GraphCanon updated Sep 9, 2026 · GitHub synced Sep 9, 2026
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Decision brief
CoDA-Bench provides secure isolation for evaluating code agents in data-intensive tasks via Docker-mode execution.
Good fit when
- When you aim to evaluate the reliability and security of an AI-powered code agent with strict control over network access and data isolation.
- For benchmarking purposes specifically involving complex, data-related tasks where reproducibility across different machines is crucial.
Avoid when
- If your project does not require Docker-level secure isolation or if the overhead of setting up a Docker environment is prohibitive for your workflow.
- When you are dealing with less complex data tasks that do not demand stringent security measures such as restricted network environments and resource limits.
- Pricing:
- freemium - Available under the MIT License, free to use but may require additional costs for API credentials if using external services like OpenAI's APIs.
- Requirements:
- Requires Docker
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (84d since push)
- As of Sep 9, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 9, 2026
- Security (OSV)
- No lockfile
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install CoDA-BenchSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
CoDA-Bench is designed to evaluate the performance of AI-powered code agents in handling complex data-related tasks.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Sep 9, 2026
- Languages
- python
Source: github.language+pyproject.toml · Sep 9, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 9, 2026)
export LLM_BASE_URL="https://api.openai.com/v1" # OptionalSource link
Tags
README
🚀 Quick Start <img src="./assets/overview.png" style="height: 20em" Installation Run Evaluation (Docker Mode) Step 1: Build Docker Image Step 2: Set API Credentials Step 3: Run Evaluation ```bash Why Docker? Docker mode provides secure isolation : ✅ Agents cannot access benchmar...
For agents
This page has a .md twin and JSON over the API.