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This commit is contained in:
Farouk Adeleke
2026-01-22 17:37:10 -08:00
parent 367fad475b
commit 175979fb15
3 changed files with 6 additions and 278 deletions
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2
config.json
View File

@@ -4,7 +4,7 @@
"lm": "openrouter/openai/gpt-5.2-codex",
"sub_lm": "openrouter/openai/gpt-5-mini",
"api_base": "https://openrouter.ai/api/v1",
"max_tokens": 32000,
"max_tokens": 50000,
"max_output_chars": 100000,
"verbose": false,
"track_usage": true

272
nanocode.py
View File

@@ -1,12 +1,8 @@
import os
import re
import glob as globlib
import subprocess
import shlex
import json
import tempfile
from modaic import PrecompiledProgram, PrecompiledConfig
import dspy
import re
# --- Modaic ---
@@ -24,29 +20,6 @@ YELLOW = "\033[33m"
RED = "\033[31m"
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."""
return f"{DIM}{'─' * min(os.get_terminal_size().columns, 80)}{RESET}"
def render_markdown(text):
"""Convert basic markdown bold syntax to ANSI bold."""
return re.sub(r"\*\*(.+?)\*\*", f"{BOLD}\\1{RESET}", text)
# --- File operations ---
@@ -200,7 +173,6 @@ AVAILABLE_MODELS = {
"8": ("Minimax M2.1", "minimax/minimax-m2.1"),
}
def select_model():
"""Interactive model selection or use environment variable."""
print(f"\n{BOLD}Select a model:{RESET}")
@@ -243,16 +215,13 @@ class CodingAssistant(dspy.Signature):
answer: str = dspy.OutputField(
desc="Your response to the user after completing the task"
)
affected_files: list[str] = dspy.OutputField(
desc="List of files that were written or modified during the task"
)
class RLMCodingConfig(PrecompiledConfig):
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 = 32000
max_tokens: int = 50000
max_output_chars: int = 100000
verbose: bool = False
track_usage: bool = True
@@ -328,243 +297,6 @@ class RLMCodingProgram(PrecompiledProgram):
new_instance.set_lm(self.lm)
self.agent = new_instance
def main():
model = select_model()
# Add openrouter/ prefix if not already present
if not model.startswith("openrouter/"):
model = f"openrouter/{model}"
config = RLMCodingConfig()
config.lm = model
agent = RLMCodingProgram(config)
print(
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"):
break
if user_input == "/c":
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"
if history:
context += "\nPrevious conversation:\n"
for h in history[-5:]: # Keep last 5 exchanges
context += f"User: {h['user']}\nAssistant: {h['assistant']}\n\n"
task = f"{context}\nCurrent task: {user_input}"
print(f"\n{CYAN}⏺{RESET} Thinking...", flush=True)
# 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})
print()
except (KeyboardInterrupt, EOFError):
break
except Exception as err:
import traceback
traceback.print_exc()
print(f"{RED}⏺ Error: {err}{RESET}")
if __name__ == "__main__":
agent = RLMCodingProgram(RLMCodingConfig())
agent.push_to_hub(MODAIC_REPO_PATH, commit_message="change signature", branch="dev")
#main()

10
program.json
View File

@@ -4,7 +4,7 @@
"train": [],
"demos": [],
"signature": {
"instructions": "You are a concise coding assistant with access to sub agents.\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.",
"instructions": "You are a concise coding assistant with access to sub agents.\n\nYou are tasked with producing the following outputs given the inputs `task`:\n- {answer}\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)` - 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:",
@@ -37,7 +37,7 @@
"launch_kwargs": {},
"train_kwargs": {},
"temperature": null,
"max_tokens": 32000,
"max_tokens": 50000,
"api_base": "https://openrouter.ai/api/v1",
"track_usage": true
}
@@ -60,10 +60,6 @@
{
"prefix": "Answer:",
"description": "Your response to the user after completing the task"
},
{
"prefix": "Affected Files:",
"description": "List of files that were written or modified during the task"
}
]
},
@@ -76,7 +72,7 @@
"launch_kwargs": {},
"train_kwargs": {},
"temperature": null,
"max_tokens": 32000,
"max_tokens": 50000,
"api_base": "https://openrouter.ai/api/v1",
"track_usage": true
}