Add support for usage limits (#409)

Co-authored-by: sydney-runkle <[email protected]>
Co-authored-by: Sydney Runkle <[email protected]>

docs/agents.md

@@ -103,6 +103,90 @@ You can also pass messages from previous runs to continue a conversation or prov
 
 ### Additional Configuration
 
+#### Usage Limits
+
+PydanticAI offers a [`settings.UsageLimits`][pydantic_ai.settings.UsageLimits] structure to help you limit your
+usage (tokens and/or requests) on model runs.
+
+You can apply these settings by passing the `usage_limits` argument to the `run{_sync,_stream}` functions.
+
+Consider the following example, where we limit the number of response tokens:
+
+```py
+from pydantic_ai import Agent
+from pydantic_ai.exceptions import UsageLimitExceeded
+from pydantic_ai.settings import UsageLimits
+
+agent = Agent('claude-3-5-sonnet-latest')
+
+result_sync = agent.run_sync(
+    'What is the capital of Italy? Answer with just the city.',
+    usage_limits=UsageLimits(response_tokens_limit=10),
+)
+print(result_sync.data)
+#> Rome
+print(result_sync.usage())
+"""
+Usage(requests=1, request_tokens=62, response_tokens=1, total_tokens=63, details=None)
+"""
+
+try:
+    result_sync = agent.run_sync(
+        'What is the capital of Italy? Answer with a paragraph.',
+        usage_limits=UsageLimits(response_tokens_limit=10),
+    )
+except UsageLimitExceeded as e:
+    print(e)
+    #> Exceeded the response_tokens_limit of 10 (response_tokens=32)
+```
+
+Restricting the number of requests can be useful in preventing infinite loops or excessive tool calling:
+
+```py
+from typing_extensions import TypedDict
+
+from pydantic_ai import Agent, ModelRetry
+from pydantic_ai.exceptions import UsageLimitExceeded
+from pydantic_ai.settings import UsageLimits
+
+
+class NeverResultType(TypedDict):
+    """
+    Never ever coerce data to this type.
+    """
+
+    never_use_this: str
+
+
+agent = Agent(
+    'claude-3-5-sonnet-latest',
+    result_type=NeverResultType,
+    system_prompt='Any time you get a response, call the `infinite_retry_tool` to produce another response.',
+)
+
+
+@agent.tool_plain(retries=5)  # (1)!
+def infinite_retry_tool() -> int:
+    raise ModelRetry('Please try again.')
+
+
+try:
+    result_sync = agent.run_sync(
+        'Begin infinite retry loop!', usage_limits=UsageLimits(request_limit=3)  # (2)!
+    )
+except UsageLimitExceeded as e:
+    print(e)
+    #> The next request would exceed the request_limit of 3
+```
+
+1. This tool has the ability to retry 5 times before erroring, simulating a tool that might get stuck in a loop.
+2. This run will error after 3 requests, preventing the infinite tool calling.
+
+!!! note
+    This is especially relevant if you're registered a lot of tools, `request_limit` can be used to prevent the model from choosing to make too many of these calls.
+
+#### Model (Run) Settings
+
 PydanticAI offers a [`settings.ModelSettings`][pydantic_ai.settings.ModelSettings] structure to help you fine tune your requests.
 This structure allows you to configure common parameters that influence the model's behavior, such as `temperature`, `max_tokens`,
 `timeout`, and more.

docs/api/models/ollama.md

@@ -32,7 +32,9 @@ result = agent.run_sync('Where were the olympics held in 2012?')
 print(result.data)
 #> city='London' country='United Kingdom'
 print(result.usage())
-#> Usage(request_tokens=57, response_tokens=8, total_tokens=65, details=None)
+"""
+Usage(requests=1, request_tokens=57, response_tokens=8, total_tokens=65, details=None)
+"""
 ```
 
 ## Example using a remote server
@@ -60,7 +62,9 @@ result = agent.run_sync('Where were the olympics held in 2012?')
 print(result.data)
 #> city='London' country='United Kingdom'
 print(result.usage())
-#> Usage(request_tokens=57, response_tokens=8, total_tokens=65, details=None)
+"""
+Usage(requests=1, request_tokens=57, response_tokens=8, total_tokens=65, details=None)
+"""
 ```
 
