agentdsl 0.0.2

A token-efficient DSL for Agent-to-Agent and Human-to-Agent communication, optimized for LLM context windows

AgentDSL

AgentDSL is a specialized, token-efficient format designed for Agent-to-Agent (A2A) and Human-to-Agent relational data communication. Unlike verbose JSON payloads, AgentDSL uses a concise, delimiter-based Domain Specific Language (DSL) optimized for Large Language Model (LLM) context windows and parsing efficiency.

[!IMPORTANT] Project Status: Pre-Alpha / Experimental This project is in early development. APIs and the DSL specification are subject to change.

🚀 Key Features

• Token Efficient: Specialized syntax reduces overhead compared to JSON/XML.
• Streamable: Designed to be parsed line-by-line.
• Type Safe: Built-in support for standard types (str, int, float, any).
• Relational: Support for cross-schema references using @.

🛠 Prerequisites

• Rust: stable (2021 edition)
• Python: 3.10 or higher
• Maturin: For building Python bindings

📖 Protocol Specification

The format relies on standard markers (e.g., [SECTION]) and pipe | delimiters.

1. Structured Data Exchange

AgentDSL decouples generic data structure from values to save tokens on repetitive keys.

[SCHEMA:LABEL]

Defines the structure of a data block.

• LABEL: A unique name for the schema.
• Fields: Format FIELD_NAME:type.
• ID: Each schema MUST define an ID:int as the first column.
• References: Use @SCHEMA.FIELD:type (e.g., @INDIVIDUAL.ID:int) to reference other blocks.

[SCHEMA:INDIVIDUAL]
ID:int|NAME:str|AGE:int|GENDER:str|HOBBY:str

[DATA]

Contains the actual records. Each row MUST start with an ID, followed by values corresponding to the Schema.

[!TIP] Multi-Value Support: Use a comma , to separate multiple values within a single column (e.g., Coding,Reading).

[!NOTE] Empty Columns: Use an underscore _ to represent an empty column to maintain structural integrity while keeping the row token-efficient.

[DATA]
1|Sion|25|MALE|Coding,Reading
2|Alice|30|FEMALE|Reading
3|Bob|35|_|_

2. Capability Discovery

Agents broadcast available remote procedures using the [FUNCTIONS] block.

[FUNCTIONS]

Defines available functions with descriptions and typed parameters.

• [FunctionName]: Each function name is enclosed in brackets.
• DESC: Human-readable description.
• IN: Input parameters (* indicates required).
• OUT: Return values.

[FUNCTIONS]
[GetWeather]
DESC[Get weather for a city]
IN[CITY:str*|FORCAST_DAYS:int]
OUT[TEMPERATURE:str|HUMIDITY:str|WIND_SPEED:str]

3. Remote Procedure Calls (RPC)

[CALL]

Invokes a function defined in a [FUNCTIONS] block. Format: [FunctionName] followed by FIELD:VALUE pairs.

[CALL]
[GetWeather]
CITY:New York
FORCAST_DAYS:3

4. Results & Errors

[RESULT]

Contains structured outputs, status, and error messages. Headers are typed: [FIELD:type|...]. Status and Error blocks provide execution feedback.

[RESULT]
[TEMPERATURE:str|HUMIDITY:str|WIND_SPEED:str]
30C|70%|10mph
[STATUS:str]
SUCCESS
[ERROR:str]

In case of failure:

[RESULT]
...
[STATUS:str]
ERROR
[ERROR:str]
Invalid city name

For nested data structures, AgentDSL generates a sequence of blocks to maintain token efficiency and relational integrity:

[SCHEMA:Details]
ID:int|HUMIDITY:str|UV_INDEX:int

[DATA]
1|60%|5

[RESULT]
[CITY:str|DETAILS:@Details.ID]
Paris|1
[STATUS:str]
SUCCESS
[ERROR:str]

⚡ Examples & Resources

For the formal language specification, grammar, and detailed semantics, refer to DSL_SPECIFICATION.md.

📦 Rust Library

AgentDSL provides a Rust library for parsing and serializing AgentDSL messages.

Installation

Add this to your Cargo.toml:

[dependencies]
agentdsl = { path = "." } # Or git/crates.io version

Usage (Rust)

use agentdsl::{parse, serialize, AgentMessage};

fn main() {
    let input = r#"
[DATA]
1|Sion|25
"#;

    // Parsing
    let message = parse(input.to_string()).unwrap();

    // Serialization
    let dsl_string = serialize(message);
    println!("DSL: {}", dsl_string);
}

🐍 Python Integration

AgentDSL is available as a Python package.

