This PR improves documentation on custom agents
- Shows example on how to create a custom agent that directly uses a
model client. In this case an example of a GeminiAssistantAgent that
directly uses the Gemini SDK model client.
- Shows that that CustomAgent can be easily added to any agentchat team
- Shows how the same CustomAgent can be made declarative by inheriting
the Component interface and implementing the required methods.
Closes#5450
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## Why are these changes needed?
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solves. -->
## Related issue number
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Fix#4731
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## Why are these changes needed?
These changes are needed because currently there's no generic way to add
`tools` to autogen studio workflows using the existing DSL and schema
other than inline python.
This API will be quite verbose, and lacks a discovery mechanism, but it
unlocks a lot of programmatic use-cases.
## Related issue number
https://github.com/microsoft/autogen/issues/5170
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Have seen discussion on Discord regarding confusion about multi-modal
support in v0.4. This change adds a small note on how to use multi-modal
messages with agents.
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## Why are these changes needed?
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solves. -->
The current implementation tries to recreate the metadata but it does it
in an incomplete way. This PR uses SK built-in kernel from function
decorator to infer the callable from the `run_json` and makes better use
of the pydantic schemas for the input and output to infer the schema of
the kernel function.
## Related issue number
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Closes#5458
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---------
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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Add clear notes on how to specify api key and modifying specifications
in AGS.
Add diagrams explaining how to switch between visual builder and JSON
mode
## Why are these changes needed?
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solves. -->
## Related issue number
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@nour-bouzid
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It is often helpful to inspect the raw request and response to/from an
LLM as agents act.
This PR, does the following:
- Updates TeamManager to yield LLMCallEvents from core library
- Run in an async background task to listen for LLMCallEvent JIT style
when a team is run
- Add events to an async queue and
- yield those events in addition to whatever actual agentchat
team.run_stream yields.
- Update the AGS UI to show those LLMCallEvents in the messages section
as a team runs
- Add settings panel to show/hide llm call events in messages.
- Minor updates to default team
<img width="1539" alt="image"
src="https://github.com/user-attachments/assets/bfbb19fe-3560-4faa-b600-7dd244e0e974"
/>
<img width="1554" alt="image"
src="https://github.com/user-attachments/assets/775624f5-ba83-46e8-81ff-512bfeed6bab"
/>
<img width="1538" alt="image"
src="https://github.com/user-attachments/assets/3becbf85-b75a-4506-adb7-e5575ebbaec4"
/>
## Why are these changes needed?
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solves. -->
## Related issue number
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Closes#5440
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## Why are these changes needed?
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solves. -->
The current stream processing of SK model adapter returns on the first
function call chunk but this behavior is incorrect end ends up returning
with an incomplete function call. The observed behavior is that the
function name and arguments are split into different chunks and this
update correctly processes the chunks in this way.
## Related issue number
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Fixes the reply in #5420
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---------
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
Don't throw an exception when model makes a mistake. Use retries, and if
not succeeding after a fixed attempts, fall back to the previous sepaker
if available, or the first participant.
Resolves#5453
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Semantic kernel prepends the plugin name to the tool name when passing
the tools to model clients and this is causing a mismatch between tool
names in SK and the AssistantAgent. Since plugin names are optional, we
have opted to remove it.
## Related issue number
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Closes#5420
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---------
Co-authored-by: Leonardo Pinheiro <lpinheiro@microsoft.com>
Restoring the grpc + Orleans server into the project
## Why are these changes needed?
This is the distributed agent runtime for .NET that can manage routing
messages amongst a fleet of grpc agent runtimes.
## Related issue number
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---------
Co-authored-by: Jack Gerrits <jack@jackgerrits.com>
Co-authored-by: Jacob Alber <jaalber@microsoft.com>
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## Why are these changes needed?
- Add ability to test teams in Team Builder view
- Update Gallery (add deep research default team, fix bug with gallery
serialization)
- UI fixes
- improve drag drop component experience
- improve new session experience (single click rather than 3 clicks to
create a session)
- fix bug with stop reason not being shown in some cases
<img width="1738" alt="Image"
src="https://github.com/user-attachments/assets/4b895df2-3bad-474e-bec6-4fbcbf1c4346"
/>
<img width="1761" alt="Image"
src="https://github.com/user-attachments/assets/65f52eb9-e926-4168-88fb-d2496c159474"
/>
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## Related issue number
Closes#5392
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Introduce a sample chat application using AgentChat and FastAPI,
demonstrating single-agent and team chat functionalities, along with
state persistence and conversation history management.
Resolves#5423
---------
Co-authored-by: Victor Dibia <victor.dibia@gmail.com>
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
Presently MagenticOne and the m1 CLI use the LocalCommandLineExecutor
(presumably copied from the agbench code, which already runs in Docker).
This pr defaults m1 to Docker, and adds a code_executor parameter to
MagenticOne, which defaults to local for now to maintain backward
compatibility -- but this behavior is immediately deprecated.
- Updated HumanEval template to use AgentChat
- Update templates to use config.yaml for model and other configuration
- Read environment from ENV.yaml (ENV.json still supported but
deprecated)
- Temporarily removed WebArena and AssistantBench. Neither had viable
Templates after `autogen_magentic_one` was removed. Templates need to be
update to AgentChat (in a future PR, but this PR is getting big enough
already)
A series of changes to the
`python/packages/autogen-ext/src/autogen_ext/agents/web_surfer/_multimodal_web_surfer.py`
file have been made to better support smaller models.
This includes changes to the prompts, state descriptions, and ordering
of messages.
Regression tasks with OpenAI models shows no change in GAIA scores,
while scores for Llama are significantly improved.
Get's SelectorGroupChat working for llama by:
1. Using a UserMessage rather than a SystemMessage
2. Normalizing how roles are presented (one agent per line)
3. Normalizing how the transcript is constructed (a blank line between
every message)
This PR fixes:
A prompting bug when no control had focus.
Awkward prompt phrasing.
Renamed page_down to scroll_down to better match other prompting and
agent descriptions.
Some agent descriptions were split over multiple lines in the M1
orchestrator. This PR ensures that each description appears on one, and
only one, line. This makes it easier for smaller models to understand.
The Tuple class is never used in CountDownAgent class.
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## Why are these changes needed?
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## Related issue number
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Co-authored-by: Victor Dibia <victordibia@microsoft.com>
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## Why are these changes needed?
Currently the way to accomplish RAG behavior with agent chat,
specifically assistant agents is with the memory interface, however
there is no way to configure it via the declarative API.
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solves. -->
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---------
Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>
