accessed: Utc::now(),
+        }
+    }
+
+    /// Add keywords to the memory entry
+    pub fn with_keywords(mut self, keywords: Vec<String>) -> Self {
+        self.keywords = keywords;
+        self
+    }
+
+    /// Set importance score
+    pub fn with_importance(mut self, importance: u8) -> Self {
+        self.importance = importance.min(10).max(1);
+        self
+    }
+
+    /// Mark as accessed
+    pub fn touch(&mut self) {
+        self.access_count += 1;
+        self.last_accessed = Utc::now();
+    }
+
+    /// Estimate token count (roughly 4 characters per token for mixed content)
+    /// More accurate estimation considering Chinese characters (1.5 tokens avg)
+    pub fn estimated_tokens(&self) -> usize {
+        let char_count = self.content.chars().count();
+        let cjk_count = self.content.chars().filter(|c| is_cjk(*c)).count();
+        let non_cjk_count = char_count - cjk_count;
+
+        // CJK: ~1.5 tokens per char, non-CJK: ~0.25 tokens per char
+        (cjk_count as f32 * 1.5 + non_cjk_count as f32 * 0.25).ceil() as usize
+    }
+}
+
+/// Extracted memory from conversation analysis
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ExtractedMemory {
+    /// Type of extracted memory
+    pub memory_type: MemoryType,
+    /// Category within the memory type
+    pub category: String,
+    /// Memory content
+    pub content: String,
+    /// Extraction confidence (0.0 - 1.0)
+    pub confidence: f32,
+    /// Source session ID
+    pub source_session: SessionId,
+    /// Keywords extracted
+    pub keywords: Vec<String>,
+}
+
+impl ExtractedMemory {
+    /// Create a new extracted memory
+    pub fn new(
+        memory_type: MemoryType,
+        category: impl Into<String>,
+        content: impl Into<String>,
+        source_session: SessionId,
+    ) -> Self {
+        Self {
+            memory_type,
+            category: category.into(),
+            content: content.into(),
+            confidence: 0.8,
+            source_session,
+            keywords: Vec::new(),
+        }
+    }
+
+    /// Set confidence score
+    pub fn with_confidence(mut self, confidence: f32) -> Self {
+        self.confidence = confidence.clamp(0.0, 1.0);
+        self
+    }
+
+    /// Add keywords
+    pub fn with_keywords(mut self, keywords: Vec<String>) -> Self {
+        self.keywords = keywords;
+        self
+    }
+
+    /// Convert to MemoryEntry for storage
+    pub fn to_memory_entry(&self, agent_id: &str) -> MemoryEntry {
+        MemoryEntry::new(agent_id, self.memory_type, &self.category, self.content.clone())
+            .with_keywords(self.keywords.clone())
+    }
+}
+
+/// Retrieval configuration
+#[derive(Debug, Clone)]
+pub struct RetrievalConfig {
+    /// Total token budget for retrieved memories
+    pub max_tokens: usize,
+    /// Token budget for preferences
+    pub preference_budget: usize,
+    /// Token budget for knowledge
+    pub knowledge_budget: usize,
+    /// Token budget for experience
+    pub experience_budget: usize,
+    /// Minimum similarity threshold (0.0 - 1.0)
+    pub min_similarity: f32,
+    /// Maximum number of results per type
+    pub max_results_per_type: usize,
+}
+
+/// Check if character is CJK
+fn is_cjk(c: char) -> bool {
+    matches!(c,
+        '\u{4E00}'..='\u{9FFF}' |  // CJK Unified Ideographs
+        '\u{3400}'..='\u{4DBF}' |  // CJK Unified Ideographs Extension A
+        '\u{20000}'..='\u{2A6DF}' | // CJK Unified Ideographs Extension B
+        '\u{F900}'..='\u{FAFF}' |   // CJK Compatibility Ideographs
+        '\u{3040}'..='\u{309F}' |   // Hiragana
+        '\u{30A0}'..='\u{30FF}' |   // Katakana
+        '\u{AC00}'..='\u{D7AF}'     // Hangul
+    )
+}
+
+impl Default for RetrievalConfig {
+    fn default() -> Self {
+        Self {
+            max_tokens: 500,
+            preference_budget: 200,
+            knowledge_budget: 200,
+            experience_budget: 100,
