aacp 1.4.0

Agent Action Compression Protocol — pipe-delimited coordination for multi-agent LLM systems

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.

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Links

Spec and site	aacp.dev
IETF Draft	draft-mackay-aacp-01
Python SDK	github.com/MackayAndrew/aacp — pip install aacp
TypeScript SDK	github.com/MackayAndrew/aacp-ts — npm install aacp-ts
LangChain	github.com/MackayAndrew/aacp-langchain
CrewAI	github.com/MackayAndrew/aacp-crewai
Community rules	github.com/MackayAndrew/aacp-community-rules — 241 rules
Registry API	registry.aacp.dev
VS Code extension	Dispatch AACP
Packet builder	dispatch.aacp.dev
Working lab	github.com/MackayAndrew/aacp-lab

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Install

# Python
pip install aacp

# TypeScript / JavaScript
npm install aacp-ts

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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

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Measured results

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

Hop	English	AACP	Claude	GPT-4o
fetch employees	56	52	-7.1%	-12.7%
fetch budgets	57	47	-17.5%	-16.0%
merge calculate	65	43	-33.8%	-31.6%
generate report	62	43	-30.6%	-33.3%
TOTAL	240	185	-22.9%	-23.7%

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.

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Framework integration results

Validated on real workflows. June 2026.

Framework	Coordination calls eliminated	Total cost saving	Deterministic
LangChain (59 hops)	59 → 0	18%	YES
CrewAI (4 hops)	4 → 0	30%	YES

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

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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

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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

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Workflow encoders

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

Encoder	Domain	Hops	Real-world basis
PayrollEncoder	HR	6	HMRC PAYE
ITEncoder	IT	5	Microsoft Entra ID
InvoiceEncoder	FIN	3	AP three-way match
ContractEncoder	LEGAL	2-3	NDA/MSA review
SalesEncoder	SALES	5	Salesforce Agentforce 2026
JMLEncoder	HR+IT	6	ConductorOne, Lumos, CloudEagle
CSResolutionEncoder	CS	5	Zendesk Resolution Platform 2026
MonthEndEncoder	FIN	6	NetSuite Autonomous Close 2026

See docs/encoder-reference.md for citations.

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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.

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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

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Multi-model lab results (Lab v3)

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

Model	Cost	Tokens in	Latency	Pass
gpt-4.1-mini	$0.0190	40,585	160s	✓ 5/5
gpt-4.1	$0.0984	41,005	124s	✓ 5/5
claude-sonnet-4-5	$0.2237	53,127	423s	✓ 5/5
gpt-4o	$0.2408	40,459	101s	✓ 5/5

Lab: github.com/MackayAndrew/aacp-lab

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Relationship to existing protocols

Protocol	Layer	AACP relationship
MCP (Anthropic/AAIF)	Tool access	AACP inside MCP payloads
A2A (Google/AAIF)	Agent routing	AACP structures A2A message content
Existing APIs / JSON	Service comms	AACP for the LLM agent coordination layer on top

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

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Roadmap

Version	Status	Focus
v1.0	Released	Pipe-delimited format, rule-based encoders
v1.1	Released	Validated benchmarks — -22.9% Claude, -23.7% GPT-4o
v1.2	Released	Fallback loop closed, PyPI + npm, IETF draft-01
v1.3	Released	8 encoders, TypeScript SDK, VS Code extension, community rules (241), lab v3, amortisation benchmark, LangChain + CrewAI integrations
v1.4	Planned	RuleRegistry (from_community, from_hosted), QBR encoder
v2.0	Planned	IETF draft-02, AAIF submission

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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.