Cognitive Memory for Autonomous AI

from smriti import SMRITI, SmritiConfig

# 1. Initialize SMRITI LTM Engine
config = SmritiConfig(
    storage_path="./agent_memory",
    llm_model="gpt-4o",
    openai_api_key="your-key-here"
)
memory = SMRITI(config=config)

# 2. Ingest observations (System 1 - Instant)
memory.encode("User prefers using PyTorch for neural networks.")
memory.encode("User is allergic to shellfish.", context="medical", private=True)

# 3. Query relevant context with dynamic retrieval
results = memory.recall("What framework does the user prefer?")
for mem in results:
    print(f"[{mem.strength:.2f}] {mem.content}")

# 4. Trigger System 2 Background Consolidation
memory.consolidate()
memory.save()

from langchain.agents import AgentExecutor, create_openai_tools_agent
from smriti.integrations.langchain import SmritiLangChainMemory

# Create SMRITI wrapper for LangChain
smriti_memory = SmritiLangChainMemory(
    storage_path="./langchain_ltm",
    api_key="your-key-here"
)

# Integrates directly into your LangChain Agent Executor
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=smriti_memory, # SMRITI handles working & long-term memory
    verbose=True
)