debug

README.md

@@ -251,7 +251,7 @@ program.push_to_hub(
 ## Dependencies
 
 - [DSPy](https://dspy.ai/) - Framework for programming language models
-- [Modaic](https://modaic.ai/) - Hub for sharing and versioning DSPy programs
+- [Modaic](https://modaic.dev/) - Hub for sharing and versioning DSPy programs
 - OpenRouter API key (for accessing language models)
 
 Install dependencies:

config.json

@@ -1,10 +1,11 @@
 {
   "model": null,
-  "max_iters": 20,
-  "lm": "openrouter/anthropic/claude-3.5-sonnet",
-  "sub_lm": "openrouter/openai/gpt-4.1",
+  "max_iters": 50,
+  "lm": "openrouter/openai/gpt-5.2-codex",
+  "sub_lm": "openrouter/openai/gpt-5-mini",
   "api_base": "https://openrouter.ai/api/v1",
-  "max_tokens": 16000,
+  "max_tokens": 32000,
   "max_output_chars": 100000,
-  "verbose": false
+  "verbose": false,
+  "track_usage": true
 }

nanocode.py

@@ -2,6 +2,9 @@ import os
 import re
 import glob as globlib
 import subprocess
+import shlex
+import json
+import tempfile
 from modaic import PrecompiledProgram, PrecompiledConfig
 import dspy
 from dspy.utils.callback import BaseCallback
@@ -24,6 +27,16 @@ MAGENTA = "\033[35m"
 
 # --- Display utilities ---
 
+LONG_PASTE_THRESHOLD = int(os.environ.get("NANOCODE_LONG_PASTE_THRESHOLD", "4000"))
+
+
+def save_long_paste(text: str) -> str:
+    fd, path = tempfile.mkstemp(prefix="nanocode_paste_", suffix=".txt")
+    with os.fdopen(fd, "w") as handle:
+        handle.write(text)
+    return path
+
+
 
 def separator():
     """Return a horizontal separator line that fits the terminal width."""
@@ -234,16 +247,6 @@ class CodingAssistant(dspy.Signature):
     )
 
 
-tools = {
-    "readfile": read_file,
-    "writefile": write_file,
-    "editfile": edit_file,
-    "globfiles": glob_files,
-    "grepfiles": grep_files,
-    "runbash": run_bash,
-}
-
-
 class ToolLoggingCallback(BaseCallback):
     """Callback that logs tool calls as they happen."""
 
@@ -274,13 +277,14 @@ class ToolLoggingCallback(BaseCallback):
 
 
 class RLMCodingConfig(PrecompiledConfig):
-    max_iters: int = 20
-    lm: str = "openrouter/anthropic/claude-3.5-sonnet"  # Default fallback
-    sub_lm: str = "openrouter/openai/gpt-4.1"  # Default fallback
+    max_iters: int = 50
+    lm: str = "openrouter/openai/gpt-5.2-codex"
+    sub_lm: str = "openrouter/openai/gpt-5-mini"
     api_base: str = "https://openrouter.ai/api/v1"
-    max_tokens: int = 16000
+    max_tokens: int = 32000
     max_output_chars: int = 100000
     verbose: bool = False
+    track_usage: bool = True
 
 
 class RLMCodingProgram(PrecompiledProgram):
@@ -289,38 +293,72 @@ class RLMCodingProgram(PrecompiledProgram):
     def __init__(self, config: RLMCodingConfig, **kwargs):
         self.config = config
         super().__init__(config, **kwargs)
+        self.tools = {
+            "read_file": read_file,
+            "write_file": write_file,
+            "edit_file": edit_file,
+            "glob_files": glob_files,
+            "grep_files": grep_files,
+            "run_bash": run_bash,
+        }
 
