many improvements

README.md

@@ -31,7 +31,7 @@ go build ./...        # produces ./bantam
 go run . prompt.txt
 ```
 
-The Go port is a single `main.go` plus four platform files (`term_linux.go`, `term_darwin.go`, `term_windows.go`, `term_other.go`) for the built-in raw-terminal line editor — zero external dependencies, same as the Python and Perl versions.
+The Go port is a single `main.go` plus four platform files (`term_linux.go`, `term_bsd.go`, `term_windows.go`, `term_other.go`) for the built-in raw-terminal line editor — zero external dependencies, same as the Python and Perl versions.
 
 ### Running Bantam
 
@@ -60,6 +60,7 @@ All implementations read the same `model.cfg` and `system.txt` from the current
    - `/list` — list saved sessions (newest first) with their ids, timestamps, message counts and summaries
    - `/load <id>` — load a saved session (exact id or unique prefix) and continue from there
    - `/compact` — summarize the conversation with the LLM and compact the context down to just the system message plus the summary
+   - `/cfg <param> [val]` — inspect or update a configuration parameter in `model.cfg` live
    - `/help` — show all supported commands
    - `/clear` — reset the conversation to just the system prompt
    - `/quit` — exit
@@ -94,12 +95,12 @@ Using these rules, everyone can build their own copy of Bantam from scratch in l
 2. Read model parameters from `model.cfg` (`key=value` format).
 3. Prepare a new message list with the system prompt (`role: "system"`).
 4. Read the first command-line parameter. If non-empty, read user prompt from the specified file, append to `messages` (`role: "user"`), run `AL(cfg, messages)`, and exit.
-5. Read user prompt from standard input (with `readline` line editing and history in `~/.bantam_history`; **Ctrl+J** inserts a real newline into the line being edited). If equal to `/quit` or EOF, exit. If equal to `/clear`, reset `messages` to step 3 and return to step 5. If equal to `/save`, write the whole `messages` array to `~/.bantam/sessions/<id>.json` (with an auto-generated summary) and return to step 5. If equal to `/list`, print saved sessions and their summaries and return to step 5. If starting with `/load`, replace `messages` with the saved session's messages (by exact id or unique prefix) and return to step 5. If equal to `/compact`, ask the LLM to summarize the conversation, replace `messages` with `[system, summary-user-message]`, and return to step 5. If equal to `/help`, print the command list and return to step 5. After every user turn and on exit, auto-save `messages` to `~/.bantam/sessions/autosave.json`.
+5. Read user prompt from standard input (with `readline` line editing and history in `~/.bantam_history`; **Ctrl+J** inserts a real newline into the line being edited). If equal to `/quit` or EOF, exit. If equal to `/clear`, reset `messages` to step 3 and return to step 5. If equal to `/save`, write the whole `messages` array to `~/.bantam/sessions/<id>.json` (with an auto-generated summary) and return to step 5. If equal to `/list`, print saved sessions and their summaries and return to step 5. If starting with `/load`, replace `messages` with the saved session's messages (by exact id or unique prefix) and return to step 5. If equal to `/compact`, ask the LLM to summarize the conversation, replace `messages` with `[system, summary-user-message]`, and return to step 5. If starting with `/cfg`, display the current value (`/cfg <param>`) or update `model.cfg` live (`/cfg <param> <val>`) and return to step 5. If equal to `/help`, print the command list and return to step 5. After every user turn and on exit, auto-save `messages` to `~/.bantam/sessions/autosave.json`.
 6. Append user prompt to `messages` (`role: "user"`), run `AL(cfg, messages)`, and go to step 5.
 
 ### Agentic loop (`AL(cfg, messages)`) function
 
-1. Call OpenAI-compatible Completions API (`POST {endpoint}/chat/completions`) using parameters from `cfg` (`model`, `temperature`, optional `api_key` bearer header).
+1. Call OpenAI-compatible Completions API (`POST {endpoint}/chat/completions`) forwarding relevant parameters from `cfg` (`model`, `temperature`, `stream`, `reasoning_effort`, etc., excluding internal agent configs like `endpoint`, `api_key`, `timeout`, `shell_timeout`, `max_al_iterations`, `color`) and optional `api_key` bearer header.
    - Set custom `User-Agent` header (`Mozilla/5.0 (compatible; Bantam/1.0)`) to avoid gateway 403 blocks.
    - Retry network/HTTP errors with Fibonacci backoff delays (`1s, 1s, 2s, 3s, 5s, 8s, 13s, 21s, 34s`).
    - If `stream=true`, parse SSE stream (`data: {...}`) for real-time reasoning and text output, bracketing reasoning with `--- reasoning start ---` / `--- reasoning end ---` markers.
@@ -187,7 +188,7 @@ MicroBantam (`mb`) is a compressed Perl 5 reference implementation of the same a
 ## Repository layout
 
 - `bantam.py` — Python reference implementation (stdlib only)
-- `main.go`, `term_linux.go`, `term_darwin.go`, `term_windows.go`, `term_other.go` — Go implementation (stdlib only, module `code.luxferre.top/luxferre/bantam`)
+- `main.go`, `term_linux.go`, `term_bsd.go`, `term_windows.go`, `term_other.go` — Go implementation (stdlib only, module `code.luxferre.top/luxferre/bantam`)
 - `bantam.pl` — Perl 5 implementation (core modules only)
 - `mb` — MicroBantam, compressed Perl 5 implementation (core modules only, under 100 SLOC)
 - `mb.tcl` — MicroBantam, Jim Tcl port (requires `jimsh` with the `json` and `ssl` extensions; under 100 SLOC)