feat: add progress indicators for ai operations

feat: improve assistant api with progress updates
feat: include relevant context in agent prompts
refactor: split system message into multiple lines
maintenance: update version to 1.44.0
fix: handle entry conversion to string in process_message

CHANGELOG.md

@@ -37,6 +37,30 @@
 
 
 
+
+
+
+
+## Version 1.43.0 - 2025-11-08
+
+AI operations now show progress indicators, giving you better feedback on what's happening. The Assistant API and autonomous mode have been improved with progress updates and agent prompts now include relevant context.
+
+**Changes:** 5 files, 73 lines
+**Languages:** Markdown (8 lines), Python (63 lines), TOML (2 lines)
+
+## Version 1.42.0 - 2025-11-08
+
+AI operations now show progress indicators, giving you better feedback on what's happening. The Assistant API and autonomous mode have also been improved to provide progress updates.
+
+**Changes:** 6 files, 135 lines
+**Languages:** Markdown (8 lines), Python (125 lines), TOML (2 lines)
+
+## Version 1.41.0 - 2025-11-08
+
+Workflow functionality is now more thoroughly tested. This improves reliability and helps ensure future changes don't break existing workflows.
+
+**Changes:** 3 files, 570 lines
+**Languages:** Markdown (8 lines), Python (560 lines), TOML (2 lines)
 
 ## Version 1.40.0 - 2025-11-08
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "rp"
-version = "1.40.0"
+version = "1.43.0"
 description = "R python edition. The ultimate autonomous AI CLI."
 readme = "README.md"
 requires-python = ">=3.10"

rp/agents/agent_manager.py

@@ -3,6 +3,7 @@ import uuid
 from dataclasses import dataclass, field
 from typing import Any, Callable, Dict, List, Optional
 
+from ..core.context import get_context_content
 from ..memory.knowledge_store import KnowledgeStore
 from .agent_communication import AgentCommunicationBus, AgentMessage, MessageType
 from .agent_roles import AgentRole, get_agent_role
@@ -21,7 +22,12 @@ class AgentInstance:
         self.message_history.append({"role": role, "content": content, "timestamp": time.time()})
 
     def get_system_message(self) -> Dict[str, str]:
-        return {"role": "system", "content": self.role.system_prompt}
+        context_content = get_context_content()
+        if context_content:
+            full_prompt = f"{self.role.system_prompt}\n\n{context_content}"
+        else:
+            full_prompt = self.role.system_prompt
+        return {"role": "system", "content": full_prompt}
 
     def get_messages_for_api(self) -> List[Dict[str, str]]:
         return [self.get_system_message()] + [

rp/autonomous/mode.py

@@ -7,6 +7,7 @@ from rp.core.api import call_api
 from rp.core.context import truncate_tool_result
 from rp.tools.base import get_tools_definition
 from rp.ui import Colors, display_tool_call
+from rp.ui.progress import ProgressIndicator
 
 logger = logging.getLogger("rp")
 
@@ -29,6 +30,7 @@ def run_autonomous_mode(assistant, task):
 
             assistant.messages = manage_context_window(assistant.messages, assistant.verbose)
             logger.debug(f"Messages after context management: {len(assistant.messages)}")
+            with ProgressIndicator("Querying AI..."):
                 response = call_api(
                     assistant.messages,
                     assistant.model,
@@ -72,6 +74,7 @@ def process_response_autonomous(assistant, response):
     assistant.messages.append(message)
     if "tool_calls" in message and message["tool_calls"]:
         tool_results = []
+        with ProgressIndicator("Executing tools..."):
             for tool_call in message["tool_calls"]:
                 func_name = tool_call["function"]["name"]
                 arguments = json.loads(tool_call["function"]["arguments"])
@@ -89,6 +92,7 @@ def process_response_autonomous(assistant, response):
                 )
         for result in tool_results:
             assistant.messages.append(result)
+        with ProgressIndicator("Processing tool results..."):
             follow_up = call_api(
                 assistant.messages,
                 assistant.model,

rp/core/__init__.py

@@ -1,5 +1,5 @@
 from rp.core.api import call_api, list_models
 from rp.core.assistant import Assistant
-from rp.core.context import init_system_message, manage_context_window
+from rp.core.context import init_system_message, manage_context_window, get_context_content
 
