Release v0.4.7 (#5094)

.gitignore

@@ -160,3 +160,10 @@ openai/
 
 # news
 CURRENT_BULLETIN.md
+
+# AgBenchmark
+agbenchmark/reports/
+
+# Nodejs
+package-lock.json
+package.json

BULLETIN.md

@@ -4,24 +4,18 @@
 📖 *User Guide*: https://docs.agpt.co.
 👩 *Contributors Wiki*: https://github.com/Significant-Gravitas/Auto-GPT/wiki/Contributing.
 
-# v0.4.6 RELEASE HIGHLIGHTS! 🚀
+# v0.4.7 RELEASE HIGHLIGHTS! 🚀
 # -----------------------------
-This release includes under-the-hood improvements and bug fixes, including better UTF-8
-special character support, workspace write access for sandboxed Python execution,
-more robust path resolution for config files and the workspace, and a full restructure
-of the Agent class, the "brain" of Auto-GPT, to make it more extensible.
+This release introduces initial REST API support, powered by e2b's agent 
+protocol SDK (https://github.com/e2b-dev/agent-protocol#sdk). 
 
-We have also released some documentation updates, including:
+It also includes improvements to prompt generation and support 
+for our new benchmarking tool, Auto-GPT-Benchmarks
+(https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks).
 
-- *How to share your system logs*
- Visit [docs/share-your-logs.md] to learn how to how to share logs with us
- via a log analyzer graciously contributed by https://www.e2b.dev/
+We've also moved our documentation to Material Theme, at https://docs.agpt.co.
 
-- *Auto-GPT re-architecture documentation*
- You can learn more about the inner-workings of the Auto-GPT re-architecture
- released last cycle, via these links:
- * [autogpt/core/README.md]
- * [autogpt/core/ARCHITECTURE_NOTES.md]
+As usual, we've squashed a few bugs and made some under-the-hood improvements.
 
-Take a look at the Release Notes on Github for the full changelog!
+Take a look at the Release Notes on Github for the full changelog:
 https://github.com/Significant-Gravitas/Auto-GPT/releases.

agbenchmark/benchmarks.py

@@ -0,0 +1,54 @@
+import os
+import sys
+from pathlib import Path
+from typing import Tuple
+
+from autogpt.agents import Agent
+from autogpt.app.main import run_interaction_loop
+from autogpt.commands import COMMAND_CATEGORIES
+from autogpt.config import AIConfig, Config, ConfigBuilder
+from autogpt.memory.vector import get_memory
+from autogpt.models.command_registry import CommandRegistry
+from autogpt.prompts.prompt import DEFAULT_TRIGGERING_PROMPT
+from autogpt.workspace import Workspace
+
+PROJECT_DIR = Path().resolve()
+
+
+def run_specific_agent(task, continuous_mode=False) -> Tuple[str, int]:
+ agent = bootstrap_agent(task, continuous_mode)
+ run_interaction_loop(agent)
+
+
+def bootstrap_agent(task, continuous_mode) -> Agent:
+ config = ConfigBuilder.build_config_from_env(workdir=PROJECT_DIR)
+ config.debug_mode = True
+ config.continuous_mode = continuous_mode
+ config.temperature = 0
+ config.plain_output = True
+ command_registry = CommandRegistry.with_command_modules(COMMAND_CATEGORIES, config)
+ config.memory_backend = "no_memory"
+ config.workspace_path = Workspace.init_workspace_directory(config)
+ config.file_logger_path = Workspace.build_file_logger_path(config.workspace_path)
+ ai_config = AIConfig(
+ ai_name="Auto-GPT",
+ ai_role="a multi-purpose AI assistant.",
+ ai_goals=[task],
+ )
+ ai_config.command_registry = command_registry
+ return Agent(
+ memory=get_memory(config),
+ command_registry=command_registry,
+ ai_config=ai_config,
+ config=config,
+ triggering_prompt=DEFAULT_TRIGGERING_PROMPT,
+ )
+
+
+if __name__ == "__main__":
+ # The first argument is the script name itself, second is the task
+ if len(sys.argv) != 2:
+ print("Usage: python script.py <task>")
+ sys.exit(1)
+ task = sys.argv[1]
+ run_specific_agent(task, continuous_mode=True)

agbenchmark/config.json

@@ -0,0 +1,4 @@
+{
+ "workspace": "auto_gpt_workspace",
+ "entry_path": "agbenchmark.benchmarks"
+}

