engramport-langchain 0.1.0

LangChain integration for EngramPort — persistent memory for AI bots and agents

engramport-langchain

Persistent memory for AI bots and agents, powered by EngramPort + MandelDB.

3 lines to give your bot a brain:

from engramport_langchain import EngramPortMemory

memory = EngramPortMemory(api_key="ek_bot_...")
memory.remember("User prefers dark mode and lives in Tampa, FL")

Install

pip install engramport-langchain

For LangChain integration:

pip install engramport-langchain[langchain]

Quick Start

Standalone (no LangChain required)

from engramport_langchain import EngramPortMemory

memory = EngramPortMemory(api_key="ek_bot_...")

# Store memories
memory.remember("User's name is Alex")
memory.remember("User prefers concise answers")
memory.remember("User lives in Tampa, FL and checks weather often")

# Semantic recall — no keyword matching needed
results = memory.recall("What do I know about this user?")
for m in results.memories:
    print(f"  {m.content} (score: {m.relevance_score:.2f})")

# Synthesize insights across all memories
insights = memory.reflect(topic="user preferences")
for i in insights.insights:
    print(f"  {i.content} (confidence: {i.confidence:.0%})")

# Check stats
stats = memory.stats()
print(f"Memories: {stats.memory_count}, Insights: {stats.insight_count}")

Async

Every method has an async counterpart:

await memory.aremember("User mentioned they love hiking")
results = await memory.arecall("outdoor activities")
insights = await memory.areflect()
stats = await memory.astats()

LangChain Integration

Drop-in memory for any LangChain conversation chain:

from engramport_langchain.langchain import EngramPortChatMemory
from langchain.chains import ConversationChain
from langchain_openai import ChatOpenAI

memory = EngramPortChatMemory(
    api_key="ek_bot_...",
    recall_limit=5,        # memories per turn
    auto_reflect=True,     # synthesize insights automatically
    reflect_interval=10,   # every 10 interactions
)

chain = ConversationChain(llm=ChatOpenAI(), memory=memory)

# Memories persist across sessions automatically
chain.invoke({"input": "My name is Alex and I live in Tampa"})
# ... restart your app ...
chain.invoke({"input": "What's the weather like where I live?"})
# Bot recalls Tampa without being told again

API Reference

Method	Description
remember(content, context?, session_id?)	Store a memory
recall(query, limit=5)	Semantic search across memories
reflect(topic?)	LLM-powered insight synthesis
stats()	Memory and insight counts

All methods return typed Pydantic models. Async versions prefixed with a.

Get Your API Key

curl -X POST https://mandeldb.com/api/v1/portal/register \
  -H "Content-Type: application/json" \
  -d '{"bot_name": "my-bot", "bot_type": "custom", "owner_email": "[e-mail]"}'

Why EngramPort?

Feature	EngramPort	Mem0
Semantic recall	Yes	Yes
Insight synthesis	Yes — /reflect	No
Cryptographic provenance (AEGIS)	Yes	No
Graph-based reasoning	Yes (MandelDB)	No
Category-aware memory decay	Yes	No
Self-hosted option	Yes	Paid only
Pricing (hobby)	Free tier	$49/mo

Links

• Documentation
• API Playground
• Enterprise Sales