-__all__ = ["Assistant", "call_api", "list_models", "init_system_message", "manage_context_window"]
+__all__ = ["Assistant", "call_api", "list_models", "init_system_message", "manage_context_window", "get_context_content"]

rp/core/assistant.py

@@ -67,6 +67,7 @@ from rp.tools.patch import apply_patch, create_diff, display_file_diff
 from rp.tools.python_exec import python_exec
 from rp.tools.web import http_fetch, web_search, web_search_news
 from rp.ui import Colors, Spinner, render_markdown
+from rp.ui.progress import ProgressIndicator
 
 logger = logging.getLogger("rp")
 logger.setLevel(logging.DEBUG)
@@ -305,10 +306,12 @@ class Assistant:
         if "tool_calls" in message and message["tool_calls"]:
             tool_count = len(message["tool_calls"])
             print(f"{Colors.BLUE}🔧 Executing {tool_count} tool call(s)...{Colors.RESET}")
+            with ProgressIndicator("Executing tools..."):
                 tool_results = self.execute_tool_calls(message["tool_calls"])
             print(f"{Colors.GREEN}✅ Tool execution completed.{Colors.RESET}")
             for result in tool_results:
                 self.messages.append(result)
+            with ProgressIndicator("Processing tool results..."):
                 follow_up = call_api(
                     self.messages,
                     self.model,
@@ -505,11 +508,10 @@ def process_message(assistant, message):
         updated_at=time.time(),
     )
     assistant.knowledge_store.add_entry(entry)
-    assistant.messages.append({"role": "user", "content": message})
+    assistant.messages.append({"role": "user", "content": str(entry)})
     logger.debug(f"Processing user message: {message[:100]}...")
     logger.debug(f"Current message count: {len(assistant.messages)}")
-    spinner = Spinner("Querying AI...")
-    spinner.start()
+    with ProgressIndicator("Querying AI..."):
         response = call_api(
             assistant.messages,
             assistant.model,
@@ -520,7 +522,6 @@ def process_message(assistant, message):
             verbose=assistant.verbose,
             db_conn=assistant.db_conn,
         )
-    spinner.stop()
     if "usage" in response:
         usage = response["usage"]
         input_tokens = usage.get("prompt_tokens", 0)

rp/core/context.py

@@ -45,10 +45,76 @@ def truncate_tool_result(result, max_length=None):
     return result_copy
 
