REPOGEO REPORT · LITE

letta-ai/letta-code

Default branch main · commit 1640c914 · scanned 5/18/2026, 6:12:17 PM

GitHub: 2,509 stars · 255 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

28 /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

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

hightopics#1

Add specific topics for AI coding agents and persistent memory

Why:

CURRENT

(none)

COPY-PASTE FIX

ai-agent, coding-agent, llm, code-generation, developer-tools, cli, desktop-app, memory-first, persistent-memory, multi-llm

highreadme#2

Reposition the README's opening paragraph to clarify its role as a multi-LLM AI coding agent with persistent memory

Why:

CURRENT

# Letta Code

Letta Code is a memory-first coding harness, designed for long-lived agents that can learn from experience.

Instead of working in independent sessions, you work with a persisted agent whose memory is portable across models (Claude, GPT, Gemini, GLM, Kimi, and more).

COPY-PASTE FIX

# Letta Code

Letta Code is an open-source, memory-first AI coding agent and harness, designed for long-lived agents that learn from experience and persist memory across sessions. It supports a wide range of LLM providers (Claude, GPT, Gemini, GLM, Kimi, and more), allowing you to work with a single agent whose memory is portable across models.

mediumabout#3

Enhance the repository description to explicitly state the problem solved

Why:

CURRENT

The memory-first coding agent

COPY-PASTE FIX

Memory-first AI coding agent for long-lived, multi-LLM agents that learn from experience and autonomously modify code.

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

Of all named tools, what % are you?

Top rival

langchain-ai/langchain

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

langchain-ai/langchain · recommended 1×
run-llama/llama_index · recommended 1×
Pinecone · recommended 1×
weaviate/weaviate · recommended 1×
qdrant/qdrant · recommended 1×

CATEGORY QUERY
What tools help build AI coding assistants with persistent memory across sessions?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Pinecone
4. Weaviate (weaviate/weaviate)
5. Qdrant (qdrant/qdrant)
6. Chroma (chroma-core/chroma)
7. PostgreSQL
8. Redis (redis/redis)
9. OpenAI API
10. Anthropic Claude API
11. Google Gemini API
AI recommended 11 alternatives but never named letta-ai/letta-code. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I create a coding agent that uses multiple LLM providers and retains context?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. AutoGen
5. Marvin
AI recommended 5 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
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 letta-ai/letta-code?
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?
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 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?

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MARKDOWN (README)

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HTML

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