Merge pull request #52 from redis/fix/docs-fixes

Fix/docs fixes

Author: abrookins

docs/CLAUDE.md CLAUDE.mddocs/CLAUDE.md renamed to CLAUDE.md

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+# Agent Examples
+
+This section provides comprehensive working examples that demonstrate real-world usage patterns of the Redis Agent Memory Server. Each example showcases different aspects of memory management, from basic conversation storage to advanced memory editing workflows.
+
+## 🧳 Travel Agent
+
+**File**: [`examples/travel_agent.py`](https://github.com/redis/agent-memory-server/blob/main/examples/travel_agent.py)
+
+A comprehensive travel assistant that demonstrates the most complete integration patterns.
+
+### Key Features
+
+- **Automatic Tool Discovery**: Uses `MemoryAPIClient.get_all_memory_tool_schemas()` to automatically discover and integrate all available memory tools
+- **Unified Tool Resolution**: Leverages `client.resolve_tool_call()` to handle all memory tool calls uniformly across different LLM providers
+- **Working Memory Management**: Session-based conversation state and structured memory storage
+- **Long-term Memory**: Persistent memory storage and semantic search capabilities
+- **Optional Web Search**: Cached web search using Tavily API with Redis caching
+
+### Available Tools
+
+The travel agent automatically discovers and uses all memory tools:
+
+1. **search_memory** - Search through previous conversations and stored information
+2. **get_working_memory** - Check current session state, stored memories, and data
+3. **add_memory_to_working_memory** - Store important information as structured memories
+4. **update_working_memory_data** - Store/update session-specific data like trip plans
+5. **web_search** (optional) - Search the internet for current travel information
+
+### Usage Examples
+
+```bash
+# Basic interactive usage
+cd examples
+python travel_agent.py
+
+# Automated demo showing capabilities
+python travel_agent.py --demo
+
+# With custom configuration
+python travel_agent.py --session-id my_trip --user-id john_doe --memory-server-url http://localhost:8001
+```
+
+### Environment Setup
+
+```bash
+# Required
+export OPENAI_API_KEY="your-openai-key"
+
+# Optional (for web search)
+export TAVILY_API_KEY="your-tavily-key"
+export REDIS_URL="redis://localhost:6379"
+```
+
+### Key Implementation Patterns
+
+```python
+# Tool auto-discovery
+memory_tools = MemoryAPIClient.get_all_memory_tool_schemas()
+
+# Unified tool resolution for any provider
+result = await client.resolve_tool_call(
+    tool_call=provider_tool_call,
+    session_id=session_id
+)
+
+if result["success"]:
+    print(result["formatted_response"])
+```
+
+## 🧠 Memory Prompt Agent
+
+**File**: [`examples/memory_prompt_agent.py`](https://github.com/redis/agent-memory-server/blob/main/examples/memory_prompt_agent.py)
+
+Demonstrates the simplified memory prompt feature for context-aware conversations without manual tool management.
+
+### Core Concept
+
+Uses `client.memory_prompt()` to automatically retrieve relevant memories and enrich prompts with contextual information.
+
+### How It Works
+
+1. **Store Messages**: All conversation messages stored in working memory
+2. **Memory Prompt**: `memory_prompt()` retrieves relevant context automatically
+3. **Enriched Context**: Memory context combined with system prompt
+4. **LLM Generation**: Enhanced context sent to LLM for personalized responses
+
+### Usage Examples
+
+```bash
+cd examples
+python memory_prompt_agent.py
+
+# With custom session
+python memory_prompt_agent.py --session-id my_session --user-id jane_doe
+```
+
+### Key Implementation Pattern
+
+```python
+# Automatic memory retrieval and context enrichment
+context = await client.memory_prompt(
+    query=user_message,
+    session_id=session_id,
+    long_term_search={
+        "text": user_message,
+        "limit": 5,
+        "user_id": user_id
+    }
+)
+
+# Enhanced prompt with memory context
+response = await openai_client.chat.completions.create(
+    model="gpt-4o",
+    messages=context.messages
+)
+```
+
+## ✏️ Memory Editing Agent
+
+**File**: [`examples/memory_editing_agent.py`](https://github.com/redis/agent-memory-server/blob/main/examples/memory_editing_agent.py)
+
+Demonstrates comprehensive memory editing capabilities through natural conversation patterns.
+
+### Core Features
+
+- **Memory Editing Workflow**: Complete lifecycle of creating, searching, editing, and deleting memories
+- **All Memory Tools**: Uses all available memory management tools including editing capabilities
+- **Realistic Scenarios**: Common patterns like corrections, updates, and information cleanup
+- **Interactive Demo**: Both automated demo and interactive modes
+
+### Memory Operations Demonstrated
+
+1. **search_memory** - Find existing memories using natural language
+2. **get_long_term_memory** - Retrieve specific memories by ID
+3. **add_memory_to_working_memory** - Store new information
+4. **edit_long_term_memory** - Update existing memories
+5. **delete_long_term_memories** - Remove outdated information
+6. **get_working_memory** - Check current session context
+
+### Common Editing Scenarios
+
+```python
+# Correction scenario
+"Actually, I work at Microsoft, not Google"
+# → Search for job memory, edit company name
+
+# Update scenario
+"I got promoted to Senior Engineer"
+# → Find job memory, update title and add promotion date
+
+# Preference change
+"I prefer tea over coffee now"
+# → Search beverage preferences, update from coffee to tea
+
+# Information cleanup
+"Delete that old job information"
+# → Search and remove outdated employment data
+```
+
+### Usage Examples
+
+```bash
+cd examples
+
+# Interactive mode (explore memory editing)
+python memory_editing_agent.py
+
+# Automated demo (see complete workflow)
+python memory_editing_agent.py --demo
+
+# Custom configuration
+python memory_editing_agent.py --session-id alice_session --user-id alice
+```
+
+### Demo Conversation Flow
+
+The automated demo shows a realistic conversation:
+
+1. **Initial Information**: User shares profile (name, job, preferences)
+2. **Corrections**: User corrects information (job company change)
+3. **Updates**: User provides updates (promotion, new title)
+4. **Multiple Changes**: User updates location and preferences
+5. **Information Retrieval**: User asks what agent remembers
+6. **Ongoing Updates**: Continued information updates
+7. **Memory Management**: Specific memory operations (show/delete)
+
+## 🏫 AI Tutor
+
+**File**: [`examples/ai_tutor.py`](https://github.com/redis/agent-memory-server/blob/main/examples/ai_tutor.py)
+
+A functional tutoring system that demonstrates episodic memory for learning tracking and semantic memory for concept management.
+
+### Core Features
+
+- **Quiz Management**: Runs interactive quizzes and stores results
+- **Learning Tracking**: Stores quiz results as episodic memories with timestamps
+- **Concept Tracking**: Tracks weak concepts as semantic memories
+- **Progress Analysis**: Provides summaries and personalized practice suggestions
+
+### Memory Patterns Used
+
+```python
+# Episodic: Per-question results with event dates
+{
+    "text": "User answered 'photosynthesis' question incorrectly",
+    "memory_type": "episodic",
+    "event_date": "2024-01-15T10:30:00Z",
+    "topics": ["quiz", "biology", "photosynthesis"]
+}
+
+# Semantic: Weak concepts for targeted practice
+{
+    "text": "User struggles with photosynthesis concepts",
+    "memory_type": "semantic",
+    "topics": ["weak_concept", "biology", "photosynthesis"]
+}
+```
+
+### Usage Examples
+
+```bash
+cd examples
+
+# Interactive tutoring session
+python ai_tutor.py
+
+# Demo with sample quiz flow
+python ai_tutor.py --demo
+
+# Custom student session
+python ai_tutor.py --user-id student123 --session-id bio_course
+```
+
+### Key Commands
+
+- **Practice**: Start a quiz on specific topics
+- **Summary**: Get learning progress summary
+- **Practice-next**: Get personalized practice recommendations based on weak areas
+
+## Getting Started with Examples
+
+### 1. Prerequisites
+
+```bash
+# Install dependencies
+cd /path/to/agent-memory-server
+uv install --all-extras
+
+# Start memory server
+uv run agent-memory server
+
+# Set required API keys
+export OPENAI_API_KEY="your-openai-key"
+```
+
+### 2. Run Examples
+
+```bash
+cd examples
+
+# Start with the travel agent (most comprehensive)
+python travel_agent.py --demo
+
+# Try memory editing workflows
+python memory_editing_agent.py --demo
+
+# Explore simplified memory prompts
+python memory_prompt_agent.py
+
+# Experience learning tracking
+python ai_tutor.py --demo
+```
+
+### 3. Customize and Extend
+
+Each example is designed to be:
+
+- **Self-contained**: Runs independently with minimal setup
+- **Configurable**: Supports custom sessions, users, and server URLs
+- **Educational**: Well-commented code showing best practices
+- **Production-ready**: Robust error handling and logging
+
+### 4. Implementation Patterns
+
+Key patterns demonstrated across examples:
+
+```python
+# Memory client setup
+client = MemoryAPIClient(
+    base_url="http://localhost:8000",
+    default_namespace=namespace,
+    user_id=user_id
+)
+
+# Tool integration
+tools = MemoryAPIClient.get_all_memory_tool_schemas()
+response = await openai_client.chat.completions.create(
+    model="gpt-4o",
+    messages=messages,
+    tools=tools
+)
+
+# Tool resolution
+for tool_call in response.choices[0].message.tool_calls:
+    result = await client.resolve_tool_call(
+        tool_call=tool_call,
+        session_id=session_id
+    )
+```
+
+## Next Steps
+
+- **Start with Travel Agent**: Most comprehensive example showing all features
+- **Explore Memory Editing**: Learn advanced memory management patterns
+- **Study Code Patterns**: Each example demonstrates different architectural approaches
+- **Build Your Own**: Use examples as templates for your specific use case
+
+All examples include detailed inline documentation and can serve as starting points for building production memory-enhanced AI applications.