<context context="81.34" cpu="0" disk_total="1081101176832" disk_used="125111209984" gpu="0" memory_total="16630431744" memory_used="991567872" stdin="0" time="2024-11-20T10:57:18Z"> <read_stdin id="3da7f743-19b7-4738-aaf9-49634618e8f7"> <![CDATA[The SIA source is located in /root/sia. Not all features are implemented yet. Look at the readme and code to find what is missing. Make sure to unit test your work.]]> </read_stdin> <single exit_code="0" id="ac690fe5-45d0-4028-8120-107c318977a9"> <![CDATA[grep -Eb "^#" /root/sia/README.md]]> <stdout> <![CDATA[0:# SIA - The Self Improving Agent 668:## Example 832:### Context 2323:### Responses 3462:## Working principles 3759:### Core Actions 3953:### Scripts 4204:#### Single-shot script 4433:#### Repeat script 4711:### Use of XML 5022:#### XML Data Flow 5705:### The SIA process 6048:### Server for debuggin and human input 6619:## Architecture 6885:### Core Components 6908:#### Agent Architecture 7521:#### Working Memory 8059:#### Command Processing 8490:#### IO Handling 8787:### Processing Flow 8810:#### Standard Agent Flow 9109:#### Web Agent Flow 9472:### Web Interface 9600:#### Server-Client Communication 9932:#### WebServer Architecture 10351:### Diagrams 10367:#### Core classes 12662:#### Standard Agent Flow 13387:#### Web Agent 15536:#### Web Agent Flow 16522:#### Entry classes 18459:#### IO Buffer classes 19156:#### Command classes ]]> </stdout> <stderr/> </single> <single exit_code="0" id="79f5a8b7-cc57-44c3-b942-c74e3bcabcf8"> <![CDATA[head -c 668 /root/sia/README.md]]> <stdout> <![CDATA[# SIA - The Self Improving Agent SIA is an agentic artificial intelligence system that autonomously completes complex tasks by writing and executing scripts. It uses a Large Language Model (LLM) which operates in a loop. Each iteration a context is updated with system info and a list of previous reasoning and actions. The agent responds with a new reasoning or an action. Context, reasoning and actions are stored in a file for each iteration. SIA can read past iterations to improve its reasoning and actions. It can improve in two ways: - By finetuning the LLM with a better reasoning or action for a given context - By modifying its own source code ]]> </stdout> <stderr/> </single> <single exit_code="0" id="c0aad1a9-48d4-46cd-bd5f-70230566b442" limit="266" stdout_truncated="true"> <![CDATA[tail -c +6619 /root/sia/README.md]]> <stdout> <![CDATA[ ## Architecture SIA follows a modular architecture centered around an agent that processes context through an LLM to generate actions. The system can run in two modes: a standard command-line mode and an interactive web mode for debugging and human feedback. ### ]]> </stdout> <stderr/> </single> <single exit_code="0" id="973d9034-7076-479a-afea-4aaf81b065be" limit="9624"> <![CDATA[tail -c +10351 /root/sia/README.md]]> <stdout> <![CDATA[ ### Diagrams #### Core classes ```mermaid classDiagram class SystemMetrics { +SystemMetrics(sample_interval float) +generate_context(context_usage float) ElementTree +stop() void -monitor_loop() void } class LLMEngine { +LLMEngine(model_path str) +set_model_path(model_path str) void +infer(system_prompt str, main_context str) Iterator~str~ } class BaseAgent { <<abstract>> -working_memory: WorkingMemory -metrics: SystemMetrics -llm: LLMEngine -parser: ResponseParser -validator: XMLValidator -action_schema: str #_compile_context() str } class WorkingMemory { -entries: List~Entry~ +WorkingMemory() +add_entry(entry Entry) void +remove_entry(id str) void +clear() void +get_entry(id str) Optional~Entry~ +get_entries() List~Entry~ +get_entries_count() int +get_entries_by_type(type Type) List~Entry~ +update() void +generate_context() List~ElementTree~ } class XMLValidator { +XMLValidator(schema str) +validate(xml str) Optional~str~ +get_valid_root_elements() Set~str~ } class ResponseParser { -io_buffer: IOBuffer +ResponseParser(io_buffer IOBuffer) +parse(xml str) Command | Entry } class Entry { <<abstract>> +id: str readonly +timestamp: datetime readonly +Entry(id str, timestamp datetime) +update() void* +generate_context() ElementTree* +cleanup() void* } class IOBuffer { <<interface>> +read() str* +write(content str) void* +buffer_length() int* } class Command { <<abstract>> +execute(memory WorkingMemory) CommandResult* } SystemMetrics "1" --* "1" BaseAgent LLMEngine "1" --* "1" BaseAgent XMLValidator "1" --* "1" BaseAgent BaseAgent "1" *-- "1" IOBuffer BaseAgent "1" *-- "1" WorkingMemory BaseAgent "1" *-- "1" ResponseParser WorkingMemory "1" *-- "*" Entry ResponseParser ..> Entry ResponseParser ..> Command ``` #### Standard Agent Flow ```mermaid stateDiagram-v2 direction LR state "Standard Agent Flow" as standard_agent_flow { [*] --> UpdateSystem: Start UpdateSystem --> CompileContext: Updated Metrics & Size CompileContext --> ProcessLLM ProcessLLM --> ValidateXML: LLM Response ValidateXML --> ParseResponse: Valid XML ValidateXML --> UpdateEntries: Invalid XML\nCreate ParseErrorEntry ParseResponse --> ExecuteCommands: Command ParseResponse --> UpdateEntries: Entry ExecuteCommands --> [*]: Stop Command ExecuteCommands --> UpdateEntries: Delete Command UpdateEntries --> UpdateSystem: Continue Loop } ``` #### Web Agent ```mermaid classDiagram class BaseAgent { <<abstract>> -working_memory: WorkingMemory -metrics: SystemMetrics -llm: LLMEngine -parser: ResponseParser -validator: XMLValidator -action_schema: str #_compile_context() str } class StandardAgent { +StandardAgent(model_path str, system_prompt str, action_schema str) +run() void } class WebAgent { +context: str +response: str readonly +current_state WebAgentState readonly +command_result Optional[CommandResult] readonly +validation_error Optional[str] readonly +add_state_change_handler(handler Callable) void +add_response_change_handler(handler Callable) void +approve_context() void +set__response(response str) void +approve_response() void } class WebAgentState { <<enumeration>> UPDATE CONTEXT_APPROVAL INFERENCE RESPONSE_APPROVAL STOPPED } class WebSocketManager { -web_sockets: Set~WebSocket~ +WebServer(agent WebAgent, io_buffer WebIOBuffer, static_files path, host str, port int) } class ClientMessage { <<enumeration>> APPROVE_CONTEXT APPROVE_RESPONSE MODIFY_RESPONSE SEND_INPUT } class ServerMessage { <<enumeration>> STATE_CHANGE CONTEXT_UPDATE RESPONSE_UPDATE OUTPUT_UPDATE VALIDATION_ERROR } class WebIOBuffer { -stdin_buffer: str -stdout_buffer: str +read() str +write(content str) void +buffer_length() int +append_stdin(content str) void +get_stdout() str +clear_stdout() void } BaseAgent <|-- WebAgent BaseAgent <|-- StandardAgent WebServer --> ClientMessage WebServer --> ServerMessage WebServer "1" *-- "1" WebIOBuffer WebServer "1" *-- "1" WebAgent WebAgent "1" *-- "1" WebAgentState ``` #### Web Agent Flow ```mermaid stateDiagram-v2 direction LR state "Web Agent Flow" as web_agent_flow { [*] --> UpdateSystem: Start UpdateSystem --> CompileContext: Updated Metrics & Size CompileContext --> WaitForContextApproval: Send Context WaitForContextApproval --> ProcessLLM: Context Approved ProcessLLM --> ValidateXML: LLM Response ValidateXML --> WaitForResponseApproval: Send Validation Result ValidateXML --> UpdateEntries: Invalid XML\nCreate ParseErrorEntry WaitForResponseApproval --> ValidateXML: Modified Response WaitForResponseApproval --> ParseResponse: Approved Response ParseResponse --> ExecuteCommands: Command ParseResponse --> UpdateEntries: Entry ExecuteCommands --> [*]: Stop Command ExecuteCommands --> UpdateEntries: Delete Command UpdateEntries --> UpdateSystem: Continue Loop } ``` #### Entry classes ```mermaid classDiagram class Entry { <<abstract>> +id: str readonly +timestamp: datetime readonly +Entry(id