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1. Get an API key

Sign up at mengram.io to get your free API key. It starts with om-.

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2. Install the SDK

pip install mengram-ai

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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-..."}

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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

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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?"},
    ]
)
Use the environment variable MENGRAM_API_KEY so you don’t have to pass the key every time: m = Mengram()