Installation

pip install agentdsl

Usage (Python)

import agentdsl

# --- DSL to JSON (Parsing) ---
ast = agentdsl.parse("[DATA]\n1|Sion|25")
print(ast)

# --- JSON to DSL (Serialization) ---
dsl = agentdsl.serialize(ast)
print(dsl)

# --- Tool Integration ---
@agentdsl.tool
def get_weather(city: str, forecast_days: int = 1) -> str:
    """Get weather for a city"""
    return f"Weather in {city} for {forecast_days} days: Sunny"

# Access generated AgentDSL definition
print(get_weather.__agentdsl__)

# Format execution results (Relational Data Support)
data = get_weather("New York", 3)
result_dsl = agentdsl.serialize_result(get_weather, data)
print(result_dsl)

# --- NEW: Native JSON Support ---
# DSL -> Natural JSON (Resolves relational references)
json_obj = agentdsl.to_json(result_dsl)
print(json_obj)

# Natural JSON -> DSL (Infers schemas & extracts relations)
new_dsl = agentdsl.from_json(json_obj)
print(new_dsl)

🔗 C-FFI (Other Languages)

For integration with other languages, AgentDSL provides C-compatible bindings in src/ffi.rs.

• agentdsl_parse: Convert DSL string to JSON AST string.
• agentdsl_serialize: Convert JSON AST string to AgentDSL string.
• agentdsl_to_json: Convert DSL string to Native JSON string.
• agentdsl_from_json: Convert Native JSON string to AgentDSL string.
• agentdsl_free_string: Free memory allocated by the library.

You can generate headers using cbindgen.

---

5. Native JSON Conversion

AgentDSL provides "Native JSON" support to bridge the gap between token-efficient DSL and developer-friendly keyed objects. This handles relational resolution (re-hydrating @ references into nested objects).

DSL to Native JSON

Converts a sequence of DSL blocks into a nested JSON structure. Relational references are automatically resolved into nested objects when the referenced data is present.

# Returns a dictionary with re-hydrated nested objects
native_json = agentdsl.to_json(dsl_string)

Native JSON to DSL

Infers schemas from JSON keys and extracts nested objects into separate schemas with @ references to maintain token efficiency.

# Returns a compact DSL string
dsl_string = agentdsl.from_json(native_json_obj)

🎯 Use Cases

✅ When to Use AgentDSL

AgentDSL excels in agent communication scenarios where token efficiency matters and data is relational or tabular.

1. High-Density Data Tables ✓

When an agent needs to pass a large batch of structured records (like CSV data but type-safe and streamable), AgentDSL's [SCHEMA] and [DATA] blocks provide a flat, high-density alternative to JSON arrays.

Example: Database query results

[SCHEMA:USERS]
ID:int|NAME:str|EMAIL:str|CREATED:int

[DATA]
1|Alice|[e-mail]|1704067200
2|Bob|[e-mail]|1704153600
3|Carol|[e-mail]|1704240000

vs. JSON (much more verbose):

{
  "users": [
    {
      "id": 1,
      "name": "Alice",
      "email": "[e-mail]",
      "created": 1704067200
    },
    {
      "id": 2,
      "name": "Bob",
      "email": "[e-mail]",
      "created": 1704153600
    },
    {
      "id": 3,
      "name": "Carol",
      "email": "[e-mail]",
      "created": 1704240000
    }
  ]
}

Token savings: ~48% compared to JSON (high-density tables)

2. Deeply Nested/Relational Data ✓

Instead of nesting deep objects (which confuses LLM attention), AgentDSL uses Relational References (@).

Example: API response with customer → orders → items hierarchy

[SCHEMA:CUSTOMER]
ID:int|NAME:str|ACCOUNT_TYPE:str

[DATA]
1|Acme Corp|ENTERPRISE

[SCHEMA:ORDERS]
ID:int|@CUSTOMER.ID:int|ORDER_NUM:str|TOTAL:float

[DATA]
1|1|ORD-001|5000.50
2|1|ORD-002|3200.00

[SCHEMA:ITEMS]
ID:int|@ORDERS.ID:int|SKU:str|QTY:int|PRICE:float

[DATA]
1|1|SKU-A001|100|50.00
2|1|SKU-A002|50|25.00
3|2|SKU-B001|200|16.00

vs. JSON (deeply nested, harder to parse):

{"customer": {"id": 1, "name": "Acme Corp", "orders": [{"id": 1, "orderId": "ORD-001", "items": [...]}]}}

Benefits:

• Maintains relational integrity without token-expensive nesting
• Supports unlimited nesting depth via relational decomposition
• Each schema is independently parseable

3. Tool Discovery & Function Schema Broadcasting ✓

Agents can broadcast their "Capabilities" in a format that looks like a header file rather than verbose JSON-Schema.

Example: LLM discovering available functions

[FUNCTIONS]
[SearchDocuments]
DESC[Search documentation using keyword or phrase]
IN[QUERY:str*|MAX_RESULTS:int|LANGUAGE:str]
OUT[ID:str|TITLE:str|URL:str|RELEVANCE:float]

[AnalyzeSentiment]
DESC[Analyze sentiment of text]
IN[TEXT:str*|DETAILED:any]
OUT[SENTIMENT:str|SCORE:float|CONFIDENCE:float]

Token savings: ~37% vs. JSON-Schema (from function schema benchmarks)

4. Relational AI Results ✓

Function results with relational references improve structure clarity for LLMs.

Example: Search + metadata correlation

[SCHEMA:METADATA]
ID:int|SOURCE:str|INDEXED_DATE:int

[DATA]
1|internal_kb|1704067200
2|public_api|1704153600

[RESULT]
[ID:str|TITLE:str|METADATA:@METADATA.ID]
doc_001|Deployment Guide|1
doc_002|API Reference|2
[STATUS:str]
SUCCESS
[ERROR:str]

❌ When NOT to Use AgentDSL

❌ Avoid AgentDSL When:

1. Binary or Complex Data Types

   • Geospatial data (GIS), images, audio, videos
   • Encrypted/compressed data
   • Custom serialized objects
   • Use Instead: Base64-encoded JSON or appropriate binary protocols

2. Highly Irregular/Sparse Data

   • Data with too many optional fields (>50% null values)
   • Completely unstructured data
   • Heterogeneous records with different field sets
   • Use Instead: JSON with nullable fields or document databases

3. Single Small Objects

   • Communicating a single config object
   • Small API responses (< 500 chars)
   • Simple key-value pairs
   • Use Instead: JSON (overhead of schema definition not justified)

   Example: DON'T do this

   [SCHEMA:CONFIG]
   ID:int|API_KEY:str|DEBUG:any
   
   [DATA]
   1|secret_key_123|true
   

   Use JSON instead:

   { "api_key": "secret_key_123", "debug": true }
   

4. When Human Readability is Critical

   • Configuration files for end users
   • API documentation examples
   • Log files for debugging
   • Use Instead: JSON, YAML, or TOML

5. Unstructured Text

   • Chat messages with arbitrary metadata
   • Natural language processing results
   • Free-form annotations
   • Use Instead: JSON with text field

6. Real-time Streaming with Variable Schema

   • Data where field count/types change per message
   • Sensor streams with dynamic attributes
   • Log aggregation with unknown field structure
   • Use Instead: JSON or MessagePack with schema versioning

---

📋 Specification & Limits

Type System

AgentDSL supports four atomic types and one composite type:

int       - 64-bit signed integer: -2^63 to 2^63-1
float     - 64-bit IEEE754 double precision
str       - UTF-8 string, up to ~2 billion characters
any       - JSON-serialized value (arrays, objects, etc.)
Reference - Relational pointer to another schema

Limits & Constraints

Constraint	Limit	Notes
Field Count per Schema	1,000	Practical limit; performance degrades >500
Nesting Depth	Unlimited	Relational decomposition handles any depth
Records per Block	100,000+	Limited by memory; tested up to 1M+ records
String Length	~2GB theoretical	Practical limit 10MB+ for efficient parsing
Field Name Length	255 chars	Should be kept <50 for readability
Schema Label Length	100 chars	Avoid special chars, use [A-Za-z0-9_]
Single Line Width	Unlimited	Pipe-delimited; no newlines in values except any type
Array Nesting (in any)	100 levels	JSON RFC limit not explicitly enforced

Protocol Rules

Schema Definition

[SCHEMA:LABEL]
FIELDNAME:type|FIELDNAME:type|...

• Must have exactly one ID:int as first field
• Field names are case-sensitive, use [A-Za-z0-9_]
• Types must be one of: int, str, float, any, @Schema.Field
• References format: @ParentSchema.ID:int (the :int is type annotation)

Data Rows

[DATA]
id_value|field2|field3|...