str, timestamp datetime) +update() void* +generate_context() ElementTree* +cleanup() void* } class ScriptEntry { +script: str readonly +stdout: str readonly +stderr: str readonly +exit_code: Optional~int~ readonly +Script(script str, id str, timestamp datetime) +update() void +generate_context() ElementTree } class RepeatEntry { +script: str readonly +stdout: str readonly +stderr: str readonly +exit_code: Optional~int~ readonly +RepeatEntry(script str, id str, timestamp datetime) +update() void +generate_context() ElementTree } class ReasoningEntry { +content: str readonly +ReasoningEntry(content str, id str, timestamp datetime) +update() void +generate_context() ElementTree } class ParseErrorEntry { +content: str readonly +error: str readonly +ParseErrorEntry(content str, error str, id str, timestamp datetime) +update() void +generate_context() ElementTree } class ReadEntry { +content: str readonly +ReadEntry(io_buffer IOBuffer, id str, timestamp datetime) +update() void +generate_context() ElementTree } class WriteEntry { +content: str readonly +WriteEntry(content str, io_buffer IOBuffer, id str, timestamp datetime) +update() void +generate_context() ElementTree } ReasoningEntry --|> Entry ParseErrorEntry --|> Entry ReadEntry --|> Entry Entry <|-- WriteEntry Entry <|-- ScriptEntry Entry <|-- RepeatEntry ``` #### IO Buffer classes ```mermaid classDiagram class IOBuffer { <<interface>> +read() str* +write(content str) void* +buffer_length() int* } class StandardIOBuffer { +StandardIOBuffer() +read() str +write(content str) void +buffer_length() int } class WebIOBuffer { -stdin_buffer: str -stdout_buffer: str +read() str +write(content str) void +buffer_length() int +append_stdin(content str) void +get_stdout() str +clear_stdout() void } IOBuffer <|.. WebIOBuffer IOBuffer <|.. StandardIOBuffer ``` #### Command classes ```mermaid classDiagram direction LR class Command { <<abstract>> +execute(memory WorkingMemory) CommandResult* } class DeleteCommand { +DeleteCommand(id str) +execute(memory WorkingMemory) CommandResult } class StopCommand { +StopCommand() +execute(memory WorkingMemory) CommandResult } class CommandResult { +message: str +success: bool +should_stop: bool +CommandResult(message str, success bool, should_stop bool) +static success() CommandResult +static failure(message str) CommandResult +static stop() CommandResult } Command <|-- DeleteCommand Command <|-- StopCommand Command -- CommandResult ``` ]]> </stdout> <stderr/> </single> <reasoning id="869e20e9-48ef-4223-b901-09796f4e0627"> <![CDATA[ The diagrams give a good overview of which classes to expect. I will explore the `/root/sia/sia` directory to see which of these classes are already implemented. ]]> </reasoning> <single exit_code="0" id="fa7effb0-a1ce-468f-b556-c971f24dca54" limit="10000"> <![CDATA[ls -l /root/sia/sia]]> <stdout> <![CDATA[total 172 -rwxr-xr-x 1 root root 0 Oct 22 11:48 __init__.py -rwxr-xr-x 1 root root 4977 Nov 19 14:20 __main__.py drwxr-xr-x 2 root root 4096 Nov 20 10:26 __pycache__ -rwxr-xr-x 1 root root 5943 Nov 15 15:54 auto_approver.py -rwxr-xr-x 1 root root 5242 Nov 13 16:31 background_entry.py -rwxr-xr-x 1 root root 3072 Nov 19 13:45 base_agent.py -rwxr-xr-x 1 root root 722 Nov 4 09:50 command.py -rwxr-xr-x 1 root root 1113 Nov 4 09:50 command_result.py -rwxr-xr-x 1 root root 5865 Nov 19 13:41 config.py -rwxr-xr-x 1 root root 1337 Nov 4 09:50 delete_command.py -rwxr-xr-x 1 root root 1537 Nov 4 09:50 entry.py -rwxr-xr-x 1 root root 2620 Nov 14 15:16 hf_llm_engine.py -rwxr-xr-x 1 root root 1082 Nov 4 09:50 io_buffer.py -rwxr-xr-x 1 root root 1683 Nov 19 13:54 iteration_logger.py -rwxr-xr-x 1 root root 385 Nov 14 14:21 llm_engine.py -rwxr-xr-x 1 root root 4018 Nov 19 14:20 local_llm_engine.py -rwxr-xr-x 1 root root 2269 Nov 19 14:20 mistral_llm_engine.py -rwxr-xr-x 1 root root 2407 Nov 19 