Becomer: LLM-agnostic persistent memory API. Store and recall memories across GPT, Claude, Gemini, or any model — zero tokens per recall.
@Becomer-net
关于 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.
基本信息
配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"becomer": {
"command": "python",
"args": [
"-m",
"becomer"
],
"env": {
"BECOMER_API_KEY": "your-key"
}
}
}
}工具
未检测到工具
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
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_idparameter - 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).
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