RRepoGEO

REPOGEO REPORT · LITE

ThousandBirdsInc/chidori

Default branch main · commit 6de0bf39 · scanned 5/21/2026, 6:02:11 PM

GitHub: 1,344 stars · 55 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 ThousandBirdsInc/chidori, 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
    Reposition the README H1 to clearly state its purpose as an AI agent framework

    Why:

    CURRENT
    #   Chidori (v3)  

**An agent framework where agents are deterministic Starlark scripts — checkpoint, replay, and resume by default.**
    COPY-PASTE FIX
    #   Chidori (v3): A Deterministic AI Agent Framework  

**Build durable, debuggable AI agents with deterministic Starlark scripts — checkpoint, replay, and resume by default.**
  • mediumreadme#2
    Add a dedicated 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## 🆚 Comparison

Chidori differentiates itself from other AI agent frameworks like LangChain and LlamaIndex by focusing on deterministic execution, replay-based debugging, and Starlark scripting for agents. Unlike frameworks that rely on complex DSLs or opaque execution, Chidori provides native control flow and a clear audit trail for every agent action, making it ideal for production systems requiring high reliability and debuggability.
  • lowreadme#3
    Expand the 'About' section to explicitly state the target audience and key use cases

    Why:

    CURRENT
    ## 📖 About

Agents look like Python.** Native control flow, variables, list comprehensions — no template DSL.
Deterministic execution.** Every side effect goes through a host function the runtime can log, cache, and replay.
Zero-co
    COPY-PASTE FIX
    ## 📖 About

Chidori is designed for developers, MLOps engineers, and data scientists who need to build robust, debuggable, and production-ready AI agents. It's particularly suited for applications requiring:

*   **Deterministic execution.** Every side effect goes through a host function the runtime can log, cache, and replay.
*   **Native control flow.** Agents look like Python, using native control flow, variables, and list comprehensions — no template DSL.
*   **Built-in debugging and replay.** Leverage replay as the foundation for testing, debugging, and resuming agent execution.
*   **Durable and observable agents.** Ensure agents are reliable and their behavior is fully auditable.

Zero-co

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 ThousandBirdsInc/chidori
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. langchain-ai/langsmith-sdk · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. wandb/wandb · recommended 1×
  5. mlflow/mlflow · recommended 1×
  • CATEGORY QUERY
    How can I build robust AI agents with built-in debugging and replay capabilities?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LangSmith (langchain-ai/langsmith-sdk)
    3. LlamaIndex (run-llama/llama_index)
    4. Weights & Biases (wandb/wandb)
    5. MLflow (mlflow/mlflow)
    6. Haystack (deepset) (deepset-ai/haystack)
    7. OpenAI Assistants API
    8. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    9. OpenTelemetry (open-telemetry/opentelemetry-python)
    10. Pydantic (pydantic/pydantic)
    11. Logging (Python's `logging` module)
    12. Pickle/JSON

    AI recommended 12 alternatives but never named ThousandBirdsInc/chidori. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework provides deterministic execution and orchestration for complex LLM-powered agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. LlamaIndex
    4. Haystack
    5. AutoGPT
    6. BabyAGI
    7. SuperAGI
    8. Microsoft Guidance
    9. Marvin

    AI recommended 9 alternatives but never named ThousandBirdsInc/chidori. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 ThousandBirdsInc/chidori?
    pass
    AI named ThousandBirdsInc/chidori explicitly

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

  • If a team adopts ThousandBirdsInc/chidori in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ThousandBirdsInc/chidori 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 ThousandBirdsInc/chidori solve, and who is the primary audience?
    pass
    AI named ThousandBirdsInc/chidori explicitly

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

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ThousandBirdsInc/chidori — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite