Show HN: Memory for LLM apps that cuts input tokens up to 80% (avg 68%)
Street AI has introduced a memory layer for LLM applications that significantly reduces input token usage. The system organizes conversation data efficiently, allowing for relevant information retrieval while minimizing the amount of data sent to the LLM API. This innovation has demonstrated an average reduction of 68% in input tokens during testing.
- ▪Street AI's memory layer sits between applications and LLM APIs, storing conversation data as signals.
- ▪The system automatically decays old data and retrieves only relevant information, leading to substantial token savings.
- ▪In a benchmark test, input tokens were reduced by 55-80% per turn, with greater savings as conversation length increased.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/Tem-Degu/streetai-memory |
| Publication time | Sat, 23 May 2026 17:37:53 +0000 |
| Retrieval time | 2026-05-23T17:52:27.506Z |
| Last seen | 2026-05-23T17:52:27.506Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 5JEsfHjNX5b_ |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Street AI Continuously learning memory layer for LLM applications. Your AI's memory grows forever. Your token bill doesn't. Street AI sits between your application and the LLM API. It stores conversation as signals organized into stacks, decays old data automatically, and retrieves only what's relevant on each turn — so you send a tiny prompt instead of the full conversation history. In our 16-turn benchmark, input tokens dropped by 55–80% per turn (average 68%), with the savings growing as the conversation lengthens. Status Alpha (0.2.0). API will change. Pin a version if you depend on it.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.