What is Elephantasm?
Elephantasm is a Long-Term Agentic Memory (LTAM) framework. It gives AI agents continuity beyond the context window — structured memory that persists, curates, and evolves across conversations.
Unlike simple RAG systems that retrieve nearest-neighbor chunks from a vector store, Elephantasm builds a layered cognitive substrate:
- Events — Raw interactions (messages, tool calls, API responses)
- Memories — Structured reflections synthesized from events
- Knowledge — Canonicalized truths extracted from memory patterns
- Identity — Emergent behavioral fingerprint
Elephantasm works with any LLM provider — OpenAI, Anthropic, local models. The SDKs handle ingest and retrieval; you keep full control of your agent logic.
How It Works
The core loop is simple:
- Ingest — Send events (conversations, tool calls) to Elephantasm
- Synthesize — The system creates structured memories from raw events
- Inject — Retrieve a deterministic memory pack to enrich your agent's context
from elephantasm import inject, ingest
# Send a conversation event
ingest(anima_id="agent-01", content="User asked about deployment")
# Get memory context for your next prompt
pack = inject(anima_id="agent-01")
if pack:
print(pack.as_prompt())Key Features
- Deterministic retrieval — Memory packs are assembled via scoring algorithms, not just vector similarity
- Four-factor recall — Importance, confidence, recency, and decay govern what surfaces
- Full provenance — Every memory links back to its source events
- Background curation — The Dreamer loop merges, archives, and promotes memories automatically
- Multi-language SDKs — Python and TypeScript clients with identical APIs
Next Steps
Ready to get started? Head to Installation to set up the SDK.