Mengram

pip install mengram-ai   # or: npm install mengram-ai

from mengram import Mengram
m = Mengram(api_key="om-...")           # Free key → mengram.io

m.add([{"role": "user", "content": "I use Python and deploy to Railway"}])
m.search("tech stack")                  # → facts
m.ask("what's my tech stack?")          # → synthesized answer + citations
m.episodes(query="deployment")          # → events
m.procedures(query="deploy")            # → workflows that evolve from failures

Install Mengram for me. Fetch the canonical install guide at
https://mengram.io/agent-install.txt and follow it precisely.
My email is YOUR_EMAIL_HERE.

pip install mengram-ai
mengram setup              # Sign up + install hooks (interactive)

Session Start  →  Loads your cognitive profile (who you are, preferences, tech stack)
Every Prompt   →  Searches past sessions for relevant context (auto-recall)
After Response →  Saves new knowledge in background (auto-save)

mengram hook status     # check what's installed
mengram hook uninstall  # remove all hooks

pip install mengram-ai

mengram setup

from mengram import Mengram

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

# Add a conversation — 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 across all 3 memory types at once
results = m.search_all("deployment issues")
# → {semantic: [...], episodic: [...], procedural: [...]}

# Upload a PDF — auto-extracts memories using vision AI
result = m.add_file("meeting-notes.pdf")
# → {"status": "accepted", "job_id": "job-...", "page_count": 12}

# Poll for completion
m.job_status(result["job_id"])

// Node.js — pass a file path
await m.addFile('./report.pdf');

// Browser — pass a File object from <input type="file">
await m.addFile(fileInput.files[0]);

# REST API
curl -X POST https://mengram.io/v1/add_file \
  -H "Authorization: Bearer om-..." \
  -F "file=@meeting-notes.pdf" \
  -F "user_id=default"

npm install mengram-ai

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

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

# Add memory
curl -X POST https://mengram.io/v1/add \
  -H "Authorization: Bearer om-..." \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "I prefer dark mode and vim keybindings"}]}'

# Search all 3 types
curl -X POST https://mengram.io/v1/search/all \
  -H "Authorization: Bearer om-..." \
  -d '{"query": "user preferences"}'

m.search("tech stack")
# → ["Uses Python 3.12", "Deploys to Railway", "PostgreSQL with pgvector"]

m.episodes(query="deployment")
# → [{summary: "DB crashed due to missing migrations", outcome: "resolved", date: "2025-05-12"}]

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

m.procedure_feedback(proc_id, success=False,
                     context="OOM error on step 3", failed_at_step=3)
# → Procedure evolves to v3 with new step added

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

result = m.ask("what programming languages do I use?")

print(result["answer"])
# 'You use Python and Rust. Python is your daily language [1] and
#  Rust is your favorite [2]. You also know Java for enterprise
#  systems [3].'

for cit in result["citations"]:
    print(f'  "{cit["text"]}" → {cit["sources"][0]["fact"]}')
# "Python and Rust" → uses Python daily for backend development
# "favorite [2]"   → Rust is favorite language
# "Java"           → specializes in Java/Spring Boot

profile = m.get_profile()
# → "You are talking to Ali, a developer in Almaty. Uses Python, PostgreSQL,
#    and Railway. Recently debugged pgvector deployment. Prefers direct
#    communication and practical next steps."

mengram import chatgpt ~/Downloads/chatgpt-export.zip --cloud   # ChatGPT history
mengram import obsidian ~/Documents/MyVault --cloud              # Obsidian vault
mengram import files notes/*.md --cloud                          # Any text/markdown

mengram hook install

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

from langchain_mengram import (
    MengramRetriever,
    MengramChatMessageHistory,
)

retriever = MengramRetriever(api_key="om-...")
docs = retriever.invoke("deployment issues")

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", tools=tools)

openclaw plugins install openclaw-mengram

mengram search "deployment" --cloud
mengram profile --cloud
mengram import chatgpt export.zip --cloud
mengram hook install