Bantam is a minimalist, dependency-free AI agent specification with two reference implementations: **Python** (`bantam.py`, ~300 SLOC) and **Go** (`main.go` + `term_*.go`, module `code.luxferre.top/luxferre/bantam`). It provides an agentic loop capable of autonomous tool execution, shell interaction, real-time response streaming, Fibonacci backoff network resilience, and subagent delegation using any OpenAI-compatible completions API.
The entire philosophy of Bantam is built upon two principles:
1. The structure must be as simple as possible for anyone to be able to reimplement the agent from a plain algorithm description.
2. The agent only needs to provide two tools: a tool to call shell commands and a tool to call itself. In theory, this should be sufficient to give LLMs the ability to handle tasks of any complexity.
Because of the second principle, Bantam itself was named after Victorinox Bantam Alox, a small and lightweight Swiss army knife with only two tools.
go install code.luxferre.top/luxferre/bantam@latest
```
This installs the `bantam` binary into `$(go env GOPATH)/bin` (make sure it is on your `PATH`). To build from a local checkout instead:
```bash
go build ./... # produces ./bantam
# or run without building:
go run . prompt.txt
```
The Go port is a single `main.go` plus two small 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 version.
In interactive mode, prompts can span multiple lines: press **Ctrl+J** to insert a real line break (the cursor moves to the next line), then **Enter** to submit the whole multi-line prompt. The Go port ships its own raw-mode line editor (arrow keys move the cursor, Up/Down browse history, Backspace edits, Ctrl+C/Ctrl+D exit), so this works everywhere without dependencies; the Python port uses `readline` when available and falls back to single-line prompts otherwise.
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
- `/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
- `/help` — show all supported commands
- `/clear` — reset the conversation to just the system prompt
- `/quit` — exit
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.
Using these rules, everyone can build their own copy of Bantam from scratch in little time in any language that supports file access, shell and HTTP(S) calls.
### High-Level Overview
1. **Initialization**: Read `system.txt` and `model.cfg`. Prepare an array of messages starting with the system prompt `{"role": "system", "content": system_prompt}`.
2. **Input Processing**: Take user prompt (via command-line file parameter or interactive stdin), append `{"role": "user", "content": prompt}`, and invoke `AL(cfg, messages)`.
3. **Agentic Loop (`AL`)**:
- Send `messages` and tool definitions to the OpenAI-compatible `/chat/completions` API endpoint with custom `User-Agent` headers.
- On network or HTTP failure, retry using Fibonacci backoff delays (`1s, 1s, 2s, 3s, 5s`).
- If `stream=true`, parse SSE data chunks (`data: {...}`) in real-time to stream reasoning content (`reasoning_content`) and response text directly to stdout, bracketing the reasoning block with `--- reasoning start ---` / `--- reasoning end ---` markers.
- Reconstruct the assistant message. If `tool_calls` exist, trace the call (`[tool call: name(args)]`), validate JSON arguments, execute the requested tool (`shell_exec` or `run_subagent`), trace the result (`[tool result: name]`), append the tool response `{"role": "tool", "tool_call_id": id, "content": result}`, and repeat the loop.
- If no tool calls remain or `max_al_iterations` is reached, return the updated messages list.
---
### Main program
1. Read system prompt from `system.txt` (default if missing).
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`.
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).
- Set custom `User-Agent` header (`Mozilla/5.0 (compatible; Bantam/1.0)`) to avoid gateway 403 blocks.
- If `stream=true`, parse SSE stream (`data: {...}`) for real-time reasoning and text output, bracketing reasoning with `--- reasoning start ---` / `--- reasoning end ---` markers.
2. Append the assistant's response message object to `messages`. If non-streaming and response has reasoning tokens (`reasoning_content` or `reasoning`), output them wrapped in `--- reasoning start ---` / `--- reasoning end ---` markers.
3. If there are pending `tool_calls` in the assistant response:
- For each tool call, output a trace log (`[tool call: name(args)]`).
- Validate JSON arguments. If invalid, format a tool-error response so the LLM can self-correct.
- Execute tool action (`shell_exec` or `run_subagent`).
- Output a trace log of the result (`[tool result: name]`).
- Append tool result message (`role: "tool"`, `tool_call_id`, `content`: result string) to `messages`.
- Loop back to step 1.
4. If no pending tool calls (or if `max_al_iterations` is reached), stop and return `messages`.
- `timeout` (HTTP timeout in seconds for LLM API calls, default 300; in the Go port it bounds connection setup and time-to-first-byte, so long streaming responses are not cut off mid-stream, matching the Python port's per-operation socket timeout)
Both implementations keep the interactive prompt safe against the classic "long line overwrites the prompt" readline bug: the Python port wraps the ANSI escapes in `\001`/`\002` (`RL_PROMPT_START_IGNORE`/`RL_PROMPT_END_IGNORE`) markers, and the Go port's built-in editor tracks the cursor with its own column math (terminal auto-wrap aware) and redraws from the first line of the buffer, so wrapped input stays clean at any terminal width.
When enabled, the interactive console uses a subtle ANSI palette: the pending-request status `...requesting...` is darkened bold, reasoning markers are cyan, reasoning text is dim, `[tool call: ...]` traces are yellow, `[tool result: ...]` headers are green, tool result bodies are dim (red for tool errors/unknown tools), and errors/network retries are red. Tool result payloads fed back to the LLM are never colored.
### Tool call definitions
#### `run_subagent` tool
- Parameters: `prompt` (string)
- Return value: string
- Action: run `AL(cfg, [{"role": "system", "content": system_prompt + "\n\nImportant: this is a child agent"}, {"role": "user", "content": prompt}])` and return the text content of the last `assistant`-role message.
#### `shell_exec` tool
- Parameters: `command` (string)
- Return value: string
- Action: run shell command specified in `command` subject to `shell_timeout` (default 120s) and return `output + '\n\nexit: ' + exit_code` string.
