codeprobe 0.2.2

Benchmark AI coding agents against your own codebase. Mine real tasks from repo history, run agents, interpret results.

codeprobe

Benchmark AI coding agents against your own codebase.

Mine real tasks from your repo history, run agents against them, and find out which setup actually works best for YOUR code — not someone else's benchmark suite.

Why codeprobe?

Existing benchmarks (SWE-bench, HumanEval) use fixed task sets that AI models may have memorized from training data. codeprobe mines tasks from your private repo history, producing benchmarks that are impossible to contaminate.

Prerequisites

codeprobe orchestrates external AI coding agents — you need at least one installed:

Agent	Install	Required env var
Claude Code	claude.ai/download	ANTHROPIC_API_KEY
GitHub Copilot	npm install -g @github/copilot-cli (>= 1.0.4)	GitHub auth via gh auth login
Codex	Included via pip install codeprobe[codex]	OPENAI_API_KEY

You also need:

• Python 3.11+
• Git (for task mining and worktree isolation)
• GitHub CLI (gh) — optional, for mining tasks from GitHub PRs with linked issues

The assess and mine --enrich commands need an LLM for scoring/enrichment. codeprobe auto-detects the best available backend:

Priority	Backend	Install	Env var
1	Anthropic SDK	pip install codeprobe[anthropic]	ANTHROPIC_API_KEY
2	OpenAI SDK	pip install codeprobe[codex]	OPENAI_API_KEY
3	Claude CLI	claude.ai/download	ANTHROPIC_API_KEY

Override with CODEPROBE_LLM_BACKEND=anthropic|openai|claude-cli. Without any backend, assess falls back to heuristic scoring.

Quick Start

pip install codeprobe

cd /path/to/your/repo

codeprobe assess .      # Score benchmarking potential (optional)
codeprobe mine .        # Extract tasks from repo history
codeprobe run .         # Run agents against tasks
codeprobe interpret .   # Get recommendations

Commands

Command	Purpose
codeprobe assess	Score a codebase's benchmarking potential
codeprobe init	Interactive wizard — choose what to compare
codeprobe mine	Mine eval tasks from merged PRs/MRs
codeprobe probe	Generate fast micro-benchmark probes (30s each)
codeprobe experiment	Manage comparison experiments (init, add-config)
codeprobe run	Execute tasks against AI agents
codeprobe interpret	Analyze results, rank configurations
codeprobe oracle-check	Compare agent answer against oracle ground truth
codeprobe scaffold	Create/validate eval task directories
codeprobe ratings	Record and analyze agent session quality ratings

Two Ways to Generate Tasks

1. SDLC Tasks (from merged PRs)

Mine real code-change tasks from your git history. Agents must reproduce known fixes and features.

codeprobe mine . --count 10 --source github
codeprobe mine . --count 5 --min-files 4    # Harder tasks (more files changed)
codeprobe mine . --enrich                    # LLM-enriched instructions

2. Micro-Benchmark Probes

Fast exact-match tasks (30s each) that test code navigation and comprehension — no agent sandbox needed.

codeprobe probe . -n 10 -l python -s 42 -o ./probes

Generates four probe types: find-function, count-callers, return-type, module-dependency.

MCP Comparison Experiments

Compare agent performance with and without MCP tools (Sourcegraph, GitHub, etc.).

Mine org-scale comprehension tasks

# Set up Sourcegraph credentials
export SOURCEGRAPH_TOKEN="your-token"

# Mine MCP-optimized tasks with Sourcegraph ground truth enrichment
codeprobe mine /path/to/repo \
  --org-scale --mcp-families --count 5 \
  --no-interactive --no-llm \
  --sg-repo github.com/sg-evals/your-repo

MCP task families: symbol-reference-trace, type-hierarchy-consumers, change-scope-audit.

Set up the experiment

# Create experiment
codeprobe experiment init /path/to/repo --name mcp-comparison

# Copy mined tasks into the experiment
cp -r /path/to/repo/.codeprobe/tasks/* /path/to/repo/mcp-comparison/tasks/

# Baseline config (no MCP, no preamble)
codeprobe experiment add-config /path/to/repo/mcp-comparison \
  --label baseline --agent claude --model claude-haiku-4-5-20251001

# Sourcegraph MCP config (preamble + MCP server)
codeprobe experiment add-config /path/to/repo/mcp-comparison \
  --label with-sourcegraph --agent claude --model claude-haiku-4-5-20251001 \
  --preamble sourcegraph \
  --mcp-config '{"mcpServers":{"sourcegraph":{"type":"http","url":"https://sourcegraph.com/.api/mcp/v1","headers":{"Authorization":"token $SOURCEGRAPH_TOKEN"}}}}'

# Run and interpret
codeprobe run /path/to/repo/mcp-comparison --agent claude --max-cost-usd 5.00
codeprobe interpret /path/to/repo/mcp-comparison

Preambles

Preambles are composable instruction templates prepended to the agent's prompt for MCP-enabled configs. Built-in preambles: sourcegraph, github.

Override built-ins by placing a .md file in:

• <task_dir>/preambles/ (per-task)
• .codeprobe/preambles/ (project-level)
• ~/.codeprobe/preambles/ (user-level)

Template variables: {{sg_repo}}, {{repo_name}}, {{repo_path}}, {{task_id}}

Key Flags

# Running
codeprobe run . --parallel 5          # Run 5 tasks concurrently (worktree-isolated)
codeprobe run . --max-cost-usd 2.00   # Stop when cost budget is reached
codeprobe run . --dry-run             # Estimate resource usage without running

# Mining
codeprobe mine . --enrich             # Use LLM to improve weak task instructions
codeprobe mine . --org-scale          # Mine comprehension tasks (not SDLC)
codeprobe mine . --mcp-families       # Include MCP-optimized task families
codeprobe mine . --sg-repo REPO       # Sourcegraph repo for ground truth enrichment

# Experiment configs
codeprobe experiment add-config . --preamble sourcegraph  # Attach MCP preamble
codeprobe experiment add-config . --mcp-config config.json  # Attach MCP server

# Output
codeprobe interpret . --format csv    # Export for pivot tables
codeprobe interpret . --format html   # Self-contained HTML report

Supported Agents

• Claude Code (--agent claude) — headless via claude -p
• GitHub Copilot (--agent copilot) — via Copilot CLI
• Codex (--agent codex) — via OpenAI API
• Custom agents via the AgentAdapter protocol

Supported Git Hosts

GitHub, GitLab, Bitbucket, Azure DevOps, Gitea/Forgejo, and local repos.

Configuration

Create a .evalrc.yaml in your repo root:

name: my-experiment
agents: [claude, copilot]
models: [claude-sonnet-4-6, claude-opus-4-6]
tasks_dir: .codeprobe/tasks

License

Apache-2.0