agbenchmark/regression_tests.json

@@ -0,0 +1,24 @@
+{
+ "TestBasicCodeGeneration": {
+ "difficulty": "basic",
+ "dependencies": [
+ "TestWriteFile"
+ ],
+ "data_path": "agbenchmark/challenges/code/d3"
+ },
+ "TestBasicMemory": {
+ "difficulty": "basic",
+ "data_path": "agbenchmark/challenges/memory/m1"
+ },
+ "TestReadFile": {
+ "difficulty": "basic",
+ "dependencies": [
+ "TestWriteFile"
+ ],
+ "data_path": "agbenchmark/challenges/interface/read_file"
+ },
+ "TestWriteFile": {
+ "dependencies": [],
+ "data_path": "agbenchmark/challenges/interface/write_file"
+ }
+}

autogpt/agents/agent.py

@@ -17,6 +17,7 @@
 from autogpt.llm.utils import count_string_tokens
 from autogpt.logs import logger
 from autogpt.logs.log_cycle import (
+ CURRENT_CONTEXT_FILE_NAME,
  FULL_MESSAGE_HISTORY_FILE_NAME,
  NEXT_ACTION_FILE_NAME,
  USER_INPUT_FILE_NAME,
@@ -109,6 +110,13 @@ def on_before_think(self, *args, **kwargs) -> ChatSequence:
  self.history.raw(),
  FULL_MESSAGE_HISTORY_FILE_NAME,
  )
+ self.log_cycle_handler.log_cycle(
+ self.ai_config.ai_name,
+ self.created_at,
+ self.cycle_count,
+ prompt.raw(),
+ CURRENT_CONTEXT_FILE_NAME,
+ )
  return prompt
 
  def execute(
@@ -285,10 +293,10 @@ def execute_command(
  # Handle non-native commands (e.g. from plugins)
  for command in agent.ai_config.prompt_generator.commands:
  if (
- command_name == command["label"].lower()
- or command_name == command["name"].lower()
+ command_name == command.label.lower()
+ or command_name == command.name.lower()
  ):
- return command["function"](**arguments)
+ return command.function(**arguments)
 
  raise RuntimeError(
  f"Cannot execute '{command_name}': unknown command."

autogpt/agents/base.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
+import re
 from abc import ABCMeta, abstractmethod
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any, Literal, Optional
 
 if TYPE_CHECKING:
  from autogpt.config import AIConfig, Config
@@ -23,6 +24,8 @@
 class BaseAgent(metaclass=ABCMeta):
  """Base class for all Auto-GPT agents."""
 
+ ThoughtProcessID = Literal["one-shot"]
+
  def __init__(
  self,
  ai_config: AIConfig,
@@ -91,6 +94,7 @@ def __init__(
  def think(
  self,
  instruction: Optional[str] = None,
+ thought_process_id: ThoughtProcessID = "one-shot",
  ) -> tuple[CommandName | None, CommandArgs | None, AgentThoughts]:
  """Runs the agent for one cycle.
 
@@ -103,9 +107,8 @@ def think(
 
  instruction = instruction or self.default_cycle_instruction
 
- prompt: ChatSequence = self.construct_prompt(instruction)
- prompt = self.on_before_think(prompt, instruction)
-
+ prompt: ChatSequence = self.construct_prompt(instruction, thought_process_id)
+ prompt = self.on_before_think(prompt, thought_process_id, instruction)
  raw_response = create_chat_completion(
  prompt,
  self.config,
@@ -115,7 +118,7 @@ def think(
  )
  self.cycle_count += 1
 
- return self.on_response(raw_response, prompt, instruction)
+ return self.on_response(raw_response, thought_process_id, prompt, instruction)
 
  @abstractmethod
  def execute(
@@ -138,6 +141,7 @@ def execute(
 
  def construct_base_prompt(
  self,
+ thought_process_id: ThoughtProcessID,
  prepend_messages: list[Message] = [],
  append_messages: list[Message] = [],
  reserve_tokens: int = 0,
@@ -179,7 +183,11 @@ def construct_base_prompt(
 
