AgentIndex icon
AgentIndex
ToolsMCP ServersHOTCategoriesTrendingNewBest ListsCompareGuides
Submit Tool
ToolsMCP ServersHOTCategoriesTrendingNewBest ListsCompareGuides
Home/
Memory & Context/
mengram
mengram logo

mengram

Active·★ 204·Apache-2.0·Updated 2026-10-26

Mengram is an AI memory layer that enables agents to remember facts, events, and workflows, with procedures automatically improving from failures. It uniquely stores semantic, episodic, and procedural memory, offering experience-driven evolution for AI agents.

mengram sits in the Memory & Context category, so it is usually better judged by workflow fit, deployment style, and team needs than by isolated features alone. Based on the available metadata, it leans most clearly toward Stores Semantic, Episodic, and Procedural Memory types.. A representative use case is Building intelligent AI agents that learn and adapt from real-world outcomes.. The listed license is Apache-2.0. GitHub stars are currently 204. Relevant tags include AI Agents, Memory Layer.

This write-up is based on the project's README and public repository data. We have not hand-tested the tool.

#AI Agents#Memory Layer#Procedural Memory#Episodic Memory#Semantic Memory#Experience-Driven Learning#LLM Integration#Cognitive Profile
$ Install
$ pip install mengram-ai
↗ Visit site★ GitHub
01

Features

01Stores Semantic, Episodic, and Procedural Memory types.
02Experience-Driven Evolution: Procedures automatically improve when they fail.
03Cognitive Profile generation for personalized LLM system prompts.
04Multi-User Isolation: Separate memory spaces for individual users in multi-tenant applications.
05Import existing data from ChatGPT, Obsidian, or text files to avoid cold-start problems.
02

Why choose it

+Stores Semantic, Episodic, and Procedural Memory types.
+Experience-Driven Evolution: Procedures automatically improve when they fail.
+Building intelligent AI agents that learn and adapt from real-world outcomes.
+Supports 8 platforms or runtime environments.
03

Trade-offs

!The final decision should still be made through side-by-side comparison with similar tools.
04

Compatibility

Python
Runtime
Verified via docs
JavaScript/TypeScript
Runtime
Verified via docs
LangChain
Integration
Verified via docs
CrewAI
Integration
Verified via docs
OpenClaw
Integration
Verified via docs
MCP Server
Integration
Verified via docs
05

Quick start

1
$ pip install mengram-ai
06

Use cases

↳Building intelligent AI agents that learn and adapt from real-world outcomes.
↳Enhancing DevOps agents by enabling procedures to evolve from deployment failures.
↳Creating personalized customer support agents that remember returning customers and their history.
↳Personalizing LLM interactions and responses using a dynamically generated cognitive profile.
↳Developing multi-tenant applications where each user requires an isolated and persistent memory space.
07

Alternatives

⚡Top VS:
VScaura★506→VSditto★299→VSheadroom★78.0k→VSengrim★300→
caura logo
caura★ 506
Caura (formerly MemClaw) — governed shared memory for AI agent fleets. Multi-agent, multi-tenant, MCP-native. Trust tiers, keystone policies, audit trails, knowledge graph, self-improving retrieval. Apache 2.0.
VSCompare→
ditto logo
ditto★ 299
Mine your Claude Code and Codex logs into a local you.md agent profile.
VSCompare→
headroom logo
headroom★ 78.0k
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. Around 20% fewer tokens for coding agents and 60-95% fewer for JSON, with the same answers. Library, proxy, and MCP server.
VSCompare→
engrim logo
engrim★ 300
The Universal Cross-Model Episodic Memory Standard. Local-first, project-scoped SQLite engine for Google Antigravity, Claude Code, Cursor, Codex CLI, and OpenCode. Zero cloud lock-in.
VSCompare→
Acontext logo
Acontext★ 3.7k
Data platform for context engineering. Context data platform that stores, observes and learns. Join the community❤️: https://discord.acontext.io
VSCompare→
mex logo
mex★ 1.8k
Persistent project memory for AI coding agents. Structured scaffold + drift detection CLI.
VSCompare→
fava-trails logo
fava-trails★ 26
🫛👣 FAVA Trails — Git-native, curated memory for AI agents via MCP. Draft isolation, promotion gate, thought lifecycle hooks, memory curation protocols, supersession chains.
VSCompare→
Zengram logo
Zengram★ 64
A Multi Agent Memory MCP That Connect Agents Across Systems and Machines
VSCompare→
See all alternatives →

Comments

Log in to leave a comment

No comments yet. Be the first!

On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases07Alternatives
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 integrations.

204Apache-2.0
Last commit2d ago
StatusActive
CategoryMemory & Context
Links
Documentation↗Discussion↗Issues↗Releases↗
Cloud Infrastructure
Ad

Deploy on DigitalOcean

Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.

Claim $200 Free Credit
© 2026 AgentIndex.app|Built by a 10-year iOS Developer.
Best by use caseAboutContactAdvertisePrivacy PolicyTerms of UseQYSGitHubBuy me a coffee ☕

Browse by Category

MCP ServersCode AssistantWorkflow AutomationRAG / Knowledge BaseMulti-AgentBrowser AutomationLLM InfraDev ToolingObservability

Not affiliated with Anthropic, OpenAI or Microsoft.