diff options
Diffstat (limited to 'packages/multillm-cli')
| -rw-r--r-- | packages/multillm-cli/README.md | 31 | ||||
| -rw-r--r-- | packages/multillm-cli/src/multillm_cli/main.py | 252 |
2 files changed, 240 insertions, 43 deletions
diff --git a/packages/multillm-cli/README.md b/packages/multillm-cli/README.md index 5f9ec94..3de6cc9 100644 --- a/packages/multillm-cli/README.md +++ b/packages/multillm-cli/README.md @@ -115,6 +115,37 @@ When using chat providers (OpenAI, Anthropic, Gemini, OpenRouter), you can enabl | `calculate` | Perform mathematical calculations | `--use-tools calculate` | | `get_current_time` | Get current date and time | `--use-tools get_current_time` | | `get_weather` | Get weather information (mock data) | `--use-tools get_weather` | +| `ask_user` | Ask the user a question interactively | `--use-tools ask_user` | + +### Interactive Tools + +The `ask_user` tool allows the model to ask you questions during execution and collect your responses. This enables truly interactive conversations where the model can clarify requirements, gather preferences, or get additional information. + +**Example:** +```bash +multillm -m openai/gpt-4o \ + -p "Help me choose a programming language for my project by asking about my requirements" \ + --use-tools ask_user +``` + +When the model calls `ask_user`, you'll see: +``` +====================================================================== +❓ QUESTION FROM ASSISTANT +====================================================================== + +What type of project are you building? + +Suggested options: + 1. Web application + 2. Desktop application + 3. Data science + 4. Command-line tool + +You can select a number or provide your own answer. + +Your answer: _ +``` ### Tool Output diff --git a/packages/multillm-cli/src/multillm_cli/main.py b/packages/multillm-cli/src/multillm_cli/main.py index b450b71..a38ebe6 100644 --- a/packages/multillm-cli/src/multillm_cli/main.py +++ b/packages/multillm-cli/src/multillm_cli/main.py @@ -65,6 +65,28 @@ BUILTIN_TOOLS = { "required": ["location"] } } + }, + "ask_user": { + "type": "function", + "function": { + "name": "ask_user", + "description": "Ask the user a question and get their response. Use this when you need user input or clarification.", + "parameters": { + "type": "object", + "properties": { + "question": { + "type": "string", + "description": "The question to ask the user" + }, + "options": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional list of suggested answers (user can still provide their own)" + } + }, + "required": ["question"] + } + } } } @@ -118,10 +140,59 @@ def get_weather(location: str, unit: str = "celsius") -> dict: } +def ask_user(question: str, options: list[str] = None) -> dict: + """ + Ask the user a question and collect their response. + + This is an interactive tool that displays a question to the user + and waits for their input. + """ + print("\n" + "=" * 70, file=sys.stderr) + print("❓ QUESTION FROM ASSISTANT", file=sys.stderr) + print("=" * 70, file=sys.stderr) + print(f"\n{question}\n", file=sys.stderr) + + if options: + print("Suggested options:", file=sys.stderr) + for i, opt in enumerate(options, 1): + print(f" {i}. {opt}", file=sys.stderr) + print("\nYou can select a number or provide your own answer.", file=sys.stderr) + + print("\nYour answer: ", file=sys.stderr, end="", flush=True) + + try: + # Read from stdin + answer = input() + + # If user entered a number and we have options, use that option + if options and answer.strip().isdigit(): + idx = int(answer.strip()) - 1 + if 0 <= idx < len(options): + answer = options[idx] + + print("=" * 70 + "\n", file=sys.stderr) + + return { + "question": question, + "answer": answer, + "selected_from_options": answer in options if options else False + } + + except (EOFError, KeyboardInterrupt): + print("\n", file=sys.stderr) + print("=" * 70 + "\n", file=sys.stderr) + return { + "question": question, + "answer": None, + "error": "User cancelled input" + } + + TOOL_FUNCTIONS = { "get_current_time": get_current_time, "calculate": calculate, "get_weather": get_weather, + "ask_user": ask_user, } @@ -220,10 +291,78 @@ async def run_with_tools( return "Maximum tool calling iterations reached" -async def run_single(model: str, prompt: str, **kwargs) -> multillm.SingleResponse: - """Run a single query against the specified model.""" +async def run_agentic( + model: str, + prompt: str, + tools: list[multillm.Tool] | None = None, + options: multillm.AgentOptions | None = None, + verbose: bool = False +) -> str: + """ + Run a query using the agentic API. + + Uses agentwrap for chat providers, native agent API for agent providers. + """ client = multillm.Client() - return await client.single(model, prompt, **kwargs) + + # For Claude, if AskUserQuestion is requested, provide custom ask_user tool instead + provider_name = model.split("/")[0] + if provider_name == "claude" and options and options.allowed_tools: + if "AskUserQuestion" in options.allowed_tools: + # Remove AskUserQuestion (SDK built-in doesn't work interactively) + options.allowed_tools = [t for t in options.allowed_tools if t != "AskUserQuestion"] + + # Add our custom ask_user tool + if not tools: + tools = [] + + # Create ask_user tool for Claude + ask_user_claude = multillm.Tool( + name="ask_user", + description="Ask the user a question and get their response. Use this when you need user input or clarification.", + parameters={ + "type": "object", + "properties": { + "question": { + "type": "string", + "description": "The question to ask the user" + }, + "options": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional suggested answers" + } + }, + "required": ["question"] + }, + handler=ask_user # Use the same handler as chat providers + ) + tools.append(ask_user_claude) + + print("ℹ️ Using custom 'ask_user' tool instead of AskUserQuestion for interactive prompting", file=sys.stderr) + + # Collect text responses + text_parts = [] + tool_uses = [] + + async for msg in client.run(model, prompt, options=options, tools=tools): + if msg.type == "text": + text_parts.append(msg.content) + elif msg.type == "tool_use": + tool_uses.append(msg) + if verbose: + print(f" → {msg.tool_name}({json.dumps(msg.tool_input)})", file=sys.stderr) + else: + print(f" → {msg.tool_name}", file=sys.stderr) + elif msg.type == "tool_result": + if verbose: + print(f" ← {msg.tool_result}", file=sys.stderr) + + # Show tool usage summary if any tools were used + if tool_uses and not verbose: + print(f"\n[Used {len(tool_uses)} tool(s)]\n", file=sys.stderr) + + return " ".join(text_parts) async def run_with_chat_tools( @@ -232,19 +371,34 @@ async def run_with_chat_tools( enabled_tools: list[str], verbose: bool = False ) -> str: - """Run with chat provider tools.""" - client = multillm.Client() - - # Build tool list from enabled tools - tools = [BUILTIN_TOOLS[name] for name in enabled_tools if name in BUILTIN_TOOLS] - - if not tools: - # No valid tools, just run normally - result = await run_single(model, prompt) - return result.text + """ + Run with chat provider tools using agentwrap. - # Run with tool loop - return await run_with_tools(client, model, prompt, tools, verbose) + Converts built-in tools to Tool objects and uses agentwrap for execution. + """ + # Build Tool objects from enabled tools + tool_objects = [] + for name in enabled_tools: + if name in BUILTIN_TOOLS: + tool_def = BUILTIN_TOOLS[name] + tool_objects.append(multillm.Tool( + name=tool_def["function"]["name"], + description=tool_def["function"]["description"], + parameters=tool_def["function"]["parameters"], + handler=TOOL_FUNCTIONS[name] + )) + + if not tool_objects: + # No valid tools, run without tools + return await run_agentic(f"agentwrap/{model}", prompt, verbose=verbose) + + # Run with agentwrap and tools + return await run_agentic( + f"agentwrap/{model}", + prompt, + tools=tool_objects, + verbose=verbose + ) def main(): @@ -254,18 +408,21 @@ def main(): formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Examples: - # Chat providers (simple queries) + # Chat providers (simple queries) - uses agentwrap internally multillm -m openai/gpt-4o -p "What is 2+2?" multillm -m anthropic/claude-sonnet-4-20250514 -p "Explain