Calera Agent Memory (calera-agent-memory)
The plug-and-play AI Memory Substrate for Python agent swarms, LangChain, and CrewAI.
Eliminates LLM context rot with sub-5ms $O(1)$ topological associative memory.
⚡ Quickstart: 3 Lines of Code
from calera_agent_memory import CaleraMemorySubstrate
# Auto-provisions a free 1,000,000-node persistent vault under Delaware UETA § 14
memory = CaleraMemorySubstrate(principal_email="developer@company.com")
# Drop directly into your agent or LLM chain
memory.save_context({"input": "Project Alpha deadline"}, {"output": "October 15, 2026"})
context = memory.load_memory_variables({"input": "When is the project due?"})
print(context["history"])
Direct Client Usage
from calera_agent_memory import CaleraMemoryClient
client = CaleraMemoryClient(api_key="your_optional_api_key")
# Claim free vault
vault_info = client.claim_free_vault("me@example.com", agent_name="my-researcher")
# Store fact into 4D topological lattice
client.store("NVDA_FY26_Rev", "Estimated at $168B based on datacenter expansion")
# Sub-5ms recall with zero token bloat
results = client.query("NVDA revenue projection")
print(results)
Key Advantages
- Sub-5ms Recall: $O(1)$ topological graph retrieval instead of slow vector database linear scans.
- Zero Hallucination Context: Retains exact structured relationships without semantic degradation.
- Free 1M Node Tier: Agents can self-provision permanent memory vaults at runtime for $0.00.
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