How do you reconstruct a past AutoBot approval or refusal today?

[kimjooyoon]

I'm asketeddy, maintainer of the experimental Gooo Go Ports project, and this question was drafted with AI assistance; I haven't deployed AutoBot.

While reading #17229, I wondered how you currently reconstruct the input, applicable policy version and approval or rejection outcome for a past run from session state and chat history.

If you have a recent non-sensitive example from maintaining or running AutoBot, which record was hardest to recover, and what workaround did you use?

[mrveiss]

hey, thanks for starting this thread and sorry for the slow reply. I honestly didn't notice it, I'd kind of written the discussions section off as a ghost town since every comment so far has come in through issues directly. so it's nice to see someone actually using it

on #17229, we're not there yet. the approval itself does survive, the gate row in postgres keeps who decided, when, the risk tier, a payload hash and which workflow step it was. but the run around it doesn't. the session row is a resume blob that expires, the per-turn context (model, authority, whether approval was required) gets rebuilt each turn and never saved, and chat history is a per-session json file you can read but can't really query across sessions

policy version is the hardest one because nothing records it at all. today the best you could do is take the approval row plus the chat file and work out from git history which policy was probably live at that timestamp. that's a guess, not a record

where we want to land is one durable record per run covering who and with what authority, policy context, data touched, model and tool calls, human decisions and the outcome, with the retention class set when the run closes. the bar is that a run can be rebuilt from that record alone without the chat files

I saw you're working on decision receipts on your side, so if you have thoughts on how to tie the policy version to a decision I'd be glad to hear them

[kimjooyoon]

Thanks for the invitation. I reviewed the installation sources at a9c47e2 and the current e187908 docs. My current test machine is macOS arm64, so I haven't run the Debian/systemd installer. I see the SLM-only Compose path has been added; bringing up that GUI alone would not exercise the main backend's approval flow.

One remaining documentation inconsistency at e187908: the Docker examples now correctly pass --env-file docker/.env.docker and the generated secrets file, but the Configuration paragraph still says to create .env.docker in the root. The backend's configuration requires docker/.env.docker. This is a source-review finding, not a reproduced installation failure.

I found the manual terminal approval scenarios. Is there a recommended minimal Linux setup for one synthetic approval run, including session/auth setup, that can work without a paid model provider? That would help me test the actual flow and report the installed commit and first failure precisely.

Separately, our small resolver example now compares 204 supplied inputs across your original Python rules, an independent Go reference, and Gooo execution/replay. It covers the pure decision rule and reason text; it does not test the installed gate, historical policy capture, or #18651.

Written with AI assistance; I have not deployed AutoBot.

[mrveiss]

thanks for the careful read. the .env.docker paragraph is fixed on main as of 678ef51 (#18555). it now says docker/.env.docker is the only Docker env file

for a run without a paid provider, the compose ollama profile (--profile ollama) gives you a local model. for session/auth, you log in with the admin credentials the install generates (on Docker they end up in docker/.env.secrets), and the setup wizard runs on first login. to be honest, I haven't run the approval flow end to end on that path myself yet, so I can't promise it gets there cleanly. if it trips, that belongs under #18581 and the first failure plus commit would be exactly what we need

nice to see the resolver comparison at 204 inputs. once the artifact shape in #18651 settles I'll post a synthetic A → B → missing-A fixture here

[mrveiss]

correction to my last reply: I checked, and on Docker the admin credentials I mentioned are only for the SLM GUI. a fresh Docker install doesn't create a main-app account at all, so you'd get stuck at login before reaching the approval flow. that's filed as #18729. with --profile ollama you'd also need to ollama pull qwen3.5:9b and nomic-embed-text by hand. I'll ping here when #18729 lands, since that's the point where an outside approval run becomes possible

[kimjooyoon]

Thanks for checking the Docker auth path and correcting the credentials. I’ve noted #18729 as the prerequisite for a main-app approval run, plus both explicit Ollama model pulls. I’ll wait for your update before trying that path on a suitable Linux environment; I haven’t deployed AutoBot yet.

The synthetic A → B → missing-A fixture after #18651 settles is the next concrete interface I can test against Gooo. I’ll keep that comparison separate from claims about the installed gate or historical reconstruction.

Written with AI assistance.

[mrveiss]

Update: the fix for the Docker login gap is approved and lands with the next merge to main (#18738, closes #18729). Once it's in, a fresh Docker install creates a main-app admin on first start. bash docker/generate-secrets.sh writes the password into docker/.env.secrets, and INSTALL.md's Docker section shows how to read it. Ollama models are only needed if you run everything locally with --profile ollama; cloud or hybrid setups need none, and picking providers is coming to the SLM setup wizard (#15494). Give it another run once that's merged.