agent-memory-client 0.14.0

Python client for the Agent Memory Server REST API

Agent Memory Client

A Python client library for the Agent Memory Server REST API, providing comprehensive memory management capabilities for AI agents and applications.

Features

• Complete API Coverage: Full support for all Agent Memory Server endpoints
• LangChain Integration: Automatic tool conversion - no manual wrapping needed!
• Memory Lifecycle Management: Explicit control over working → long-term memory promotion
• Batch Operations: Efficient bulk operations with built-in rate limiting
• Auto-Pagination: Seamless iteration over large result sets
• Client-Side Validation: Pre-flight validation to catch errors early
• Enhanced Convenience Methods: Simplified APIs for common operations
• Type Safety: Full type hints for better development experience
• Async-First: Built for modern async Python applications

Installation

# Basic installation
pip install agent-memory-client

# With LangChain integration
pip install agent-memory-client langchain-core

Quick Start

import asyncio
from agent_memory_client import create_memory_client, ClientMemoryRecord, MemoryTypeEnum

async def main():
    # Create a client instance
    client = await create_memory_client(
        base_url="http://localhost:8000",
        default_namespace="my-app"
    )

    try:
        # Create some memories
        memories = [
            ClientMemoryRecord(
                text="User prefers dark mode",
                memory_type=MemoryTypeEnum.SEMANTIC,
                topics=["preferences", "ui"]
            ),
            ClientMemoryRecord(
                text="User completed onboarding on 2024-01-15",
                memory_type=MemoryTypeEnum.EPISODIC,
                topics=["onboarding", "milestones"]
            )
        ]

        # Store in long-term memory
        await client.create_long_term_memory(memories)

        # Search memories
        results = await client.search_long_term_memory(
            text="user interface preferences",
            limit=10
        )

        print(f"Found {len(results.memories)} relevant memories")
        for memory in results.memories:
            print(f"- {memory.text} (distance: {memory.dist})")

    finally:
        await client.close()

# Run the example
asyncio.run(main())

LangChain Integration

No manual tool wrapping needed! The client provides automatic conversion to LangChain-compatible tools:

from agent_memory_client import create_memory_client
from agent_memory_client.integrations.langchain import get_memory_tools
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI

async def create_memory_agent():
    # Initialize memory client
    memory_client = await create_memory_client("http://localhost:8000")

    # Get LangChain-compatible tools (automatic conversion!)
    tools = get_memory_tools(
        memory_client=memory_client,
        session_id="my_session",
        user_id="alice"
    )

    # Create agent with memory tools
    llm = ChatOpenAI(model="gpt-4o")
    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are a helpful assistant with persistent memory."),
        ("human", "{input}"),
        MessagesPlaceholder("agent_scratchpad"),
    ])

    agent = create_tool_calling_agent(llm, tools, prompt)
    executor = AgentExecutor(agent=agent, tools=tools)

    # Use the agent
    result = await executor.ainvoke({
        "input": "Remember that I love pizza"
    })

    return executor

# No @tool decorators needed - everything is automatic!

Benefits:

• ✅ No manual @tool decorator wrapping
• ✅ Automatic type conversion and validation
• ✅ Session and user context automatically injected
• ✅ Works seamlessly with LangChain agents

See the LangChain Integration Guide for more details.

Core API

Client Setup

from agent_memory_client import MemoryAPIClient, MemoryClientConfig

# Manual configuration
config = MemoryClientConfig(
    base_url="http://localhost:8000",
    timeout=30.0,
    default_namespace="my-app"
)
client = MemoryAPIClient(config)

# Or use the helper function
client = await create_memory_client(
    base_url="http://localhost:8000",
    default_namespace="my-app"
)

Working Memory Operations

from agent_memory_client import WorkingMemory, MemoryMessage

# Create working memory with messages
working_memory = WorkingMemory(
    session_id="user-session-123",
    messages=[
        MemoryMessage(role="user", content="Hello!"),
        MemoryMessage(role="assistant", content="Hi there! How can I help?")
        # created_at timestamps are automatically set for proper chronological ordering
    ],
    namespace="chat-app"
)

# Store working memory
response = await client.put_working_memory("user-session-123", working_memory)

# Retrieve working memory
memory = await client.get_working_memory("user-session-123")

# Convenience method for data storage
await client.set_working_memory_data(
    session_id="user-session-123",
    data={"user_preferences": {"theme": "dark", "language": "en"}}
)

Long-Term Memory Operations

from agent_memory_client import ClientMemoryRecord, MemoryTypeEnum

# Create memories
memories = [
    ClientMemoryRecord(
        text="User enjoys science fiction books",
        memory_type=MemoryTypeEnum.SEMANTIC,
        topics=["books", "preferences"],
        user_id="user-123"
    )
]

# Store memories
await client.create_long_term_memory(memories)

# Search with filters
from agent_memory_client.filters import Topics, UserId

results = await client.search_long_term_memory(
    text="science fiction",
    topics=Topics(any=["books", "entertainment"]),
    user_id=UserId(eq="user-123"),
    limit=20
)

Enhanced Features

Memory Lifecycle Management

# Explicitly promote working memories to long-term storage
await client.promote_working_memories_to_long_term(
    session_id="user-session-123",
    memory_ids=["memory-1", "memory-2"]  # Optional: specific memories
)

Batch Operations

# Bulk create with rate limiting
memory_batches = [batch1, batch2, batch3]
results = await client.bulk_create_long_term_memories(
    memory_batches=memory_batches,
    batch_size=50,
    delay_between_batches=0.1
)

Auto-Pagination

# Iterate through all results automatically
async for memory in client.search_all_long_term_memories(
    text="user preferences",
    batch_size=100
):
    print(f"Memory: {memory.text}")

Client-Side Validation

from agent_memory_client.exceptions import MemoryValidationError

try:
    # Validate before sending
    client.validate_memory_record(memory)
    client.validate_search_filters(limit=10, offset=0)
except MemoryValidationError as e:
    print(f"Validation error: {e}")

Enhanced Convenience Methods

# Update working memory data with merge strategies
await client.update_working_memory_data(
    session_id="user-session-123",
    data_updates={"new_setting": "value"},
    merge_strategy="deep_merge"  # "replace", "merge", or "deep_merge"
)

# Append messages
new_messages = [
    {"role": "user", "content": "What's the weather?"},
    {"role": "assistant", "content": "It's sunny today!"}
]

await client.append_messages_to_working_memory(
    session_id="user-session-123",
    messages=new_messages
)

Advanced Filtering

from agent_memory_client.filters import (
    SessionId, Namespace, Topics, Entities,
    CreatedAt, LastAccessed, UserId, MemoryType
)
from datetime import datetime, timezone

# Complex search with filters
results = await client.search_long_term_memory(
    text="machine learning",
    session_id=SessionId(in_=["session-1", "session-2"]),
    namespace=Namespace(eq="ai-research"),
    topics=Topics(any=["ml", "ai"], none=["deprecated"]),
    entities=Entities(all=["tensorflow", "python"]),
    created_at=CreatedAt(gte=datetime(2024, 1, 1, tzinfo=timezone.utc)),
    user_id=UserId(eq="researcher-123"),
    memory_type=MemoryType(eq="semantic"),
    distance_threshold=0.8,
    limit=50
)

Recency-Aware Search

from agent_memory_client.models import RecencyConfig

# Search with recency-aware ranking
recency_config = RecencyConfig(
    recency_boost=True,
    semantic_weight=0.8,           # Weight for semantic similarity
    recency_weight=0.2,            # Weight for recency score
    freshness_weight=0.6,          # Weight for freshness component
    novelty_weight=0.4,            # Weight for novelty/age component
    half_life_last_access_days=7,  # Last accessed decay half-life
    half_life_created_days=30,     # Creation date decay half-life
    server_side_recency=True       # Use server-side optimization
)

results = await client.search_long_term_memory(
    text="project updates",
    recency=recency_config,
    limit=10
)

Error Handling

from agent_memory_client.exceptions import (
    MemoryClientError,
    MemoryValidationError,
    MemoryNotFoundError,
    MemoryServerError
)

try:
    memory = await client.get_working_memory("nonexistent-session")
except MemoryNotFoundError:
    print("Session not found")
except MemoryServerError as e:
    print(f"Server error {e.status_code}: {e}")
except MemoryClientError as e:
    print(f"Client error: {e}")

Context Manager Usage

async with create_memory_client("http://localhost:8000") as client:
    # Client will be automatically closed when exiting the context
    results = await client.search_long_term_memory("search query")

Development

Running Tests

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=agent_memory_client

Code Quality

# Lint code
ruff check agent_memory_client/

# Format code
ruff format agent_memory_client/

# Type checking
mypy agent_memory_client/

Requirements

• Python 3.10+
• httpx >= 0.25.0
• pydantic >= 2.0.0
• python-ulid >= 3.0.0

License

Apache 2.0 License - see LICENSE file for details.

Contributing

Contributions are welcome! Please see the main repository for contribution guidelines.

Links

• Agent Memory Server - The server this client connects to
• Issues - Bug reports and feature requests