 1. The name of the model running on the remote server

docs/api/settings.md

@@ -5,3 +5,4 @@
       inherited_members: true
       members:
         - ModelSettings
+        - UsageLimits

docs/results.md

@@ -19,7 +19,9 @@ result = agent.run_sync('Where were the olympics held in 2012?')
 print(result.data)
 #> city='London' country='United Kingdom'
 print(result.usage())
-#> Usage(request_tokens=57, response_tokens=8, total_tokens=65, details=None)
+"""
+Usage(requests=1, request_tokens=57, response_tokens=8, total_tokens=65, details=None)
+"""
 ```
 
 _(This example is complete, it can be run "as is")_

pydantic_ai_slim/pydantic_ai/__init__.py

@@ -1,8 +1,18 @@
 from importlib.metadata import version
 
 from .agent import Agent
-from .exceptions import ModelRetry, UnexpectedModelBehavior, UserError
+from .exceptions import AgentRunError, ModelRetry, UnexpectedModelBehavior, UsageLimitExceeded, UserError
 from .tools import RunContext, Tool
 
-__all__ = 'Agent', 'Tool', 'RunContext', 'ModelRetry', 'UnexpectedModelBehavior', 'UserError', '__version__'
+__all__ = (
+    'Agent',
+    'RunContext',
+    'Tool',
+    'AgentRunError',
+    'ModelRetry',
+    'UnexpectedModelBehavior',
+    'UsageLimitExceeded',
+    'UserError',
+    '__version__',
+)
 __version__ = version('pydantic_ai_slim')

pydantic_ai_slim/pydantic_ai/agent.py

@@ -22,7 +22,7 @@
     result,
 )
 from .result import ResultData
-from .settings import ModelSettings, merge_model_settings
+from .settings import ModelSettings, UsageLimits, merge_model_settings
 from .tools import (
     AgentDeps,
     RunContext,
@@ -191,6 +191,7 @@ async def run(
         model: models.Model | models.KnownModelName | None = None,
         deps: AgentDeps = None,
         model_settings: ModelSettings | None = None,
+        usage_limits: UsageLimits | None = None,
         infer_name: bool = True,
     ) -> result.RunResult[ResultData]:
         """Run the agent with a user prompt in async mode.
@@ -211,8 +212,9 @@ async def run(
             message_history: History of the conversation so far.
             model: Optional model to use for this run, required if `model` was not set when creating the agent.
             deps: Optional dependencies to use for this run.
-            infer_name: Whether to try to infer the agent name from the call frame if it's not set.
             model_settings: Optional settings to use for this model's request.
+            usage_limits: Optional limits on model request count or token usage.
+            infer_name: Whether to try to infer the agent name from the call frame if it's not set.
 
         Returns:
             The result of the run.
@@ -237,12 +239,14 @@ async def run(
             for tool in self._function_tools.values():
                 tool.current_retry = 0
 
-            usage = result.Usage()
-
+            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)
+
                 run_step += 1
                 with _logfire.span('preparing model and tools {run_step=}', run_step=run_step):
                     agent_model = await self._prepare_model(model_used, deps, messages)
@@ -254,6 +258,8 @@ async def run(
 
                 messages.append(model_response)
                 usage += request_usage
+                usage.requests += 1
+                usage_limits.check_tokens(request_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, deps, messages)
@@ -284,6 +290,7 @@ def run_sync(
         model: models.Model | models.KnownModelName | None = None,
         deps: AgentDeps = None,
         model_settings: ModelSettings | None = None,
+        usage_limits: UsageLimits | None = None,
         infer_name: bool = True,
     ) -> result.RunResult[ResultData]:
         """Run the agent with a user prompt synchronously.
@@ -308,8 +315,9 @@ async def main():
             message_history: History of the conversation so far.
             model: Optional model to use for this run, required if `model` was not set when creating the agent.
             deps: Optional dependencies to use for this run.
-            infer_name: Whether to try to infer the agent name from the call frame if it's not set.
             model_settings: Optional settings to use for this model's request.
+            usage_limits: Optional limits on model request count or token usage.
+            infer_name: Whether to try to infer the agent name from the call frame if it's not set.
 
         Returns:
             The result of the run.
@@ -322,8 +330,9 @@ async def main():
                 message_history=message_history,
                 model=model,
                 deps=deps,
-                infer_name=False,
                 model_settings=model_settings,
+                usage_limits=usage_limits,
+                infer_name=False,
             )
         )
 
@@ -336,6 +345,7 @@ async def run_stream(
         model: models.Model | models.KnownModelName | None = None,
         deps: AgentDeps = None,
         model_settings: ModelSettings | None = None,
+        usage_limits: UsageLimits | 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.
@@ -357,8 +367,9 @@ async def main():
             message_history: History of the conversation so far.
             model: Optional model to use for this run, required if `model` was not set when creating the agent.
             deps: Optional dependencies to use for this run.
-            infer_name: Whether to try to infer the agent name from the call frame if it's not set.
             model_settings: Optional settings to use for this model's request.
+            usage_limits: Optional limits on model request count or token usage.
+            infer_name: Whether to try to infer the agent name from the call frame if it's not set.
 
         Returns:
             The result of the run.
@@ -387,16 +398,19 @@ async def main():
 
