Claude Memory Tool

from praisonaiagents import Agent

agent = Agent(
    name="assistant",
    instructions="Remember user preferences across conversations.",
    llm="anthropic/claude-sonnet-4-20250514",
)
agent.start("What did I tell you about my project last week?")

.praison/claude_memory/{user_id}/
└── memories/
    ├── preferences.md      # User preferences
    ├── projects/           # Project-specific memories
    │   └── ai_research.md
    └── learnings.md        # Key learnings

from praisonaiagents import Agent
from praisonaiagents import ClaudeMemoryTool

# Default configuration
agent = Agent(
    name="Assistant",
    llm="anthropic/claude-sonnet-4-20250514",
    memory={"claude_memory": True}  # Uses default settings
)

# Custom configuration
memory_tool = ClaudeMemoryTool(
    user_id="custom_user",
    base_path=".custom/memory",
    max_file_size=1024 * 1024,  # 1MB
    max_files=100
)

agent = Agent(
    name="Assistant",
    llm="anthropic/claude-sonnet-4-20250514",
    memory={"claude_memory": memory_tool}
)

from praisonaiagents import Agent

agent = Agent(
    name="Research Assistant",
    role="AI Researcher",
    goal="Research topics and build knowledge over time",
    llm="anthropic/claude-sonnet-4-20250514",
    memory={
        "claude_memory": True,
        "user_id": "researcher_001"
    }
)

# First session - research and store findings
result1 = agent.start("""
Research the latest developments in AI agents.
Store key findings in your memory for future reference.
""")

# Later session - recall and build upon previous research
result2 = agent.start("""
What did we learn about AI agents previously?
Now research how memory systems are implemented.
""")

from praisonaiagents import ClaudeMemoryTool

tool = ClaudeMemoryTool(
    user_id="default",           # User identifier
    base_path=".praison/claude_memory",  # Storage location
    max_file_size=512 * 1024,    # Max file size (512KB)
    max_files=50                 # Max files per user
)

# Get tool definition for API
definition = tool.get_tool_definition()
# Returns: {"type": "memory_20250818", "name": "memory"}

# Get beta header
header = tool.get_beta_header()
# Returns: "context-management-2025-06-27"

# Process tool call from Claude
result = tool.process_tool_call({
    "command": "view",
    "path": "/memories"
})