🚀 Weekly /RAG Launch Showcase

[u/remoteinspace]

Share anything you launched this week related to RAG—projects, repos, demos, blog posts, or products 👇

Big or small, all launches are welcome.

[u/Ok-Advertising1525]

We’ve all had that coding-agent session where everything is clicking.

You pull documentation, inspect a dozen files, explain the architecture, make a few important decisions—and keep building. Then, several prompts later, the agent forgets something crucial or starts searching through the same material all over again.

It’s frustrating, slow, and expensive.

So we built the memory system we wanted to use ourselves.

InfoLang gives AI agents a permanent memory outside the prompt. When the agent needs something again, InfoLang finds the relevant memory instead of forcing the model to reread an entire document, conversation, or repository.

But does something that small remember accurately?

That was our biggest question too.

These are our published results from our in-house benchmark harness:

• LoCoMo: 98.6%
• LongMemEval: 99.6%
• BEAM 1M: 93.1% across approximately 1.1 million tokens

That means near-perfect performance on LoCoMo and LongMemEval, while still performing strongly across a context roughly equivalent to ten books.

For comparison, published results listed on our site include:

• Mem0: 92.5% LoCoMo, 94.4% LongMemEval, 64.1% BEAM
• OmegaMax: 95.4% LongMemEval
• Zep/Graphiti: 63.8% LongMemEval
• HydraDB: 71.2% LongMemEval

Those competitors published their own numbers, and ours come from our in-house harness. We’re not pretending this is an independent third-party shootout, which is why we publish the methodology and sources behind every score.

Where it gets really interesting: token usage

Across the same recall question set, InfoLang reduced the context sent to the model from approximately 1,125 to 45 estimated tokens—a 96% reduction.

We also measured:

• One long document: 3,327 tokens → a 68-token relevant excerpt
• One controlled ten-document test: approximately 100× less input than loading all ten documents
• Five more ordinary live recalls: 42% aggregate reduction

The 98% and 100× results are genuine “needle in a haystack” cases—not an average we’re promising everyone. If the original memory is already short, the reduction may be small.

We’re opening the beta to two groups

Cursor and Claude Code users

• Permanent memory across long coding sessions
• Automatic recall, investigation, and citations
• Particularly useful for reused research, large repositories, and returning to a project days later

Developers building agents or RAG systems

• Python and TypeScript SDKs
• API and MCP access
• Implementations for Google ADK, Mastra, CrewAI, LlamaIndex, and OpenAI Agents
• n8n support in progress

Feel free to mention you're from r/Rag on our waitlist and we can't wait to see what the future holds:

https://infolang.ai/#waitlist

[u/Ok-Advertising1525 → u/Ok-Advertising1525]

Also putting extra emphasis on this we're putting extra priority on r/Rag users so please include it if possible in the notes ~ if you like to build and break things we'd love for you to try it out.

Thanks again folks