aacp

AACP — Agent Action Compression Protocol

Deterministic coordination for multi-agent LLM systems.

Status: v1.3 · Benchmarks measured · MIT Licensed · IETF Internet-Draft filed

When agents coordinate in natural language, every instruction varies on every run, costs tokens, and produces no reliable audit trail. AACP replaces that with typed, validated, deterministic packets. Same input. Same output. Every time.

---

Links

---

Install

# Python
pip install aacp

# TypeScript / JavaScript
npm install aacp-ts

---

The problem

Natural language (56 tokens) — varies on every run, not auditable:
"Please retrieve the employee salary records for the period ending
March 2026. I need all active employees, their departments, cost
centres, base salary, any changes made this month, and pension
contribution rates. Return as JSON array."

AACP v1.1 (52 tokens) — identical every run, validated, auditable:
FETCH|HR|return:HR-Agent|p:1|aacp:1.1|res:emp_salary|period:2026-03|filter:status=active|fmt:json

---

Measured results

Token counts from live API usage_metadata. 4-hop payroll workflow. May 2026.

Token reduction is a secondary benefit. The primary value is determinism and auditability -- every packet is identical across runs and machine-readable without post-processing.

---

Framework integration results

Validated on real workflows. June 2026.

• aacp-langchain — pip install aacp-langchain
• aacp-crewai — pip install aacp-crewai

---

Quick start (Python)

from aacp.encoders.workflows.payroll import PayrollEncoder

enc = PayrollEncoder()
pkt = enc.fetch_employees(period="2026-03")
print(pkt.packet)
# FETCH|HR|return:HR-Agent|p:1|aacp:1.1|res:emp_salary|period:2026-03|filter:status=active|fmt:json
print(f"Cost: ${pkt.api_cost_usd:.2f}")  # $0.00

# Novel instructions — fallback to LLM, logged to registry
from aacp.encoders.fallback import FallbackEncoder
enc = FallbackEncoder()
pkt = enc.encode_english(
    english="Cross-reference contractors against the approved vendor list.",
    domain="FIN",
    return_agent="FIN-Agent"
)
# First call: LLM once, ~$0.002
# Every subsequent identical call: $0.00 from registry cache

Quick start (TypeScript)

import { PayrollEncoder, AACPValidator, AACPDecoder } from "aacp-ts";

const enc = new PayrollEncoder();
const pkt = enc.fetchEmployees("2026-03");
console.log(pkt.packet);
console.log(pkt.apiCostUsd); // 0

---

Packet format

TASK|DOM|return:AGENT|p:PRIORITY|aacp:VERSION|key:value|key:value...

Valid TASK: FETCH PROC FLAG RESOLVE LOG SEND BUILD MERGE CALC REPORT ACK SYNC

Valid DOM: HR FIN SALES LEGAL IT CS MKT

---

Workflow encoders

8 encoders across 6 domains, each grounded in a documented real-world platform. Available in Python and TypeScript. Zero LLM cost.

See docs/encoder-reference.md for citations.

---

Three-tier fallback encoder

Tier 1 — Hash cache:     exact match → $0.00
Tier 2 — Pattern match:  registry or builtin → $0.00
Tier 3 — LLM fallback:   novel → LLM once → logged → ~$0.002
                          next call → Tier 1 → $0.00

Amortisation benchmark: 6 LLM calls across 240 encoding operations. 91.6% saving.

---

Community rule library

241 pre-validated rules across 7 domains. MIT licensed. Zero API cost.

# Coming in v1.4:
from aacp import RuleRegistry
registry = RuleRegistry.from_community()
registry = RuleRegistry.from_hosted()  # registry.aacp.dev

Browse: github.com/MackayAndrew/aacp-community-rules API: registry.aacp.dev

---

Multi-model lab results (Lab v3)

5 workflows, 4 models, all passing. June 2026.

Lab: github.com/MackayAndrew/aacp-lab

---

Relationship to existing protocols

AACP fills the coordination content layer -- the layer between routing (A2A, MCP) and task execution (the LLM doing actual work).

---

Roadmap

---

Licence

MIT — free to use, implement, extend, and fork.

AACP is an independent protocol proposal. Not affiliated with Anthropic, Google, IBM, or the Linux Foundation, though designed to complement MCP, A2A, and ACP.