RRepoGEO

REPOGEO REPORT · LITE

dirac-run/dirac

Default branch master · commit c4f1df66 · scanned 5/28/2026, 8:28:17 AM

GitHub: 1,255 stars · 68 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface dirac-run/dirac, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening definition of the project

    Why:

    CURRENT
    # 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.
    COPY-PASTE FIX
    # Dirac - Accurate & Highly Token Efficient Open Source AI Agent
    
    Dirac is an open-source coding agent singularly focused on efficiency and context curation. It reduces API costs by 50-80% compared to other agents while simultaneously improving code quality, utilizing hash-anchored parallel edits, AST manipulation, and advanced optimizations.
    
    > **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.
  • hightopics#2
    Add descriptive topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-agent, coding-assistant, llm-agent, code-refactoring, developer-tools, open-source, generative-ai, large-language-models
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://dirac.run/

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface dirac-run/dirac
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot Enterprise
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot Enterprise · recommended 1×
  2. Tabnine Enterprise · recommended 1×
  3. Codeium Enterprise · recommended 1×
  4. JetBrains AI Assistant · recommended 1×
  5. OpenAI API · recommended 1×
  • CATEGORY QUERY
    Looking for an AI coding assistant that reduces API costs and improves code quality.
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Enterprise
    2. Tabnine Enterprise
    3. Codeium Enterprise
    4. JetBrains AI Assistant
    5. OpenAI API

    AI recommended 5 alternatives but never named dirac-run/dirac. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source AI agents are best for efficient code refactoring and context management?
    you: not recommended
    AI recommended (in order):
    1. Cursor
    2. Continue (continue-dev/continue)
    3. OpenDevin (OpenDevin/OpenDevin)
    4. GPT-Engineer (gpt-engineer-org/gpt-engineer)
    5. Auto-GPT (Significant-Gravitas/Auto-GPT)
    6. AgentGPT (reworkd/AgentGPT)
    7. SuperAGI (TransformerOptimus/SuperAGI)
    8. Ollama (ollama/ollama)

    AI recommended 8 alternatives but never named dirac-run/dirac. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of dirac-run/dirac?
    pass
    AI named dirac-run/dirac explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts dirac-run/dirac in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dirac-run/dirac explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo dirac-run/dirac solve, and who is the primary audience?
    pass
    AI named dirac-run/dirac explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Brand-free category queries5 vs 2 in Lite
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