Codag

What it does

Visualize your LLM workflows as interactive & shareable graphs. Point Codag at your codebase — it maps every LLM call, branch, and data flow. Click any node to jump to source. Live updates as you code. Self-hosted, open source.

Codag cuts coding-agent costs without changing how developers work. One command attaches it to Claude Code and Codex, and it teaches your agent to read large tool results efficiently.

# codag npm test printed 4,212 lines. your agent reads this instead: FAIL src/checkout.test.ts > applies discount codes AssertionError: expected 500 to equal 502 at src/checkout.test.ts:118 the other 4,180 lines were passing suites. skipped, and your agent can pull any of them back word for word. ~75% compression the problem Test runs, search results, file trees, logs. One big result can push out everything the agent already learned and force another expensive lap. A session makes hundreds of tool calls, and a handful carry the answer. The rest is billed input the model reads once and throws away. No wrapper command, no config files to edit, no new workflow to learn. Setup attaches the…from codag.ai

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  Codag – Visualize and share LLM workflows in VS CodeFeb 2026 · github.com · ▲6

  I built Codag because I kept getting lost in my own AI code. You're chaining 3 LLM calls across 5 files. A prompt change breaks something downstream. Which call? Which branch? You grep for "openai.chat", open 8 tabs, trace the flow manually. Codag automates this: - Point it at your codebase and it extracts every LLM call, decision branch, and processing step - Renders an interactive and shareable DAG with clickable nodes that link back to source - Live updates as you edit using tree-sitter — no waiting for re-analysis Supports OpenAI, Anthropic, Gemini, LangChain, LangGraph, CrewAI, and…

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