CLI-Anything: Building Agent-Native Software from Zero to Production

Transform any codebase into a deterministic, AI-controllable CLI with structured JSON output—step-by-step guide for engineers

Table of Contents

• What We're Building: The Agent-Native CLI Pipeline
• Prerequisites: Tools, Versions, and Environment Setup
• Phase 1: Codebase Analysis and CLI Design
• Phase 2: Automated CLI Implementation and Testing
• Phase 3: Publishing to CLI-Hub and Agent Integration
• Advanced Configuration: Performance and Determinism
• Testing & Validation: Ensuring Agent Reliability
• Error Handling & Debugging: Production-Grade Resilience
• Production Hardening: Security, Scaling, and Compliance
• Monitoring & Observability: Metrics for Agent Systems
• Cost & Performance: Optimization Strategies
• Next Steps: Extensions and Hyperion Consulting CTA

What We're Building: The Agent-Native CLI Pipeline

The Agent-Native Imperative

The modern software ecosystem faces a fundamental architectural mismatch: applications designed for human interaction through graphical interfaces must now serve autonomous agents as primary users. This shift demands more than superficial API wrappers or brittle GUI automation—it requires a ground-up reimagining of how software exposes its capabilities. CLI-Anything addresses this need through an automated 7-phase pipeline that transforms any codebase into an agent-native Command Line Interface (CLI), complete with structured JSON output, standardized error handling, and workflow-aware state management.

At its core, CLI-Anything solves three critical problems in agent-software interaction:

1. Determinism: GUI automation tools like Selenium or PyAutoGUI introduce non-deterministic failure modes due to timing dependencies and visual element detection. CLI-Anything's generated interfaces provide atomic, idempotent operations with predictable outcomes.

2. Observability: Agents require machine-readable output for decision-making. While most CLIs emit unstructured text, CLI-Anything enforces a --json flag on every command, producing consistently formatted responses with status codes, error messages, and typed data payloads CLI Anything.

3. Discoverability: Agents cannot infer functionality from visual interfaces. CLI-Anything's automated documentation pipeline generates OpenAPI schemas, Markdown references, and CLI-Hub registry entries that enable programmatic discovery of capabilities.

The 7-Phase Automated Pipeline

CLI-Anything's pipeline operates as a deterministic state machine that processes a target application's codebase through seven distinct phases. Each phase produces verifiable artifacts that feed into subsequent stages, with comprehensive test coverage ensuring end-to-end reliability. The following Mermaid diagram illustrates the pipeline's architecture and data flow:

Phase 1: Analyze (SENSE Layer)

The pipeline begins with static analysis of the target application's codebase, extracting both structural and behavioral information:

1. Abstract Syntax Tree (AST) Parsing: Using Python's ast module and Tree-sitter grammars, CLI-Anything builds a complete symbol table of functions, classes, and data structures. For C/C++ applications (e.g., Blender), it leverages Clang's LibTooling for accurate AST generation GitHub - HKUDS/CLI-Anything.

2. GUI Action Mapping: Through a combination of:

   • Qt/Cocoa/GTK introspection (for desktop apps)
   • WebDriver-based DOM analysis (for Electron apps)
   • Accessibility API inspection (macOS AXUIElement, Windows IAccessible) CLI-Anything creates a registry of user-triggerable actions with their input/output signatures.

3. Dependency Graph: A directed acyclic graph (DAG) of function calls and data flows is constructed using static call graph analysis (via pycg for Python, CodeViz for C++).

Failure Mode: False positives in GUI action detection occur when accessibility APIs expose non-user-facing controls. CLI-Anything mitigates this through a confidence scoring system (0-1) based on:

• Action frequency in user telemetry (if available)
• Presence in application menus/toolbars
• Input parameter complexity (user-facing actions typically have fewer, simpler parameters)

Phase 2: Design (REASON Layer)

The design phase translates analysis artifacts into a CLI specification:

1. Command Grouping: Using hierarchical clustering on the AST, related functions are grouped into subcommands. For example, GIMP's layer operations become gimp layer create|delete|merge.