+            min_similarity: 0.7,
+            max_results_per_type: 5,
+        }
+    }
+}
+
+impl RetrievalConfig {
+    /// Create a config with custom token budget
+    pub fn with_budget(max_tokens: usize) -> Self {
+        let pref = (max_tokens as f32 * 0.4) as usize;
+        let knowledge = (max_tokens as f32 * 0.4) as usize;
+        let exp = max_tokens.saturating_sub(pref).saturating_sub(knowledge);
+
+        Self {
+            max_tokens,
+            preference_budget: pref,
+            knowledge_budget: knowledge,
+            experience_budget: exp,
+            min_similarity: 0.7,
+            max_results_per_type: 5,
+        }
+    }
+}
+
+/// Retrieval result containing memories by type
+#[derive(Debug, Clone, Default)]
+pub struct RetrievalResult {
+    /// Retrieved preferences
+    pub preferences: Vec<MemoryEntry>,
+    /// Retrieved knowledge
+    pub knowledge: Vec<MemoryEntry>,
+    /// Retrieved experience
+    pub experience: Vec<MemoryEntry>,
+    /// Total tokens used
+    pub total_tokens: usize,
+}
+
+impl RetrievalResult {
+    /// Check if result is empty
+    pub fn is_empty(&self) -> bool {
+        self.preferences.is_empty()
+            && self.knowledge.is_empty()
+            && self.experience.is_empty()
+    }
+
+    /// Get total memory count
+    pub fn total_count(&self) -> usize {
+        self.preferences.len() + self.knowledge.len() + self.experience.len()
+    }
+
+    /// Calculate total tokens from entries
+    pub fn calculate_tokens(&self) -> usize {
+        let tokens: usize = self.preferences.iter()
+            .chain(self.knowledge.iter())
+            .chain(self.experience.iter())
+            .map(|m| m.estimated_tokens())
+            .sum();
+        tokens
+    }
+}
+
+/// Extraction configuration
+#[derive(Debug, Clone)]
+pub struct ExtractionConfig {
+    /// Extract preferences from conversation
+    pub extract_preferences: bool,
+    /// Extract knowledge from conversation
+    pub extract_knowledge: bool,
+    /// Extract experience from conversation
+    pub extract_experience: bool,
+    /// Minimum confidence threshold for extraction
+    pub min_confidence: f32,
+}
+
+impl Default for ExtractionConfig {
+    fn default() -> Self {
+        Self {
+            extract_preferences: true,
+            extract_knowledge: true,
+            extract_experience: true,
+            min_confidence: 0.6,
+        }
+    }
+}
+
+/// Growth statistics for an agent
+#[derive(Debug, Clone, Default, Serialize, Deserialize)]
+pub struct GrowthStats {
+    /// Total number of memories
+    pub total_memories: usize,
+    /// Number of preferences
+    pub preference_count: usize,
+    /// Number of knowledge entries
+    pub knowledge_count: usize,
+    /// Number of experience entries
+    pub experience_count: usize,
+    /// Total sessions processed
+    pub sessions_processed: usize,
+    /// Last learning timestamp
+    pub last_learning_time: Option<DateTime<Utc>>,
+    /// Average extraction confidence
+    pub avg_confidence: f32,
+}
+
+/// OpenViking URI builder
+pub struct UriBuilder;
+
+impl UriBuilder {
+    /// Build a preference URI
+    pub fn preference(agent_id: &str, category: &str) -> String {
+        format!("agent://{}/preferences/{}", agent_id, category)
+    }
+
+    /// Build a knowledge URI
+    pub fn knowledge(agent_id: &str, domain: &str) -> String {
+        format!("agent://{}/knowledge/{}", agent_id, domain)
+    }
+
+    /// Build an experience URI
+    pub fn experience(agent_id: &str, skill_id: &str) -> String {
+        format!("agent://{}/experience/{}", agent_id, skill_id)
+    }
+
+    /// Build a session URI
+    pub fn session(agent_id: &str, session_id: &str) -> String {
+        format!("agent://{}/sessions/{}", agent_id, session_id)
+    }
+
+    /// Parse agent ID from URI
+    pub fn parse_agent_id(uri: &str) -> Option<&str> {
+        uri.strip_prefix("agent://")?
