Adds README to multi-agent-collaboration example

examples/multi-agent-collaboration/README.md

@@ -0,0 +1,66 @@
+# Multi Agent Collaboration
+
+This example resembles the example from following [cookbook](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/multi-agent-collaboration.ipynb).
+
+There are three implementations:
+
+1. `hamilton_application.py` -- this uses [Hamilton](https://github.com/dagworks-inc/hamilton) inside the actions.
+2. `lecl_application.py` -- this uses LangChain's LCEL inside the actions.
+3. `application.py` -- this tries to simplify the graph to have tool calling happen inside the actions.
+
+# `hamilton_application.py` vs `lecl_application.py`:
+
+- They should be functionally equivalent, except that langchain uses deprecated
+openai tool constructs underneath, while Hamilton uses the non-deprecated function calling
+constructs.
+- Compare the two examples to see the code. Burr however doesn't change.
+
+## show me the prompts
+With Hamilton the prompts can be found in `func_agent.py`.
+
+With LangChain that's difficult. You'll need to dive into their code to see what ends up being sent.
+
+# Tracing
+You'll see that both `hamilton_application.py` and `lecl_application.py`
+have some lightweight `tracing` set up. This is a simple way to plug into Burr's
+tracer functionality -- this will allow you to see more in the Burr UI.
+
+More functionality is on the roadmap!
+
+# Running the example
+
+Install the dependencies:
+
+```bash
+pip install "burr[start]" -r requirements.txt
+```
+
+Make sure you have the API Keys in your environment:
+
+```bash
+export OPENAI_API_KEY=YOUR_KEY
+export TAVILY_API_KEY=YOUR_KEY
+```
+
+
+To run the example, you can do:
+
+```bash
+python hamilton_application.py
+```
+Application run:
+![hamilton image](hamilton-multi-agent-v2.png)
+
+or
+```bash
+python lecl_application.py
+```
+Application run:
+![lcel image](lcel-multi-agent.png)
+
+or
+```bash
+python application.py
+```
+Application run:
+![simpler hamilton image](hamilton-multi-agent.png)

examples/multi-agent-collaboration/application.py

@@ -10,7 +10,9 @@
 from burr.tracking import client as burr_tclient
 
 # Initialize some things needed for tools.
+# see func_agent.py for the code for this pipeline
 tool_dag = driver.Builder().with_modules(func_agent).build()
+# Will run code provided by the LLM.
 repl = PythonREPL()
 
 
@@ -30,7 +32,14 @@ def python_repl(code: str) -> dict:
 
 @action(reads=["query", "messages"], writes=["messages", "next_hop"])
 def chart_generator(state: State) -> tuple[dict, State]:
+    """
+    Generates a chart based on the provided query and updates the state.
+
+    :param state: The current state of the application.
+    :return: A tuple containing the result of the tool execution and the updated state.
+    """
     query = state["query"]
+    # see func_agent.py for the code for this pipeline
     result = tool_dag.execute(
         ["executed_tool_calls", "parsed_tool_calls", "llm_function_message"],
         inputs={
@@ -63,7 +72,14 @@ def chart_generator(state: State) -> tuple[dict, State]:
 
 @action(reads=["query", "messages"], writes=["messages", "next_hop"])
 def researcher(state: State) -> tuple[dict, State]:
+    """
+    Performs research based on the provided query and updates the state.
+
+    :param state: The current state of the application.
+    :return: A tuple containing the result of the tool execution and the updated state.
+    """
     query = state["query"]
+    # see func_agent.py for the code for this pipeline
     result = tool_dag.execute(
         ["executed_tool_calls", "parsed_tool_calls", "llm_function_message"],
         inputs={
@@ -92,16 +108,20 @@ def researcher(state: State) -> tuple[dict, State]:
 
 @action(reads=[], writes=[])
 def terminal_step(state: State) -> tuple[dict, State]:
+    """One can express a terminal action like this."""
     return {}, state
 
 
 class PrintStepHook(PostRunStepHook):
+    """Example of a post run step hook that prints the state and action."""
+
     def post_run_step(self, *, state: "State", action: "Action", **future_kwargs):
         print("action=====\n", action)
         print("state======\n", state)
 
 
 def default_state_and_entry_point() -> tuple[dict, str]:
+    """Default state and entry point for the application."""
     return {
         "messages": [],
         "query": "Fetch the UK's GDP over the past 5 years,"
@@ -112,6 +132,11 @@ def default_state_and_entry_point() -> tuple[dict, str]:
 
 
 def main(app_instance_id: str = None):
+    """
+    Builds the application and runs it.
+
+    :param app_instance_id: The ID of the application instance to load the state from.
+    """
     project_name = "demo:hamilton-multi-agent"
     if app_instance_id:
         state, entry_point = burr_tclient.LocalTrackingClient.load_state(
@@ -138,12 +163,12 @@ def main(app_instance_id: str = None):
                 "researcher",
                 "terminal",
                 core.expr("next_hop == 'complete'"),
-            ),  # core.expr("'FINAL ANSWER' in messages[-1]['content']")),
+            ),
             (
                 "chart_generator",
                 "terminal",
                 core.expr("next_hop == 'complete'"),
-            ),  # core.expr("'FINAL ANSWER' in messages[-1]['content']")),
+            ),
         )
         .with_entrypoint(entry_point)
         .with_hooks(PrintStepHook())
@@ -157,8 +182,13 @@ def main(app_instance_id: str = None):
 
 
 if __name__ == "__main__":
-    # Add an app_id to restart from last sequence in that state
-    # e.g. fine the ID in the UI and then put it in here "app_4d1618d2-79d1-4d89-8e3f-70c216c71e63"
+    """
+    The entry point of the application.
+
+    If an app_id is provided, the application will restart from the last
+    sequence in that state.
+    E.g. fine the ID in the UI and then put it in here "app_4d1618d2-79d1-4d89-8e3f-70c216c71e63"
+    """
     _app_id = None
     main(_app_id)
 

examples/multi-agent-collaboration/func_agent.py

@@ -1,3 +1,8 @@
+"""
+Hamilton module that defines the pipeline for
+hitting an LLM model and asking it what to do.
+"""
+
 import inspect
 import json
 from typing import Callable
@@ -11,18 +16,29 @@ def llm_client() -> openai.OpenAI:
 
 
 def tool_names(tool_function_specs: list[dict]) -> list[str]:
+    """Get the names of the tools from the tool function specs."""
     return [tool["function"]["name"] for tool in tool_function_specs]
 
 
 def _langchain_tool_spec(tool: Callable) -> dict:
+    """Converts a tool to a langchain tool spec."""
     t = convert_to_openai_function(tool)
     # print(t)
     return t
 
 
 def _tool_function_spec(tool: Callable) -> dict:
+    """Converts a python function into a specification for function calling.
+
+    This is a little hacky. But it works.
+
+    It takes a function, introspects it, and returns a spec.
+
+    :param tool:
+    :return:
+    """
     # TODO: maybe just get people to wrap any external tool in a function
-    # to make it clear WTF is going on.
+    # to make it clear what is going on.
     if hasattr(tool, "name") and hasattr(tool, "description") and hasattr(tool, "args_schema"):
         return {"type": "function", "function": _langchain_tool_spec(tool)}
     func_sig = inspect.signature(tool)
@@ -83,10 +99,12 @@ def _tool_function_spec(tool: Callable) -> dict:
 
 
 def tool_function_specs(tools: list[Callable]) -> list[dict]:
+    """Converts a list of tools into a list of tool function specs."""
     return [_tool_function_spec(tool) for tool in tools]
 
 
 def base_system_prompt(tool_names: list[str], system_message: str) -> str:
+    """Creates the base system prompt for the pipeline."""
     return (
         "You are a helpful AI assistant, collaborating with other assistants."
         " Use the provided tools to progress towards answering the question."
@@ -118,6 +136,13 @@ def get_current_weather(location: str, unit: str = "fahrenheit") -> str:
 
 
 def message_history(base_system_prompt: str, user_query: str, messages: list[dict]) -> list[dict]:
+    """Creates the message history for the LLM model.
+
+    :param base_system_prompt:
+    :param user_query:
+    :param messages:
+    :return:
+    """
     base = [
         {"role": "system", "content": base_system_prompt},
         {"role": "user", "content": user_query},
@@ -152,21 +177,20 @@ def llm_function_response(
         tool_choice="auto",
     )
     return response
-    # tool_calls = response_message.tool_calls
-    # return response.choices[0].message.content
 
 
 def llm_function_message(
     llm_function_response: openai.types.chat.chat_completion.ChatCompletion,
 ) -> dict:
+    """Parses the LLM response message. Does extra parsing for tool invocations."""
     response_message = llm_function_response.choices[0].message
     if response_message.tool_calls:
         return {
             "role": response_message.role,
             "content": None,
             "tool_calls": [
                 {
-                    "id": t.uid,
+                    "id": t.id,
                     "type": "function",
                     "function": {"name": t.function.name, "arguments": t.function.arguments},
                 }
@@ -193,7 +217,7 @@ def parsed_tool_calls(
     if tool_calls:
         for tool_call in tool_calls:
             func_call = {
-                "id": tool_call.uid,
+                "id": tool_call.id,
                 "function_name": tool_call.function.name,
                 "function_args": tool_call.function.arguments,
             }
@@ -244,6 +268,7 @@ def executed_tool_calls(
 
 
 if __name__ == "__main__":
+    # some code to test a few things.
     jspec = _tool_function_spec(get_current_weather)
     import pprint