Lossless codec for AI agent messages – 36% fewer tokens, overhead counted
The a2acompress project introduces a lossless, reversible codec designed to compress agent-to-agent handoff messages in multi‑agent pipelines. It achieves a 36% reduction in token count on held‑out ToolBench trajectories by encoding structure such as JSON keys and tool catalogs while counting all decoding overhead. The system includes a benchmark harness that enforces byte‑exact round‑trips, disallows invented tokens, and reports full token costs using the real cl100k_base encoder.
- ▪a2acompress reduces token usage by 36.6% on a benchmark of 196 ToolBench trajectories, cutting from 140,661 to 89,235 tokens.
- ▪The codec’s token savings include payload, reconstruction instructions, and any dictionary, ensuring the reported cost reflects the entire transmission.
- ▪All configurations must decode back to identical canonical JSON, and any learned dictionary is omitted if it does not provide net savings.
- ▪The implementation provides a CLI for fetching data, optimizing, encoding, decoding, and verifying results, and it works with standard GPT and Claude APIs without custom token IDs.
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| Original publisher | GitHub |
| Canonical URL | https://github.com/reh8n/a2acompress |
| Publication time | Thu, 13 Aug 2026 04:33:28 +0000 |
| Retrieval time | 2026-08-13T04:46:05.481Z |
| Last seen | 2026-08-13T04:46:05.481Z |
| 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 | thnBPvDZf-Mq · 1 stories |
| 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 |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
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| 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
a2acompress A compact, lossless wire format for agent-to-agent handoffs — cuts 36% of real cl100k_base tokens on held-out ToolBench trajectories, with every byte of overhead counted against it. Multi-agent pipelines (planner → builder → reviewer, tool loops, LangGraph / CrewAI / AutoGen-style orchestration) burn most of their context window re-sending structure: JSON keys, quoting, restated identifiers, tool catalogs, and outputs quoted verbatim two handoffs later. If you control both sides of a handoff, that structure is free to compress — as long as the compression is exactly reversible and you are honest about what decoding costs. a2acompress is that codec, plus the benchmark harness that keeps it honest.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.