+            .split('/')
+            .next()
+    }
+
+    /// Parse memory type from URI
+    pub fn parse_memory_type(uri: &str) -> Option<MemoryType> {
+        let after_agent = uri.strip_prefix("agent://")?;
+        let mut parts = after_agent.split('/');
+        parts.next()?; // Skip agent_id
+
+        match parts.next()? {
+            "preferences" => Some(MemoryType::Preference),
+            "knowledge" => Some(MemoryType::Knowledge),
+            "experience" => Some(MemoryType::Experience),
+            "sessions" => Some(MemoryType::Session),
+            _ => None,
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_memory_type_display() {
+        assert_eq!(format!("{}", MemoryType::Preference), "preferences");
+        assert_eq!(format!("{}", MemoryType::Knowledge), "knowledge");
+        assert_eq!(format!("{}", MemoryType::Experience), "experience");
+        assert_eq!(format!("{}", MemoryType::Session), "sessions");
+    }
+
+    #[test]
+    fn test_memory_entry_creation() {
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "communication-style",
+            "User prefers concise responses".to_string(),
+        );
+
+        assert_eq!(entry.uri, "agent://test-agent/preferences/communication-style");
+        assert_eq!(entry.importance, 5);
+        assert_eq!(entry.access_count, 0);
+    }
+
+    #[test]
+    fn test_memory_entry_touch() {
+        let mut entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Knowledge,
+            "domain",
+            "content".to_string(),
+        );
+
+        entry.touch();
+        assert_eq!(entry.access_count, 1);
+    }
+
+    #[test]
+    fn test_estimated_tokens() {
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "test",
+            "This is a test content that should be around 10 tokens".to_string(),
+        );
+
+        // ~40 chars / 4 = ~10 tokens
+        assert!(entry.estimated_tokens() > 5);
+        assert!(entry.estimated_tokens() < 20);
+    }
+
+    #[test]
+    fn test_retrieval_config_default() {
+        let config = RetrievalConfig::default();
+        assert_eq!(config.max_tokens, 500);
+        assert_eq!(config.preference_budget, 200);
+        assert_eq!(config.knowledge_budget, 200);
+        assert_eq!(config.experience_budget, 100);
+    }
+
+    #[test]
+    fn test_retrieval_config_with_budget() {
+        let config = RetrievalConfig::with_budget(1000);
+        assert_eq!(config.max_tokens, 1000);
+        assert!(config.preference_budget >= 350);
+        assert!(config.knowledge_budget >= 350);
+    }
+
+    #[test]
+    fn test_uri_builder() {
+        let pref_uri = UriBuilder::preference("agent-1", "style");
+        assert_eq!(pref_uri, "agent://agent-1/preferences/style");
+
+        let knowledge_uri = UriBuilder::knowledge("agent-1", "rust");
+        assert_eq!(knowledge_uri, "agent://agent-1/knowledge/rust");
+
+        let exp_uri = UriBuilder::experience("agent-1", "browser");
+        assert_eq!(exp_uri, "agent://agent-1/experience/browser");
+
+        let session_uri = UriBuilder::session("agent-1", "session-123");
+        assert_eq!(session_uri, "agent://agent-1/sessions/session-123");
+    }
+
+    #[test]
+    fn test_uri_parser() {
+        let uri = "agent://agent-1/preferences/style";
+        assert_eq!(UriBuilder::parse_agent_id(uri), Some("agent-1"));
+        assert_eq!(UriBuilder::parse_memory_type(uri), Some(MemoryType::Preference));
+
+        let invalid_uri = "invalid-uri";
+        assert!(UriBuilder::parse_agent_id(invalid_uri).is_none());
+        assert!(UriBuilder::parse_memory_type(invalid_uri).is_none());
+    }
+
+    #[test]
+    fn test_retrieval_result() {
+        let result = RetrievalResult::default();
+        assert!(result.is_empty());
+        assert_eq!(result.total_count(), 0);
+
+        let result = RetrievalResult {
+            preferences: vec![MemoryEntry::new(
+                "agent-1",
+                MemoryType::Preference,
+                "style",
+                "test".to_string(),
+            )],
+            knowledge: vec![],
+            experience: vec![],
+            total_tokens: 0,
+        };
+        assert!(!result.is_empty());
+        assert_eq!(result.total_count(), 1);
+    }
+}

crates/zclaw-growth/src/viking_adapter.rs

@@ -0,0 +1,362 @@
+//! OpenViking Adapter - Interface to the OpenViking memory system
+//!
+//! This module provides the `VikingAdapter` which wraps the OpenViking
+//! context database for storing and retrieving agent memories.
