/cont logic

README.md

@@ -61,8 +61,9 @@ All implementations read `model.cfg` (or `.bantam.cfg`, which takes priority if
    ```
 
    Sessions are saved under `~/.bantam/sessions/` and can be managed with these commands:
-   - `/save` — save the entire conversation to a new session file (auto-id like `20260808-190038`) and generate its summary
-   - `/list` — list saved sessions (newest first) with their ids, timestamps, message counts and summaries
+   - `/save [name]` — save the entire conversation to a session file (named `name`, or defaulting to the MD5 hash of the current project directory) and generate its summary
+   - `/continue` (alias `/cont`) — continue/autoload the session corresponding to the current project directory
+   - `/list` — list saved sessions (newest first) with their ids, timestamps, message counts and summaries (marking the current project's session)
    - `/load <id>` — load a saved session (exact id or unique prefix) and continue from there
    - `/compact` — compact context down to the system message and a concise summary using the LLM; the compaction prompt is appended to the conversation to derive the summary, then the conversation is reset to `[system, summary-user-message]` (a fresh prefix, so downstream prompt-cache hits depend on the provider and are not guaranteed)
    - `/cfg <param> [val]` — inspect or update a configuration parameter live (writes to `.bantam.cfg`)
@@ -79,7 +80,7 @@ All implementations read `model.cfg` (or `.bantam.cfg`, which takes priority if
 
    Pressing **Ctrl+C** during an active inference run or long-running shell execution cleanly interrupts the turn without appending partial or malformed responses to the conversation context.
 
-   The current conversation is also **auto-saved** to `~/.bantam/sessions/autosave.json` after every turn, on `/clear`, `/load`, `/compact`, and on exit — so you can always `/load autosave` to resume where you left off.
+   The current conversation is also **auto-saved** to `~/.bantam/sessions/<project-md5>.json` after every turn, on `/clear`, `/load`, `/compact`, `/continue`, and on exit — so you can always run `/continue` (or `/cont`) to resume where you left off.
 
 3. File input mode:
    ```bash
@@ -113,7 +114,7 @@ Using these rules, everyone can build their own copy of Bantam from scratch in l
 2. Read model parameters from the config file (`.bantam.cfg` if present, else `model.cfg`) in `key=value` format and discover context window size.
 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. If prefixed with `!`, execute the shell command directly via `shell_exec` and exit. Otherwise, append to `messages` (`role: "user"`), run `AL(cfg, messages)`, display token usage, 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 by appending the compaction prompt to derive the summary, replace `messages` with `[system, summary-user-message]`, and return to step 5. If starting with `/cfg`, display the current value (`/cfg <param>`) or update the config live by writing to `.bantam.cfg` (`/cfg <param> <val>`) and return to step 5. If equal to `/models`, query the `/models` path on the current inference endpoint and print a plain list of supported model IDs (the currently configured model marked with a leading `* `), then return to step 5. If starting with `!`, execute the command directly via `shell_exec` without adding the result to `messages` 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 starting with `/save`, write the whole `messages` array to `~/.bantam/sessions/<name>.json` (or `<project-md5>.json` if no name is given) and return to step 5. If equal to `/continue` or `/cont`, load the session corresponding to the current project's MD5 hash and return to step 5. If equal to `/list`, print saved sessions and their summaries (marking current project session) 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 by appending the compaction prompt to derive the summary, replace `messages` with `[system, summary-user-message]`, and return to step 5. If starting with `/cfg`, display the current value (`/cfg <param>`) or update the config live by writing to `.bantam.cfg` (`/cfg <param> <val>`) and return to step 5. If equal to `/models`, query the `/models` path on the current inference endpoint and print a plain list of supported model IDs (the currently configured model marked with a leading `* `), then return to step 5. If starting with `!`, execute the command directly via `shell_exec` without adding the result to `messages` 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/<project-md5>.json`.
 6. Append user prompt to `messages` (`role: "user"`), run `AL(cfg, messages)`, display token usage, check 60% context threshold for auto-compaction, and go to step 5.
 
 ### Agentic loop (`AL(cfg, messages)`) function