daveloop 1.2.0

Self-healing debug agent powered by Claude Code CLI

DaveLoop

DaveLoop is a Claude CLI based debug agent designed to solve bugs in cases where Claude Code fails to one-shot them. It feeds itself bugs iteratively until it resolves them or gets blocked. Each iteration builds on the previous one with full context thanks to the --continue flag.

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How It Works

1. You give it a bug description
2. It analyzes, makes a hypothesis, and tries a fix
3. If not fixed, it loops again with new context
4. Keeps going until it outputs [DAVELOOP:RESOLVED]

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Why Use It

• Claude Code sometimes needs multiple attempts to fix complex bugs
• Race conditions, subtle logic errors, multi-file refactors
• You dont want to manually copy-paste context every iteration
• Autonomous operation means you dont need to press enter for permissions all the time

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Key Features

• Persistent Context - uses claude --continue so it remembers everything
• Exit Signals - explicitly tells you when done or blocked
• Real-time Streaming - watch it think and debug live
• Pragmatic Exits - if environment is broken, it documents the fix and exits
• 4-Level Reasoning - KNOWN, UNKNOWN, HYPOTHESIS, and WHY

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The 4-Level Reasoning Protocol

The reasoning protocol forces systematic debugging:

1. Prevents random changes - cant just try stuff without stating why
2. Builds knowledge incrementally - each iterations KNOWN grows
3. Explicit about uncertainty - UNKNOWN list gets smaller or changes focus
4. Testable hypotheses - you can verify if the guess matches symptoms
5. Clear action items - NEXT ACTION is concrete and measurable

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INSTALL

Via pip

    pip install daveloop                                                                                                            

How to Use

Basic Usage

python daveloop.py "your bug description here"

Example:

python daveloop.py "routes/order.ts has a race condition on wallet balance. two concurrent orders can overdraw the account"

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From a File

If you have a detailed bug report:

python daveloop.py --file bug-report.txt

The file should contain the bug description. Can be as detailed as you want - stack traces, error logs, reproduction steps, whatever.

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From Claude Code Chat

Just talk naturally to Claude Code:

"daveloop this: mongodb connection error in lookup artist node"

Or:

"run daveloop on the jwt validation bug"

Claude Code will automatically run:

python daveloop.py "mongodb connection error in lookup artist node"

No special commands needed. Just mention "daveloop" and describe the bug.

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With Options

# Custom working directory
python daveloop.py "fix the bug" --dir /path/to/your/project

# Limit iterations (default is 20)
python daveloop.py "fix the bug" --max-iterations 10

# All together
python daveloop.py --file bug.txt --dir ./my-app --max-iterations 15

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If Claude CLI Not Found

DaveLoop automatically searches for Claude CLI in common locations. But if you get:

ERROR: Claude CLI not found!

Please install Claude Code CLI or set CLAUDE_CLI_PATH environment variable:
  Windows: set CLAUDE_CLI_PATH=C:\path\to\claude.cmd
  Linux/Mac: export CLAUDE_CLI_PATH=/path/to/claude

Install from: https://github.com/anthropics/claude-code

Option 1: Set Environment Variable (Recommended)

Find where Claude CLI is installed:

Windows:

where claude.cmd

Linux/Mac:

which claude

Then set the path:

Windows (temporary - current session):

set CLAUDE_CLI_PATH=C:\Users\YourName\AppData\Roaming\npm\claude.cmd

Windows (permanent):

setx CLAUDE_CLI_PATH "C:\Users\YourName\AppData\Roaming\npm\claude.cmd"

Linux/Mac (add to ~/.bashrc or ~/.zshrc):

export CLAUDE_CLI_PATH=/usr/local/bin/claude

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Option 2: Add to PATH

Windows:

1. Search for "environment variables" in start menu
2. Click "Environment Variables" button
3. Under "User variables", find "Path"
4. Add the directory containing claude.cmd
5. Restart terminal

Linux/Mac:

# Add to ~/.bashrc or ~/.zshrc
export PATH="$PATH:/path/to/claude/directory"

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Option 3: Install/Reinstall Claude CLI

If Claude CLI isnt installed:

npm install -g @anthropics/claude-code

After setting the path, run DaveLoop again.