  return prompt
 
- def construct_prompt(self, cycle_instruction: str) -> ChatSequence:
+ def construct_prompt(
+ self,
+ cycle_instruction: str,
+ thought_process_id: ThoughtProcessID,
+ ) -> ChatSequence:
  """Constructs and returns a prompt with the following structure:
  1. System prompt
  2. Message history of the agent, truncated & prepended with running summary as needed
@@ -196,14 +204,86 @@ def construct_prompt(self, cycle_instruction: str) -> ChatSequence:
  cycle_instruction_tlength = count_message_tokens(
  cycle_instruction_msg, self.llm.name
  )
- prompt = self.construct_base_prompt(reserve_tokens=cycle_instruction_tlength)
+
+ append_messages: list[Message] = []
+
+ response_format_instr = self.response_format_instruction(thought_process_id)
+ if response_format_instr:
+ append_messages.append(Message("system", response_format_instr))
+
+ prompt = self.construct_base_prompt(
+ thought_process_id,
+ append_messages=append_messages,
+ reserve_tokens=cycle_instruction_tlength,
+ )
 
  # ADD user input message ("triggering prompt")
  prompt.append(cycle_instruction_msg)
 
  return prompt
 
- def on_before_think(self, prompt: ChatSequence, instruction: str) -> ChatSequence:
+ # This can be expanded to support multiple types of (inter)actions within an agent
+ def response_format_instruction(self, thought_process_id: ThoughtProcessID) -> str:
+ if thought_process_id != "one-shot":
+ raise NotImplementedError(f"Unknown thought process '{thought_process_id}'")
+
+ RESPONSE_FORMAT_WITH_COMMAND = """```ts
+ interface Response {
+ thoughts: {
+ // Thoughts
+ text: string;
+ reasoning: string;
+ // Short markdown-style bullet list that conveys the long-term plan
+ plan: string;
+ // Constructive self-criticism
+ criticism: string;
+ // Summary of thoughts to say to the user
+ speak: string;
+ };
+ command: {
+ name: string;
+ args: Record<string, any>;
+ };
+ }
+ ```"""
+
+ RESPONSE_FORMAT_WITHOUT_COMMAND = """```ts
+ interface Response {
+ thoughts: {
+ // Thoughts
+ text: string;
+ reasoning: string;
+ // Short markdown-style bullet list that conveys the long-term plan
+ plan: string;
+ // Constructive self-criticism
+ criticism: string;
+ // Summary of thoughts to say to the user
+ speak: string;
+ };
+ }
+ ```"""
+
+ response_format = re.sub(
+ r"\n\s+",
+ "\n",
+ RESPONSE_FORMAT_WITHOUT_COMMAND
+ if self.config.openai_functions
+ else RESPONSE_FORMAT_WITH_COMMAND,
+ )
+
+ use_functions = self.config.openai_functions and self.command_registry.commands
+ return (
+ f"Respond strictly with JSON{', and also specify a command to use through a function_call' if use_functions else ''}. "
+ "The JSON should be compatible with the TypeScript type `Response` from the following:\n"
+ f"{response_format}\n"
+ )
+
+ def on_before_think(
+ self,
+ prompt: ChatSequence,
+ thought_process_id: ThoughtProcessID,
+ instruction: str,
+ ) -> ChatSequence:
  """Called after constructing the prompt but before executing it.
 
  Calls the `on_planning` hook of any enabled and capable plugins, adding their
@@ -238,7 +318,11 @@ def on_before_think(self, prompt: ChatSequence, instruction: str) -> ChatSequenc
  return prompt
 
  def on_response(
- self, llm_response: ChatModelResponse, prompt: ChatSequence, instruction: str
+ self,
+ llm_response: ChatModelResponse,
+ thought_process_id: ThoughtProcessID,
+ prompt: ChatSequence,
+ instruction: str,
  ) -> tuple[CommandName | None, CommandArgs | None, AgentThoughts]:
  """Called upon receiving a response from the chat model.
 
@@ -261,7 +345,9 @@ def on_response(
  ) # FIXME: support function calls
 
  try:
- return self.parse_and_process_response(llm_response, prompt, instruction)
+ return self.parse_and_process_response(
+ llm_response, thought_process_id, prompt, instruction
+ )
  except SyntaxError as e:
  logger.error(f"Response could not be parsed: {e}")
  # TODO: tune this message
@@ -276,7 +362,11 @@ def on_response(
 
  @abstractmethod
  def parse_and_process_response(
- self, llm_response: ChatModelResponse, prompt: ChatSequence, instruction: str
+ self,
+ llm_response: ChatModelResponse,
+ thought_process_id: ThoughtProcessID,
+ prompt: ChatSequence,
+ instruction: str,
  ) -> tuple[CommandName | None, CommandArgs | None, AgentThoughts]:
  """Validate, parse & process the LLM's response.