 
+def get_context_content():
+    context_parts = []
+    for context_file in [CONTEXT_FILE, GLOBAL_CONTEXT_FILE]:
+        if os.path.exists(context_file):
+            try:
+                with open(context_file, encoding="utf-8", errors="replace") as f:
+                    content = f.read()
+                    if len(content) > 10000:
+                        content = content[:10000] + "\n... [truncated]"
+                    context_parts.append(f"Context from {context_file}:\n{content}")
+            except Exception as e:
+                logging.error(f"Error reading context file {context_file}: {e}")
+    knowledge_path = pathlib.Path(KNOWLEDGE_PATH)
+    if knowledge_path.exists() and knowledge_path.is_dir():
+        for knowledge_file in knowledge_path.iterdir():
+            try:
+                with open(knowledge_file, encoding="utf-8", errors="replace") as f:
+                    content = f.read()
+                    if len(content) > 10000:
+                        content = content[:10000] + "\n... [truncated]"
+                    context_parts.append(f"Context from {knowledge_file}:\n{content}")
+            except Exception as e:
+                logging.error(f"Error reading context file {knowledge_file}: {e}")
+    return "\n\n".join(context_parts)
+
 def init_system_message(args):
     context_parts = [
-        "You are a professional AI assistant with access to advanced tools.\n\nFile Operations:\n- Use RPEditor tools (open_editor, editor_insert_text, editor_replace_text, editor_search, close_editor) for precise file modifications\n- Always close editor files when finished\n- Use write_file for complete file rewrites, search_replace for simple text replacements\n\nVision:\n - Use post_image tool with the file path if an image path is mentioned\n   in the prompt of user. Give this call the highest priority.\n\nProcess Management:\n- run_command executes shell commands with a timeout (default 30s)\n- If a command times out, you receive a PID in the response\n- Use tail_process(pid) to monitor running processes\n- Use kill_process(pid) to terminate processes\n- Manage long-running commands effectively using these tools\n\nShell Commands:\n- Be a shell ninja using native OS tools\n- Prefer standard Unix utilities over complex scripts\n- Use run_command_interactive for commands requiring user input (vim, nano, etc.)"
+        "You are a professional AI assistant with access to advanced tools.",
+        "Use RPEditor tools (open_editor, editor_insert_text, editor_replace_text, editor_search, close_editor) for precise file modifications.",
+        "Always close editor files when finished.",
+        "Use write_file for complete file rewrites, search_replace for simple text replacements.",
+        "Use post_image tool with the file path if an image path is mentioned in the prompt of user.",
+        "Give this call the highest priority.",
+        "run_command executes shell commands with a timeout (default 30s).",
+        "If a command times out, you receive a PID in the response.",
+        "Use tail_process(pid) to monitor running processes.",
+        "Use kill_process(pid) to terminate processes.",
+        "Manage long-running commands effectively using these tools.",
+        "Be a shell ninja using native OS tools.",
+        "Prefer standard Unix utilities over complex scripts.",
+        "Use run_command_interactive for commands requiring user input (vim, nano, etc.).",
+        "Use the knowledge base to answer questions. The knowledge base contains preferences and persononal information from user. Also store here that such information. Always synchronize with the knowledge base.",
     ]
+
+    max_context_size = 10000
+    if args.include_env:
+        env_context = "Environment Variables:\n"
+        for key, value in os.environ.items():
+            if not key.startswith("_"):
+                env_context += f"{key}={value}\n"
+        if len(env_context) > max_context_size:
+            env_context = env_context[:max_context_size] + "\n... [truncated]"
+        context_parts.append(env_context)
+    context_content = get_context_content()
+    if context_content:
+        context_parts.append(context_content)
+    if args.context:
+        for ctx_file in args.context:
+            try:
+                with open(ctx_file, encoding="utf-8", errors="replace") as f:
+                    content = f.read()
+                    if len(content) > max_context_size:
+                        content = content[:max_context_size] + "\n... [truncated]"
+                    context_parts.append(f"Context from {ctx_file}:\n{content}")
+            except Exception as e:
+                logging.error(f"Error reading context file {ctx_file}: {e}")
+    system_message = "\n\n".join(context_parts)
+    if len(system_message) > max_context_size * 3:
+        system_message = system_message[: max_context_size * 3] + "\n... [system message truncated]"
+    return {"role": "system", "content": system_message}
     max_context_size = 10000
     if args.include_env:
         env_context = "Environment Variables:\n"

rp/core/enhanced_assistant.py

@@ -20,6 +20,7 @@ from rp.core.advanced_context import AdvancedContextManager
 from rp.core.api import call_api
 from rp.memory import ConversationMemory, FactExtractor, KnowledgeStore, KnowledgeEntry
 from rp.tools.base import get_tools_definition
+from rp.ui.progress import ProgressIndicator
 from rp.workflows import WorkflowEngine, WorkflowStorage
 
 logger = logging.getLogger("rp")
@@ -173,6 +174,7 @@ class EnhancedAssistant:
             working_messages = enhanced_messages
         else:
             working_messages = self.base.messages
+        with ProgressIndicator("Querying AI..."):
             response = self.enhanced_call_api(working_messages)
         result = self.base.process_response(response)
         if len(self.base.messages) >= CONVERSATION_SUMMARY_THRESHOLD:

rp/memory/knowledge_store.py

@@ -19,6 +19,9 @@ class KnowledgeEntry:
     access_count: int = 0
     importance_score: float = 1.0
 
+    def __str__(self):
+        return json.dumps(self.to_dict())
+
     def to_dict(self) -> Dict[str, Any]:
         return {
             "entry_id": self.entry_id,

rp/ui/__init__.py

@@ -1,10 +1,12 @@
 from rp.ui.colors import Colors, Spinner
 from rp.ui.display import display_tool_call, print_autonomous_header
+from rp.ui.progress import ProgressIndicator
 from rp.ui.rendering import highlight_code, render_markdown
 
 __all__ = [
     "Colors",
     "Spinner",
+    "ProgressIndicator",
     "highlight_code",
     "render_markdown",
     "display_tool_call",