elephantasm 0.1.0

Long-Term Agentic Memory SDK - Give your AI agents persistent, evolving memory

elephantasm

Long-Term Agentic Memory SDK for Python.

Give your AI agents persistent, evolving memory across sessions.

Installation

pip install elephantasm

Quick Start

from elephantasm import inject, extract, EventType

# Get memory context for LLM injection
pack = inject()
system_prompt = f"You are a helpful assistant.\n\n{pack.as_prompt()}"

# Capture events during conversation
extract(EventType.MESSAGE_IN, "Hello!", role="user")
extract(EventType.MESSAGE_OUT, "Hi there!", role="assistant")

Configuration

Set these environment variables:

Variable	Required	Default	Description
ELEPHANTASM_API_KEY	Yes	-	Your API key (starts with sk_live_)
ELEPHANTASM_ANIMA_ID	No	-	Default anima ID for operations
ELEPHANTASM_ENDPOINT	No	https://api.elephantasm.com	API endpoint URL
ELEPHANTASM_TIMEOUT	No	30	Request timeout in seconds

Usage

Module-Level Functions

The simplest way to use Elephantasm:

from elephantasm import inject, extract, create_anima, EventType

# Create an anima (agent entity)
anima = create_anima("my-assistant", description="Personal helper")

# Set ELEPHANTASM_ANIMA_ID or pass anima_id explicitly
pack = inject(anima_id=str(anima.id))

# Capture events
extract(EventType.MESSAGE_IN, "User message", anima_id=str(anima.id))
extract(EventType.MESSAGE_OUT, "Assistant response", anima_id=str(anima.id))

Explicit Client

For more control, use the Elephantasm class:

from elephantasm import Elephantasm, EventType

# Initialize with credentials
client = Elephantasm(
    api_key="sk_live_...",
    anima_id="your-anima-id",
)

# Or use as context manager
with Elephantasm(api_key="sk_live_...") as client:
    pack = client.inject()
    client.extract(EventType.MESSAGE_IN, "Hello!")

Memory Pack

The MemoryPack object contains assembled context for LLM injection:

pack = inject()

# Get formatted prompt string
prompt = pack.as_prompt()

# Access structured data
print(f"Session memories: {pack.session_memory_count}")
print(f"Knowledge items: {pack.knowledge_count}")
print(f"Token count: {pack.token_count}/{pack.max_tokens}")

# Access identity context
if pack.identity:
    print(f"Personality: {pack.identity.personality_type}")

# Access individual memories
for mem in pack.session_memories:
    print(f"- {mem.summary} (score: {mem.score:.2f})")

Event Types

from elephantasm import EventType

EventType.MESSAGE_IN   # User messages
EventType.MESSAGE_OUT  # Assistant responses
EventType.TOOL_CALL    # Tool invocations
EventType.TOOL_RESULT  # Tool outputs
EventType.SYSTEM       # System events

Error Handling

from elephantasm import Elephantasm
from elephantasm.exceptions import (
    AuthenticationError,
    NotFoundError,
    RateLimitError,
    ValidationError,
    ServerError,
)

try:
    pack = inject()
except AuthenticationError:
    print("Invalid API key")
except NotFoundError:
    print("Anima not found")
except RateLimitError:
    print("Rate limit exceeded, retry later")
except ValidationError as e:
    print(f"Invalid request: {e}")
except ServerError:
    print("Server error, retry later")

Examples

See the examples/ directory for complete examples:

• basic_usage.py - Simple event capture and pack retrieval
• langchain_agent.py - Integration with LangChain
• multi_anima.py - Multi-tenant per-user agents

API Reference

Elephantasm Class

class Elephantasm:
    def __init__(
        self,
        api_key: str | None = None,      # Falls back to ELEPHANTASM_API_KEY
        anima_id: str | None = None,      # Falls back to ELEPHANTASM_ANIMA_ID
        endpoint: str | None = None,      # Falls back to ELEPHANTASM_ENDPOINT
        timeout: int | None = None,       # Falls back to ELEPHANTASM_TIMEOUT
    ): ...

    def inject(
        self,
        anima_id: str | None = None,      # Override default anima
        query: str | None = None,         # Semantic search query
        preset: str | None = None,        # "conversational" or "self_determined"
    ) -> MemoryPack: ...

    def extract(
        self,
        event_type: str | EventType,      # Event type
        content: str,                      # Event content
        anima_id: str | None = None,      # Override default anima
        session_id: str | None = None,    # Group related events
        role: str | None = None,          # user, assistant, system, tool
        author: str | None = None,        # Username, model name, etc.
        occurred_at: datetime | None = None,
        meta: dict | None = None,
        importance_score: float | None = None,
    ) -> Event: ...

    def create_anima(
        self,
        name: str,
        description: str | None = None,
        meta: dict | None = None,
    ) -> Anima: ...

    def close(self) -> None: ...

Module Functions

def inject(anima_id=None, query=None, preset=None) -> MemoryPack: ...
def extract(event_type, content, anima_id=None, **kwargs) -> Event: ...
def create_anima(name, description=None, meta=None) -> Anima: ...

Development

# Clone and install
git clone https://github.com/hejijunhao/elephantasm-py.git
cd elephantasm-py
pip install -e ".[dev]"

# Run tests
pytest -v

# Lint
ruff check elephantasm tests

Links

• Documentation
• API Reference
• GitHub
• PyPI

License

Apache 2.0