feat: Enhance tool calling, classification, and progress reporting

[MichaelAnders]

CRITICAL: All test scripts now require NODE_ENV=test environment variable. This ensures tests run in test mode with proper isolation from production code paths (e.g., disables live API calls, mocking, test fixtures). See package.json test:* scripts.

Apply comprehensive improvements to Lynkr's tool execution pipeline, including per-model tool parsers (based on vLLM), LLM-based classification, real-time progress monitoring, and advanced agent routing with dual-provider support for cloud-based tool execution.

Key Changes

Test Infrastructure

• NODE_ENV=test: Added to ALL test scripts (test:unit, test:memory, etc.)
  • Ensures isolated test environment without production side effects
  • Enables test fixtures and mocking frameworks
  • Prevents accidental API calls during testing
  • IMPORTANT: This is a breaking change if tests are run without this var

New Major Features

Tool Calling & Parsing (vLLM-Inspired)

• Per-model tool parsers for GLM-4.7, Qwen3, and generic models
  • Implementation follows vLLM's ToolParser hierarchy (Apache 2.0 license)
  • GLM-4.7 parser: Handles native XML format + fallback patterns
  • Qwen3 parser: Markdown extraction with robust error handling
  • Generic parser: Extensible base for any model format
• Ollama fallback handling for malformed responses
• Tool call deduplication and cleaning

Dual-Provider Tool Execution

• TOOL_EXECUTION_PROVIDER: Route tool calls to specialized providers
  • Enables using cheap/fast/local models for chat while using reliable models (Claude Sonnet) for tool calling
  • Reduces token usage and improves tool accuracy
• TOOL_EXECUTION_COMPARE_MODE: Compare tool calls from both providers
• OLLAMA_CLOUD_ENDPOINT: Support for cloud-based Ollama models
  • Enables "cloud-only" setups without local Ollama
  • Automatic routing: cloud models use cloud endpoint
  • Hybrid support: mix local and cloud models in same session

Tool Classification

• LLM-based tool needs classification (whitelist + LLM fallback)
• Per-model classification accuracy with pattern matching
• Tool execution provider routing based on classification
• Workspace access permission system for external file operations

Progress Reporting (Real-Time Monitoring)

• WebSocket server (port 8765) broadcasting execution events
• Progress events: agent loop, model invocation, tool execution
• Built-in Python listener (tools/progress-listener.py)
  • Color-coded output with timestamps
  • Agent hierarchy tracking (parent/child relationships)
  • Token and duration metrics
  • Remote monitoring support
• Event tracking for debugging and observability

Configuration Enhancements

New environment variables (see .env.example for defaults):

• OLLAMA_CLOUD_ENDPOINT: Cloud Ollama instance URL
• OLLAMA_API_KEY: Cloud Ollama API authentication
• TOOL_EXECUTION_PROVIDER: Provider for tool calling decisions
• TOOL_EXECUTION_MODEL: Model override for tool execution
• TOOL_EXECUTION_COMPARE_MODE: Enable provider comparison
• POLICY_MAX_DURATION_MS: Single agent loop turn timeout
• POLICY_TOOL_LOOP_THRESHOLD: Max tool results before termination
• POLICY_MAX_TOOL_CALLS_PER_REQUEST: Parallel tool call limit
• TOOL_NEEDS_CLASSIFICATION_*: Classification whitelist and LLM config

Files Changed

61 files modified (9,061 insertions, 432 deletions):

Test Infrastructure

• package.json: NODE_ENV=test on all test:* scripts

Core Parser System (vLLM-Based)

• src/parsers/base-tool-parser.js: Base class hierarchy
• src/parsers/glm47-tool-parser.js: GLM-4.7 tool parsing
• src/parsers/generic-tool-parser.js: Extensible generic parser
• src/parsers/index.js: Parser registry and selection

Tool Execution & Classification

• src/tools/tool-call-cleaner.js: Response cleanup and deduplication
• src/tools/tool-classification-*.js: Classification system
• src/agents/tool-agent-mapper.js: Tool-agent relationship mapping

Provider & Routing

• src/clients/ollama-utils.js: Dual endpoint support (local + cloud)
• src/api/router.js: Provider routing and conversion
• src/providers/context-window.js: NEW - Context detection

Progress & Observability

• src/progress/server.js: NEW - WebSocket server
• src/progress/emitter.js: NEW - Event broadcasting
• src/progress/client.js: NEW - Client monitoring
• tools/progress-listener.py: NEW - Python listener tool

Configuration & Documentation

• .env.example: OLLAMA_CLOUD_ENDPOINT, TOOL_EXECUTION_, POLICY_
• config/tool-whitelist-*.json: Classification patterns

Tests (14 new files, 490/490 passing)

• Tool parser tests (GLM, Qwen3, generic)
• Tool classification and accuracy tests
• Dual endpoint and cloud Ollama tests
• Tool execution provider tests with comparison mode
• Subagent auto-spawning tests
• Progress reporting integration tests

Attribution

• Per-model tool parsers: Based on vLLM's tool calling implementation (Apache License 2.0, https://github.com/vllm-project/vllm)
• Progress reporting: Real-time WebSocket event system
• Agent routing: Dual-provider architecture for cost optimization

on Ollama

[MichaelAnders]

Two things:

1. vLLM tool calling implementations are added - please adjust if needed the code to reflect it properly (I mentioned vLLM in "Attribution)
2. With these changes GLM-4.7 can be used for some code analysis&corrections - sometimes it gets stuck ("Let me do XYZ..." responses) which can be overcome with "do XYZ" - that is a WIP I'll look into (several ideas), but 1st this has to be merged. Then other models will be enabled using the vLLM parser implementations which are really good!

[veerareddyvishal144]

Thanks @MichaelAnders for your contribution I am merging it.