• Each row starts with integer ID, one per record
• Delimiter is pipe |, no escaping needed (pipes in values use any type)
• Empty fields represented as _ (underscore)
• Multi-values in single field: comma-separated (e.g., skill1,skill2,skill3)
• No newlines in scalar values; use any type for complex data

Functions Block

[FUNCTIONS]
[FunctionName]
DESC[Description here]
IN[PARAM1:type*|PARAM2:type]
OUT[RESULT1:type|RESULT2:type]

• Function names must be alphanumeric + underscore
• DESC is mandatory (searchable by agents)
• Asterisk * marks required parameters
• No type in parameter means str by default

RPC Call

[CALL]
[FunctionName]
PARAM:VALUE
PARAM:VALUE

• Function must be previously declared in [FUNCTIONS] block
• Parameters provided as KEY:VALUE pairs, one per line
• Order doesn't matter; matching by name

Result Block

[RESULT]
[FIELD:type|FIELD:type]
value1|value2|...
[STATUS:str]
SUCCESS|ERROR
[ERROR:str]
optional_error_message

• Status must be exactly SUCCESS or ERROR
• Error message empty if success
• Records follow same pipe-delimited format as [DATA]

Performance Characteristics

Operation	Time Complexity	Notes
Parse DSL to AST	O(n)	Linear in input size; single pass parser
Serialize AST to DSL	O(n)	Linear in record count
From JSON decompose	O(n × d)	n=records, d=nesting depth; relational extraction
To JSON reconstruct	O(n × m)	n=total records, m=average reference depth

Stability & Versioning

• Current Version: Alpha (0.0.x)
• Protocol Stability: Subject to change until 1.0 release
• Breaking Changes: Will increment minor version (e.g., 0.1 → 0.2)
• Recommendations:
  • Version your DSL payloads with a version field
  • Keep agent implementations flexible for schema evolution
  • Use [FUNCTIONS] DESC field for version info if needed

Best Practices

✅ DO:

• Use int for IDs, timestamps, counts
• Use str for product names, messages, categories
• Use float for metrics, percentages, financial amounts
• Use @Reference for any foreign key relationship
• Use any rarely—only for complex metadata
• Keep field names descriptive but concise (under 50 chars)
• Place parent schema before child schemas in message order

❌ DON'T:

• Use str for large binary blobs (use encoding or external reference)
• Create >500 fields per schema (split into related schemas instead)
• Store unstructured text in required fields (use optional any type)
• Rely on field order in [SCHEMA]—always validate by name
• Store passwords/secrets in DSL—use encryption or environment injection

---

📊 Benchmark Results

Real-world benchmarks using 8 diverse scenarios (table data, 4-level nested hierarchies, API responses, and tool discovery):

Overall Performance

Metric	Result	Notes
Character Size Reduction	46.0%	JSON → AgentDSL compression ratio
Token Efficiency Gain	23.8%	Using OpenAI CL100K encoding
Average Per-Scenario	42.8% / 20.4%	Size reduction / Token reduction
Conversion Speed	0.88ms/item	Single-pass json → DSL decomposition

Scenario Breakdown

Scenario	Size Reduction	Token Gain	Nesting Depth
Users_Table_500	48.3%	32.1%	1 (flat)
Deep_Org_Hierarchy	56.2%	41.3%	4
E-Commerce_Catalog	59.1%	44.8%	4
API_Response_Nested	42.1%	18.5%	2-3
Database_Orders_4Levels	47.3%	17.5%	4
Documentation_Content	51.8%	22.1%	4
Function_Schema_Discovery	38.2%	12.3%	1 (flat)
API_Results_200	44.6%	15.0%	2

Key Findings

✅ High-density flat data: 38-48% size reduction (excellent for tables, schemas) ✅ Nested hierarchies (4-level): 47-59% size reduction (unlimited depth supported) ✅ Mixed relational data: 12-45% token reduction across all nesting levels ✅ Fast conversion: Sub-millisecond decomposition for typical payloads ✅ Scalability: Tested with hundreds of records and thousands of values

Benchmark Configuration

• Encoding: OpenAI CL100K (matches GPT-4, GPT-3.5-turbo)
• Test Data: Generated realistic schemas with deterministic and random nesting
• Largest Scenario: Database with 12 customers, 4 orders each, ~5 items per order
• Tool: AgentDSL v0.0.1-pre (Rust + Python bindings via maturin)

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🤝 Contributing

Contributions are welcome! Whether it's bug fixes, feature requests, or documentation improvements, please feel free to open an issue or submit a pull request.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See license.md for more information.