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GitHub - dirac-run/dirac: Coding Agent singularly focused efficiency and context curation. Reduces API costs by 50-80% vs other agent AND improves the code quality at the same time. Uses Hash Anchored edits, massively parallel operations, AST manipulation and many many other optimizations. https://dirac.run/

▲ 393 points 148 comments by GodelNumbering 4w ago HN discussion ↗

Pangram verdict · v3.3

We believe that this document is primarily human-written, with a small amount of AI content detected

28 %

AI likelihood · overall

Human
93% human-written 7% AI-generated
SEGMENTS · HUMAN 3 of 5
SEGMENTS · AI 1 of 5
WORD COUNT 763
PEAK AI % 100% · §5
Analyzed
Apr 27
backend: pangram/v3.3
Segments scanned
5 windows
avg 153 words each
Distribution
93 / 7%
human / AI fraction
Verdict
Human
Pangram v3.3

Article text · 763 words · 5 segments analyzed

Human AI-generated
§1 Human · 13%

Dirac - Accurate & Highly Token Efficient Open Source AI Agent

Dirac topped the Terminal-Bench-2 leaderboard for gemini-3-flash-preview with a 65.2% score!

It is a well studied phenomenon that any given model's reasoning ability degrades with the context length. If we can keep context tightly curated, we improve both accuracy and cost while making larger changes tractable in a single task. Dirac is an open-source coding agent built with this in mind. It reduces API costs by 64.8% on average while producing better and faster work. Using hash-anchored parallel edits, AST manipulation, and a suite of advanced optimizations. Oh, and no MCP. Our goal: Optimize for bang-for-the-buck on tooling with bare minimum prompting instead of going blindly minimalistic. 📊 Evals Dirac is benchmarked against other leading open-source agents on complex, real-world refactoring tasks. Dirac consistently achieves 100% accuracy at a fraction of the cost. These evals are run on public github repos and should be reproducible by anyone.

🏆 Leaderboard Success: Dirac recently topped the Terminal-Bench-2 leaderboard with a 65.2% score using gemini-3-flash-preview. This outperforms both Google's official baseline (47.6%) and the top closed-source agent Junie CLI (64.3%). This was achieved without any benchmark-specific info or any AGENTS.md files being inserted.

Note on the cost table below: A bug was discovered in Cline, the parent repo, after running these evals (issue #10314). We have submitted a PR #10315 to fix this. This bug caused the evals for Dirac and Cline to slightly underreport the numbers ($0.03 vs $0.05 per million token cache read). Although there won't be a large difference, we will update the evals soon.

§2 Human · 16%

Task (Repo) Files* Cline Kilo Ohmypi Opencode Pimono Roo Dirac

Task1 (transformers) 8 🟢 (diff) [$0.37] 🔴 (diff) [N/A] 🟡 (diff) [$0.24] 🟢 (diff) [$0.20] 🟢 (diff) [$0.34] 🟢 (diff) [$0.49] 🟢 (diff) [$0.13]

Task2 (vscode) 21 🟢 (diff) [$0.67] 🟡 (diff) [$0.78] 🟢 (diff) [$0.63] 🟢 (diff) [$0.40] 🟢 (diff) [$0.48] 🟡 (diff) [$0.58] 🟢 (diff) [$0.23]

Task3 (vscode) 12 🟡 (diff) [$0.42] 🟢 (diff) [$0.70] 🟢 (diff) [$0.64] 🟢 (diff) [$0.32] 🟢 (diff) [$0.25] 🟡 (diff) [$0.45] 🟢 (diff) [$0.16]

Task4 (django) 14 🟢 (diff) [$0.36] 🟢 (diff) [$0.42] 🟡 (diff) [$0.32] 🟢 (diff) [$0.24] 🟡 (diff) [$0.24] 🟢 (diff) [$0.17] 🟢 (diff) [$0.08]

Task5 (vscode) 3 🔴 (diff) [N/A] 🟢 (diff) [$0.71] 🟢 (diff) [$0.43] 🟢 (diff) [$0.53]

§3 Human · 22%

🟢 (diff) [$0.50] 🟢 (diff) [$0.36] 🟢 (diff) [$0.17]

Task6 (transformers) 25 🟢 (diff) [$0.87] 🟡 (diff) [$1.51] 🟢 (diff) [$0.94] 🟢 (diff) [$0.90] 🟢 (diff) [$0.52] 🟢 (diff) [$1.44] 🟢 (diff) [$0.34]

Task7 (vscode) 13 🟡 (diff) [$0.51] 🟢 (diff) [$0.77] 🟢 (diff) [$0.74] 🟢 (diff) [$0.67] 🟡 (diff) [$0.45] 🟢 (diff) [$1.05] 🟢 (diff) [$0.25]

Task8 (transformers) 3 🟢 (diff) [$0.25] 🟢 (diff) [$0.19] 🟢 (diff) [$0.17] 🟢 (diff) [$0.26] 🟢 (diff) [$0.23] 🟢 (diff) [$0.29] 🟢 (diff) [$0.12]

Total Correct

5/8 5/8 6/8 8/8 6/8 6/8 8/8

Avg Cost

$0.49 $0.73 $0.51 $0.44 $0.38 $0.60 $0.18

🟢 Success | 🟡 Incomplete | 🔴 Failure

Cost Comparison: Dirac is 64.8% cheaper than the competition (a 2.8x cost reduction). * Expected number of files to be modified/created to complete the task. See evals/README.md for detailed task descriptions and methodology.

§4 Mixed · 41%

🚀 Key Features

📦 Installation VS Code Extension Install Dirac from the VS Code Marketplace. CLI (Terminal) Install the Dirac CLI globally using npm: npm install -g dirac-cli Alternatively, use our official installation script (macOS/Linux): curl -fsSL https://raw.githubusercontent.com/dirac-run/dirac/master/scripts/install.sh | bash 🚀 CLI Quick Start

Authenticate: dirac auth

Run your first task: dirac "Analyze the architecture of this project"

Configuration (Environment Variables) You can provide API keys via environment variables to skip the dirac auth step. This is ideal for CI/CD or non-persistent environments:

ANTHROPIC_API_KEY OPENAI_API_KEY OPENROUTER_API_KEY GEMINI_API_KEY GROQ_API_KEY MISTRAL_API_KEY XAI_API_KEY (x.ai) HF_TOKEN (HuggingFace) ... and others (see src/shared/storage/env-config.ts for the full list).

Common Commands

dirac "prompt": Start an interactive task. dirac -p "prompt": Run in Plan Mode to see the strategy before executing. dirac -y "prompt": Yolo Mode (auto-approve all actions, great for simple fixes). git diff | dirac "Review these changes": Pipe context directly into Dirac. dirac history: View and resume previous tasks.

🛠️ Getting Started

Open the Dirac sidebar in VS Code. Configure your preferred AI provider (Anthropic, OpenAI, OpenRouter, etc.).

§5 AI · 100%

Start a new task by describing what you want to build or fix. Watch Dirac go!

📄 License Dirac is open source and licensed under the Apache License 2.0. 🤝 Acknowledgments Dirac is a fork of the excellent Cline project. We are grateful to the Cline team and contributors for their foundational work.

Built with ❤️ by Max Trivedi at Dirac Delta Labs