+
+use crate::types::MemoryEntry;
+use async_trait::async_trait;
+use serde::{de::DeserializeOwned, Serialize};
+use std::collections::HashMap;
+use std::sync::Arc;
+use zclaw_types::Result;
+
+/// Search options for find operations
+#[derive(Debug, Clone, Default)]
+pub struct FindOptions {
+    /// Scope to search within (URI prefix)
+    pub scope: Option<String>,
+    /// Maximum results to return
+    pub limit: Option<usize>,
+    /// Minimum similarity threshold
+    pub min_similarity: Option<f32>,
+}
+
+/// VikingStorage trait - core storage operations (dyn-compatible)
+#[async_trait]
+pub trait VikingStorage: Send + Sync {
+    /// Store a memory entry
+    async fn store(&self, entry: &MemoryEntry) -> Result<()>;
+
+    /// Get a memory entry by URI
+    async fn get(&self, uri: &str) -> Result<Option<MemoryEntry>>;
+
+    /// Find memories by query with options
+    async fn find(&self, query: &str, options: FindOptions) -> Result<Vec<MemoryEntry>>;
+
+    /// Find memories by URI prefix
+    async fn find_by_prefix(&self, prefix: &str) -> Result<Vec<MemoryEntry>>;
+
+    /// Delete a memory by URI
+    async fn delete(&self, uri: &str) -> Result<()>;
+
+    /// Store metadata as JSON string
+    async fn store_metadata_json(&self, key: &str, json: &str) -> Result<()>;
+
+    /// Get metadata as JSON string
+    async fn get_metadata_json(&self, key: &str) -> Result<Option<String>>;
+}
+
+/// OpenViking adapter implementation
+#[derive(Clone)]
+pub struct VikingAdapter {
+    /// Storage backend
+    backend: Arc<dyn VikingStorage>,
+}
+
+impl VikingAdapter {
+    /// Create a new Viking adapter with a storage backend
+    pub fn new(backend: Arc<dyn VikingStorage>) -> Self {
+        Self { backend }
+    }
+
+    /// Create with in-memory storage (for testing)
+    pub fn in_memory() -> Self {
+        Self {
+            backend: Arc::new(InMemoryStorage::new()),
+        }
+    }
+
+    /// Store a memory entry
+    pub async fn store(&self, entry: &MemoryEntry) -> Result<()> {
+        self.backend.store(entry).await
+    }
+
+    /// Get a memory entry by URI
+    pub async fn get(&self, uri: &str) -> Result<Option<MemoryEntry>> {
+        self.backend.get(uri).await
+    }
+
+    /// Find memories by query
+    pub async fn find(&self, query: &str, options: FindOptions) -> Result<Vec<MemoryEntry>> {
+        self.backend.find(query, options).await
+    }
+
+    /// Find memories by URI prefix
+    pub async fn find_by_prefix(&self, prefix: &str) -> Result<Vec<MemoryEntry>> {
+        self.backend.find_by_prefix(prefix).await
+    }
+
+    /// Delete a memory
+    pub async fn delete(&self, uri: &str) -> Result<()> {
+        self.backend.delete(uri).await
+    }
+
+    /// Store metadata (typed)
+    pub async fn store_metadata<T: Serialize>(&self, key: &str, value: &T) -> Result<()> {
+        let json = serde_json::to_string(value)?;
+        self.backend.store_metadata_json(key, &json).await
+    }
+
+    /// Get metadata (typed)
+    pub async fn get_metadata<T: DeserializeOwned>(&self, key: &str) -> Result<Option<T>> {
+        match self.backend.get_metadata_json(key).await? {
+            Some(json) => {
+                let value: T = serde_json::from_str(&json)?;
+                Ok(Some(value))
+            }
+            None => Ok(None),
+        }
+    }
+}
+
+/// In-memory storage backend (for testing and development)
+pub struct InMemoryStorage {
+    memories: std::sync::RwLock<HashMap<String, MemoryEntry>>,
+    metadata: std::sync::RwLock<HashMap<String, String>>,
+}
+
+impl InMemoryStorage {
+    /// Create a new in-memory storage
+    pub fn new() -> Self {
+        Self {
+            memories: std::sync::RwLock::new(HashMap::new()),
+            metadata: std::sync::RwLock::new(HashMap::new()),
+        }
+    }
+}
+
+impl Default for InMemoryStorage {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+#[async_trait]
+impl VikingStorage for InMemoryStorage {
+    async fn store(&self, entry: &MemoryEntry) -> Result<()> {
+        let mut memories = self.memories.write().unwrap();
+        memories.insert(entry.uri.clone(), entry.clone());
+        Ok(())
+    }
+
+    async fn get(&self, uri: &str) -> Result<Option<MemoryEntry>> {
+        let memories = self.memories.read().unwrap();
+        Ok(memories.get(uri).cloned())
+    }
+
+    async fn find(&self, query: &str, options: FindOptions) -> Result<Vec<MemoryEntry>> {
+        let memories = self.memories.read().unwrap();
+
+        let mut results: Vec<MemoryEntry> = memories
+            .values()
+            .filter(|entry| {
+                // Apply scope filter
+                if let Some(ref scope) = options.scope {
+                    if !entry.uri.starts_with(scope) {
+                        return false;
+                    }
+                }
+
+                // Simple text matching (in real implementation, use semantic search)
+                if !query.is_empty() {
+                    let query_lower = query.to_lowercase();
+                    let content_lower = entry.content.to_lowercase();
+                    let keywords_match = entry.keywords.iter().any(|k| k.to_lowercase().contains(&query_lower));
+
+                    content_lower.contains(&query_lower) || keywords_match
+                } else {
+                    true
+                }
+            })
+            .cloned()
+            .collect();
+
