RRepoGEO

REPOGEO REPORT · LITE

letta-ai/letta-code

Default branch main · commit cefab973 · scanned 6/30/2026, 12:57:23 AM

GitHub: 2,779 stars · 316 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
33 /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
2 / 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 letta-ai/letta-code, 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 clarify agent framework purpose

    Why:

    CURRENT
    Letta Code is a stateful agent harness for creating agents that are more like people than tools. Letta Code agents have memory, identity, and a sense of experience over time. They learn and evolve over long horizons through rewriting their own memory, skills, prompts, and even the harness itself (through mods).
    COPY-PASTE FIX
    Letta Code is an open-source framework for building stateful AI agents that are more like people than tools. Unlike generic code generation or codebase interaction tools, Letta Code agents possess memory, identity, and the ability to learn and adapt over long horizons by rewriting their own memory, skills, prompts, and even the harness itself (through mods).
  • hightopics#2
    Add specific agent framework topics

    Why:

    CURRENT
    agent-memory, ai, claude, codex, continual-learning, letta, memgpt, stateful-agents
    COPY-PASTE FIX
    ai-agents, agent-framework, stateful-ai, self-improving-ai, continual-learning, agent-memory, memgpt, letta
  • mediumreadme#3
    Add a clarification on 'Code' in Letta Code

    Why:

    COPY-PASTE FIX
    Crucially, 'Code' in Letta Code refers to agents dynamically modifying their own operational context and logic, not to a general-purpose code generation tool for human developers. (Add this sentence immediately after the initial description in the README.)

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 letta-ai/letta-code
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. langchain-ai/langchain · recommended 2×
  3. run-llama/llama_index · recommended 1×
  4. facebookresearch/faiss · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to build AI agents that maintain state and learn over long interactions?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Faiss (facebookresearch/faiss)
    4. Pinecone
    5. Weaviate (weaviate/weaviate)
    6. OpenAI API
    7. Anthropic Claude API
    8. Google Gemini API
    9. Redis (redis/redis)
    10. PostgreSQL
    11. Ray (ray-project/ray)
    12. Dask (dask/dask)

    AI recommended 12 alternatives but never named letta-ai/letta-code. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for developing self-improving AI agents that adapt their own code.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym (openai/gym)
    2. Baselines (openai/baselines)
    3. GPT-4
    4. AutoGPT (Significant-Gravitas/AutoGPT)
    5. BabyAGI (yoheinakajima/babyagi)
    6. LangChain (langchain-ai/langchain)
    7. Hugging Face Transformers (huggingface/transformers)
    8. CodeLlama (meta-llama/codellama)
    9. StarCoder (bigcode-project/starcoder)
    10. GPT-2
    11. GPT-3
    12. RLlib (ray-project/ray)
    13. Ray (ray-project/ray)
    14. AlphaCode

    AI recommended 14 alternatives but never named letta-ai/letta-code. 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 letta-ai/letta-code?
    pass
    AI did not name letta-ai/letta-code — likely talking about a different project

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

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

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

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letta-ai/letta-code — 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