-        # tool logging for introspections on multi-turn conversations
-        dspy.settings.configure(callbacks=[ToolLoggingCallback()])
-        lm = dspy.LM(
-            self.config.lm,
+        self.lm = dspy.LM(
+            model=self.config.lm,
             api_base=self.config.api_base,
             max_tokens=self.config.max_tokens,
+            track_usage=self.config.track_usage,
         )
-        sub_lm = dspy.LM(
-            self.config.sub_lm,
+        self.sub_lm = dspy.LM(
+            model=self.config.sub_lm,
             api_base=self.config.api_base,
             max_tokens=self.config.max_tokens,
+            track_usage=self.config.track_usage,
         )
         agent = dspy.RLM(
             CodingAssistant,
-            sub_lm=sub_lm,
-            tools=tools,
+            sub_lm=self.sub_lm,
+            tools=self.tools,
             max_output_chars=self.config.max_output_chars,
             max_iterations=self.config.max_iters,
             verbose=self.config.verbose,
         )
-
-        agent.set_lm(lm)
+        agent.set_lm(self.lm)
+        print(f"Using model: {self.lm.model}")
+        print(f"Using sub-model: {self.sub_lm.model}")
         self.agent = agent
 
     def forward(self, task: str) -> str:
         assert task, "Task cannot be empty"
         return self.agent(task=task)
 
+    def get_tools(self):
+        return self.tools
+
+    def set_tool(self, name: str, tool: callable):
+        self.tools[name] = tool
+        self.reload_repl_tools()
+
+    def remove_tool(self, name: str):
+        if name in self.tools:
+            del self.tools[name]
+            self.reload_repl_tools()
+
+    def reload_repl_tools(
+        self,
+    ):  # we need to create a new instance for tool mutations to be passed back into the REPL
+        new_instance = dspy.RLM(
+            CodingAssistant,
+            sub_lm=self.sub_lm,
+            tools=self.tools,
+            max_output_chars=self.config.max_output_chars,
+            max_iterations=self.config.max_iters,
+            verbose=self.config.verbose,
+        )
+        new_instance.set_lm(self.lm)
+        self.agent = new_instance
+
+
 def main():
-    model = os.getenv("MODEL")
-    if model is None:
     model = select_model()
 
     # Add openrouter/ prefix if not already present
@@ -332,18 +370,41 @@ def main():
 
     agent = RLMCodingProgram(config)
     print(
-        f"{BOLD}nanocode-dspy{RESET} | {DIM}{agent.config.lm} | {os.getcwd()}{RESET}\n"
+        f"{BOLD}NANOCODE DSPY{RESET} | {DIM}{agent.config.lm} | {os.getcwd()}{RESET}\n"
     )
 
     # Conversation history for context
     history = []
 
+    # MCP servers registry
+    mcp_servers = {}
+
+    def register_mcp_server(name, server):
+        tool_names = []
+        for tool in server.tools:
+            tool_name = f"{name}_{tool.__name__}"
+            agent.set_tool(tool_name, tool)
+            tool_names.append(tool_name)
+        return tool_names
+
     while True:
         try:
             print(separator())
             user_input = input(f"{BOLD}{BLUE}❯{RESET} ").strip()
             print(separator())
 