+        // Sort by importance and access count
+        results.sort_by(|a, b| {
+            b.importance
+                .cmp(&a.importance)
+                .then_with(|| b.access_count.cmp(&a.access_count))
+        });
+
+        // Apply limit
+        if let Some(limit) = options.limit {
+            results.truncate(limit);
+        }
+
+        Ok(results)
+    }
+
+    async fn find_by_prefix(&self, prefix: &str) -> Result<Vec<MemoryEntry>> {
+        let memories = self.memories.read().unwrap();
+
+        let results: Vec<MemoryEntry> = memories
+            .values()
+            .filter(|entry| entry.uri.starts_with(prefix))
+            .cloned()
+            .collect();
+
+        Ok(results)
+    }
+
+    async fn delete(&self, uri: &str) -> Result<()> {
+        let mut memories = self.memories.write().unwrap();
+        memories.remove(uri);
+        Ok(())
+    }
+
+    async fn store_metadata_json(&self, key: &str, json: &str) -> Result<()> {
+        let mut metadata = self.metadata.write().unwrap();
+        metadata.insert(key.to_string(), json.to_string());
+        Ok(())
+    }
+
+    async fn get_metadata_json(&self, key: &str) -> Result<Option<String>> {
+        let metadata = self.metadata.read().unwrap();
+        Ok(metadata.get(key).cloned())
+    }
+}
+
+/// OpenViking levels for storage
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum VikingLevel {
+    /// L0: Raw data (original content)
+    L0,
+    /// L1: Summarized content
+    L1,
+    /// L2: Keywords and metadata
+    L2,
+}
+
+impl std::fmt::Display for VikingLevel {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            VikingLevel::L0 => write!(f, "L0"),
+            VikingLevel::L1 => write!(f, "L1"),
+            VikingLevel::L2 => write!(f, "L2"),
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::types::MemoryType;
+
+    #[tokio::test]
+    async fn test_in_memory_storage_store_and_get() {
+        let storage = InMemoryStorage::new();
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "style",
+            "test content".to_string(),
+        );
+
+        storage.store(&entry).await.unwrap();
+        let retrieved = storage.get(&entry.uri).await.unwrap();
+
+        assert!(retrieved.is_some());
+        assert_eq!(retrieved.unwrap().content, "test content");
+    }
+
+    #[tokio::test]
+    async fn test_in_memory_storage_find() {
+        let storage = InMemoryStorage::new();
+
+        let entry1 = MemoryEntry::new(
+            "agent-1",
+            MemoryType::Knowledge,
+            "rust",
+            "Rust programming tips".to_string(),
+        );
+        let entry2 = MemoryEntry::new(
+            "agent-1",
+            MemoryType::Knowledge,
+            "python",
+            "Python programming tips".to_string(),
+        );
+
+        storage.store(&entry1).await.unwrap();
+        storage.store(&entry2).await.unwrap();
+
+        let results = storage
+            .find(
+                "Rust",
+                FindOptions {
+                    scope: Some("agent://agent-1".to_string()),
+                    limit: Some(10),
+                    min_similarity: None,
+                },
+            )
+            .await
+            .unwrap();
+
+        assert_eq!(results.len(), 1);
+        assert!(results[0].content.contains("Rust"));
+    }
+
+    #[tokio::test]
+    async fn test_in_memory_storage_delete() {
+        let storage = InMemoryStorage::new();
+        let entry = MemoryEntry::new(
+            "test-agent",
+            MemoryType::Preference,
+            "style",
+            "test".to_string(),
+        );
+
+        storage.store(&entry).await.unwrap();
+        storage.delete(&entry.uri).await.unwrap();
+
+        let retrieved = storage.get(&entry.uri).await.unwrap();
+        assert!(retrieved.is_none());
+    }
+
+    #[tokio::test]
+    async fn test_metadata_storage() {
+        let storage = InMemoryStorage::new();
+
+        #[derive(Serialize, serde::Deserialize)]
+        struct TestData {
+            value: String,
+        }
+
+        let data = TestData {
+            value: "test".to_string(),
+        };
+
+        storage.store_metadata_json("test-key", &serde_json::to_string(&data).unwrap()).await.unwrap();
+        let json = storage.get_metadata_json("test-key").await.unwrap();
+
+        assert!(json.is_some());
+        let retrieved: TestData = serde_json::from_str(&json.unwrap()).unwrap();
+        assert_eq!(retrieved.value, "test");
+    }
+
+    #[tokio::test]
+    async fn test_viking_adapter_typed_metadata() {
+        let adapter = VikingAdapter::in_memory();
+
+        #[derive(Serialize, serde::Deserialize)]
+        struct TestData {
+            value: String,
+        }
+
+        let data = TestData {
+            value: "test".to_string(),
+        };
+
+        adapter.store_metadata("test-key", &data).await.unwrap();
+        let retrieved: Option<TestData> = adapter.get_metadata("test-key").await.unwrap();
+
+        assert!(retrieved.is_some());
+        assert_eq!(retrieved.unwrap().value, "test");
+    }
+
+    #[test]
+    fn test_viking_level_display() {
+        assert_eq!(format!("{}", VikingLevel::L0), "L0");
+        assert_eq!(format!("{}", VikingLevel::L1), "L1");
+        assert_eq!(format!("{}", VikingLevel::L2), "L2");
+    }
+}

crates/zclaw-growth/tests/integration_test.rs

@@ -0,0 +1,412 @@
+//! Integration tests for ZCLAW Growth System
+//!