14:20 openai_llm_engine.py -rwxr-xr-x 1 root root 1632 Nov 13 15:40 parse_error_entry.py -rwxr-xr-x 1 root root 1744 Nov 13 15:39 read_entry.py -rwxr-xr-x 1 root root 1323 Nov 13 15:38 reasoning_entry.py -rwxr-xr-x 1 root root 4127 Nov 19 14:20 repeat_entry.py -rwxr-xr-x 1 root root 5916 Nov 19 14:20 response_parser.py -rwxr-xr-x 1 root root 4173 Nov 19 14:20 single_entry.py -rwxr-xr-x 1 root root 2074 Nov 4 09:50 standard_io_buffer.py -rwxr-xr-x 1 root root 908 Nov 4 09:50 stop_command.py -rwxr-xr-x 1 root root 4152 Nov 14 13:36 system_metrics.py -rwxr-xr-x 1 root root 2510 Nov 12 16:17 util.py -rwxr-xr-x 1 root root 7818 Nov 19 13:54 web_agent.py -rwxr-xr-x 1 root root 2039 Nov 12 16:17 web_io_buffer.py -rwxr-xr-x 1 root root 6968 Nov 15 12:48 web_socket_manager.py -rwxr-xr-x 1 root root 3638 Nov 13 16:35 working_memory.py -rwxr-xr-x 1 root root 1688 Nov 13 15:41 write_entry.py -rwxr-xr-x 1 root root 3992 Nov 4 09:50 xml_validator.py ]]> </stdout> <stderr/> </single> <single exit_code="0" id="c83a4145-a43d-4b78-856b-6d81da1fad76" limit="3072"> <![CDATA[cat /root/sia/sia/base_agent.py]]> <stdout> <![CDATA[from abc import ABC import xml.etree.ElementTree as ET from .llm_engine import LlmEngine from .response_parser import ResponseParser from .system_metrics import SystemMetrics from .util import pretty_print_element from .working_memory import WorkingMemory from .xml_validator import XMLValidator class BaseAgent(ABC): """ Abstract base class for SIA agents. Provides core functionality for maintaining working memory, system metrics, and coordinating components for LLM inference. """ def __init__( self, system_prompt: str, action_schema: str, working_memory: WorkingMemory, metrics: SystemMetrics, llm: LlmEngine, validator: XMLValidator, parser: ResponseParser, ): """ Initialize agent with required components. """ self._system_prompt = system_prompt self._action_schema = action_schema self._working_memory = working_memory self._metrics = metrics self._llm = llm self._validator = validator self._parser = parser def __del__(self): """Clean up resources on deletion.""" if hasattr(self, '_metrics'): self._metrics.stop() @property def system_prompt(self) -> str: """Get the system prompt.""" return f"{self._system_prompt}\n{self._action_schema}" def _compile_context(self) -> str: """ Compile the current context for LLM inference. Includes system metrics and working memory entries. Returns: str: Complete context as XML string """ memory_context = self._working_memory.generate_context() metrics_data = self._metrics.get_metrics() # Create context element context = ET.Element("context") context.set("time", metrics_data["timestamp"]) context.set("cpu", str(metrics_data["cpu"])) context.set("gpu", str(metrics_data["gpu"])) context.set("memory_used", str(metrics_data["memory_used"])) context.set("memory_total", str(metrics_data["memory_total"])) context.set("disk_used", str(metrics_data["disk_used"])) context.set("disk_total", str(metrics_data["disk_total"])) context.set("stdin", str(self._parser.io_buffer.buffer_length())) context.set("context", "100") for entry in memory_context: context.append(entry) context_str = pretty_print_element(context) # Calculate token usage percentage token_count = self._llm.token_count(self.system_prompt, context_str) token_limit = self._llm.token_limit() context_usage = (float(token_count) / float(token_limit)) * 100.0 # Update context usage metric context.set("context", str(round(context_usage, 2))) return pretty_print_element(context)]]> </stdout> <stderr/> </single> </context><reasoning> Looking at BaseAgent I can see it is a close match to the diagram with some small differences. I will create a notes directory where I will list differences between the schemas and the implementation. I will go over each file or class in the diagram in alphabetical order. </reasoning>