Autolith: A programming agent with a live runtime
lambda-symbolics.com
lambda-symbolics.com
I see there is a section on RLMs; have you ran Autolith via agentic benchmarks? I would love to see comparisons with Prime Agent.
In Autolith, the top level agent is traditional, but has RLM tools which it can use for the things RLM is good at, namely exploratory work, processing a lot of files at once, backward context research and so on.
I rarely find myself thinking "ah I need the agent interface to change". And the vague generality of how agents work play into making it fairly easy to "just" have it rely on some external tooling to do anything special.
Maybe this is just a counterargument to the lisp philosophy as a whole but... well... I use Emacs for example and am fine with a model of "an agent can look at my Emacs config" rather than "my agent _is running my Emacs session_".
Well, having the agent write out tools and call those tools is something I consider to be under self-modification also.
But there are other things, when it's useful:
- You want the agent to update without restarting a session and e.g. killing child LISP REPLs and long-running sub-agents
- There is a bug in the harness that bothers you (Autolith records papercuts and can generally solve them via self-modification)
- You want to temporarily or permanently hook into literally any part of the agent lifecycle
- You are of a ricing persuassion and want to change how the agent looks (very surface level, but I have seen people do it)
- Related to the hooking point, you want to integrate Autolith with something else or make it emit something. We cannot predict all the knobs where you might do this, or how selectively you might do it, but self-modification lets you do it
- You want to add support for yourself for non-standard or proprietary/secret providers
That's off the top of my head. The secondary benefit is that this is great at developing the agent itself as a project. It can try/triage the changes it's working on, try its own tests, probe at things, and so on. This is why by far, the changes submitted to Autolith developed by Autolith are by far the highest quality out of all clanker-submitted changes.
wtf does this mean lol
I didn’t actually do anything with that idea yet but may look at the idea in Elixir this weekend.
I was thinking about Smalltalk as well before I made Autolith. I ended up going with Common Lisp because I know Lisp much better (last time I used smalltalk was like, whew, 2014 or so) and because it has better platform support and ecosystem (at least in my experience).
I think Elixir could be great, I knew a guy who was trying to do an agent in Elixir, but sadly didn't get far.
Keep me posted if you get anywhere! And if you'd like to try Autolith, I am happy to help with issues/questions on our Zulip, haha
Never heard about Jolt, and I love Chez Scheme, it was my first Lisp!
Would you like to come to our Zulip at https://zulip.lambda-symbolics.com? We can exchange ideas for our harnesses
The key trick is that workflows are represented as state machines which are just data structures. So, the LLM can easily inspect and change them to add or remove behaviors as it sees fit.
I have a specific supervisor role whose sole job is to watch how implementer agents are doing and whether they're making progress. When it sees them get stuck, its job is to unblock them.
This idea almost gets us there.
Could the next step be to make it the program itself?
This is explicitly called out as only weakly supported in that blog post:
- You should use a popular language
- There's weak support for this statementIt makes sense, needing to train the model on things that aren't already in its weighs takes up valuable context. Until we have models that update their weights based on what they've seen in their recent sessions and learn like people, this will be a problem.
For now, though, between the results I'm seeing here, and the lack of need to look at code, I think this kills off any reason for me to use less popular languages.
I don't think there's really ever a downside to leaning in and making use of a language or system that works for you. Trying to tell people they should just use the popular thing is, imo, bad advice to turn hackers and experimenters into boring people.
AI changes the constraints here for now, since it can't permanently learn things. I'm waiting until that changes, but right now it's better to use what it knows out of the box if you want good results.
A better language doesn't buy me anything other than performance; the reason to stick an AI in here is to remove interactions with the code. I don't care what the AI chooses to use, as long as it gets results.
coincidentally, "good code" in popular lang is rarely directly attributed to only that part; and it's also about the underlying principles it tries to follow in the code... another example; is it typescript that's good, or are "types" inherently making things/feedback loops easier to reason about in llms? (only using ts here for all example because it's probably one of the most "trained on" pl)
I think there's an optimal ratio somewhere
These days I moved up the ladder of abstraction, so I don't really look; the main criteria I have is how the LLM gets things done.
If you give that to an LLM, it is then also able to iterate and develop faster.
The best that people have said about lisp is that evidence LLMs perform worse with it is weak.
You can’t tell that with a few uncontrolled runs
In fact, I've had much easier time maintaining LLM assisted programs in Scheme and Clojure than other languages I've tried using because functional style naturally leads to low coupling. And that makes controlling context far easier than the rats nest of shared state that you have in imperative languages.
The niche language thing is really not a problem at all any more. If you're working in some esolang it doesn't take more than a 1-2k token primer in the context to get great results, and lisp is popular enough to not even need that.
The benefit of having the agent directly in the image like with Autolith here is that it can directly inspect all defined symbols and explore and orient itself automatically. Really doesn't need much guidance to get great results.
This all correct, I'd also add that in my experience, the GPTs are even better at Lisp, namely in the counting parentheses department.
Which is not an issue that much per-se because in Autolith, the harness detects Lisp file edits (CL, Scheme, Clojure) and gives hints when the edits lead to unbalanced files
(The heuristic is pretty simple, we detect if there's a mismatch, and if yes, it provide hints where the extra/missing might be based on indentation)
Do you have benchmarks on non-trivial tasks (say, generating zstd) that show it does any better than rust?
Autolith can spawn managed Lisp REPLs either from saved images (so it can do checkpoints) and triage changes before committing them to files, and then run test suites in the same REPL, it's been very useful for this.
I have been trying Scheme and CL with LLMs for the last three years or so, and in recent months, I have finally decided that they are good enough.
My idea is that well, it's good enough that I can now produce more training data just by using Autolith with the most basic claude/gpt subs, haha