Becomer: LLM-agnostic persistent memory API. Store and recall memories across GPT, Claude, Gemini, or any model — zero tokens per recall.

Name: Becomer: LLM-agnostic persistent memory API. Store and recall memories across GPT, Claude, Gemini, or any model — zero tokens per recall.
Author: Becomer-net

@Becomer-net

Repository

About Becomer: LLM-agnostic persistent memory API. Store and recall memories across GPT, Claude, Gemini, or any model — zero tokens per recall.

BECOMER gives any LLM persistent memory across sessions — without spending a single token on retrieval.

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "becomer": {
      "command": "python",
      "args": [
        "-m",
        "becomer"
      ],
      "env": {
        "BECOMER_API_KEY": "your-key"
      }
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Becomer?

Becomer is a persistent memory API for AI applications. It lets you store and recall factual memories across any LLM (GPT, Claude, Gemini, etc.) using a proprietary retrieval engine. It is designed for developers who need long-term, zero-token recall in chatbots, assistants, or agentic workflows. Becomer is available as a Python SDK, an MCP server (for Claude Desktop, Cursor, etc.), and a REST API.

How to use Becomer?

Install the Python package via pip install becomer, obtain a free API key from becomer.net, then instantiate a Client and call .store() / .recall() in your code. For MCP integration, add a JSON configuration to your MCP host pointing to python -m becomer with the environment variable BECOMER_API_KEY. No code changes are needed when using the MCP server.

Key features of Becomer

LLM-agnostic persistent memory with zero token cost per recall
Achieves 94.4% on the LongMemEval benchmark
Multi-tenant isolation via user_id parameter
Three integration modes: Python SDK, MCP, and REST API
Zero dependencies – pure Python standard library only
LangChain, OpenAI, Anthropic, and other framework examples provided

Use cases of Becomer

Give a chatbot memory across sessions without modifying the LLM provider
Store user preferences (language, theme, dietary restrictions) for consistent behavior
Build multi-user AI products where each user’s memory is fully isolated
Replace context-window stuffing with a dedicated, high-accuracy memory layer

FAQ from Becomer

What makes Becomer different from a vector store?

Becomer uses a proprietary retrieval engine optimized for long-term conversational memory, outperforming the next-best solution by 3 points on the LongMemEval benchmark (94.4% vs 91.4%). It is designed specifically for storing and recalling factual statements about users, not for general document search.

What are the runtime dependencies?

Becomer is pure Python with zero external dependencies – only the standard library is required.

Where is my data stored?

All memories are stored on Becomer’s servers via API calls to becomer.net. The README does not specify self-hosting options.

What are the rate limits and pricing?

The free plan includes 1,000 API calls per month. The Pro plan offers 50,000 calls/month for ₹1,140/month (~$14). Multi-tenancy requires only one master key; billing counts against that key.

How do I authenticate and what transports are supported?

Authentication is via an API key (bk-your-api-key) passed to the client or set as an environment variable (BECOMER_API_KEY). Supported transports: Python SDK (direct), MCP (stdio via python -m becomer), and REST API (HTTP POST to becomer.net/v1/store and becomer.net/v1/recall).

Comments

More AI & Agents MCP servers

欢迎来到智言平台

Shy2593666979

AgentChat 是一个基于 LLM 的智能体交流平台，内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作，Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Memory、HITL、Skill、Milvus 和 ElasticSearch 等技术，实现高效的知识检索与工具调用，使用 FastAPI 构建高性能后端服务。

797