Agent API updates and doc file

loopgpt/agent.md

@@ -0,0 +1,46 @@
+# Agent
+
+This file describes how to use Agent objects.
+
+## Agent State
+
+An Agent's state can be one of the following:
+- `START`: Agent is initialized.
+- `IDLE`: No tool is staged and Agent is waiting for input.
+- `TOOL_STAGED`: Agent has staged a tool for execution.
+- `STOP`: If `task_complete` is executed.
+
+## Initialize Agent
+
+An agent can be initialized using:
+```python
+import loopgpt
+agent = loopgpt.Agent()
+```
+
+## Chat with Agent
+
+`agent.chat()` deals with sending prompts to the agent and executing commands. It returns the Agent's response (see [loopgpt/constants.py](https://github.com/farizrahman4u/loopgpt/blob/main/loopgpt/constants.py) for the response format).
+
+It takes two arguments:
+- **`**message: Optional[str]`**: The message to send to the agent. Defaults to `None`.
+- **`run_tool: bool`**: If specified as `True`, any staged command will be executed. Defaults to `False`.
+
+### Prompts
+
+There are two kinds of prompts that are attached with the `message` argument:
+- **`agent.init_prompt`**: This prompt is sent along with the first message and in case `message` is `None`.
+- **`agent.next_prompt`**: This prompt is sent along with the subsequent messages.
+
+### Staged tool
+
+The staged tool (if any), can be accessed through `agent.staging_tool` and the response that staged the tool is stored in `agent.staging_response`.
+You can see the name and arguments of the staged tool using `agent.staging_tool.get("name")` and `agent.staging_tool.get("args")` respectively.
+
+To run the staged tool, just do:
+
+```python
+agent.chat(run_tool=True)
+```
+
+The tool's response can be found at `agent.tool_response`.

loopgpt/agent.py

@@ -5,6 +5,7 @@
     DEFAULT_AGENT_DESCRIPTION,
     NEXT_PROMPT,
     NEXT_PROMPT_SMALL,
+    AgentStates
 )
 from loopgpt.memory import from_config as memory_from_config
 from loopgpt.models import OpenAIModel, from_config as model_from_config
@@ -59,6 +60,7 @@ def __init__(
         self.progress = []
         self.plan = []
         self.constraints = []
+        self.state = AgentStates.START
 
     def _get_non_user_messages(self, n):
         msgs = [
@@ -130,24 +132,35 @@ def _get_compressed_history(self):
         user_msgs = [i for i in range(len(hist)) if hist[i]["role"] == "user"]
         hist = [hist[i] for i in range(len(hist)) if i not in user_msgs]
         return hist
+
+    def get_full_message(self, message: Optional[str]):
+        if self.state == AgentStates.START:
+            return self.init_prompt + "\n\n" + (message or "")
+        else:
+            return self.next_prompt + "\n\n" + (message or "")
 
     @spinner
-    def chat(self, message: Optional[str] = None, run_tool=False) -> Union[str, Dict]:
-        if message is None:
-            message = self.init_prompt
+    def chat(self, message: Optional[str] = None, run_tool=False) -> Optional[Union[str, Dict]]:
+        if self.state == AgentStates.STOP:
+            raise ValueError(
+                "This agent has completed its tasks. It will not accept any more messages."
+                " You can do `agent.clear_state()` to start over with the same goals."
+            )
+        message = self.get_full_message(message)
         if self.staging_tool:
             tool = self.staging_tool
             if run_tool:
+                output = self.run_staging_tool()
+                self.tool_response = output
                 if tool.get("name") == "task_complete":
                     self.history.append(
                         {
                             "role": "system",
                             "content": "Completed all user specified tasks.",
                         }
                     )
-                    message = ""
-                output = self.run_staging_tool()
-                self.tool_response = output
+                    self.state = AgentStates.STOP
+                    return
                 if tool.get("name") != "do_nothing":
                     pass
                     # TODO We dont have enough space for this in gpt3
@@ -186,11 +199,19 @@ def chat(self, message: Optional[str] = None, run_tool=False) -> Union[str, Dict
                 ):
                     self.staging_tool = {"name": "task_complete", "args": {}}
                     self.staging_response = resp
+                    self.state = AgentStates.STOP
             else:
-                if "name" in resp:
-                    resp = {"command": resp}
-                self.staging_tool = resp["command"]
-                self.staging_response = resp
+                if isinstance(resp, dict):
+                    if "name" in resp:
+                        resp = {"command": resp}
+                    if "command" in resp:
+                        self.staging_tool = resp["command"]
+                        self.staging_response = resp
+                        self.state = AgentStates.TOOL_STAGED
+                    else:
+                        self.state = AgentStates.IDLE
+                else:
+                    self.state = AgentStates.IDLE
 
             progress = resp.get("thoughts", {}).get("progress")
             if progress:
@@ -321,6 +342,7 @@ def clear_state(self):
         self.staging_response = None
         self.tool_response = None
         self.progress = None
+        self.state = AgentStates.START
         self.history.clear()
         self.sub_agents.clear()
         self.memory.clear()
@@ -407,6 +429,7 @@ def config(self, include_state=True):
             "description": self.description,
             "goals": self.goals[:],
             "constraints": self.constraints[:],
+            "state": self.state,
             "model": self.model.config(),
             "temperature": self.temperature,
             "tools": [tool.config() for tool in self.tools.values()],
@@ -435,6 +458,7 @@ def from_config(cls, config):
         agent.description = config["description"]
         agent.goals = config["goals"][:]
         agent.constraints = config["constraints"][:]
+        agent.state = config["state"]
         agent.temperature = config["temperature"]
         agent.model = model_from_config(config["model"])
         agent.tools = {tool.id: tool for tool in map(tool_from_config, config["tools"])}

loopgpt/constants.py

@@ -86,6 +86,12 @@
     "Always execute plans to completion",
 ]
 
+class AgentStates:
+    START = "START"
+    IDLE = "IDLE"
+    TOOL_STAGED = "TOOL_STAGED"
+    STOP = "STOP"
+
 # SPINNER
 SPINNER_ENABLED = True
 SPINNER_START_DELAY = 2

loopgpt/loops/repl.py

@@ -171,21 +171,19 @@ def cli(agent, continuous=False):
                         if cmd == "task_complete":
                             return
                         print_line("system", f"Executing command: {cmd}")
-                        resp = agent.chat(agent.next_prompt, True)
+                        resp = agent.chat(run_tool=True)
                         print_line("system", f"{cmd} output: {agent.tool_response}")
                     elif yn == "n":
                         feedback = input(
                             "Enter feedback (Why not execute the command?): "
                         )
                         if feedback.lower().strip() == "exit":
                             return
-                        next_prompt = agent.next_prompt
-                        feedback = next_prompt + "\n\n" + feedback
                         resp = agent.chat(feedback, False)
                     write_divider()
                     continue
         write_divider()
         inp = input(INPUT_PROMPT)
         if inp.lower().strip() == "exit":
             return
-        resp = agent.chat(agent.next_prompt + "\n\n" + inp)
+        resp = agent.chat(inp)