chore: Add tool rules example (#1998)

Co-authored-by: Sarah Wooders <sarahwooders@gmail.com>

examples/docs/agent_advanced.py

@@ -5,14 +5,18 @@ client = create_client()
 
 # create a new agent
 agent_state = client.create_agent(
+    # agent's name (unique per-user, autogenerated if not provided)
     name="agent_name",
+    # in-context memory representation with human/persona blocks
     memory=ChatMemory(human="Name: Sarah", persona="You are a helpful assistant that loves emojis"),
+    # LLM model & endpoint configuration
     llm_config=LLMConfig(
         model="gpt-4",
         model_endpoint_type="openai",
         model_endpoint="https://api.openai.com/v1",
-        context_window=8000,
+        context_window=8000,  # set to <= max context window
     ),
+    # embedding model & endpoint configuration (cannot be changed)
     embedding_config=EmbeddingConfig(
         embedding_endpoint_type="openai",
         embedding_endpoint="https://api.openai.com/v1",
@@ -20,9 +24,12 @@ agent_state = client.create_agent(
         embedding_dim=1536,
         embedding_chunk_size=300,
     ),
+    # system instructions for the agent (defaults to `memgpt_chat`)
     system=gpt_system.get_system_text("memgpt_chat"),
-    tools=[],
+    # whether to include base letta tools (default: True)
     include_base_tools=True,
+    # list of additional tools (by name) to add to the agent
+    tools=[],
 )
 print(f"Created agent with name {agent_state.name} and unique ID {agent_state.id}")
 

examples/docs/agent_basic.py

@@ -3,19 +3,8 @@ from letta import EmbeddingConfig, LLMConfig, create_client
 client = create_client()
 
 # set automatic defaults for LLM/embedding config
-client.set_default_llm_config(
-    LLMConfig(model="gpt-4o-mini", model_endpoint_type="openai", model_endpoint="https://api.openai.com/v1", context_window=128000)
-)
-client.set_default_embedding_config(
-    EmbeddingConfig(
-        embedding_endpoint_type="openai",
-        embedding_endpoint="https://api.openai.com/v1",
-        embedding_model="text-embedding-ada-002",
-        embedding_dim=1536,
-        embedding_chunk_size=300,
-    )
-)
-
+client.set_default_llm_config(LLMConfig.default_config(model_name="gpt-4"))
+client.set_default_embedding_config(EmbeddingConfig.default_config(model_name="text-embedding-ada-002"))
 
 # create a new agent
 agent_state = client.create_agent()

examples/docs/tools.py

@@ -1,23 +1,14 @@
 from letta import EmbeddingConfig, LLMConfig, create_client
+from letta.schemas.tool_rule import TerminalToolRule
 
 client = create_client()
 # set automatic defaults for LLM/embedding config
-client.set_default_llm_config(
-    LLMConfig(model="gpt-4", model_endpoint_type="openai", model_endpoint="https://api.openai.com/v1", context_window=8000)
-)
-client.set_default_embedding_config(
-    EmbeddingConfig(
-        embedding_endpoint_type="openai",
-        embedding_endpoint="https://api.openai.com/v1",
-        embedding_model="text-embedding-ada-002",
-        embedding_dim=1536,
-        embedding_chunk_size=300,
-    )
-)
+client.set_default_llm_config(LLMConfig.default_config(model_name="gpt-4"))
+client.set_default_embedding_config(EmbeddingConfig.default_config(model_name="text-embedding-ada-002"))
 
 
 # define a function with a docstring
-def roll_d20() -> str:
+def roll_d20(self) -> str:
     """
     Simulate the roll of a 20-sided die (d20).
 
@@ -38,10 +29,22 @@ def roll_d20() -> str:
     return output_string
 
 
-tool = client.create_tool(roll_d20, name="roll_dice")
+# create a tool from the function
+tool = client.create_tool(roll_d20)
+print(f"Created tool with name {tool.name}")
 
 # create a new agent
-agent_state = client.create_agent(tools=[tool.name])
+agent_state = client.create_agent(
+    # create the agent with an additional tool
+    tools=[tool.name],
+    # add tool rules that terminate execution after specific tools
+    tool_rules=[
+        # exit after roll_d20 is called
+        TerminalToolRule(tool_name=tool.name),
+        # exit after send_message is called (default behavior)
+        TerminalToolRule(tool_name="send_message"),
+    ],
+)
 print(f"Created agent with name {agent_state.name} with tools {agent_state.tools}")
 