+//! Tests the complete flow: store → find → inject
+
+use std::sync::Arc;
+use zclaw_growth::{
+    FindOptions, MemoryEntry, MemoryRetriever, MemoryType, PromptInjector,
+    RetrievalConfig, RetrievalResult, SqliteStorage, VikingAdapter,
+};
+use zclaw_types::AgentId;
+
+/// Test complete memory lifecycle
+#[tokio::test]
+async fn test_memory_lifecycle() {
+    let storage = Arc::new(SqliteStorage::in_memory().await);
+    let adapter = Arc::new(VikingAdapter::new(storage));
+
+    // Create agent ID and use its string form for storage
+    let agent_id = AgentId::new();
+    let agent_str = agent_id.to_string();
+
+    // 1. Store a preference
+    let pref = MemoryEntry::new(
+        &agent_str,
+        MemoryType::Preference,
+        "communication-style",
+        "用户偏好简洁的回复，不喜欢冗长的解释".to_string(),
+    )
+    .with_keywords(vec!["简洁".to_string(), "沟通风格".to_string()])
+    .with_importance(8);
+
+    adapter.store(&pref).await.unwrap();
+
+    // 2. Store knowledge
+    let knowledge = MemoryEntry::new(
+        &agent_str,
+        MemoryType::Knowledge,
+        "rust-expertise",
+        "用户是 Rust 开发者，熟悉 async/await 和 trait 系统".to_string(),
+    )
+    .with_keywords(vec!["Rust".to_string(), "开发者".to_string()]);
+
+    adapter.store(&knowledge).await.unwrap();
+
+    // 3. Store experience
+    let experience = MemoryEntry::new(
+        &agent_str,
+        MemoryType::Experience,
+        "browser-skill",
+        "浏览器技能在搜索技术文档时效果很好".to_string(),
+    )
+    .with_keywords(vec!["浏览器".to_string(), "技能".to_string()]);
+
+    adapter.store(&experience).await.unwrap();
+
+    // 4. Retrieve memories - directly from adapter first
+    let direct_results = adapter
+        .find(
+            "Rust",
+            FindOptions {
+                scope: Some(format!("agent://{}", agent_str)),
+                limit: Some(10),
+                min_similarity: Some(0.1),
+            },
+        )
+        .await
+        .unwrap();
+    println!("Direct find results: {:?}", direct_results.len());
+
+    let retriever = MemoryRetriever::new(adapter.clone());
+    // Use lower similarity threshold for testing
+    let config = RetrievalConfig {
+        min_similarity: 0.1,
+        ..RetrievalConfig::default()
+    };
+    let retriever = retriever.with_config(config);
+    let result = retriever
+        .retrieve(&agent_id, "Rust 编程")
+        .await
+        .unwrap();
+
+    println!("Knowledge results: {:?}", result.knowledge.len());
+    println!("Preferences results: {:?}", result.preferences.len());
+    println!("Experience results: {:?}", result.experience.len());
+
+    // Should find the knowledge entry
+    assert!(!result.knowledge.is_empty(), "Expected to find knowledge entries but found none. Direct results: {}", direct_results.len());
+    assert!(result.knowledge[0].content.contains("Rust"));
+
+    // 5. Inject into prompt
+    let injector = PromptInjector::new();
+    let base_prompt = "你是一个有帮助的 AI 助手。";
+    let enhanced = injector.inject_with_format(base_prompt, &result);
+
+    // Enhanced prompt should contain memory context
+    assert!(enhanced.len() > base_prompt.len());
+}
+
+/// Test semantic search ranking
+#[tokio::test]
+async fn test_semantic_search_ranking() {
+    let storage = Arc::new(SqliteStorage::in_memory().await);
+    let adapter = Arc::new(VikingAdapter::new(storage.clone()));
+
+    // Store multiple entries with different relevance
+    let entries = vec![
+        MemoryEntry::new(
+            "agent-1",
+            MemoryType::Knowledge,
+            "rust-basics",
+            "Rust 是一门系统编程语言，注重安全性和性能".to_string(),
+        )
+        .with_keywords(vec!["Rust".to_string(), "系统编程".to_string()]),
+        MemoryEntry::new(
+            "agent-1",
+            MemoryType::Knowledge,
+            "python-basics",
+            "Python 是一门高级编程语言，易于学习".to_string(),
+        )
+        .with_keywords(vec!["Python".to_string(), "高级语言".to_string()]),
+        MemoryEntry::new(
+            "agent-1",
