mengram-ai 2.15.1

Human-like memory for AI — semantic, episodic & procedural. Experience-driven procedures, Cognitive Profile, unified search, memory agents. Free open-source Mem0 alternative.

Give your AI agents memory that actually learns

Website · Get API Key · Docs · Console · Examples

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.episodes(query="deployment")          # → events
m.procedures(query="deploy")            # → workflows that evolve from failures

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Why Mengram?

Every AI memory tool stores facts. Mengram stores 3 types of memory — and procedures evolve when they fail.

	Mengram	Mem0	Zep	Letta
Semantic memory (facts, preferences)	Yes	Yes	Yes	Yes
Episodic memory (events, decisions)	Yes	No	No	Partial
Procedural memory (workflows)	Yes	No	No	No
Procedures evolve from failures	Yes	No	No	No
Cognitive Profile	Yes	No	No	No
Multi-user isolation	Yes	Yes	Yes	No
Knowledge graph	Yes	Yes	Yes	Yes
LangChain + CrewAI + MCP	Yes	Partial	Partial	Partial
Import ChatGPT / Obsidian	Yes	No	No	No
Pricing	Free tier	$19-249/mo	Enterprise	Self-host

Get Started in 30 Seconds

1. Get a free API key at mengram.io (email or GitHub)

2. Install

pip install mengram-ai

3. Use

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: [...]}

JavaScript / TypeScript

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: [...] }

REST API (curl)

# 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"}'

3 Memory Types

Semantic — facts, preferences, knowledge

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

Episodic — events, decisions, outcomes

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

Procedural — workflows that evolve

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 ✅

This happens automatically when you report failures:

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

Or fully automatic — just add conversations and Mengram detects failures and evolves procedures:

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

Cognitive Profile

One API call generates a system prompt from all memories:

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."

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

Import Existing Data

Kill the cold-start problem:

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

Integrations

MCP Server — Claude Desktop, Cursor, Windsurf { "mcpServers": { "mengram": { "command": "mengram", "args": ["server", "--cloud"], "env": { "MENGRAM_API_KEY": "om-..." } } } } 21 tools for memory management.	LangChain — pip install langchain-mengram from langchain_mengram import ( MengramRetriever, MengramChatMessageHistory, ) retriever = MengramRetriever(api_key="om-...") docs = retriever.invoke("deployment issues")
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", tools=tools)	OpenClaw openclaw plugins install openclaw-mengram Auto-recall before every turn, auto-capture after. 12 tools, slash commands, Graph RAG. GitHub · npm

Multi-User Isolation

One API key, many users — each sees only their own data:

m.add([...], user_id="alice")
m.add([...], user_id="bob")

m.search_all("preferences", user_id="alice")  # Only Alice's memories
m.get_profile(user_id="alice")                 # Alice's cognitive profile

Async Client

Non-blocking Python client built on httpx:

from mengram import AsyncMengram

async with AsyncMengram() as m:
    await m.add([{"role": "user", "content": "I use async/await"}])
    results = await m.search("async")
    profile = await m.get_profile()

Install with pip install mengram-ai[async].

Metadata Filters

Filter search results by metadata:

results = m.search("config", filters={"agent_id": "support-bot", "app_id": "prod"})

Webhooks

Get notified when memories change:

m.create_webhook(
    url="https://your-app.com/hook",
    event_types=["memory_add", "memory_update"],
)

Agent Templates

Clone, set API key, run in 5 minutes:

Template	Stack	What it shows
DevOps Agent	Python SDK	Procedures that evolve from deployment failures
Customer Support	CrewAI	Agent with 5 memory tools, remembers returning customers
Personal Assistant	LangChain	Cognitive profile + auto-saving chat history

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

API Reference

Endpoint	Description
POST /v1/add	Add memories (auto-extracts all 3 types)
POST /v1/search	Semantic search
POST /v1/search/all	Unified search (semantic + episodic + procedural)
GET /v1/episodes/search	Search events and decisions
GET /v1/procedures/search	Search workflows
PATCH /v1/procedures/{id}/feedback	Report outcome — triggers evolution
GET /v1/procedures/{id}/history	Version history + evolution log
GET /v1/profile	Cognitive Profile
GET /v1/triggers	Smart Triggers (reminders, contradictions, patterns)
POST /v1/agents/run	Memory agents (Curator, Connector, Digest)
GET /v1/me	Account info

Full interactive docs: mengram.io/docs

Community

• GitHub Issues — bug reports, feature requests
• API Docs — interactive Swagger UI
• Examples — ready-to-run agent templates

License

Apache 2.0 — free for commercial use.

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Get your free API key · Built by Ali Baizhanov · mengram.io