Why Mengram?

Every AI memory tool stores facts. Mengram stores 3 types — and procedures evolve from failures.

Quick Start

pip install mengram-ai

from cloud.client import CloudMemory

m = CloudMemory(api_key="om-...")  # Free key → mengram.io/dashboard

# Add a conversation — Mengram auto-extracts facts, events, and workflows
m.add([
    {"role": "user", "content": "Deployed to Railway today. Build passed but forgot migrations — DB crashed. Fixed by adding a pre-deploy check."},
])

# Search facts
m.search("deployment setup")

# Search events — what happened?
m.episodes(query="deployment")
# → [{summary: "Deployed to Railway, DB crashed due to missing migrations", outcome: "resolved", ...}]

# Search workflows — how to do it?
m.procedures(query="deploy")
# → [{name: "Deploy to Railway", steps: ["build", "run migrations", "push", "verify"], ...}]

# Unified search — all 3 types at once
m.search_all("deployment issues")
# → {semantic: [...], episodic: [...], procedural: [...]}

JavaScript / TypeScript:

npm install mengram-ai

const { MengramClient } = require('mengram-ai');
const m = new MengramClient('om-...');

await m.add([{ role: 'user', content: 'Fixed OOM with Redis cache' }]);
const all = await m.searchAll('database issues');
// → { semantic: [...], episodic: [...], procedural: [...] }

Experience-Driven Procedures

The feature no one else has. Procedures learn from real outcomes — not static runbooks.

Week 1:  "Deploy" → build → push → deploy
                                         ↓ FAILURE: forgot migrations, DB crashed
Week 2:  "Deploy" v2 → build → run migrations → push → deploy
                                                          ↓ FAILURE: OOM on Railway
Week 3:  "Deploy" v3 → build → run migrations → check memory → push → deploy ✅

This happens automatically when you report failures:

# Report failure with context → procedure evolves to a new version
m.procedure_feedback(proc_id, success=False,
                     context="OOM error on step 3", failed_at_step=3)

# View version history
history = m.procedure_history(proc_id)
# → {versions: [v1, v2, v3], evolution_log: [{change: "step_added", reason: "prevent OOM"}]}

Or fully automatic — add conversations and Mengram detects failures, links them to procedures, and evolves:

m.add([{"role": "user", "content": "Deploy to Railway failed again — OOM on the build step"}])
# → Episode auto-linked to "Deploy" procedure → failure detected → v3 created

Cognitive Profile

One API call generates a system prompt from all your memories:

profile = m.get_profile()
# → "You are talking to Ali, a developer in Almaty building Mengram.
#    He uses Python, PostgreSQL, and Railway. Recently debugged pgvector deployment.
#    Workflows: deploys via build→twine→npm→git. Communicate directly, focus on practical next steps."

Insert into any LLM's system prompt for instant personalization.

Import Existing Data

Kill the cold-start problem — import your ChatGPT history, Obsidian vault, or text files:

# ChatGPT export (Settings → Data Controls → Export)
mengram import chatgpt ~/Downloads/chatgpt-export.zip --cloud

# Obsidian vault
mengram import obsidian ~/Documents/MyVault --cloud

# Any text/markdown files
mengram import files notes/*.md --cloud

Works with Python SDK too:

m = CloudMemory(api_key="om-...")
m.import_chatgpt("export.zip")
m.import_obsidian("~/Documents/MyVault")
m.import_files(["notes.md", "journal.txt"])

Integrations

MCP Server (Claude Desktop, Cursor, Windsurf)

{
  "mcpServers": {
    "mengram": {
      "command": "mengram",
      "args": ["server", "--cloud"],
      "env": { "MENGRAM_API_KEY": "om-..." }
    }
  }
}

LangChain

from integrations.langchain import MengramChatMessageHistory, MengramRetriever

# Drop-in message history — auto-saves to Mengram
history = MengramChatMessageHistory(api_key="om-...", session_id="session-1")

# RAG retriever — searches all 3 memory types
retriever = MengramRetriever(api_key="om-...")

CrewAI

from integrations.crewai import create_mengram_tools

tools = create_mengram_tools(api_key="om-...")
# → 5 tools: search, remember, profile, save_workflow, workflow_feedback

agent = Agent(role="Support Engineer", tools=tools)

OpenClaw

openclaw plugins install openclaw-mengram

{
  "plugins": {
    "entries": {
      "openclaw-mengram": {
        "enabled": true,
        "config": { "apiKey": "${MENGRAM_API_KEY}" }
      }
    },
    "slots": { "memory": "openclaw-mengram" }
  }
}

Auto-recall before every turn, auto-capture after every turn. 6 tools, slash commands, CLI. GitHub · npm

Agent Templates

Ready-to-run examples — clone, set API key, run in 5 minutes:

cd examples/devops-agent && pip install -r requirements.txt
export MENGRAM_API_KEY=om-...
python main.py

API Reference

All endpoints require Authorization: Bearer om-... — your key identifies you, no user_id needed.

Full interactive docs: mengram.io/docs

License

Apache 2.0 — free for commercial use.