+            MemoryType::Knowledge,
+            "rust-async",
+            "Rust 的 async/await 语法用于异步编程".to_string(),
+        )
+        .with_keywords(vec!["Rust".to_string(), "async".to_string(), "异步".to_string()]),
+    ];
+
+    for entry in &entries {
+        adapter.store(entry).await.unwrap();
+    }
+
+    // Search for "Rust 异步编程"
+    let results = adapter
+        .find(
+            "Rust 异步编程",
+            FindOptions {
+                scope: Some("agent://agent-1".to_string()),
+                limit: Some(10),
+                min_similarity: Some(0.1),
+            },
+        )
+        .await
+        .unwrap();
+
+    // Rust async entry should rank highest
+    assert!(!results.is_empty());
+    assert!(results[0].content.contains("async") || results[0].content.contains("Rust"));
+}
+
+/// Test memory importance and access count
+#[tokio::test]
+async fn test_importance_and_access() {
+    let storage = Arc::new(SqliteStorage::in_memory().await);
+    let adapter = Arc::new(VikingAdapter::new(storage.clone()));
+
+    // Create entries with different importance
+    let high_importance = MemoryEntry::new(
+        "agent-1",
+        MemoryType::Preference,
+        "critical",
+        "这是非常重要的偏好".to_string(),
+    )
+    .with_importance(10);
+
+    let low_importance = MemoryEntry::new(
+        "agent-1",
+        MemoryType::Preference,
+        "minor",
+        "这是不太重要的偏好".to_string(),
+    )
+    .with_importance(2);
+
+    adapter.store(&high_importance).await.unwrap();
+    adapter.store(&low_importance).await.unwrap();
+
+    // Access the low importance one multiple times
+    for _ in 0..5 {
+        let _ = adapter.get(&low_importance.uri).await;
+    }
+
+    // Search should consider both importance and access count
+    let results = adapter
+        .find(
+            "偏好",
+            FindOptions {
+                scope: Some("agent://agent-1".to_string()),
+                limit: Some(10),
+                min_similarity: None,
+            },
+        )
+        .await
+        .unwrap();
+
+    assert_eq!(results.len(), 2);
+}
+
+/// Test prompt injection with token budget
+#[tokio::test]
+async fn test_prompt_injection_token_budget() {
+    let mut result = RetrievalResult::default();
+
+    // Add memories that exceed budget
+    for i in 0..10 {
+        result.preferences.push(
+            MemoryEntry::new(
+                "agent-1",
+                MemoryType::Preference,
+                &format!("pref-{}", i),
+                "这是一个很长的偏好描述，用于测试 token 预算控制功能。".repeat(5),
+            ),
+        );
+    }
+
+    result.total_tokens = result.calculate_tokens();
+
+    // Budget is 500 tokens by default
+    let injector = PromptInjector::new();
+    let base = "Base prompt";
+    let enhanced = injector.inject_with_format(base, &result);
+
+    // Should include memory context
+    assert!(enhanced.len() > base.len());
+}
+
+/// Test metadata storage
+#[tokio::test]
+async fn test_metadata_operations() {
+    let storage = Arc::new(SqliteStorage::in_memory().await);
+    let adapter = Arc::new(VikingAdapter::new(storage));
+
+    // Store metadata using typed API
+    #[derive(serde::Serialize, serde::Deserialize, PartialEq, Debug)]
+    struct Config {
+        version: String,
+        auto_extract: bool,
+    }
+
+    let config = Config {
+        version: "1.0.0".to_string(),
+        auto_extract: true,
+    };
+
+    adapter.store_metadata("agent-config", &config).await.unwrap();
+
+    // Retrieve metadata
+    let retrieved: Option<Config> = adapter.get_metadata("agent-config").await.unwrap();
+    assert!(retrieved.is_some());
+
+    let parsed = retrieved.unwrap();
+    assert_eq!(parsed.version, "1.0.0");
+    assert_eq!(parsed.auto_extract, true);
+}
+
+/// Test memory deletion and cleanup
+#[tokio::test]
+async fn test_memory_deletion() {
+    let storage = Arc::new(SqliteStorage::in_memory().await);
+    let adapter = Arc::new(VikingAdapter::new(storage));
+