 # Message an agent

examples/tool_rule_usage.py

@@ -0,0 +1,132 @@
+import os
+import uuid
+
+from letta import create_client
+from letta.schemas.letta_message import FunctionCallMessage
+from letta.schemas.tool_rule import InitToolRule, TerminalToolRule, ToolRule
+from tests.helpers.endpoints_helper import (
+    assert_invoked_send_message_with_keyword,
+    setup_agent,
+)
+from tests.helpers.utils import cleanup
+from tests.test_endpoints import llm_config_dir
+
+"""
+This example shows how you can constrain tool calls in your agent.
+
+Please note that this currently only works reliably for models with Structured Outputs (e.g. gpt-4o).
+
+Start by downloading the dependencies.
+```
+poetry install --all-extras
+```
+"""
+
+# Tools for this example
+# Generate uuid for agent name for this example
+namespace = uuid.NAMESPACE_DNS
+agent_uuid = str(uuid.uuid5(namespace, "agent_tool_graph"))
+config_file = os.path.join(llm_config_dir, "openai-gpt-4o.json")
+
+"""Contrived tools for this test case"""
+
+
+def first_secret_word(self: "Agent"):
+    """
+    Call this to retrieve the first secret word, which you will need for the second_secret_word function.
+    """
+    return "v0iq020i0g"
+
+
+def second_secret_word(self: "Agent", prev_secret_word: str):
+    """
+    Call this to retrieve the second secret word, which you will need for the third_secret_word function. If you get the word wrong, this function will error.
+
+    Args:
+        prev_secret_word (str): The secret word retrieved from calling first_secret_word.
+    """
+    if prev_secret_word != "v0iq020i0g":
+        raise RuntimeError(f"Expected secret {"v0iq020i0g"}, got {prev_secret_word}")
+
+    return "4rwp2b4gxq"
+
+
+def third_secret_word(self: "Agent", prev_secret_word: str):
+    """
+    Call this to retrieve the third secret word, which you will need for the fourth_secret_word function. If you get the word wrong, this function will error.
+
+    Args:
+        prev_secret_word (str): The secret word retrieved from calling second_secret_word.
+    """
+    if prev_secret_word != "4rwp2b4gxq":
+        raise RuntimeError(f"Expected secret {"4rwp2b4gxq"}, got {prev_secret_word}")
+
+    return "hj2hwibbqm"
+
+
+def fourth_secret_word(self: "Agent", prev_secret_word: str):
+    """
+    Call this to retrieve the last secret word, which you will need to output in a send_message later. If you get the word wrong, this function will error.
+
+    Args:
+        prev_secret_word (str): The secret word retrieved from calling third_secret_word.
+    """
+    if prev_secret_word != "hj2hwibbqm":
+        raise RuntimeError(f"Expected secret {"hj2hwibbqm"}, got {prev_secret_word}")
+
+    return "banana"
+
+
+def auto_error(self: "Agent"):
+    """
+    If you call this function, it will throw an error automatically.
+    """
+    raise RuntimeError("This should never be called.")
+
+
+def main():
+    # 1. Set up the client
+    client = create_client()
+    cleanup(client=client, agent_uuid=agent_uuid)
+
+    # 2. Add all the tools to the client
+    functions = [first_secret_word, second_secret_word, third_secret_word, fourth_secret_word, auto_error]
+    tools = []
+    for func in functions:
+        tool = client.create_tool(func)
+        tools.append(tool)
+    tool_names = [t.name for t in tools[:-1]]
+
+    # 3. Create the tool rules. It must be called in this order, or there will be an error thrown.
+    tool_rules = [
+        InitToolRule(tool_name="first_secret_word"),
+        ToolRule(tool_name="first_secret_word", children=["second_secret_word"]),
+        ToolRule(tool_name="second_secret_word", children=["third_secret_word"]),
+        ToolRule(tool_name="third_secret_word", children=["fourth_secret_word"]),
+        ToolRule(tool_name="fourth_secret_word", children=["send_message"]),
+        TerminalToolRule(tool_name="send_message"),
+    ]
+
+    # 4. Create the agent
+    agent_state = setup_agent(client, config_file, agent_uuid=agent_uuid, tools=[t.name for t in tools], tool_rules=tool_rules)
+
+    # 5. Ask for the final secret word
+    response = client.user_message(agent_id=agent_state.id, message="What is the fourth secret word?")
+
+    # 6. Here, we thoroughly check the correctness of the response
+    tool_names += ["send_message"]  # Add send message because we expect this to be called at the end
+    for m in response.messages:
+        if isinstance(m, FunctionCallMessage):
+            # Check that it's equal to the first one
+            assert m.function_call.name == tool_names[0]
+            # Pop out first one
+            tool_names = tool_names[1:]
+
+    # Check final send message contains "banana"
+    assert_invoked_send_message_with_keyword(response.messages, "banana")
+    print(f"Got successful response from client: \n\n{response}")
+    cleanup(client=client, agent_uuid=agent_uuid)
+
+
+if __name__ == "__main__":
+    main()