async/await" multillm -m gemini/gemini-2.0-flash-exp -p "What is Python?" - # With built-in tools (for chat providers) + # With built-in tools (for chat providers) - uses agentwrap with tool execution multillm -m openai/gpt-4o -p "What time is it?" --use-tools get_current_time multillm -m openai/gpt-4o -p "Calculate 15 * 23" --use-tools calculate multillm -m openai/gpt-4o -p "What's the weather in Tokyo?" --use-tools get_weather multillm -m openai/gpt-4o -p "What's 5+5 and the current time?" --use-tools calculate get_current_time - # Agent providers (with tools) + # Interactive tools (ask user questions) + multillm -m openai/gpt-4o -p "Ask me about my preferences and create a summary" --use-tools ask_user + + # Native agent providers (Claude with built-in tools) multillm -m claude/default -p "What Python version?" --allowed-tools Bash multillm -m claude/default -p "List files" --allowed-tools Bash Glob --max-turns 5 multillm -m claude/default -p "Read README.md" --allowed-tools Read @@ -273,16 +430,23 @@ Examples: # With stdin cat file.txt | multillm -m openai/gpt-4o -p "Summarize:" --with-stdin - # Permission modes + # Permission modes (for native agents) multillm -m claude/default -p "Create hello.py" --allowed-tools Write --permission-mode acceptEdits # Verbose mode multillm -m openai/gpt-4o -p "Calculate 5*5" --use-tools calculate --verbose +Note: + - Chat providers (openai, google, anthropic, etc.) are automatically wrapped with + agentic capabilities using the 'agentwrap' provider + - Native agent providers (claude) use their built-in agentic features + - Use --use-tools for chat providers, --allowed-tools for native agents + Available Built-in Tools (for chat providers with --use-tools): get_current_time Get current date and time calculate Perform mathematical calculations get_weather Get weather information (mock data) + ask_user Ask the user a question and get their response (interactive) Available Tools (for agent providers with --allowed-tools): Read, Write, Edit, Bash, Glob, Grep, Task, WebFetch, WebSearch, @@ -346,37 +510,39 @@ Available Tools (for agent providers with --allowed-tools): prompt = f"{prompt}\n--- USER STDIN BEGIN ---\n{stdin_content}" try: - # Check if this is a chat provider with tools + # Determine if this is a chat or agent provider + provider_name = args.model.split("/")[0] + is_agent_provider = provider_name in ["claude"] # Native agent providers + if args.use_tools: - # Use tool calling workflow for chat providers + # Use tool calling workflow for chat providers with agentwrap result_text = asyncio.run( run_with_chat_tools(args.model, prompt, args.use_tools, args.verbose) ) print(result_text) else: - # Build kwargs for agent options - kwargs = {} - if args.max_turns is not None: - kwargs["max_turns"] = args.max_turns - if args.allowed_tools: - kwargs["allowed_tools"] = args.allowed_tools - if args.permission_mode: - kwargs["permission_mode"] = args.permission_mode - - # Use single() for normal queries or agent providers - result = asyncio.run(run_single(args.model, prompt, **kwargs)) - - # Show tool usage for agent providers (inline) - if result.tool_calls: - print(f"\n[Agent used {len(result.tool_calls)} tool(s)]") - for tc in result.tool_calls: - if args.verbose: - print(f" → {tc['function']['name']}({json.dumps(tc['function'].get('arguments', {}))})") - else: - print(f" → {tc['function']['name']}") - print() + # Build agent options + options = None + if args.max_turns is not None or args.allowed_tools or args.permission_mode: + options = multillm.AgentOptions( + max_turns=args.max_turns, + allowed_tools=args.allowed_tools, + permission_mode=args.permission_mode, + ) + + # Determine which model string to use + if is_agent_provider: + # Use agent provider directly (claude) + model_to_use = args.model + else: + # Use agentwrap for chat providers + model_to_use = f"agentwrap/{args.model}" - print(result.text) + # Run with agentic API + result_text = asyncio.run( + run_agentic(model_to_use, prompt, options=options, verbose=args.verbose) + ) + print(result_text) except Exception as e: print(f"Error: {e}", file=sys.stderr) |