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.

Interactive Technical Guide (Notebook)

File: examples/agent_memory_server_interactive_guide.ipynb

The Interactive Technical Guide is a comprehensive, cell-by-cell walkthrough of the Agent Memory Server. Unlike the standalone scripts below, this notebook is designed for hands-on exploration in an interactive environment — each section builds on the previous one, with executable code cells and inline explanations.

Format

The notebook uses the Jupytext percent format, where cells are delimited by # %% markers. You can open it directly in:

VS Code (with the Jupyter extension)
JetBrains IDEs (PyCharm, DataSpell)
JupyterLab (via jupytext --to notebook conversion)

Prerequisites

Before running the notebook, ensure the following services are available:

Redis 8 running locally (via docker-compose up redis -d)

Agent Memory Server running in development mode:

DISABLE_AUTH=true uv run agent-memory api --task-backend=asyncio

Environment variables configured:

export OPENAI_API_KEY=<your-key>
export DISABLE_AUTH=true

Python dependencies installed:

pip install agent-memory-client httpx openai

Sections

The guide is organized into twelve sections, each covering a distinct aspect of the memory system:

Section	Topic	Description
1	Problem & Solution	Introduces the statelessness problem and the two-tier memory architecture (working memory and long-term memory).
2	Quick Start	Configures the SDK client, verifies server connectivity, and explains the Redis key structure used internally.
3	Integration Patterns Overview	Describes the three integration patterns — Code-Driven, LLM-Driven, and Background Extraction — and when to use each.
4	Pattern 1: Code-Driven (SDK)	Demonstrates deterministic memory operations using the `agent_memory_client` SDK: creating sessions, seeding long-term memories, retrieving context with `memory_prompt()`, and storing conversations.
5	Pattern 2: LLM-Driven (Tools)	Shows how to expose memory operations as OpenAI-compatible tool schemas, let the model decide when to store or retrieve memories, and resolve tool calls with `resolve_tool_call()`.
6	Pattern 3: Background Extraction	Covers automatic memory extraction from conversations using discrete and custom extraction strategies, including debounce configuration.
7	Combining Patterns	Discusses how production applications typically combine multiple patterns (e.g., code-driven context hydration with LLM-driven storage).
8	Working Memory Deep Dive	Explores session management, working memory summarization (trigger conditions, token allocation, progressive summarization), and structured data storage.
9	Long-Term Memory Deep Dive	Covers semantic, keyword, and hybrid search modes; `hybrid_alpha` tuning; recency boost configuration; and filtered search by topics, entities, or timestamps.
10	Memory Types & Contextual Grounding	Explains semantic vs. episodic memory types, event dating, and how contextual grounding enriches stored memories.
11	Extraction Strategy Comparison	Provides a side-by-side comparison of discrete (default) and custom extraction strategies using a vehicle rental scenario.
12	Production Considerations	Covers authentication, background task workers, and LLM provider configuration for deployment.

Key concepts demonstrated

Two-tier memory architecture: Working memory (session-scoped, ephemeral) automatically promotes structured memories to long-term storage (persistent, searchable).
Search mode comparison: Executable cells that run the same query across semantic, keyword, and hybrid search modes, illustrating how each mode surfaces different results.
Recency boost tuning: Shows how to adjust or disable the recency weight to control whether newer memories are prioritized over older, semantically relevant ones.
Tool schema integration: Generates OpenAI-compatible function schemas from the SDK and demonstrates the full tool-call lifecycle (schema → LLM decision → execution → response).
Extraction strategy configuration: Compares what discrete vs. custom extraction strategies produce from the same input conversation.

Running the guide

cd examples

# VS Code: open the file directly — the Jupyter extension recognizes percent-format cells
code agent_memory_server_interactive_guide.ipynb

# JupyterLab: convert to standard notebook format first
jupytext --to notebook agent_memory_server_interactive_guide.ipynb
jupyter lab agent_memory_server_interactive_guide.ipynb

🧳 Travel Agent

File: 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 with semantic, keyword, and hybrid 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:

search_memory - Search through previous conversations and stored information (supports semantic, keyword, and hybrid search modes)
get_or_create_working_memory - Check current working memory session
lazily_create_long_term_memory - Store important information as structured memories (promoted to long-term storage later)
update_working_memory_data - Store/update session-specific data like trip plans
get_long_term_memory - Retrieve a specific long-term memory by ID
eagerly_create_long_term_memory - Create long-term memories directly for immediate storage
edit_long_term_memory - Update existing long-term memories
delete_long_term_memories - Remove long-term memories
get_current_datetime - Get current UTC datetime for grounding relative time expressions
web_search (optional) - Search the internet for current travel information

Usage Examples

# 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

# 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

# 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

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

Store Messages: All conversation messages stored in working memory
Memory Prompt: memory_prompt() retrieves relevant context automatically
Enriched Context: Memory context combined with system prompt
LLM Generation: Enhanced context sent to LLM for personalized responses

