RRepoGEO

REPOGEO REPORT · LITE

wrtnlabs/agentica

Default branch main · commit dc91f430 · scanned 6/20/2026, 11:56:42 PM

GitHub: 1,028 stars · 62 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 wrtnlabs/agentica, 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 README opening to highlight production readiness and DAG workflows

    Why:

    CURRENT
    Agentic AI framework specialized in AI Function Calling. Don't be afraid of AI agent development. Just list functions from three protocols below.
    COPY-PASTE FIX
    Agentica is a TypeScript AI Function Calling Framework designed for building **production-ready, reliable, and observable AI agents** using a **DAG-based approach** for structured workflows. It simplifies complex agent development by integrating functions from TypeScript classes, Swagger/OpenAPI documents, and MCP servers.
  • mediumreadme#2
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

While frameworks like LangChain.js, LlamaIndex.TS, and AutoGen offer broad AI agent capabilities, Agentica distinguishes itself with a strong emphasis on **production readiness, reliability, and observability** through its **DAG-based workflow orchestration**. We focus on providing a robust foundation for complex, mission-critical AI applications, particularly for TypeScript developers integrating with existing APIs via OpenAPI or custom classes.
  • lowtopics#3
    Add topics related to production readiness and robust agent development

    Why:

    CURRENT
    agent, agentic, agentic-ai, agentic-framework, ai, chatbot, claude, function-calling, llama, llm-function-calling, multi-agent-system, openai, openapi, rag, retrieval-augmented-generation, swagger, typescript
    COPY-PASTE FIX
    agent, agentic, agentic-ai, agentic-framework, ai, chatbot, claude, function-calling, llama, llm-function-calling, multi-agent-system, openai, openapi, rag, retrieval-augmented-generation, swagger, typescript, production-ready, robust-ai, workflow-orchestration

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 wrtnlabs/agentica
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchainjs
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchainjs · recommended 1×
  2. run-llama/LlamaIndexTS · recommended 1×
  3. openai/openai-node · recommended 1×
  4. microsoft/TypeChat · recommended 1×
  5. microsoft/autogen · recommended 1×
  • CATEGORY QUERY
    How to build robust AI agents using function calling in a TypeScript environment?
    you: not recommended
    AI recommended (in order):
    1. LangChain.js (langchain-ai/langchainjs)
    2. LlamaIndex.TS (run-llama/LlamaIndexTS)
    3. OpenAI SDK (TypeScript) (openai/openai-node)
    4. TypeChat (microsoft/TypeChat)
    5. Autogen (microsoft/autogen)
    6. Zod (colinhacks/zod)

    AI recommended 6 alternatives but never named wrtnlabs/agentica. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's the best framework for integrating LLMs with existing APIs using OpenAPI specifications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Functions
    4. Google Gemini Functions
    5. FastAPI
    6. openapi-python-client
    7. datamodel-code-generator
    8. Microsoft Semantic Kernel
    9. Haystack

    AI recommended 9 alternatives but never named wrtnlabs/agentica. 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 wrtnlabs/agentica?
    pass
    AI named wrtnlabs/agentica explicitly

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

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

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

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wrtnlabs/agentica — 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