multi-agent usage

pydantic_ai_slim/pydantic_ai/agent.py

@@ -184,6 +184,7 @@ async def run(
         deps: AgentDeps = None,
         model_settings: ModelSettings | None = None,
         usage_limits: UsageLimits | None = None,
+        usage: result.Usage | None = None,
         infer_name: bool = True,
     ) -> result.RunResult[ResultData]:
         """Run the agent with a user prompt in async mode.
@@ -206,6 +207,7 @@ async def run(
             deps: Optional dependencies to use for this run.
             model_settings: Optional settings to use for this model's request.
             usage_limits: Optional limits on model request count or token usage.
+            usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.
             infer_name: Whether to try to infer the agent name from the call frame if it's not set.
 
         Returns:
@@ -226,20 +228,19 @@ async def run(
             model_name=model_used.name(),
             agent_name=self.name or 'agent',
         ) as run_span:
-            run_context = RunContext(deps, 0, [], None, model_used)
+            run_context = RunContext(deps, 0, [], None, model_used, usage or result.Usage())
             messages = await self._prepare_messages(user_prompt, message_history, run_context)
             run_context.messages = messages
 
             for tool in self._function_tools.values():
                 tool.current_retry = 0
 
-            usage = result.Usage(requests=0)
             model_settings = merge_model_settings(self.model_settings, model_settings)
             usage_limits = usage_limits or UsageLimits()
 
             run_step = 0
             while True:
-                usage_limits.check_before_request(usage)
+                usage_limits.check_before_request(run_context.usage)
 
                 run_step += 1
                 with _logfire.span('preparing model and tools {run_step=}', run_step=run_step):
@@ -251,9 +252,8 @@ async def run(
                     model_req_span.set_attribute('usage', request_usage)
 
                 messages.append(model_response)
-                usage += request_usage
-                usage.requests += 1
-                usage_limits.check_tokens(request_usage)
+                run_context.usage.incr(request_usage, requests=1)
+                usage_limits.check_tokens(run_context.usage)
 
                 with _logfire.span('handle model response', run_step=run_step) as handle_span:
                     final_result, tool_responses = await self._handle_model_response(model_response, run_context)
@@ -266,10 +266,10 @@ async def run(
                     if final_result is not None:
                         result_data = final_result.data
                         run_span.set_attribute('all_messages', messages)
-                        run_span.set_attribute('usage', usage)
+                        run_span.set_attribute('usage', run_context.usage)
                         handle_span.set_attribute('result', result_data)
                         handle_span.message = 'handle model response -> final result'
-                        return result.RunResult(messages, new_message_index, result_data, usage)
+                        return result.RunResult(messages, new_message_index, result_data, run_context.usage)
                     else:
                         # continue the conversation
                         handle_span.set_attribute('tool_responses', tool_responses)
@@ -285,6 +285,7 @@ def run_sync(
         deps: AgentDeps = None,
         model_settings: ModelSettings | None = None,
         usage_limits: UsageLimits | None = None,
+        usage: result.Usage | None = None,
         infer_name: bool = True,
     ) -> result.RunResult[ResultData]:
         """Run the agent with a user prompt synchronously.
@@ -311,6 +312,7 @@ async def main():
             deps: Optional dependencies to use for this run.
             model_settings: Optional settings to use for this model's request.
             usage_limits: Optional limits on model request count or token usage.
+            usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.
             infer_name: Whether to try to infer the agent name from the call frame if it's not set.
 
         Returns:
@@ -326,6 +328,7 @@ async def main():
                 deps=deps,
                 model_settings=model_settings,
                 usage_limits=usage_limits,
+                usage=usage,
                 infer_name=False,
             )
         )
@@ -340,6 +343,7 @@ async def run_stream(
         deps: AgentDeps = None,
         model_settings: ModelSettings | None = None,
         usage_limits: UsageLimits | None = None,
+        usage: result.Usage | None = None,
         infer_name: bool = True,
     ) -> AsyncIterator[result.StreamedRunResult[AgentDeps, ResultData]]:
         """Run the agent with a user prompt in async mode, returning a streamed response.
@@ -363,6 +367,7 @@ async def main():
             deps: Optional dependencies to use for this run.
             model_settings: Optional settings to use for this model's request.
             usage_limits: Optional limits on model request count or token usage.
+            usage: Optional usage to start with, useful for resuming a conversation or agents used in tools.
             infer_name: Whether to try to infer the agent name from the call frame if it's not set.
 
         Returns:
@@ -385,28 +390,27 @@ async def main():
             model_name=model_used.name(),
             agent_name=self.name or 'agent',
         ) as run_span:
-            run_context = RunContext(deps, 0, [], None, model_used)
+            run_context = RunContext(deps, 0, [], None, model_used, usage or result.Usage())
             messages = await self._prepare_messages(user_prompt, message_history, run_context)
             run_context.messages = messages
 
             for tool in self._function_tools.values():
                 tool.current_retry = 0
 
-            usage = result.Usage()
             model_settings = merge_model_settings(self.model_settings, model_settings)
             usage_limits = usage_limits or UsageLimits()
 
             run_step = 0
             while True:
                 run_step += 1
-                usage_limits.check_before_request(usage)
+                usage_limits.check_before_request(run_context.usage)
 
                 with _logfire.span('preparing model and tools {run_step=}', run_step=run_step):
                     agent_model = await self._prepare_model(run_context)
 
                 with _logfire.span('model request {run_step=}', run_step=run_step) as model_req_span:
                     async with agent_model.request_stream(messages, model_settings) as model_response:
-                        usage.requests += 1
+                        run_context.usage.requests += 1
                         model_req_span.set_attribute('response_type', model_response.__class__.__name__)
                         # We want to end the "model request" span here, but we can't exit the context manager
                         # in the traditional way
@@ -442,7 +446,6 @@ async def on_complete():
                                 yield result.StreamedRunResult(
                                     messages,
                                     new_message_index,
-                                    usage,
                                     usage_limits,
                                     result_stream,
                                     self._result_schema,
@@ -466,8 +469,8 @@ async def on_complete():
                                 handle_span.message = f'handle model response -> {tool_responses_str}'
                                 # the model_response should have been fully streamed by now, we can add its usage
                                 model_response_usage = model_response.usage()
-                                usage += model_response_usage
-                                usage_limits.check_tokens(usage)
+                                run_context.usage.incr(model_response_usage)
+                                usage_limits.check_tokens(run_context.usage)
 
     @contextmanager
     def override(

pydantic_ai_slim/pydantic_ai/result.py

@@ -2,6 +2,7 @@
 
 from abc import ABC, abstractmethod
 from collections.abc import AsyncIterator, Awaitable, Callable
+from copy import copy
 from dataclasses import dataclass, field
 from datetime import datetime
 from typing import Generic, Union, cast
@@ -63,25 +64,33 @@ class Usage:
     details: dict[str, int] | None = None
     """Any extra details returned by the model."""
 
-    def __add__(self, other: Usage) -> Usage:
-        """Add two Usages together.
+    def incr(self, incr_usage: Usage, *, requests: int = 0) -> None:
+        """Increment the usage in place.
 
-        This is provided so it's trivial to sum usage information from multiple requests and runs.
+        Args:
+            incr_usage: The usage to increment by.
+            requests: The number of requests to increment by in addition to `incr_usage.requests`.
         """
-        counts: dict[str, int] = {}
+        self.requests += requests
         for f in 'requests', 'request_tokens', 'response_tokens', 'total_tokens':
             self_value = getattr(self, f)
-            other_value = getattr(other, f)
+            other_value = getattr(incr_usage, f)
             if self_value is not None or other_value is not None:
-                counts[f] = (self_value or 0) + (other_value or 0)
+                setattr(self, f, (self_value or 0) + (other_value or 0))
 
-        details = self.details.copy() if self.details is not None else None
-        if other.details is not None:
-            details = details or {}
-            for key, value in other.details.items():
-                details[key] = details.get(key, 0) + value
+        if incr_usage.details:
+            self.details = self.details or {}
+            for key, value in incr_usage.details.items():
+                self.details[key] = self.details.get(key, 0) + value
 
-        return Usage(**counts, details=details or None)
+    def __add__(self, other: Usage) -> Usage:
+        """Add two Usages together.
+
+        This is provided so it's trivial to sum usage information from multiple requests and runs.
+        """
+        new_usage = copy(self)
+        new_usage.incr(other)
+        return new_usage
 
 
 @dataclass
@@ -136,8 +145,6 @@ def usage(self) -> Usage:
 class StreamedRunResult(_BaseRunResult[ResultData], Generic[AgentDeps, ResultData]):
     """Result of a streamed run that returns structured data via a tool call."""
 
-    usage_so_far: Usage
-    """Usage of the run up until the last request."""
     _usage_limits: UsageLimits | None
     _stream_response: models.EitherStreamedResponse
     _result_schema: _result.ResultSchema[ResultData] | None
@@ -306,7 +313,7 @@ def usage(self) -> Usage:
         !!! note
             This won't return the full usage until the stream is finished.
         """
-        return self.usage_so_far + self._stream_response.usage()
+        return self._run_ctx.usage + self._stream_response.usage()
 
     def timestamp(self) -> datetime:
         """Get the timestamp of the response."""

pydantic_ai_slim/pydantic_ai/settings.py

@@ -136,6 +136,6 @@ def check_tokens(self, usage: Usage) -> None:
                 f'Exceeded the response_tokens_limit of {self.response_tokens_limit} ({response_tokens=})'
             )
 
-        total_tokens = request_tokens + response_tokens
+        total_tokens = usage.total_tokens or 0
         if self.total_tokens_limit is not None and total_tokens > self.total_tokens_limit:
             raise UsageLimitExceeded(f'Exceeded the total_tokens_limit of {self.total_tokens_limit} ({total_tokens=})')

pydantic_ai_slim/pydantic_ai/tools.py

@@ -4,7 +4,7 @@
 import inspect
 from collections.abc import Awaitable
 from dataclasses import dataclass, field
-from typing import Any, Callable, Generic, TypeVar, Union, cast
+from typing import TYPE_CHECKING, Any, Callable, Generic, TypeVar, Union, cast
 
 from pydantic import ValidationError
 from pydantic_core import SchemaValidator
@@ -13,6 +13,9 @@
 from . import _pydantic, _utils, messages as _messages, models
 from .exceptions import ModelRetry, UnexpectedModelBehavior
 
