AI Memory Like a
Human Brain.

from cloud.client import CloudMemory

m = CloudMemory(api_key="om-...")

# Save — auto-extracts facts, events, workflows
m.add([
    {"role": "user", "content": "Fixed OOM with Redis cache"},
    {"role": "assistant", "content": "Got it."},
])

# Unified search — all 3 memory types
results = m.search_all("database issues")
# → {semantic: [...], episodic: [...], procedural: [...]}

# Cognitive Profile — instant personalization
profile = m.get_profile()
# → ready system prompt for any LLM

# Multi-user isolation — one API key, many users
m.add([...], user_id="alice")
m.search_all("prefs", user_id="alice")  # only Alice's data

const { MengramClient } = require('mengram-ai');

const m = new MengramClient('om-...');

// Save — auto-extracts facts, events, workflows
await m.add([
    { role: 'user', content: 'Fixed OOM with Redis cache' },
    { role: 'assistant', content: 'Got it.' },
]);

// Unified search — all 3 memory types
const all = await m.searchAll('database issues');
// → {semantic: [...], episodic: [...], procedural: [...]}

// Multi-user isolation — one API key, many users
await m.add([...], { userId: 'alice' });
await m.searchAll('prefs', { userId: 'alice' }); // only Alice's data

from integrations.langchain import MengramMemory

# Replaces ConversationBufferMemory
memory = MengramMemory(
    api_key="om-...",
    use_profile=True,  # Cognitive Profile
)

chain = ConversationChain(llm=llm, memory=memory)
chain.predict(input="I deployed on Railway")

# Next call — Mengram provides relevant context
# from semantic + episodic + procedural memory
chain.predict(input="How did my deploy go?")
# → Memory: facts, the deployment event, deploy workflow

from integrations.langchain import MengramChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

chain_with_memory = RunnableWithMessageHistory(
    chain,
    lambda sid: MengramChatMessageHistory(
        api_key="om-...",
        session_id=sid,
    ),
    input_messages_key="input",
    history_messages_key="history",
)

from crewai import Agent, Crew
from integrations.crewai import create_mengram_tools

tools = create_mengram_tools(
    api_key="om-...",
)

agent = Agent(
    role="Support Engineer",
    goal="Help users with technical issues",
    tools=tools,
)

# Agent completes workflow → Mengram saves as procedure
# Next time → agent finds optimal path with success tracking
crew = Crew(agents=[agent], tasks=[...])

{
  "plugins": {
    "entries": {
      "openclaw-mengram": {
        "enabled": true,
        "config": {
          "apiKey": "${MENGRAM_API_KEY}"
        }
      }
    },
    "slots": { "memory": "openclaw-mengram" }
  }
}
// Auto-recall: memories injected before every agent turn
// Auto-capture: new info saved after every turn