2. State Model: A finite state machine (FSM) is derived from the application's core data structures. CLI-Anything represents this as a JSON Schema with:

   {
     "type": "object",
     "properties": {
       "current_document": {"$ref": "#/definitions/Document"},
       "clipboard": {"$ref": "#/definitions/ClipboardItem"},
       "undo_stack": {"type": "array", "items": {"$ref": "#/definitions/Action"}}
     },
     "required": ["current_document"]
   }
   

   Copy

3. Input/Output Schemas: Every command's parameters and return values are formalized as JSON Schemas. CLI-Anything enforces:

   • Primitive type constraints (e.g., width: {"type": "integer", "minimum": 1})
   • Enumerated values for discrete options (e.g., format: {"enum": ["png", "jpg", "tiff"]})
   • File path validation using pathlib.Path patterns

Trade-off: The design phase must balance completeness with usability. Overly granular command groupings (e.g., separate commands for every GIMP filter) create cognitive overload for agents. CLI-Anything uses a command density metric (commands per logical group) to automatically split or merge groups when this metric exceeds 15.

Phase 3: Implement (COMPUTE Layer)

The implementation phase generates a production-ready Click CLI with these key characteristics:

1. Unified REPL Interface: All generated CLIs inherit from a base AgentCLI class that provides:

   • Command history (via readline)
   • Progress indicators (spinners for long-running operations)
   • Structured help (--help generates both human-readable text and machine-readable JSON)
   • Tab completion for subcommands and parameters

2. JSON Output Contract: Every command supports a --json flag that returns:

   {
     "status": "success|error",
     "code": 200,
     "data": {"layer_id": 42, "name": "background"},
     "warnings": ["deprecated_parameter: use 'format' instead of 'type'"],
     "timestamp": "2026-04-05T14:30:45Z"
   }
   

   Copy

3. State Management: Commands receive and update application state through a context object:

   @cli.command()
   @click.pass_context
   def layer_create(ctx, name: str, width: int, height: int):
       """Create a new layer in the current document."""
       state = ctx.obj["state"]
       layer = state["current_document"].create_layer(name, width, height)
       return {"layer_id": layer.id}
   

   Copy

Benchmark: Startup time measurements for generated CLIs show consistent performance across applications:

Phase 4-7: Test, Document, Publish (ORCHESTRATE Layer)

The remaining phases ensure the generated CLI meets production standards:

1. Test Planning: Generates a TEST.md file with:

   • Unit test templates for each command
   • End-to-end scenario outlines (e.g., "Create a document, add three layers, export as PNG")
   • Edge case matrices (invalid inputs, state transitions)

2. Test Implementation: Produces a pytest suite with:

   • 1,073 unit tests (testing individual commands in isolation)
   • 435 end-to-end tests (validating multi-command workflows)
   • Property-based tests using Hypothesis for input validation

3. Documentation: Updates TEST.md with:

   • Command reference tables
   • Example invocations
   • OpenAPI 3.1 specification for programmatic access

4. Publication: Creates:

   • setup.py with PyPI metadata
   • Dockerfile for containerized deployment
   • CLI-Hub registry entry (registry.json)

Failure Mode: Test generation occasionally produces flaky tests when applications have non-deterministic behavior (e.g., Blender's physics simulations). CLI-Anything addresses this through:

• Timeouts for long-running operations
• Retry logic with exponential backoff
• Golden master testing for visual outputs

CLI-Hub: The Central Registry (CONNECT Layer)

CLI-Hub serves as the connective tissue in the Physical AI Stack, enabling agents to discover, install, and operate agent-native CLIs through a standardized interface. The registry's architecture follows these principles:

1. Decentralized Contribution: Developers add new CLIs by submitting a PR with a registry.json file:

   {
     "name": "gimp-cli",
     "version": "2.10.34",
     "description": "Agent-native CLI for GIMP",
     "install": {
       "pip": "gimp-cli>=2.10.0",
       "npx": "@clianything/gimp@latest"
     },
     "commands": ["layer", "filter", "export"],
     "tags": ["creative", "image-editing"],
     "license": "GPL-3.0"
   }
   

   Copy

2. Automated Validation: CLI-Hub's CI pipeline verifies CLI responsiveness (--help returns within 500ms) CLI-Anything/CONTRIBUTING.md.

3. Agent Integration: Agents interact with CLI-Hub through:

   • REST API (/api/v1/search?q=image+editing)
   • CLI (clihub install gimp-cli)
   • Python SDK (from clihub import Registry)

Adoption Metrics: As of March 2026, CLI-Hub hosts agent-native CLIs for 18 applications across domains:

End-to-End Workflow: From Codebase to Agent Execution

The following sequence diagram illustrates a complete workflow where an AI agent uses CLI-Anything to perform image editing in GIMP: