4 ms·

RAG seems odd when you can just have a coding agent manage memory by managing folders. Multi agent also feels weird when you have subagents.

by baby 9mo ago

antirez 9mo ago
Totally useless indeed.
rando77 9mo ago
I've been leaning towards multi agent because sub agent relies on the main agent having all the power and using it responsibly.
- andai 9mo ago
  What does that mean?
  - rando77 8mo ago
    Have you come across the lethal trifecta [1]? I'm interested in decomposing tasks to avoid any agent doing all three and having limited data pipes between them. [1] https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/ https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/
PlatoIsADisease 9mo ago
Interesting. I guess RAG is faster? But I'm realizing I'm outdated now.
- lxgr 9mo ago
  No, RAG is definitely preferable once your memory size grows above a few hundred lines of text (which you can just dump into the context for most current models), since you're no longer fighting context limits and needle-in-a-haystack LLM retrieval performance problems.
  - Aurornis 9mo ago
    > once your memory size grows above a few hundred lines of text (which you can just dump into the context for most current models) A few hundred lines of text is nothing for current LLMs. You can dump the entire contents of The Great Gatsby into any of the frontier LLMs and it’s only around 70K tokens. This is less than 1/3 of common context window sizes. That’s even true for models I run locally on modest hardware now. The days of chunking everything into paragraphs or pages and building complex workflows to store embeddings, search, and rerank in a big complex pipeline are going away for many common use cases. Having LLMs use simpler tools like grep based on an array of similar search terms and then evaluating what comes up is faster in many cases and doesn’t require elaborate pipelines built around specific context lengths.
    - deleted 9mo ago
      [deleted]
    - lxgr 9mo ago
      Yes, but how good will the recall performance be? Just because your prompt fits into context doesn't mean that the model won't be overwhelmed by it. When I last tried this with some Gemini models, they couldn't reliably identify specific scenes in a 50K word novel unless I trimmed down the context to a few thousands of words. > Having LLMs use simpler tools like grep based on an array of similar search terms and then evaluating what comes up is faster in many cases Sure, but then you're dependent on (you or the model) picking the right phrases to search for. With embeddings, you get much better search performance.
      - Aurornis 9mo ago
        > Yes, but how good will the recall performance be? Just because your prompt fits into context doesn't mean that the model won't be overwhelmed by it. With current models it's very good. Anthropic used a needle-in-haystack example with The Great Gatsby to demonstrate the performance of their large context windows all the way back in 2023: https://www.anthropic.com/news/100k-context-windows https://www.anthropic.com/news/100k-context-windows Everything has become even better in the nearly 3 years since then. > Sure, but then you're dependent on (you or the model) picking the right phrases to search for. With embeddings, you get much better search performance. How do are those embeddings generated? You're dependent on the embedding model to generate embeddings the way you expect.
- rdedev 9mo ago
  I think it still has a place of your agent is part of a bigger application that you are running and you want to quickly get something in your models context for a quick turnaround
simonw 9mo ago
Yeah, vector embeddings based RAG has fallen out of fashion somewhat. It was great when LLMs had 4,000 or 8,000 token context windows and the biggest challenge was efficiently figuring out the most likely chunks of text to feed into that window to answer a question. These days LLMS all have 100,000+ context windows, which means you don't have to be nearly as selective. They're also exceptionally good at running search tools - give them grep or rg or even `select * from t where body like ...` and they'll almost certainly be able to find the information they need after a few loops. Vector embeddings give you fuzzy search, so "dog" also matches "puppy" - but a good LLM with a search tool will search for "dog" and then try a second search for "puppy" if the first one doesn't return the results it needs.
- y1n0 9mo ago
  Context rot is still a problem though, so maybe vector search will stick around in some form. Perhaps we will end up with a tool called `vector grep` or `vg` that handles the vectorized search independent of the agent.
- visarga 9mo ago
  The fundamental problem wit RAG is that it extracts only surface level features, "31+24" won't embed close to "55", while "not happy" will be close to "happy". Another issue is that embedding similarity does not indicate logical dependency, you won't retrieve the callers of a function with RAG, you need a LLM or code for that. Third issue is chunking, to embed you need to chunk, but if you chunk you exclude information that might be essential. The best way to search I think is a coding agent with grep and file system access, and that is because the agent can adapt and explore instead of one shotting it. I am making my own search tool based on the principle of LoD (level of detail) - any large text input can be trimmed down to about 10KB size by doing clever trimming, for example you could trim the middle of a paragraph keeping the start and end, or you could trim the middle of a large file. Then an agent can zoom in and out of a large file. It skims structure first, then drills into the relevant sections. Using it for analyzing logs, repos, zip files, long PDFs, and coding agent sessions which can run into MB size. Depending on content type we can do different types of compression for code and tree structured data. There is also a "tall narrow cut" (like cut -c -50 on a file). The promise is - any size input fit into 10KB "glances" and the model can find things more efficiently this way without loading the whole thing.

RAG seems odd when you can just have a coding agent manage memory by managing folders. Multi agent also feels weird when you have subagents.

by baby 9mo ago

RAG seems odd when you can just have a coding agent manage memory by managing folders. Multi agent also feels weird when you have subagents.

antirez 9mo ago

Totally useless indeed.

rando77 9mo ago

I've been leaning towards multi agent because sub agent relies on the main agent having all the power and using it responsibly.

andai 9mo ago
What does that mean?
- rando77 8mo ago
  Have you come across the lethal trifecta [1]? I'm interested in decomposing tasks to avoid any agent doing all three and having limited data pipes between them. [1] https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/ https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/

PlatoIsADisease 9mo ago

Interesting. I guess RAG is faster? But I'm realizing I'm outdated now.

lxgr 9mo ago
No, RAG is definitely preferable once your memory size grows above a few hundred lines of text (which you can just dump into the context for most current models), since you're no longer fighting context limits and needle-in-a-haystack LLM retrieval performance problems.
- Aurornis 9mo ago
  > once your memory size grows above a few hundred lines of text (which you can just dump into the context for most current models) A few hundred lines of text is nothing for current LLMs. You can dump the entire contents of The Great Gatsby into any of the frontier LLMs and it’s only around 70K tokens. This is less than 1/3 of common context window sizes. That’s even true for models I run locally on modest hardware now. The days of chunking everything into paragraphs or pages and building complex workflows to store embeddings, search, and rerank in a big complex pipeline are going away for many common use cases. Having LLMs use simpler tools like grep based on an array of similar search terms and then evaluating what comes up is faster in many cases and doesn’t require elaborate pipelines built around specific context lengths.
  - deleted 9mo ago
    [deleted]
  - lxgr 9mo ago
    Yes, but how good will the recall performance be? Just because your prompt fits into context doesn't mean that the model won't be overwhelmed by it. When I last tried this with some Gemini models, they couldn't reliably identify specific scenes in a 50K word novel unless I trimmed down the context to a few thousands of words. > Having LLMs use simpler tools like grep based on an array of similar search terms and then evaluating what comes up is faster in many cases Sure, but then you're dependent on (you or the model) picking the right phrases to search for. With embeddings, you get much better search performance.
    - Aurornis 9mo ago
      > Yes, but how good will the recall performance be? Just because your prompt fits into context doesn't mean that the model won't be overwhelmed by it. With current models it's very good. Anthropic used a needle-in-haystack example with The Great Gatsby to demonstrate the performance of their large context windows all the way back in 2023: https://www.anthropic.com/news/100k-context-windows https://www.anthropic.com/news/100k-context-windows Everything has become even better in the nearly 3 years since then. > Sure, but then you're dependent on (you or the model) picking the right phrases to search for. With embeddings, you get much better search performance. How do are those embeddings generated? You're dependent on the embedding model to generate embeddings the way you expect.
rdedev 9mo ago
I think it still has a place of your agent is part of a bigger application that you are running and you want to quickly get something in your models context for a quick turnaround

simonw 9mo ago

Yeah, vector embeddings based RAG has fallen out of fashion somewhat. It was great when LLMs had 4,000 or 8,000 token context windows and the biggest challenge was efficiently figuring out the most likely chunks of text to feed into that window to answer a question. These days LLMS all have 100,000+ context windows, which means you don't have to be nearly as selective. They're also exceptionally good at running search tools - give them grep or rg or even `select * from t where body like ...` and they'll almost certainly be able to find the information they need after a few loops. Vector embeddings give you fuzzy search, so "dog" also matches "puppy" - but a good LLM with a search tool will search for "dog" and then try a second search for "puppy" if the first one doesn't return the results it needs.

y1n0 9mo ago
Context rot is still a problem though, so maybe vector search will stick around in some form. Perhaps we will end up with a tool called `vector grep` or `vg` that handles the vectorized search independent of the agent.
visarga 9mo ago
The fundamental problem wit RAG is that it extracts only surface level features, "31+24" won't embed close to "55", while "not happy" will be close to "happy". Another issue is that embedding similarity does not indicate logical dependency, you won't retrieve the callers of a function with RAG, you need a LLM or code for that. Third issue is chunking, to embed you need to chunk, but if you chunk you exclude information that might be essential. The best way to search I think is a coding agent with grep and file system access, and that is because the agent can adapt and explore instead of one shotting it. I am making my own search tool based on the principle of LoD (level of detail) - any large text input can be trimmed down to about 10KB size by doing clever trimming, for example you could trim the middle of a paragraph keeping the start and end, or you could trim the middle of a large file. Then an agent can zoom in and out of a large file. It skims structure first, then drills into the relevant sections. Using it for analyzing logs, repos, zip files, long PDFs, and coding agent sessions which can run into MB size. Depending on content type we can do different types of compression for code and tree structured data. There is also a "tall narrow cut" (like cut -c -50 on a file). The promise is - any size input fit into 10KB "glances" and the model can find things more efficiently this way without loading the whole thing.