Ross ROSS = Recommend OSS · open-source software intelligence for agents

parcadei/llm-tldr

95% token savings. 155x faster queries. 16 languages. LLMs can't read your entire codebase. TLDR extracts structure, traces dependencies, and gives them exactly what they need. observed · 2026-08-28

github.com/parcadei/llm-tldr · Python · AGPL-3.0 (copyleft) · archived observed · 2026-08-28

Health v2 · maintenance only

10/100

  • Activity 62
  • Release rhythm 35
  • Longevity 16

Flags: no_releases archived

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 236
  • days_rel: n/a
  • days_push: 228
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1170 stars · 114 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python CLI tool and daemon that indexes codebases into a 5-layer analysis stack (AST, call graph, control flow, data flow, program dependence) using tree-sitter, plus a FAISS semantic index for natural-language code search. It serves token-efficient, LLM-ready code context to AI agents, claiming 95% token savings and millisecond queries across 16-17 languages.

Use cases

  • give my coding agent only the relevant parts of a large codebase
  • reduce token usage when feeding code to Claude or GPT
  • find where JWT validation is implemented via semantic code search
  • trace what affects a specific line before refactoring
  • generate LLM-ready summaries of project structure and dependencies
  • index a multi-language monorepo for AI-assisted development

When to choose

  • you use AI coding agents on codebases too large for the context window
  • you want fast semantic and structural code queries with a persistent daemon
  • you need call graph, data flow, or program slicing analysis across many languages

When to avoid

  • you need a full IDE-grade static analysis or type checker
  • your project is small enough to fit directly in the LLM context
  • you require a permissive license (AGPL-3.0)
  • you need deep analysis for languages outside its supported set

Facets

cli-tool · maturity active

parser search-engine rag mcp developer-tools developer-tools large-language-models parsers apis cli python cross-platform static-analysis tree-sitter call-graph token-optimization code-indexing semantic-search context-window ai-agents

2 sources

Member repositories

RepositoryRoleHealth v2
parcadei/llm-tldrmain10

For agents

markdown · JSON · MCP: product_card(name="parcadei/llm-tldr")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem