muonry 0.1.7

Muonry - Sequential AI coding assistant with planning, websearch, and resilient fallbacks

Muonry – Parallel‑Capable AI Coding Assistant

Muonry is a reliable AI coding assistant built on Bhumi with optional planning and now parallel tool calling. It favors a clean, straightforward approach that actually works. It is also an open‑sourced Claude Code–style coding assistant.

✨ Key Features

• ⚡ Parallel Tool Calling – Run multiple tools concurrently in one turn (enabled by default)
• 🎯 Sequential Execution – Reliable step-by-step execution when needed
• 🧠 Optional Planning – Cerebras-powered task breakdown for complex projects
• 🔧 Rich Tool Set – File operations, shell commands, code patching
• 📋 Smart Planning – AI-powered task decomposition with sequential execution
• 🧵 Bounded Concurrency – Semaphore-based limits with per-call progress updates
• 📊 Compact Codebase – 1,238 lines of focused, maintainable code
• 🛡️ Rate‑Limit Fallback – Automatically retries with a fallback model on rate limits
• 🪓 Context Trimming – Sliding‑window message trimming to avoid context overflow (~131k)
• ✅ Satya Validation – Robust schema validation for planner outputs (dict/model safe)
• 🔎 Websearch Improvements – Structured results and fallback parsing for Title/URL blocks

🚀 Quick Start

1. Set API Keys:

   export GROQ_API_KEY=your_groq_key
   export CEREBRAS_API_KEY=your_cerebras_key  # Optional for multi-model
   export EXA_API_KEY=your_exa_key            # Optional for websearch tool
   export MUONRY_MAX_CONTEXT_CHARS=120000     # Optional: context cap (chars)
   

2. Run the Assistant:

   python assistant.py
   

💬 Usage

Interactive Chat

Simply run python assistant.py and start chatting! The assistant automatically handles:

Simple Tasks → Direct execution:

💬 You: Read config.json
🤖 Assistant: [reads file directly]

Complex Tasks → Planning + execution (parallel + sequential as appropriate):

💬 You: Create 6 Fire Nation stories in a folder
🧠 Planning task with 6 steps...
📋 Plan created: 1. Create folder, 2-6. Generate stories
💻 [Executes each step with the right mix of parallel + sequential]

Available Tools

• File Operations: read_file, write_file, apply_patch
• System Commands: run_shell, get_system_info, grep, search_replace
• Planning: planner (automatic for complex tasks)
• Development: update_plan
• Web Search: websearch (requires EXA_API_KEY or api_key param)
• Interactive Shell: interactive_shell (PTY; scripted answers, env)
• Quick Checks: quick_check (syntax/health checks)

Parallel tool calling

• Muonry can execute a batch of tools in parallel either when models return tool_calls or explicitly via the parallel tool.
• Env flags:
  • MUONRY_PARALLEL_TOOLS (default: 1)
  • MUONRY_PARALLEL_CONCURRENCY (default: 5)
  • MUONRY_PARALLEL_TIMEOUT_MS (default: 60000)

Example using the parallel tool:

{
  "name": "parallel",
  "arguments": {
    "calls": [
      {"name": "read_file", "arguments": {"file_path": "README.md"}},
      {"name": "read_file", "arguments": {"file_path": "pyproject.toml"}},
      {"name": "get_system_info", "arguments": {}}
    ],
    "concurrency": 3,
    "timeout_ms": 60000
  }
}

🎯 Execution Model: Parallel + Sequential

1. Simple Detection: AI recognizes simple vs complex tasks automatically
2. Optional Planning: For complex tasks, uses Cerebras to break them into steps
3. Parallel where independent: Independent tool calls are batched and run in parallel
4. Sequential where dependent: Steps that depend on prior results are executed in order
5. Reliable Results: Bounded concurrency, progress events, and fallbacks

Example Output:

💬 You: Create 6 Fire Nation stories in a folder
🧠 Planning task with 6 steps...
📋 Plan created successfully
💻 Shell: mkdir -p "fire nation" (exit 0)
📝 Writing story 1: The First Flame...
📝 Writing story 2: The Phoenix Crown...
✅ All 6 stories created successfully!

📊 Architecture

Core Components

• assistant.py – Main assistant. Handles chat loop, model fallback, context trimming, and parallel integration (auto + parallel tool).
• tools/toolset.py – Consolidated tool implementations (planner, shell, patching, file ops, quick checks, interactive shell, etc.).
• tools/orchestratorv2.py – Provider‑agnostic parallel executor (ParallelToolExecutor) with progress callbacks.
• tools/websearch.py – Exa-powered web search with structured JSON output and fallback Title/URL parsing.
• tools/apply_patch.py, tools/shell.py, tools/update_plan.py, etc. – Supporting modules used by toolset.py.

Models & Fallback

• Primary execution model: groq/moonshotai/kimi-k2-instruct (requires GROQ_API_KEY).
• Fallback model on rate-limit: cerebras/qwen-3-coder-480b (auto retry once).
• Planner model: cerebras/qwen-3-235b-a22b-thinking-2507 (requires CEREBRAS_API_KEY).

Error Handling & Limits

• Rate-limit handling: Auto-detects rate limit errors; switches to fallback model and retries once.
• Context length: Sliding-window trimming keeps the latest messages within MUONRY_MAX_CONTEXT_CHARS (default 120k, below ~131k cap).
• Planner validation: Satya schema validation with safe conversion of model/dict step objects.

Web Search

• websearch returns compact JSON: title, url, published_date, author, snippet, and limited text.
• If the provider returns a text block, the tool extracts Title/URL pairs to preserve clickable sources.

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Muonry is a compact, reliable assistant with first-class parallel tool calling and pragmatic guardrails.