https://github.com/alibaizhanov/mengram
Human-like memory for AI agents — semantic, episodic & procedural. Experience-driven procedures that learn from failures. Free API, Python & JS SDKs, LangChain, CrewAI & OpenClaw integrations.
https://github.com/alibaizhanov/mengram
ai-agents ai-memory cognitive-profile crewai episodic-memory langchain llm mcp-server memory-layer procedural-memory rag semantic-memory
Last synced: 20 days ago
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Human-like memory for AI agents — semantic, episodic & procedural. Experience-driven procedures that learn from failures. Free API, Python & JS SDKs, LangChain, CrewAI & OpenClaw integrations.
- Host: GitHub
- URL: https://github.com/alibaizhanov/mengram
- Owner: alibaizhanov
- License: apache-2.0
- Created: 2026-02-10T19:20:33.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-05-13T13:46:55.000Z (21 days ago)
- Last Synced: 2026-05-13T15:37:16.345Z (21 days ago)
- Topics: ai-agents, ai-memory, cognitive-profile, crewai, episodic-memory, langchain, llm, mcp-server, memory-layer, procedural-memory, rag, semantic-memory
- Language: Python
- Homepage: https://mengram.io
- Size: 2.31 MB
- Stars: 165
- Watchers: 4
- Forks: 23
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Notice: NOTICE
Awesome Lists containing this project
- awesome-llmops - Mengram - source memory infrastructure for AI agents. Provides semantic (entities/facts), episodic (conversations), and procedural (learned behaviors) memory with auto-reflection. Python SDK, JS SDK, MCP server, and REST API. |  | (LLMOps / Observability)
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README
### Give your AI agents memory that actually learns
[](https://pypi.org/project/mengram-ai/)
[](https://www.npmjs.com/package/mengram-ai)
[](LICENSE)
[](https://pypi.org/project/mengram-ai/)
**[Website](https://mengram.io)** · **[Get API Key](https://mengram.io/#signup)** · **[Docs](https://mengram.io/docs)** · **[Console](https://mengram.io/dashboard)** · **[Examples](examples/)**
```bash
pip install mengram-ai # or: npm install mengram-ai
```
```python
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
```
Native multilingual: ask in Russian, Chinese, Spanish, Japanese — Mengram retrieves and answers across 23 languages (Cohere multilingual embeddings + rerank).
---
## Claude Code — Zero-Config Memory
Two commands. Claude Code remembers everything across sessions automatically.
```bash
pip install mengram-ai
mengram setup # Sign up + install hooks (interactive)
```
Or manually: `export MENGRAM_API_KEY=om-...` → `mengram hook install`
What happens:
```
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)
```
No manual saves. No tool calls. Claude just knows what you worked on yesterday.
```bash
mengram hook status # check what's installed
mengram hook uninstall # remove all hooks
```
---
## Why Mengram?
Every AI memory tool stores facts. Mengram stores **3 types of memory** — and procedures **evolve when they fail**.
| | Mengram | claude-mem | Mem0 | Zep | Letta |
|---|:---:|:---:|:---:|:---:|:---:|
| Semantic memory (facts, preferences) | **Yes** | Yes | Yes | Yes | Yes |
| **Episodic memory (events, decisions)** | **Yes** | Partial | No | No | Partial |
| **Procedural memory (workflows)** | **Yes** | No | No | No | No |
| **Procedures evolve from failures** | **Yes** | No | No | No | No |
| **Cognitive Profile** | **Yes** | No | No | No | No |
| **Native multilingual (23 languages)** | **Yes** | No | No | No | No |
| **Ask & Citations (synthesized answer)** | **Yes** | No | No | No | No |
| Multi-user isolation | **Yes** | No | Yes | Yes | No |
| Knowledge graph | **Yes** | No | Yes | Yes | Yes |
| Claude Code hooks (auto-save/recall) | **Yes** | **Yes** | No | No | No |
| LangChain + CrewAI + MCP | **Yes** | No | Partial | Partial | Partial |
| **Import ChatGPT / Obsidian** | **Yes** | No | No | No | No |
| Pricing | **Free tier** | Free / OSS | $19-249/mo | Enterprise | Self-host |
## Get Started in 30 Seconds
**1. Install**
```bash
pip install mengram-ai
```
**2. Setup** (creates account + installs Claude Code hooks)
```bash
mengram setup
```
Or get a key manually at [mengram.io](https://mengram.io/#signup) and `export MENGRAM_API_KEY=om-...`
**3. Use**
```python
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: [...]}
```
File Upload (PDF, DOCX, TXT, MD)
```python
# 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"])
```
```javascript
// Node.js — pass a file path
await m.addFile('./report.pdf');
// Browser — pass a File object from
await m.addFile(fileInput.files[0]);
```
```bash
# REST API
curl -X POST https://mengram.io/v1/add_file \
-H "Authorization: Bearer om-..." \
-F "file=@meeting-notes.pdf" \
-F "user_id=default"
```
JavaScript / TypeScript
```bash
npm install mengram-ai
```
```javascript
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)
```bash
# 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
```python
m.search("tech stack")
# → ["Uses Python 3.12", "Deploys to Railway", "PostgreSQL with pgvector"]
```
### Episodic — events, decisions, outcomes
```python
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:
```python
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:
```python
m.add([{"role": "user", "content": "Deploy failed again — OOM on the build step"}])
# → Episode created → linked to "Deploy" procedure → failure detected → v3 created
```
## Ask Your Memory (RAG built-in)
`m.ask()` returns a synthesized answer with citations — not a raw fact list.
Mengram embeds your query, retrieves the top relevant facts, and uses
Cohere Chat to write a grounded answer with native source attribution.
```python
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
```
Multilingual: ask in any of 23 languages, get an answer in the same language with citations linking back to facts in the original language they were stored. Premium feature (Pro / Growth / Business).
## Cognitive Profile
One API call generates a system prompt from all memories:
```python
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:
```bash
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
**Claude Code** — Auto-memory hooks
```bash
mengram hook install
```
3 hooks: profile on start, recall on every prompt, save after responses. Zero manual effort.
[Docs](https://mengram.io/docs/claude-code)
**MCP Server** — Claude Desktop, Cursor, Windsurf
```json
{
"mcpServers": {
"mengram": {
"command": "mengram",
"args": ["server", "--cloud"],
"env": { "MENGRAM_API_KEY": "om-..." }
}
}
}
```
29 tools for memory management.
**LangChain** — `pip install langchain-mengram`
```python
from langchain_mengram import (
MengramRetriever,
MengramChatMessageHistory,
)
retriever = MengramRetriever(api_key="om-...")
docs = retriever.invoke("deployment issues")
```
**CrewAI**
```python
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**
```bash
openclaw plugins install openclaw-mengram
```
Auto-recall before every turn, auto-capture after. 12 tools, slash commands, Graph RAG.
[GitHub](https://github.com/alibaizhanov/openclaw-mengram) · [npm](https://www.npmjs.com/package/openclaw-mengram)
**CLI** — Full command-line interface
```bash
mengram search "deployment" --cloud
mengram profile --cloud
mengram import chatgpt export.zip --cloud
mengram hook install
```
[Docs](https://mengram.io/docs/cli)
**Claude Managed Agents** — MCP memory for hosted agents
```json
{
"mcp_servers": [{
"type": "url",
"name": "mengram",
"url": "https://mengram.io/mcp/sse"
}]
}
```
29 memory tools via MCP. [Docs](https://mengram.io/docs/managed-agents)
**n8n** — HTTP nodes for any workflow
```
POST https://mengram.io/v1/add
POST https://mengram.io/v1/search
```
No code needed — drag and drop memory into any n8n workflow.
[Docs](https://mengram.io/docs/n8n)
## Multi-User Isolation
One API key, many users — each sees only their own data:
```python
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:
```python
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:
```python
results = m.search("config", filters={"agent_id": "support-bot", "app_id": "prod"})
```
## Webhooks
Get notified when memories change:
```python
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](examples/devops-agent/)** | Python SDK | Procedures that evolve from deployment failures |
| **[Customer Support](examples/customer-support-agent/)** | CrewAI | Agent with 5 memory tools, remembers returning customers |
| **[Personal Assistant](examples/personal-assistant/)** | LangChain | Cognitive profile + auto-saving chat history |
```bash
cd examples/devops-agent && pip install -r requirements.txt
export MENGRAM_API_KEY=om-...
python main.py
```
## Use with AI Agents
Mengram works as a persistent memory backend for autonomous agents. Your agent stores what it learns, and recalls it on the next run — getting smarter over time.
```python
from mengram import Mengram
m = Mengram(api_key="om-...")
# Agent completes a task → store what happened
m.add([
{"role": "user", "content": "Apply to Acme Corp on Greenhouse"},
{"role": "assistant", "content": "Applied successfully. Had to use React Select workaround for dropdowns."},
])
# → Extracts: fact ("applied to Acme Corp"), episode ("Greenhouse application"),
# procedure ("React Select dropdown workaround")
# Next run → agent recalls what worked before
context = m.search_all("Greenhouse application tips")
# → Returns past procedures, failures, and successful strategies
# Report outcome → procedures evolve
m.procedure_feedback(proc_id, success=False,
context="Dropdown fix stopped working")
# → Procedure auto-evolves to a new version
```
Works with any agent framework — CrewAI, LangChain, AutoGPT, custom loops. The agent just calls `add()` after actions and `search()` before decisions.
## Self-Hosted (Ollama)
When running locally with Ollama, use models with **8B+ parameters** and **8K+ context window**. The extraction prompt is ~4,000 tokens — smaller models will hallucinate or mix examples with real data.
| Model | Parameters | Works? |
|-------|-----------|--------|
| `llama3.1:8b` | 8B | Yes |
| `mistral:7b` | 7B | Yes |
| `gemma2:9b` | 9B | Yes |
| `llama3.1:70b` | 70B | Best |
| `phi4-mini:3.8b` | 3.8B | No — context too small |
## API Reference
| Endpoint | Description |
|---|---|
| `POST /v1/add` | Add memories (auto-extracts all 3 types) |
| `POST /v1/add_text` | Add memories from plain text |
| `POST /v1/add_file` | Upload file (PDF, DOCX, TXT, MD) — vision AI extraction |
| `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](https://mengram.io/docs)**
### Quota Headers
Every authenticated response includes usage headers:
| Header | Description |
|--------|-------------|
| `X-Quota-Add-Used` | Add calls used this month |
| `X-Quota-Add-Limit` | Add calls allowed this month |
| `X-Quota-Search-Used` | Search calls used this month |
| `X-Quota-Search-Limit` | Search calls allowed this month |
SDKs expose this via `.quota`:
```python
m.search("test")
print(m.quota) # {"add": {"used": 5, "limit": 30}, "search": {"used": 12, "limit": 100}}
```
## Community
- **[GitHub Issues](https://github.com/alibaizhanov/mengram/issues)** — bug reports, feature requests
- **[API Docs](https://mengram.io/docs)** — interactive Swagger UI
- **[Examples](examples/)** — ready-to-run agent templates
## License
Apache 2.0 — free for commercial use.
---
**[Get your free API key](https://mengram.io/#signup)** · Built by **[Ali Baizhanov](https://github.com/alibaizhanov)** · **[mengram.io](https://mengram.io)**