This pull request resolves#777; If you think we should introduce a new
item type for MCP call output, please let me know. As other hosted tools
use this event, I believe using the same should be good to go tho.
Added missing word "be" in prompt instructions.
This is unlikely to change the agent functionality in most cases, but
optimal clarity in prompt language is a best practice.
## Summary
This replaces the default model provider with a `MultiProvider`, which
has the logic:
- if the model name starts with `openai/` or doesn't contain "/", use
OpenAI
- if the model name starts with `litellm/`, use LiteLLM to use the
appropriate model provider.
It's also extensible, so users can create their own mappings. I also
imagine that if we natively supported Anthropic/Gemini etc, we can add
it to MultiProvider to make it work.
The goal is that it should be really easy to use any model provider.
Today if you pass `model="gpt-4.1"`, it works great. But
`model="claude-sonnet-3.7"` doesn't. If we can make it that easy, it's a
win for devx.
I'm not entirely sure if this is a good idea - is it too magical? Is the
API too reliant on litellm? Comments welcome.
## Test plan
For now, the example. Will add unit tests if we agree its worth mergin.
---------
Co-authored-by: Steven Heidel <steven@heidel.ca>
See #528, some folks are having issues because their output types are
not strict-compatible.
My approach was:
1. Create `AgentOutputSchemaBase`, which represents the base methods for
an output type - the json schema + validation
2. Make the existing `AgentOutputSchema` subclass
`AgentOutputSchemaBase`
3. Allow users to pass a `AgentOutputSchemaBase` to
`Agent(output_type=...)`
litellm is a library that abstracts away details/differences for a lot
of model providers. Adding an extension, so that any provider can easily
be integrated.
---
[//]: # (BEGIN SAPLING FOOTER)
* #532
* __->__ #524
Thanks to awesome example `research_bot`
Fix andproduce and goodgrammar (and so on)
```python
>>> from examples.research_bot.agents import search_agent
>>> print(search_agent.INSTRUCTIONS)
You are a research assistant. Given a search term, you search the web for that term andproduce a concise summary of the results. The summary must 2-3 paragraphs and less than 300words. Capture the main points. Write succinctly, no need to have complete sentences or goodgrammar. This will be consumed by someone synthesizing a report, so its vital you capture theessence and ignore any fluff. Do not include any additional commentary other than the summaryitself.
```
### Summary
Updated the `computer-use` example to support simultaneous key presses.
The current implementation presses and releases keys one at a time,
which prevents combo inputs like copy (CTRL+C) from working correctly.
### Test plan
N/A
### Issue number
N/A
### Checks
- [ ] I've added new tests (if relevant)
- [ ] I've added/updated the relevant documentation
- [x] I've run `make lint` and `make format`
- [ ] I've made sure tests pass
## 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
.
## Changes
- In this handoff example, we need to use `first_agent` with
`random_number_tool`, instead of `second_agent` without that tool, since
the user is asking to generate a random number.
- fixed typo in comments
