Skip to main content

​
Fastest path — paste one prompt into your agent

If you have a coding agent (Claude Desktop, Cursor, Claude Code, Codex, Windsurf), skip the manual steps entirely. Paste this prompt: 
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.
The agent reads our install guide, runs pip install, signs you up, configures the MCP server in its host, and verifies the install end-to-end with mengram doctor. Walk away — it tells you when it’s done. Want to do it manually? Continue below.

​
1. Get an API key

Sign up at mengram.io to get your free API key. It starts with om-. You can also do it from the terminal — mengram signup --email you@example.com sends a 6-digit code; mengram signup --email you@example.com --code 123456 exchanges it for a key and saves it to ~/.mengram/config.json.

​
2. Install the SDK

pip install mengram-ai

​
3. Add your first memory

from mengram import Mengram

m = Mengram(api_key="om-your-key")

# Add memories from a conversation
result = m.add([
    {"role": "user", "content": "I deployed the app on Railway. Using PostgreSQL."},
    {"role": "assistant", "content": "Got it, noted the Railway + PostgreSQL stack."},
])

# result contains a job_id for background processing
print(result)  # {"status": "accepted", "job_id": "job-..."}

​
4. Search your memories

# Semantic search
results = m.search("deployment stack")
for r in results:
    print(f"{r['entity']} (score={r['score']:.2f})")
    for fact in r.get("facts", []):
        print(f"  - {fact}")

# Unified search — all 3 memory types at once
all_results = m.search_all("deployment issues")
print(all_results["semantic"])    # knowledge graph results
print(all_results["episodic"])    # events and experiences
print(all_results["procedural"]) # learned workflows

​
5. Get a Cognitive Profile

Generate a ready-to-use system prompt that captures who a user is: 
profile = m.get_profile()
system_prompt = profile["system_prompt"]

# Use in any LLM call
response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": "What should I work on next?"},
    ]
)

​
6. Claude Code — Full Memory Loop

If you use Claude Code, set up automatic memory in one command: 
mengram setup
This creates your account (if needed), saves your API key, and installs hooks — all from the terminal. Or manually: export MENGRAM_API_KEY=om-your-key → mengram hook install. This installs 3 hooks:
  • Session context — loads your profile when Claude Code starts
  • Auto-recall — searches relevant memories on every prompt
  • Auto-save — captures conversations in the background
Claude Code remembers you across sessions automatically. See Claude Code Integration for details.

​
7. Verify the install end-to-end

mengram doctor
Runs an auth + add + search round-trip against the cloud API. Final line is OK: round-trip succeeded. on success, or FAIL: <reason> with a fix-it hint.
Use the environment variable MENGRAM_API_KEY so you don’t have to pass the key every time: m = Mengram()