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
### Summary:
1. Add the MCP dep for python 3.10, since it doesn't support 3.9 and
below
2. Create MCPServer, which is the agents SDK representation of an MCP
server
3. Create implementations for HTTP-SSE and StdIO servers, directly
copying the [MCP SDK
example](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/clients/simple-chatbot/mcp_simple_chatbot/main.py)
4. Add a util to transform MCP tools into Agent SDK tools
Note: I added optional caching support to the servers. That way, if you
happen to know a server's tools don't change, you can just cache them.
### Test Plan:
Checks pass. I added tests at the end of the stack.
---
#324#322#321
-> #320#319
### Summary:
1. Add the MCP dep for python 3.10, since it doesn't support 3.9 and below
2. Create MCPServer, which is the agents SDK representation of an MCP server
3. Create implementations for HTTP-SSE and StdIO servers, directly copying the [MCP SDK example](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/clients/simple-chatbot/mcp_simple_chatbot/main.py)
4. Add a util to transform MCP tools into Agent SDK tools
Note: I added optional caching support to the servers. That way, if you happen to know a server's tools don't change, you can just cache them.
### Test Plan:
Checks pass. I added tests at the end of the stack.
Currently, when we set `parallel_tool_calls=False` in the
`model_settings`, the responses API is called with `parallel_tool_calls
== NotGiven`, which defaults to true on the Response API side
(https://platform.openai.com/docs/api-reference/responses/create#responses-create-parallel_tool_calls).
This PR attempts to fix that.
```
agent = Agent(
...,
model_settings=ModelSettings(
parallel_tool_calls=False,
),
)
```
# Fix potential infinite tool call loop by resetting tool_choice after
tool execution
## Summary
This PR fixes an issue where setting `tool_choice` to "required" or a
specific function name could cause models to get stuck in an infinite
tool call loop.
When `tool_choice` is set to force tool usage, this setting persists
across model invocations. This PR automatically resets `tool_choice` to
"auto" after tool execution, allowing the model to decide whether to
make additional tool calls in subsequent turns.
Unlike using `tool_use_behavior="stop_on_first_tool"`, this approach
lets the model continue processing tool results while preventing forced
repeated tool calls.
## Test plan
- Added tests to verify tool_choice reset behavior for both agent and
run_config settings
- Added integration test to verify the solution prevents infinite loops
- All tests pass
## Checks
- [x] I've added new tests for the fix
- [x] I've updated the relevant documentation (added comment in code)
- [x] I've run `make lint` and `make format`
- [x] I've made sure tests pass
We don't really need mypy on 3.9 (unit tests would catch any real
issues), and it causes issues with the rest of this stack.
---
[//]: # (BEGIN SAPLING FOOTER)
* #324
* #322
* #321
* #320
* __->__ #319
### 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.
### Summary:
1. Add the MCP dep for python 3.10, since it doesn't support 3.9 and below
2. Create MCPServer, which is the agents SDK representation of an MCP server
3. Create implementations for HTTP-SSE and StdIO servers, directly copying the [MCP SDK example](https://github.com/modelcontextprotocol/python-sdk/blob/main/examples/clients/simple-chatbot/mcp_simple_chatbot/main.py)
4. Add a util to transform MCP tools into Agent SDK tools
Note: I added optional caching support to the servers. That way, if you happen to know a server's tools don't change, you can just cache them.
### Test Plan:
Checks pass. I added tests at the end of the stack.
This update moves the tool_choice reset logic to a more appropriate location within the RunImpl class, ensuring that the original agent's model_settings remains unmodified during the reset process. The logic now checks for problematic scenarios before creating a modified copy of the agent's settings, maintaining expected behavior across sequential runs. This change enhances clarity and efficiency in handling tool choices.
Addresses previous feedback regarding the modification of the agent instance and improves the overall structure of the reset logic.
This fixes the issue where the original agent's model_settings was being directly modified during the tool choice reset process. The original implementation caused the agent's tool_choice to unintentionally reset to "auto" for subsequent runs, which could be unexpected behavior.
The fix creates new copies of the agent and model settings objects using dataclasses.replace() instead of modifying the original objects. This ensures that the tool choice reset is limited to the current run only, maintaining the expected behavior for sequential runs with the same agent.
Addresses feedback from @baderalfahad about the agent instance being modified when it should maintain its original state between runs.
- Refactor tool_choice reset to target only problematic edge cases
- Replace manual ModelSettings recreation with dataclasses.replace
- Fix line length and error handling lint issues in tests
Fix circular dependency in voice streamed example by renaming agents.py
to my_workflow.py
Fix circular dependency in voice streamed example by renaming agents
#291
