🧠RAT
Doriandarko/RAT-retrieval-augmented-thinking
RAT is a powerful tool that improves AI responses by leveraging DeepSeek's reasoning capabilities to guide other models through a structured thinking process.
⭐ Emerging: 661 stars
View on GitHub ↗repo profile
momentum
durability
since we covered it
why it's a big deal
- Lets you pair DeepSeek-R1's chain of reasoning with a separate answering model, so you get R1-style thinking on top of whatever OpenRouter model or Claude you actually want to reply.
- Useful for developers who find a single model's raw output shallow and want a reasoning pass to guide the final answer without switching their whole stack to R1.
- Runs as an interactive CLI where you can swap the response model on the fly and toggle whether the reasoning trace is shown.
under the hood
- Splits the pipeline into a reasoning stage from DeepSeek-R1 and a response stage from an OpenRouter model that consumes that reasoning as context.
- Extracts R1's thinking by setting the final response token to 1, capturing the reasoning trace while suppressing R1's own answer.
- Written in Python requiring 3.11 or higher, configured through DEEPSEEK_API_KEY and OPENROUTER_API_KEY, with an optional Claude version that uses Anthropic message prefilling and ANTHROPIC_API_KEY.
Radar summary, generated from the project's public sources
star history
- PR#1 550 2025-02-05
- now 661 + 111 since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Better known than its recent output, coasting a little on attention.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
-
Enhance AI Responses Through Structured Reasoning
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