About · v0.1-draft
A small, honest protocol.
What AACP is
AACP, Agent Action Compression Protocol, is a draft specification for a compact, bracketed key:value format used between agents in a multi-agent LLM system. It targets the coordination layer only: routing, references, hand-off metadata, structured intent.
The thesis is narrow: agent-to-agent prose is wasteful. Replacing it with a parseable, extensible token format reduces coordination cost by 85–91% on the workflows we measured, improves auditability, and gives a clear surface for validation.
What AACP is not
- It is not a way to reduce the cost of the model doing the actual task work.
- It is not a replacement for MCP, A2A, or function-calling specs, it's complementary.
- It is not a binary compression scheme. It's plain text designed for humans to read.
- It is not appropriate for emotional or relational context, those compress poorly.
- It is not production-validated. v0.1 is a draft. Live benchmarks in progress.
Relationship to other work
| Protocol | Layer | Relationship |
|---|---|---|
| MCP | Tool / context | Complementary. AACP packets can be the payload between MCP-aware agents. |
| A2A | Agent transport | Complementary. AACP is the wire-format on top of A2A. |
| Gibberlink | Acoustic / audio | Different problem (transport medium). AACP is text-layer. |
Prior work & related research
The problem AACP addresses is independently recognised in published research and practitioner documentation.
| Source | Finding | Relationship to AACP |
|---|---|---|
| EcoLANG Mou et al., Fudan University, May 2025 · arXiv:2505.06904 | "There exists redundancy in current agent communication: when expressing the same intention, agents tend to use lengthy and repetitive language." Achieved >20% token reduction through evolved compression language for social simulation. | Same observed problem, different angle. EcoLANG compresses evolved natural language; AACP uses a structured packet schema and targets business workflow coordination, measuring 91% on coordination tokens. |
| Framework practitioners LangGraph vs CrewAI vs AutoGen comparisons, 2025–2026 | CrewAI's role-based prompts inflate token count by ~30–50% vs hand-tuned alternatives for equivalent tasks. AutoGen's multi-turn conversation model inflates further, every agent turn involves a full LLM call with accumulated conversation history. | Confirms coordination-layer verbosity is observed across mainstream frameworks, not just in academic settings. |
AACP's position · EcoLANG compresses evolved natural language. AACP uses a structured packet schema. Both address the same observed problem from different angles. Neither MCP nor A2A address the verbosity of message content, that is the gap AACP fills.
Roadmap
| Version | Focus |
|---|---|
| v0.1 validated | May 2026. First benchmark: payroll workflow, 91.4% coordination token reduction measured (Claude Sonnet 4.5, 3 runs). |
| v0.1-draft | Format, validator, demo, honest framing. Current. |
| v0.2 | Live API benchmarks across 3 model families. Published methodology. |
| v0.3 | Reference encoder/decoder libraries (TS + Python). |
| v0.4 | Reserved key registry. Domain key conventions. |
| v1.0 | Stable spec, conformance test suite, ecosystem proposals. |
Open questions
- How should multi-packet conversations reference prior packets? Hash? Sequence?
- Is there a useful binary envelope for very high-throughput inter-agent buses?
- What does a conformance test suite look like?
- How do we encode partial / streaming intent without losing the structural advantage?
Issues and PRs welcome on GitHub.
Licence: MIT.