Memory and knowledge · MCP server
Iranti: MCP server, setup and security
Iranti is an MCP server published on GitHub that lets an AI agent share persistent facts and resolve memory conflicts between coding agents. Its repository has 5 stars and its last commit is from July 6, 2026, but it declares no licence.
| Adoption (stars) | 7/40 | |
|---|---|---|
| Maintenance | 18/30 | |
| Framework (licence, registry, official) | 10/30 |
What Iranti does
According to its repository: “Persistent shared memory for coding agents: facts stored as entity/key/value triples with hybrid semantic search, task checkpoints and conflict resolution.”
The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. Iranti is listed under Memory and knowledge, a category of 250 servers in the directory, where it ranks #234 by stars.
Installing and connecting Iranti
Distribution modes declared in the official MCP registry:
- npm package (Node.js, usually started with
npx)
It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: TypeScript. Listed systems: macOS, Windows, Linux.
The registry lists one secret variable to provide (API key, access token). Keep them in the client's environment variables, never in a shared file or a repository.
Security: what to check
Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.
Points found for Iranti:
- Secrets: the server acts with your credentials. Create a dedicated key with minimum rights and an expiry date.
- No licence: without a licence the code is not legally reusable, even if it is public. Avoid it in a company setting without the author's agreement.
Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.
Project activity and maintenance
Created on February 28, 2026, the nfemmanuel/iranti repository has 5 stars and 0 forks, with 1 open issues. It has more stars than 13% of the servers in the directory. Last change 4 months ago. Version published in the registry: 0.3.35 (April 13, 2026).
Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.
Verdict
Iranti is still a modest project: read its code and compare it with the alternatives before using it.
Strengths
- Listed in the official MCP registry
Limitations
- Requires an API key or token
- No licence declared
- Few visible users
Avoid if…
- Company use without the author's agreement
- You do not want to give credentials to an agent
Alternatives
- Context763.6K ★
- Basic memory4.3K ★
- Hindsight53.4K ★
- Cognee32.7K ★
- Skill Seekers15.2K ★
- Honcho7.8K ★
Learn more
Frequently asked questions
How do I install Iranti?
Iranti is distributed as npm package (Node.js, usually started with npx). Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of nfemmanuel/iranti.
Is Iranti free?
The code is public but has no declared licence: it is not formally free to reuse. It needs an API key: the service it connects to may be paid.
Is Iranti safe?
Points to watch: secrets and no licence. Give it minimum rights and approve sensitive actions.
Is it an official server?
Nothing says so: it is a project published by nfemmanuel on GitHub. Check who maintains it before giving it access.
What are the alternatives to Iranti?
In the same category: Context7, Basic memory, Hindsight, and Cognee. Context7 and Basic memory score higher on our grid.
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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score