             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)
 
                 with _logfire.span('preparing model and tools {run_step=}', run_step=run_step):
                     agent_model = await self._prepare_model(model_used, deps, messages)
 
                 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
                         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
@@ -435,6 +449,7 @@ async def on_complete():
                                     messages,
                                     new_message_index,
                                     usage,
+                                    usage_limits,
                                     result_stream,
                                     self._result_schema,
                                     deps,
@@ -456,7 +471,9 @@ async def on_complete():
                                 tool_responses_str = ' '.join(r.part_kind for r in tool_responses)
                                 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
-                                usage += model_response.usage()
+                                model_response_usage = model_response.usage()
+                                usage += model_response_usage
+                                usage_limits.check_tokens(usage)
 
     @contextmanager
     def override(

pydantic_ai_slim/pydantic_ai/exceptions.py

@@ -2,7 +2,7 @@
 
 import json
 
-__all__ = 'ModelRetry', 'UserError', 'UnexpectedModelBehavior'
+__all__ = 'ModelRetry', 'UserError', 'AgentRunError', 'UnexpectedModelBehavior', 'UsageLimitExceeded'
 
 
 class ModelRetry(Exception):
@@ -30,7 +30,25 @@ def __init__(self, message: str):
         super().__init__(message)
 
 
-class UnexpectedModelBehavior(RuntimeError):
+class AgentRunError(RuntimeError):
+    """Base class for errors occurring during an agent run."""
+
+    message: str
+    """The error message."""
+
+    def __init__(self, message: str):
+        self.message = message
+        super().__init__(message)
+
+    def __str__(self) -> str:
+        return self.message
+
+
+class UsageLimitExceeded(AgentRunError):
+    """Error raised when a Model's usage exceeds the specified limits."""
+
+
+class UnexpectedModelBehavior(AgentRunError):
     """Error caused by unexpected Model behavior, e.g. an unexpected response code."""
 
     message: str

pydantic_ai_slim/pydantic_ai/models/anthropic.py

@@ -282,11 +282,11 @@ def _map_message(messages: list[ModelMessage]) -> tuple[str, list[MessageParam]]
                                 MessageParam(
                                     role='user',
                                     content=[
-                                        ToolUseBlockParam(
-                                            id=_guard_tool_call_id(t=part, model_source='Anthropic'),
-                                            input=part.model_response(),
-                                            name=part.tool_name,
-                                            type='tool_use',
+                                        ToolResultBlockParam(
+                                            tool_use_id=_guard_tool_call_id(t=part, model_source='Anthropic'),
+                                            type='tool_result',
+                                            content=part.model_response(),
+                                            is_error=True,
                                         ),
                                     ],
                                 )

pydantic_ai_slim/pydantic_ai/models/function.py

@@ -237,7 +237,7 @@ def get(self, *, final: bool = False) -> ModelResponse:
         return ModelResponse(calls, timestamp=self._timestamp)
 
     def usage(self) -> result.Usage:
-        return result.Usage()
+        return _estimate_usage([self.get()])
 
     def timestamp(self) -> datetime:
         return self._timestamp
@@ -255,24 +255,24 @@ def _estimate_usage(messages: Iterable[ModelMessage]) -> result.Usage:
         if isinstance(message, ModelRequest):
             for part in message.parts:
                 if isinstance(part, (SystemPromptPart, UserPromptPart)):
-                    request_tokens += _string_usage(part.content)
+                    request_tokens += _estimate_string_usage(part.content)
                 elif isinstance(part, ToolReturnPart):
-                    request_tokens += _string_usage(part.model_response_str())
+                    request_tokens += _estimate_string_usage(part.model_response_str())
                 elif isinstance(part, RetryPromptPart):
-                    request_tokens += _string_usage(part.model_response())
+                    request_tokens += _estimate_string_usage(part.model_response())
                 else:
                     assert_never(part)
         elif isinstance(message, ModelResponse):
             for part in message.parts:
                 if isinstance(part, TextPart):
-                    response_tokens += _string_usage(part.content)
+                    response_tokens += _estimate_string_usage(part.content)
                 elif isinstance(part, ToolCallPart):
                     call = part
                     if isinstance(call.args, ArgsJson):
                         args_str = call.args.args_json
                     else:
                         args_str = pydantic_core.to_json(call.args.args_dict).decode()
-                    response_tokens += 1 + _string_usage(args_str)
+                    response_tokens += 1 + _estimate_string_usage(args_str)
                 else:
                     assert_never(part)
         else:
@@ -282,5 +282,5 @@ def _estimate_usage(messages: Iterable[ModelMessage]) -> result.Usage:
     )
 
 
-def _string_usage(content: str) -> int:
+def _estimate_string_usage(content: str) -> int:
     return len(re.split(r'[\s",.:]+', content))