+if TYPE_CHECKING:
+    from .result import Usage
+
 __all__ = (
     'AgentDeps',
     'RunContext',
@@ -45,6 +48,8 @@ class RunContext(Generic[AgentDeps]):
     """Name of the tool being called."""
     model: models.Model
     """The model used in this run."""
+    usage: Usage
+    """LLM usage associated with the run."""
 
     def replace_with(
         self, retry: int | None = None, tool_name: str | None | _utils.Unset = _utils.UNSET

tests/test_usage_limits.py

@@ -1,11 +1,20 @@
+import functools
+import operator
 import re
 from datetime import timezone
 
 import pytest
 from inline_snapshot import snapshot
 
-from pydantic_ai import Agent, UsageLimitExceeded
-from pydantic_ai.messages import ArgsDict, ModelRequest, ModelResponse, ToolCallPart, ToolReturnPart, UserPromptPart
+from pydantic_ai import Agent, RunContext, UsageLimitExceeded
+from pydantic_ai.messages import (
+    ArgsDict,
+    ModelRequest,
+    ModelResponse,
+    ToolCallPart,
+    ToolReturnPart,
+    UserPromptPart,
+)
 from pydantic_ai.models.test import TestModel
 from pydantic_ai.result import Usage
 from pydantic_ai.settings import UsageLimits
@@ -97,3 +106,67 @@ async def ret_a(x: str) -> str:
             UsageLimitExceeded, match=re.escape('Exceeded the response_tokens_limit of 10 (response_tokens=11)')
         ):
             await result.get_data()
+
+
+def test_usage_so_far(set_event_loop: None) -> None:
+    test_agent = Agent(TestModel())
+
+    with pytest.raises(
+        UsageLimitExceeded, match=re.escape('Exceeded the total_tokens_limit of 105 (total_tokens=163)')
+    ):
+        test_agent.run_sync(
+            'Hello, this prompt exceeds the request tokens limit.',
+            usage_limits=UsageLimits(total_tokens_limit=105),
+            usage=Usage(total_tokens=100),
+        )
+
+
+async def test_multi_agent_usage_no_incr():
+    delegate_agent = Agent(TestModel(), result_type=int)
+
+    controller_agent1 = Agent(TestModel())
+    run_1_usages: list[Usage] = []
+
+    @controller_agent1.tool
+    async def delegate_to_other_agent1(ctx: RunContext[None], sentence: str) -> int:
+        delegate_result = await delegate_agent.run(sentence)
+        delegate_usage = delegate_result.usage()
+        run_1_usages.append(delegate_usage)
+        assert delegate_usage == snapshot(Usage(requests=1, request_tokens=51, response_tokens=4, total_tokens=55))
+        return delegate_result.data
+
+    result1 = await controller_agent1.run('foobar')
+    assert result1.data == snapshot('{"delegate_to_other_agent1":0}')
+    run_1_usages.append(result1.usage())
+    assert result1.usage() == snapshot(Usage(requests=2, request_tokens=103, response_tokens=13, total_tokens=116))
+
+    controller_agent2 = Agent(TestModel())
+
+    @controller_agent2.tool
+    async def delegate_to_other_agent2(ctx: RunContext[None], sentence: str) -> int:
+        delegate_result = await delegate_agent.run(sentence, usage=ctx.usage)
+        delegate_usage = delegate_result.usage()
+        assert delegate_usage == snapshot(Usage(requests=2, request_tokens=102, response_tokens=9, total_tokens=111))
+        return delegate_result.data
+
+    result2 = await controller_agent2.run('foobar')
+    assert result2.data == snapshot('{"delegate_to_other_agent2":0}')
+    assert result2.usage() == snapshot(Usage(requests=3, request_tokens=154, response_tokens=17, total_tokens=171))
+
+    # confirm the usage from result2 is the sum of the usage from result1
+    assert result2.usage() == functools.reduce(operator.add, run_1_usages)
+
+
+async def test_multi_agent_usage_sync():
+    """As in `test_multi_agent_usage_async`, with a sync tool."""
+    controller_agent = Agent(TestModel())
+
+    @controller_agent.tool
+    def delegate_to_other_agent(ctx: RunContext[None], sentence: str) -> int:
+        new_usage = Usage(requests=5, request_tokens=2, response_tokens=3, total_tokens=4)
+        ctx.usage.incr(new_usage)
+        return 0
+
+    result = await controller_agent.run('foobar')
+    assert result.data == snapshot('{"delegate_to_other_agent":0}')
+    assert result.usage() == snapshot(Usage(requests=7, request_tokens=105, response_tokens=16, total_tokens=120))