+            tmp_paste_path = None
+            if len(user_input) > LONG_PASTE_THRESHOLD:
+                tmp_paste_path = save_long_paste(user_input)
+                print(
+                    f"{YELLOW}⏺ Long paste detected ({len(user_input)} chars). Saved to {tmp_paste_path}{RESET}"
+                )
+                user_input = (
+                    f"The user pasted a long input ({len(user_input)} chars). "
+                    f"It has been saved to {tmp_paste_path}. "
+                    "Use read_file to view it. The file will be deleted after this response."
+                )
+
             if not user_input:
                 continue
             if user_input in ("/q", "exit"):
@@ -352,6 +413,144 @@ def main():
                 history = []
                 print(f"{GREEN}⏺ Cleared conversation{RESET}")
                 continue
+            if user_input == "/model":
+                print(f"\n{BOLD}Current model: {agent.config.lm}{RESET}")
+                print(f"\n{BOLD}Select a new model:{RESET}")
+                for key, (name, model_id) in AVAILABLE_MODELS.items():
+                    print(f"  {BLUE}{key}{RESET}. {name} ({DIM}{model_id}{RESET})")
+                print(f"  {BLUE}c{RESET}. Custom model (enter manually)")
+                print(f"  {BLUE}k{RESET}. Keep current model")
+
+                choice = input(f"\n{BOLD}{BLUE}❯{RESET} Enter choice: ").strip().lower()
+
+                if choice == "k":
+                    print(f"{GREEN}⏺ Keeping current model: {agent.config.lm}{RESET}")
+                    continue
+                elif choice in AVAILABLE_MODELS:
+                    name, model_id = AVAILABLE_MODELS[choice]
+                    new_model = (
+                        model_id
+                        if model_id.startswith("openrouter/")
+                        else f"openrouter/{model_id}"
+                    )
+                    config.lm = new_model
+                    agent = RLMCodingProgram(config)
+                    for server_name, info in mcp_servers.items():
+                        info["tools"] = register_mcp_server(server_name, info["server"])
+                    print(f"{GREEN}⏺ Switched to: {name} ({new_model}){RESET}")
+                elif choice == "c":
+                    custom_model = input(
+                        f"{BOLD}{BLUE}❯{RESET} Enter model ID: "
+                    ).strip()
+                    if custom_model:
+                        new_model = (
+                            custom_model
+                            if custom_model.startswith("openrouter/")
+                            else f"openrouter/{custom_model}"
+                        )
+                        config.lm = new_model
+                        agent = RLMCodingProgram(config)
+                        for server_name, info in mcp_servers.items():
+                            info["tools"] = register_mcp_server(
+                                server_name, info["server"]
+                            )
+                        print(f"{GREEN}⏺ Switched to custom model: {new_model}{RESET}")
+                    else:
+                        print(f"{RED}⏺ Invalid model ID, keeping current model{RESET}")
+                else:
+                    print(f"{RED}⏺ Invalid choice, keeping current model{RESET}")
+                continue
+
+            if user_input.startswith("/add-mcp"):
+                parts = shlex.split(user_input)
+                args = parts[1:]
+                if not args:
+                    print(
+                        f"{YELLOW}⏺ Usage: /add-mcp <name> <server> [--auth <auth>|--oauth] [--headers '<json>'] [--auto-auth|--no-auto-auth]{RESET}"
+                    )
+                    continue
+
+                name = None
+                auth = None
+                headers = None
+                auto_auth = None
+                positional = []
+                i = 0
+                while i < len(args):
+                    if args[i] in ("--name", "-n") and i + 1 < len(args):
+                        name = args[i + 1]
+                        i += 2
+                    elif args[i].startswith("--auth="):
+                        auth = args[i].split("=", 1)[1]
+                        i += 1
+                    elif args[i] == "--auth" and i + 1 < len(args):
+                        auth = args[i + 1]
+                        i += 2
+                    elif args[i] == "--oauth":
+                        auth = "oauth"
+                        i += 1
+                    elif args[i] == "--auto-auth":
+                        auto_auth = True
+                        i += 1
+                    elif args[i] == "--no-auto-auth":
+                        auto_auth = False
+                        i += 1
+                    elif args[i].startswith("--headers="):
+                        headers = json.loads(args[i].split("=", 1)[1])
+                        i += 1
+                    elif args[i] == "--headers" and i + 1 < len(args):
+                        headers = json.loads(args[i + 1])
+                        i += 2
+                    else:
+                        positional.append(args[i])
+                        i += 1
+
+                server_cmd = None
+                if positional:
+                    if name is None and len(positional) >= 2:
+                        name = positional[0]
+                        server_cmd = " ".join(positional[1:])
+                    else:
+                        server_cmd = " ".join(positional)
+
+                if not server_cmd:
+                    print(
+                        f"{YELLOW}⏺ Usage: /add-mcp <name> <server> [--auth <auth>|--oauth] [--headers '<json>'] [--auto-auth|--no-auto-auth]{RESET}"
+                    )
+                    continue
+
+                if not name:
+                    name = re.sub(r"[^a-zA-Z0-9_]+", "_", server_cmd).strip("_")
+                    if not name:
+                        name = f"mcp_{len(mcp_servers) + 1}"
+
+                if name in mcp_servers:
+                    for tool_name in mcp_servers[name]["tools"]:
+                        agent.remove_tool(tool_name)
+
+                try:
+                    from mcp2py import load
+
+                    kwargs = {}
+                    if auth is not None:
+                        kwargs["auth"] = auth
+                    if headers:
+                        kwargs["headers"] = headers
+                    if auto_auth is not None:
+                        kwargs["auto_auth"] = auto_auth
+
+                    server = load(server_cmd, **kwargs)
+                    tool_names = register_mcp_server(name, server)
+                    mcp_servers[name] = {"server": server, "tools": tool_names}
+
+                    print(
+                        f"{GREEN}⏺ Added MCP server '{name}' with {len(tool_names)} tools{RESET}"
+                    )
+                    print(f"{GREEN}⏺ Tools: {list(agent.tools.keys())}{RESET}")
+                except Exception as err:
+                    print(f"{RED}⏺ Failed to add MCP server: {err}{RESET}")
+
+                continue
 
             # Build context from history
             context = f"Working directory: {os.getcwd()}\n"
@@ -364,12 +563,22 @@ def main():
 
             print(f"\n{CYAN}⏺{RESET} Thinking...", flush=True)
 
-            # Run the ReAct agent
+            # Run the RLM agent
+            try:
                 result = agent(task=task)
+            finally:
+                if tmp_paste_path:
+                    try:
+                        os.remove(tmp_paste_path)
+                    except OSError:
+                        pass
 
             # Display the answer
             print(f"\n{CYAN}⏺{RESET} {render_markdown(result.answer)}")
 
+            # Display usage
+            print(f"\n{MAGENTA}⏺ Debug Prediction: {result}{RESET}")
+
             # Save to history
             history.append({"user": user_input, "assistant": result.answer})
 
@@ -386,5 +595,5 @@ def main():
 
 if __name__ == "__main__":
     agent = RLMCodingProgram(RLMCodingConfig())
-    agent.push_to_hub(MODAIC_REPO_PATH, commit_message="Switch to RLM instead of ReAct", tag="v0.0.2")
+    agent.push_to_hub(MODAIC_REPO_PATH, commit_message="debug", tag="v0.0.5")
     #main()

program.json

@@ -4,7 +4,7 @@
     "train": [],
     "demos": [],
     "signature": {
-      "instructions": "You are a concise coding assistant. Help the user with their coding task by using the available tools to read, write, edit files, search the codebase, and run commands.\n\nYou are tasked with producing the following outputs given the inputs `task`:\n- {answer}\n- {affected_files}        # note: the value you produce must adhere to the JSON schema: {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n\nYou have access to a Python REPL environment. Write Python code and it will be executed. You will see the output, then write more code based on what you learned. This is an iterative process.\n\nAvailable:\n- Variables: `task` (your input data)\n- `llm_query(prompt)` - query a sub-LLM (~500K char capacity) for semantic analysis\n- `llm_query_batched(prompts)` - query multiple prompts concurrently (much faster for multiple queries)\n- `print()` - ALWAYS print to see results\n- `SUBMIT(answer, affected_files)` - submit final output when done\n- Standard libraries: re, json, collections, math, etc.\n\nIMPORTANT: This is ITERATIVE. Each code block you write will execute, you'll see the output, then you decide what to do next. Do NOT try to solve everything in one step.\n\n1. EXPLORE FIRST - Look at your data before processing it. Print samples, check types/lengths, understand the structure.\n2. ITERATE - Write small code snippets, observe outputs, then decide next steps. State persists between iterations.\n3. VERIFY BEFORE SUBMITTING - If results seem wrong (zeros, empty, unexpected), reconsider your approach.\n4. USE llm_query FOR SEMANTICS - String matching finds WHERE things are; llm_query understands WHAT things mean.\n5. MINIMIZE RETYPING (INPUTS & OUTPUTS) - When values are long, precise, or error-prone (IDs, numbers, code, quotes), re-access them via variables and parse/compute in code instead of retyping. Use small, targeted prints to sanity-check, but avoid manual copying when variables can carry the exact value.\n6. SUBMIT ONLY AFTER SEEING OUTPUTS - SUBMIT ends the current run immediately. If you need to inspect printed output, run it in one step, review the result, then call SUBMIT in a later step.\n\nYou have max 50 sub-LLM calls. When done, call SUBMIT() with your output.\nAdditional tools available (use these instead of standard library equivalents):\n- `readfile(path: str, offset: int, limit: int) -> str` - Read file contents with line numbers.\n- `writefile(path: str, content: str) -> str` - Write content to a file.\n- `editfile(path: str, old: str, new: str, replace_all: bool) -> str` - Replace text in a file.\n- `globfiles(pattern: str, path: str) -> str` - Find files matching a glob pattern, sorted by modification time.\n- `grepfiles(pattern: str, path: str) -> str` - Search files for a regex pattern.\n- `runbash(cmd: str) -> str` - Run a shell command and return output.",