+    let entry = MemoryEntry::new(
+        "agent-1",
+        MemoryType::Knowledge,
+        "temp",
+        "Temporary knowledge".to_string(),
+    );
+
+    adapter.store(&entry).await.unwrap();
+
+    // Verify stored
+    let retrieved = adapter.get(&entry.uri).await.unwrap();
+    assert!(retrieved.is_some());
+
+    // Delete
+    adapter.delete(&entry.uri).await.unwrap();
+
+    // Verify deleted
+    let retrieved = adapter.get(&entry.uri).await.unwrap();
+    assert!(retrieved.is_none());
+
+    // Verify not in search results
+    let results = adapter
+        .find(
+            "Temporary",
+            FindOptions {
+                scope: Some("agent://agent-1".to_string()),
+                limit: Some(10),
+                min_similarity: None,
+            },
+        )
+        .await
+        .unwrap();
+
+    assert!(results.is_empty());
+}
+
+/// Test cross-agent isolation
+#[tokio::test]
+async fn test_agent_isolation() {
+    let storage = Arc::new(SqliteStorage::in_memory().await);
+    let adapter = Arc::new(VikingAdapter::new(storage));
+
+    // Store memories for different agents
+    let agent1_memory = MemoryEntry::new(
+        "agent-1",
+        MemoryType::Knowledge,
+        "secret",
+        "Agent 1 的秘密信息".to_string(),
+    );
+
+    let agent2_memory = MemoryEntry::new(
+        "agent-2",
+        MemoryType::Knowledge,
+        "secret",
+        "Agent 2 的秘密信息".to_string(),
+    );
+
+    adapter.store(&agent1_memory).await.unwrap();
+    adapter.store(&agent2_memory).await.unwrap();
+
+    // Agent 1 should only see its own memories
+    let results = adapter
+        .find(
+            "秘密",
+            FindOptions {
+                scope: Some("agent://agent-1".to_string()),
+                limit: Some(10),
+                min_similarity: None,
+            },
+        )
+        .await
+        .unwrap();
+
+    assert_eq!(results.len(), 1);
+    assert!(results[0].content.contains("Agent 1"));
+
+    // Agent 2 should only see its own memories
+    let results = adapter
+        .find(
+            "秘密",
+            FindOptions {
+                scope: Some("agent://agent-2".to_string()),
+                limit: Some(10),
+                min_similarity: None,
+            },
+        )
+        .await
+        .unwrap();
+
+    assert_eq!(results.len(), 1);
+    assert!(results[0].content.contains("Agent 2"));
+}
+
+/// Test Chinese text handling
+#[tokio::test]
+async fn test_chinese_text_handling() {
+    let storage = Arc::new(SqliteStorage::in_memory().await);
+    let adapter = Arc::new(VikingAdapter::new(storage));
+
+    let entry = MemoryEntry::new(
+        "中文测试",
+        MemoryType::Knowledge,
+        "中文知识",
+        "这是一个中文测试，包含关键词：人工智能、机器学习、深度学习。".to_string(),
+    )
+    .with_keywords(vec!["人工智能".to_string(), "机器学习".to_string()]);
+
+    adapter.store(&entry).await.unwrap();
+
+    // Search with Chinese query
+    let results = adapter
+        .find(
+            "人工智能",
+            FindOptions {
+                scope: Some("agent://中文测试".to_string()),
+                limit: Some(10),
+                min_similarity: Some(0.1),
+            },
+        )
+        .await
+        .unwrap();
+
+    assert!(!results.is_empty());
+    assert!(results[0].content.contains("人工智能"));
+}
+
+/// Test find by prefix
+#[tokio::test]
+async fn test_find_by_prefix() {
+    let storage = Arc::new(SqliteStorage::in_memory().await);
+    let adapter = Arc::new(VikingAdapter::new(storage));
+
+    // Store multiple entries under same agent
+    for i in 0..5 {
+        let entry = MemoryEntry::new(
+            "agent-1",
+            MemoryType::Knowledge,
+            &format!("topic-{}", i),
+            format!("Content for topic {}", i),
+        );
+        adapter.store(&entry).await.unwrap();
+    }
+
+    // Find all entries for agent-1
+    let results = adapter
+        .find_by_prefix("agent://agent-1")
+        .await
+        .unwrap();
+
+    assert_eq!(results.len(), 5);
+}