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What Happens When You Run It

1. Banner shows up - you see the DAVELOOP ASCII art
2. Session info - working dir, max iterations, prompt loaded
3. Bug report - your description echoed back
4. Iteration 1 starts - progress bar shows up
5. You see the reasoning - KNOWN, UNKNOWN, HYPOTHESIS, NEXT ACTION
6. You see the actions - file reads, edits, bash commands
7. Iteration completes - either continues or exits

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Reading the Output

Output is color coded:

• Blue - reasoning blocks and actions
• White - normal text and code
• Dim - less important details
• Green - success messages
• Red - errors
• Yellow - warnings

Key things to watch for:

• Reasoning blocks - shows how its thinking through the problem
• Tool usage - what files its reading/editing
• Exit signals - [Exit signal detected: RESOLVED] means its done

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When It Finishes

Three Possible Outcomes:

1. Success - Bug is Fixed

★ ★ ★ BUG SUCCESSFULLY RESOLVED ★ ★ ★

Bug fixed in 3 iteration(s)!

**2. Blocked - Needs Human Help**

ERROR: Claude is blocked - needs human help

Check the logs to see what it tried. Usually means:
- Environment issues (missing dependencies)
- Need clarification on requirements
- Need access to external systems

**3. Max Iterations - Ran Out of Attempts**

WARNING: Max iterations (20) reached without resolution

Check logs in `logs/` directory. Either:
- Increase max iterations
- Provide more context about the bug
- Manually help it past a blocker

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## Logs Location

Every session creates logs:

logs/ 20240127_143022_iteration_01.log <- first attempt 20240127_143022_iteration_02.log <- second attempt 20240127_143022_summary.md <- overview

Session ID format: `YYYYMMDD_HHMMSS`

Useful for:
- Seeing what the agent tried
- Debugging why it got stuck
- Understanding its reasoning process
- Proving to your team that the AI actually fixed the bug

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## Tips for Good Bug Descriptions

**Bad:**
```bash
python daveloop.py "fix the bug"

Too vague. What bug? Where?

Better:

python daveloop.py "wallet balance goes negative when two users checkout simultaneously"

Has symptom and context.

Best:

python daveloop.py "RACE CONDITION: routes/order.ts wallet payment (lines 139-148). Balance check at line 141 happens BEFORE decrement   
at line 142. Two concurrent $100 orders both pass the check and overdraw wallet to -$100. Need atomic check+decrement."

Has:

• Bug type (race condition)
• Location (file and lines)
• Reproduction (concurrent orders)
• Root cause (non-atomic operations)
• Suggested fix direction (atomic operation)

More context = faster resolution = fewer iterations

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Interrupting the Agent

If you need to stop it:

• Press Ctrl+C once - graceful shutdown
• Press Ctrl+C twice - force kill

Logs are saved even if interrupted.

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Testing Before Production

Run on test bugs first:

# Simple test
python daveloop.py "create a file test.txt with 'hello world' and output [DAVELOOP:RESOLVED]"

Should resolve in 1-2 iterations. If it works, youre good to go.

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Using with SWE-bench

For testing against real-world benchmark bugs:

python daveloop_swebench.py --file django_hash_task.json --max-iterations 15

Comes with pre-configured bugs from:

• Django ORM
• Pytest AST rewriting
• SymPy code generation
• Sklearn edge cases

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Tested On

• Juice-Shop security vulnerabilities (race conditions, NoSQL injection, ReDoS, path traversal)
• SWE-bench real-world bugs (Django ORM, Pytest AST, SymPy C-code generation)
• Production n8n workflow errors (MongoDB connection, webhook failures)

Success rate significantly higher than one-shot attempts because of the iterative + reasoning approach