+      "instructions": "You are a concise coding assistant. Help the user with their coding task by using the available tools to read, write, edit files, search the codebase, and run commands.\n\nYou are tasked with producing the following outputs given the inputs `task`:\n- {answer}\n- {affected_files}        # note: the value you produce must adhere to the JSON schema: {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n\nYou have access to a Python REPL environment. Write Python code and it will be executed. You will see the output, then write more code based on what you learned. This is an iterative process.\n\nAvailable:\n- Variables: `task` (your input data)\n- `llm_query(prompt)` - query a sub-LLM (~500K char capacity) for semantic analysis\n- `llm_query_batched(prompts)` - query multiple prompts concurrently (much faster for multiple queries)\n- `print()` - ALWAYS print to see results\n- `SUBMIT(answer, affected_files)` - submit final output when done\n- Standard libraries: re, json, collections, math, etc.\n\nIMPORTANT: This is ITERATIVE. Each code block you write will execute, you'll see the output, then you decide what to do next. Do NOT try to solve everything in one step.\n\n1. EXPLORE FIRST - Look at your data before processing it. Print samples, check types/lengths, understand the structure.\n2. ITERATE - Write small code snippets, observe outputs, then decide next steps. State persists between iterations.\n3. VERIFY BEFORE SUBMITTING - If results seem wrong (zeros, empty, unexpected), reconsider your approach.\n4. USE llm_query FOR SEMANTICS - String matching finds WHERE things are; llm_query understands WHAT things mean.\n5. MINIMIZE RETYPING (INPUTS & OUTPUTS) - When values are long, precise, or error-prone (IDs, numbers, code, quotes), re-access them via variables and parse/compute in code instead of retyping. Use small, targeted prints to sanity-check, but avoid manual copying when variables can carry the exact value.\n6. SUBMIT ONLY AFTER SEEING OUTPUTS - SUBMIT ends the current run immediately. If you need to inspect printed output, run it in one step, review the result, then call SUBMIT in a later step.\n\nYou have max 50 sub-LLM calls. When done, call SUBMIT() with your output.\nAdditional tools available (use these instead of standard library equivalents):\n- `read_file(path: str, offset: int, limit: int) -> str` - Read file contents with line numbers.\n- `write_file(path: str, content: str) -> str` - Write content to a file.\n- `edit_file(path: str, old: str, new: str, replace_all: bool) -> str` - Replace text in a file.\n- `glob_files(pattern: str, path: str) -> str` - Find files matching a glob pattern, sorted by modification time.\n- `grep_files(pattern: str, path: str) -> str` - Search files for a regex pattern.\n- `run_bash(cmd: str) -> str` - Run a shell command and return output.",
       "fields": [
         {
           "prefix": "Variables Info:",
@@ -29,7 +29,7 @@
       ]
     },
     "lm": {
-      "model": "openrouter/anthropic/claude-3.5-sonnet",
+      "model": "openrouter/openai/gpt-5.2-codex",
       "model_type": "chat",
       "cache": true,
       "num_retries": 3,
@@ -37,8 +37,9 @@
       "launch_kwargs": {},
       "train_kwargs": {},
       "temperature": null,
-      "max_tokens": 16000,
-      "api_base": "https://openrouter.ai/api/v1"
+      "max_tokens": 32000,
+      "api_base": "https://openrouter.ai/api/v1",
+      "track_usage": true
     }
   },
   "agent.extract": {
@@ -67,7 +68,7 @@
       ]
     },
     "lm": {
-      "model": "openrouter/anthropic/claude-3.5-sonnet",
+      "model": "openrouter/openai/gpt-5.2-codex",
       "model_type": "chat",
       "cache": true,
       "num_retries": 3,
@@ -75,8 +76,9 @@
       "launch_kwargs": {},
       "train_kwargs": {},
       "temperature": null,
-      "max_tokens": 16000,
-      "api_base": "https://openrouter.ai/api/v1"
+      "max_tokens": 32000,
+      "api_base": "https://openrouter.ai/api/v1",
+      "track_usage": true
     }
   },
   "metadata": {

pyproject.toml

@@ -4,4 +4,4 @@ version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
-dependencies = ["dspy>=3.1.2", "modaic>=0.10.3"]
+dependencies = ["dspy>=3.1.2", "fastmcp>=2.14.3", "mcp2py>=0.6.0", "modaic>=0.10.4"]