Usage Examples

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

# 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

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

search_memory - Find existing memories using natural language (supports semantic, keyword, and hybrid search modes)
get_long_term_memory - Retrieve specific memories by ID
lazily_create_long_term_memory - Store new information (promoted to long-term storage later)
eagerly_create_long_term_memory - Create long-term memories directly for immediate storage
edit_long_term_memory - Update existing memories
delete_long_term_memories - Remove outdated information
get_or_create_working_memory - Check current working memory session
update_working_memory_data - Store/update session-specific data
get_current_datetime - Get current UTC datetime for grounding relative time expressions

Common Editing Scenarios

# 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

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:

Initial Information: User shares profile (name, job, preferences)
Corrections: User corrects information (job company change)
Updates: User provides updates (promotion, new title)
Multiple Changes: User updates location and preferences
Information Retrieval: User asks what agent remembers
Ongoing Updates: Continued information updates
Memory Management: Specific memory operations (show/delete)

🏫 AI Tutor

File: 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

# 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

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

🔗 LangChain Integration Example

File: examples/langchain_integration_example.py

Demonstrates how to use the agent_memory_client LangChain integration to create memory-enabled agents without manual tool wrapping.

Core Concept

Uses get_memory_tools() from agent_memory_client.integrations.langchain to automatically generate LangChain-compatible tools, then creates an agent with create_agent (LangGraph-based).

Key Features

Automatic Tool Generation: No manual @tool wrappers needed — get_memory_tools() handles it
Modern LangGraph Agent: Uses create_agent from langchain.agents (not the deprecated AgentExecutor)
State Persistence: Demonstrates MemorySaver checkpointer for multi-turn conversations
Search Modes: Supports semantic, keyword, and hybrid search via search_memory

Usage Examples

cd examples
python langchain_integration_example.py

Key Implementation Pattern

from agent_memory_client import create_memory_client
from agent_memory_client.integrations.langchain import get_memory_tools
from langchain.agents import create_agent

memory_client = await create_memory_client("http://localhost:8000")
tools = get_memory_tools(memory_client=memory_client, session_id="session", user_id="user")

agent = create_agent(
    ChatOpenAI(model="gpt-4o"), tools,
    system_prompt="You are a helpful assistant with persistent memory."
)

result = await agent.ainvoke({"messages": [("human", "Remember I love pizza")]})
print(result["messages"][-1].content)

📊 Recent Messages Limit Demo

File: examples/recent_messages_limit_demo.py

Demonstrates the recent_messages_limit parameter for efficiently retrieving only the most recent N messages from working memory.

Core Concept

When working memory grows large, retrieving all messages is expensive. The recent_messages_limit parameter lets you fetch only the N most recent messages, useful for context windows and UI displays.

Key Features

Efficient Retrieval: Fetch only the messages you need instead of the full history
SDK & HTTP Integration: Uses agent_memory_client to store working memory and raw HTTP requests to retrieve it
Multiple Scenarios: Tests various limits (3, 5, 20, 2) to show how recent_messages_limit changes the results
Direct API Verification: Uses the raw HTTP API for retrieval so you can inspect the exact responses from the server

Usage Examples

cd examples
python recent_messages_limit_demo.py

Key Implementation Pattern

from agent_memory_client import create_memory_client

client = await create_memory_client(base_url="http://localhost:8000")

# Get only the 3 most recent messages
memory = await client.get_working_memory(
    session_id="my-session",
    namespace="demo",
    context_window_max=3
)

Getting Started with Examples

1. Prerequisites

# Install dependencies
cd /path/to/agent-memory-server
uv sync --all-extras

# Start memory server (disable auth for local development)
DISABLE_AUTH=true uv run agent-memory api --task-backend=asyncio

# Set required API keys
export OPENAI_API_KEY="your-openai-key"

2. Run Examples

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

# LangChain integration (requires langchain, langchain_openai)
python langchain_integration_example.py

# Recent messages limit feature demo
python recent_messages_limit_demo.py

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:

# 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 the Interactive Guide: Best for learning the full system end-to-end in an exploratory environment
Start with Travel Agent: Most comprehensive standalone 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.

FilesExpand file tree

agent-examples.md

Latest commit

History

agent-examples.md

File metadata and controls

Agent Examples

Interactive Technical Guide (Notebook)

Format

Prerequisites

Sections

Key concepts demonstrated

Running the guide

🧳 Travel Agent

Key Features

Available Tools

Usage Examples

Environment Setup

Key Implementation Patterns

🧠 Memory Prompt Agent

Core Concept

How It Works

Usage Examples

Key Implementation Pattern

✏️ Memory Editing Agent

Core Features

Memory Operations Demonstrated

Common Editing Scenarios

Usage Examples

Demo Conversation Flow

🏫 AI Tutor

Core Features

Memory Patterns Used

Usage Examples

Key Commands

🔗 LangChain Integration Example

Core Concept

Key Features

Usage Examples

Key Implementation Pattern

📊 Recent Messages Limit Demo

Core Concept

Key Features

Usage Examples

Key Implementation Pattern

Getting Started with Examples

1. Prerequisites

2. Run Examples

3. Customize and Extend

4. Implementation Patterns

Next Steps