axon-lang 0.1.0

A programming language for AI cognition — compiles to prompts, not machine code.

AXON
A programming language whose primitives are cognitive primitives of AI.

persona · intent · flow · reason · anchor · refine · memory · tool · probe · weave · validate · context

---

What is AXON?

AXON is a compiled language that targets LLMs instead of CPUs. It has a formal EBNF grammar, a lexer, parser, AST, intermediate representation, multiple compiler backends (Anthropic, OpenAI, Gemini, Ollama), and a runtime with semantic type checking, retry engines, and execution tracing.

It is not a Python library, a LangChain wrapper, or a YAML DSL.

persona LegalExpert {
    domain: ["contract law", "IP", "corporate"]
    tone: precise
    confidence_threshold: 0.85
    refuse_if: [speculation, unverifiable_claim]
}

anchor NoHallucination {
    require: source_citation
    confidence_floor: 0.75
    unknown_response: "Insufficient information"
}

flow AnalyzeContract(doc: Document) -> StructuredReport {
    step Extract {
        probe doc for [parties, obligations, dates, penalties]
        output: EntityMap
    }
    step Assess {
        reason {
            chain_of_thought: enabled
            given: Extract.output
            ask: "Are there ambiguous or risky clauses?"
            depth: 3
        }
        output: RiskAnalysis
    }
    step Check {
        validate Assess.output against: ContractSchema
        if confidence < 0.8 -> refine(max_attempts: 2)
        output: ValidatedAnalysis
    }
    step Report {
        weave [Extract.output, Check.output]
        format: StructuredReport
        include: [summary, risks, recommendations]
    }
}

---

Architecture

.axon source → Lexer → Tokens → Parser → AST
                                           │
                              Type Checker (semantic validation)
                                           │
                              IR Generator → AXON IR (JSON-serializable)
                                           │
                              Backend (Anthropic │ OpenAI │ Gemini │ Ollama)
                                           │
                              Runtime (Executor + Validators + Tracer)
                                           │
                              Typed Output (validated, traced result)

12 Cognitive Primitives

Primitive	Keyword	What it represents
Persona	persona	Cognitive identity of the model
Context	context	Working memory / session config
Intent	intent	Atomic semantic instruction
Flow	flow	Composable pipeline of cognitive steps
Reason	reason	Explicit chain-of-thought
Anchor	anchor	Hard constraint (never violable)
Validate	validate	Semantic validation gate
Refine	refine	Adaptive retry with failure context
Memory	memory	Persistent semantic storage
Tool	tool	External invocable capability
Probe	probe	Directed information extraction
Weave	weave	Semantic synthesis of multiple outputs

Semantic Type System

Types represent meaning, not data structures:

Epistemic:    FactualClaim · Opinion · Uncertainty · Speculation
Content:      Document · Chunk · EntityMap · Summary · Translation
Analysis:     RiskScore(0..1) · ConfidenceScore(0..1) · SentimentScore(-1..1)
Structural:   Party · Obligation · Risk (user-defined)
Compound:     StructuredReport

Opinion can never satisfy a FactualClaim slot. Uncertainty propagates — any computation with Uncertainty produces Uncertainty.

---

Project Structure

axon-constructor/
├── axon/
│   ├── compiler/
│   │   ├── lexer.py              # Source → Token stream
│   │   ├── tokens.py             # Token type enum
│   │   ├── parser.py             # Tokens → AST (recursive descent)
│   │   ├── ast_nodes.py          # AST node class hierarchy
│   │   ├── type_checker.py       # Semantic type validation
│   │   ├── ir_generator.py       # AST → AXON IR
│   │   └── ir_nodes.py           # IR node definitions
│   ├── backends/
│   │   ├── base_backend.py       # Abstract backend interface
│   │   ├── anthropic.py          # Claude
│   │   ├── openai.py             # GPT
│   │   ├── gemini.py             # Gemini
│   │   └── ollama.py             # Local models
│   ├── runtime/
│   │   ├── executor.py           # Flow execution engine
│   │   ├── context_mgr.py        # Mutable state between steps
│   │   ├── semantic_validator.py # Output type validation
│   │   ├── retry_engine.py       # Backoff + failure context
│   │   ├── memory_backend.py     # Abstract + InMemoryBackend
│   │   ├── tracer.py             # 14 event types, JSON trace
│   │   ├── runtime_errors.py     # 6-level error hierarchy
│   │   └── tools/
│   │       ├── base_tool.py      # BaseTool ABC + ToolResult
│   │       ├── registry.py       # RuntimeToolRegistry (cached)
│   │       ├── dispatcher.py     # IR → runtime tool bridge
│   │       ├── stubs/            # 8 tools (6 stubs + 2 real)
│   │       └── backends/         # 3 production backends
│   └── stdlib/                   # Built-in personas, flows, anchors
└── tests/                        # 731 tests

---

Installation

# From PyPI
pip install axon-lang

# With real tool backends (WebSearch, etc.)
pip install axon-lang[tools]

# Verify
axon version

From Source

git clone https://github.com/bemarking/axon-constructor.git
cd axon-constructor
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows
pip install -e ".[tools,dev]"  # editable install

Required API Keys

Key	For	Get it at
SERPER_API_KEY	WebSearch backend	serper.dev
ANTHROPIC_API_KEY	Claude backend	console.anthropic.com
OPENAI_API_KEY	GPT backend	platform.openai.com
GEMINI_API_KEY	Gemini backend	aistudio.google.com

None are required for development — stubs work without keys.

---

CLI Usage

# Validate syntax: lex + parse + type-check
axon check program.axon

# Compile to IR JSON
axon compile program.axon                     # → program.ir.json
axon compile program.axon --stdout             # pipe to stdout
axon compile program.axon -b openai            # target backend
axon compile program.axon -o custom.json       # custom output path

# Execute end-to-end (requires API key for chosen backend)
axon run program.axon                          # default: anthropic
axon run program.axon -b gemini                # choose backend
axon run program.axon --trace                  # save execution trace
axon run program.axon --tool-mode hybrid       # stub | real | hybrid

# Pretty-print an execution trace
axon trace program.trace.json

# Version
axon version

Python API

from axon import Lexer, Parser, TypeChecker, IRGenerator, get_backend

source = open("program.axon").read()
tokens  = Lexer(source).tokenize()
ast     = Parser(tokens).parse()
errors  = TypeChecker(ast).check()
ir      = IRGenerator().generate(ast)
backend = get_backend("anthropic")
result  = backend.compile(ir)

---

Tests

# Full suite
pytest tests/ -v

# By layer
pytest tests/test_lexer.py tests/test_parser.py         # Phase 1: Language core
pytest tests/test_ir_nodes.py tests/test_backends.py     # Phase 2: Compiler
pytest tests/test_executor.py tests/test_retry.py        # Phase 3: Runtime
pytest tests/test_tool_stubs.py tests/test_tool_backends.py  # Phase 4: Tools

Current Status

731 passed, 2 known failures (IR serialization edge cases)

Phase	Tests	What's covered
1	83	Lexer, Parser, AST, Type Checker
2	164	IR Generator, Compiler Backends
3	115	Executor, Context, Retry, Tracer, Validator
4	88	Tool infra (53) + Real backends (35)
misc	281	Stdlib, integration, edge cases

---

Tool System

AXON tools bridge compile-time IRUseTool nodes with runtime implementations.

Registry Modes

from axon.runtime.tools import create_default_registry

# Safe for tests — no API calls, no I/O
registry = create_default_registry(mode="stub")

# Real backends where available, stubs elsewhere
registry = create_default_registry(mode="hybrid")

# Only real backends (fails if deps missing)
registry = create_default_registry(mode="real")

Available Backends

Tool	Stub	Real Backend	Requires
WebSearch	✅	Serper.dev (httpx)	SERPER_API_KEY
FileReader	✅	Local filesystem	—
CodeExecutor	✅	subprocess + asyncio	—
Calculator	—	stdlib (real)	—
DateTime	—	stdlib (real)	—
PDFExtractor	✅	—	—
ImageAnalyzer	✅	—	—
APICall	✅	—	—

---

Error Hierarchy

Level 1: ValidationError    — output type mismatch
Level 2: ConfidenceError    — confidence below floor
Level 3: AnchorBreachError  — anchor constraint violated
Level 4: RefineExhausted    — max retry attempts exceeded
Level 5: RuntimeError       — model call failed
Level 6: TimeoutError       — execution time limit exceeded

---

Runtime Self-Healing

AXON features a native self-healing mechanism for L3 Semantic Gates. When the LLM output violates a hard constraint (AnchorBreachError) or fails structural semantic validation (ValidationError), the AXON RetryEngine automatically intercepts the failure.

Instead of crashing or silently failing, the engine re-injects the exact failure_context (e.g., "Anchor breach detected: Hedging without citation") back into the LLM's next prompt. This creates a closed feedback loop where the model adaptively corrects its logic and structurally self-heals in real-time.

Production Guarantees:

• Strict Boundaries: The correction loop strictly respects the refine limits explicitly defined in the execution configuration. If the model fails to heal within the permitted attempts, AXON deterministically raises a RefineExhaustedError (containing the last failed state) to escalate the failure, preventing infinite execution loops.
• Anchor Dependency: The healing capability is directly proportional to the precision of the defined Anchors. AXON provides the robust recovery mechanism, but ambiguous or poorly defined constraints may cause the model to optimize for passing validation syntactically while failing semantically. Clear, logical Anchors are required.

---

Roadmap

Phase	What	Status
0	Spec, grammar, type system	✅ Done
1	Lexer, Parser, AST, Type Checker	✅ Done
2	IR Generator, Compiler Backends	✅ Done
3	Runtime (7 modules)	✅ Done
4	Standard Library	🔧 In progress
5	CLI, REPL, VSCode Extension	🔧 In progress
6	Test Suite, Hardening, Docs	⬜ Planned

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Design Principles

1. Declarative over imperative — describe what, not how
2. Semantic over syntactic — types carry meaning, not layout
3. Composable cognition — blocks compose like neurons
4. Configurable determinism — spectrum from exploration to precision
5. Failure as first-class citizen — retry, refine, fallback are native

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How it Compares

	LangChain	DSPy	Guidance	AXON
Own language + grammar	❌	❌	❌	✅
Semantic type system	❌	Partial	❌	✅
Formal anchors	❌	❌	❌	✅
Persona as type	❌	❌	❌	✅
Reasoning as primitive	❌	Partial	❌	✅
Native multi-model	Partial	Partial	❌	✅

---

License

MIT

Authors

Ricardo Velit