[Error occurred during streaming: 1 validation error for ResponseUsage
input_tokens_details
Input should be a valid dictionary or instance of InputTokensDetails [type=model_type, input_value=InputTokensDetails(prompt...ens=17, cached_tokens=0), input_type=InputTokensDetails]
For further information visit https://errors.pydantic.dev/2.10/v/model_type]
Location: Traceback (most recent call last):
File /Users/alias/Alias/research/openai-agents-python/src/cai/cli.py, line 209, in process_streamed_response
async for event in result.stream_events():
File /Users/alias/Alias/research/openai-agents-python/src/cai/sdk/agents/result.py, line 186, in stream_events
raise self._stored_exception
File /Users/alias/Alias/research/openai-agents-python/src/cai/sdk/agents/run.py, line 539, in _run_streamed_impl
turn_result = await cls._run_single_turn_streamed(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File /Users/alias/Alias/research/openai-agents-python/src/cai/sdk/agents/run.py, line 641, in _run_single_turn_streamed
async for event in model.stream_response(
File /Users/alias/Alias/research/openai-agents-python/src/cai/sdk/agents/models/openai_chatcompletions.py, line 421, in stream_response
ResponseUsage(
File /Users/alias/Alias/research/openai-agents-python/env/lib/python3.12/site-packages/pydantic/main.py, line 214, in __init__
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
pydantic_core._pydantic_core.ValidationError: 1 validation error for ResponseUsage
input_tokens_details
Input should be a valid dictionary or instance of InputTokensDetails [type=model_type, input_value=InputTokensDetails(prompt...ens=17, cached_tokens=0), input_type=InputTokensDetails]
For further information visit https://errors.pydantic.dev/2.10/v/model_type
Signed-off-by: Víctor Mayoral Vilches <v.mayoralv@gmail.com>
- agents can't translate directly it seems, see current error
- in addition, it appears streaming mode leads to other problems
Signed-off-by: Víctor Mayoral Vilches <v.mayoralv@gmail.com>
Towards #345
## Summary:
Using a `dict` or `Mapping` isn't strict-mode compliant. But we were
checking for the literal `True` whereas the value can also be an array,
for example. Fix that.
## Test Plan:
Unit tests
## Summary:
Adds tracing and tests for tracing.
- Tools are added to the agents
- Theres a span for the mcp tools lookup
- Functions have MCP data
## Test Plan:
Unit tests
.
## Summary:
Adds tracing and tests for tracing.
- Tools are added to the agents
- Theres a span for the mcp tools lookup
- Functions have MCP data
## Test Plan:
Unit tests
.
This pull request introduces functionality for visualizing agent
structures using Graphviz. The changes include adding a new dependency,
implementing functions to generate and draw graphs, and adding tests for
these functions.
New functionality for visualizing agent structures:
* Added `graphviz` as a new dependency in `pyproject.toml`.
* Implemented functions in `src/agents/visualizations.py` to generate
and draw graphs for agents using Graphviz. These functions include
`get_main_graph`, `get_all_nodes`, `get_all_edges`, and `draw_graph`.
Testing the new visualization functionality:
* Added tests in `tests/test_visualizations.py` to verify the
correctness of the graph generation and drawing functions. The tests
cover `get_main_graph`, `get_all_nodes`, `get_all_edges`, and
`draw_graph`.
For example, given the following code:
```python
from agents import Agent, function_tool
from agents.visualizations import draw_graph
@function_tool
def get_weather(city: str) -> str:
return f"The weather in {city} is sunny."
spanish_agent = Agent(
name="Spanish agent",
instructions="You only speak Spanish.",
)
english_agent = Agent(
name="English agent",
instructions="You only speak English",
)
triage_agent = Agent(
name="Triage agent",
instructions="Handoff to the appropriate agent based on the language of the request.",
handoffs=[spanish_agent, english_agent],
tools=[get_weather],
)
draw_graph(triage_agent)
```
Generates the following image:
<img width="614" alt="Screenshot 2025-03-13 at 18 36 23"
src="https://github.com/user-attachments/assets/d01fe502-6886-4efb-aaf8-c92e4524b0fe"
/>
Previously the stream was closing as soon as all the audio was added but
didn't wait for it to be finished. Additionally the audio might seem
chopped off if there is no additional silence so this PR also adds one
second of silence before exiting the program.
## Summary:
#263 added this behavior. The goal was to prevent infinite loops when
tool choice was set. The key change I'm making is:
1. Making it configurable on the agent.
2. Doing bookkeeping in the Runner to track this, to prevent mutating
agents.
3. Not resetting the global tool choice in RunConfig.
## Test Plan:
Unit tests.
.
## Summary:
#263 added this behavior. The goal was to prevent infinite loops when tool choice was set. The key change I'm making is:
1. Making it configurable on the agent.
2. Doing bookkeeping in the Runner to track this, to prevent mutating agents.
3. Not resetting the global tool choice in RunConfig.
## Test Plan:
Unit tests.
.
### Summary:
Spins up a stdio server with some local files, then asks the model
questions.
### Test Plan:
Run the example, see it work.
(repeat of #322)
### Summary:
This enables users to **use** MCP inside the SDK.
1. You add a list of MCP servers to `Agent`, via `mcp_server=[...]`
2. When an agent runs, we look up its MCP tools and add them to the list
of tools.
3. When a tool call occurs, we call the relevant MCP server.
Notes:
1. There's some refactoring to make sure we send the full list of tools
to the Runner/Model etc.
2. Right now, you could have a locally defined tool that conflicts with
an MCP defined tool. I didn't add errors for that, will do in a
followup.
### Test Plan:
See unit tests. Also has an end to end example next PR.
---
[//]: # (BEGIN SAPLING FOOTER)
* #324
* #322
* __->__ #321
* #320
Víctor Mayoral Vilches
Ali Waleed
Martin Jagusch
amri369
Steven Heidel
Rohan Mehta
pakrym-oai
Dmitry Pimenov
Dominik Kundel
Martín Bravo