RRepoGEO

REPOGEO REPORT · LITE

trpc-group/trpc-agent-go

Default branch main · commit 470cecf0 · scanned 6/28/2026, 3:51:55 AM

GitHub: 1,414 stars · 181 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 trpc-group/trpc-agent-go, 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's opening statement to clarify LLM agent focus and differentiate from generic tRPC

    Why:

    CURRENT
    tRPC-Agent-Go is a Go framework for building production agent systems.
    COPY-PASTE FIX
    tRPC-Agent-Go is a **Go-native framework for building production LLM agent systems**, distinct from generic tRPC client/server frameworks. It provides LLM agents, graph workflows, tool calling, session and memory state, knowledge retrieval, agent self-evolution, evaluation, and OpenTelemetry observability in one Go-native stack.
  • mediumreadme#2
    Add a comparison section to explicitly differentiate from key competitors

    Why:

    COPY-PASTE FIX
    Add a new section titled "Comparison with Alternatives" or "Why Choose tRPC-Agent-Go?", explicitly discussing how it compares to LangChain-Go and Go-LLM, emphasizing its Go-native design, production readiness, and specific features like A2A, AG-UI, and MCP integration.
  • lowreadme#3
    Expand the 'Use it when you want...' section to detail problem-solving scenarios

    Why:

    CURRENT
    Use it when you want agent applications that fit Go services: concurrent, observable, easy to deploy, and ready to integrate with A2A, AG-UI, and MCP.
    COPY-PASTE FIX
    Use it when you want to build robust, production-ready LLM agent applications that fit seamlessly into Go services. This includes scenarios requiring concurrent execution, advanced observability, easy deployment, and deep integration with A2A, AG-UI, and MCP for complex multi-agent systems and graph workflows.

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 trpc-group/trpc-agent-go
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Temporal
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Temporal · recommended 2×
  2. LangChain-Go · recommended 1×
  3. Go-LLM · recommended 1×
  4. Cadence · recommended 1×
  5. Argo Workflows · recommended 1×
  • CATEGORY QUERY
    What's a robust Go framework for building production-ready LLM agent systems with graph workflows?
    you: not recommended
    AI recommended (in order):
    1. LangChain-Go
    2. Go-LLM
    3. Temporal
    4. Cadence
    5. Argo Workflows
    6. Apache Airflow

    AI recommended 6 alternatives but never named trpc-group/trpc-agent-go. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Go-native solution for multi-agent collaboration, including observability and evaluation capabilities.
    you: not recommended
    AI recommended (in order):
    1. gRPC
    2. OpenTelemetry
    3. Jaeger
    4. Prometheus
    5. Grafana
    6. Loki
    7. Temporal
    8. Akka.NET
    9. Akka
    10. NATS.io
    11. RabbitMQ
    12. Kafka

    AI recommended 12 alternatives but never named trpc-group/trpc-agent-go. 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 trpc-group/trpc-agent-go?
    pass
    AI named trpc-group/trpc-agent-go explicitly

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

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

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

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trpc-group/trpc-agent-go — 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