mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Eduard van Valkenburg
0cd40f8354
* [BREAKING] Refactor middleware layering and raw clients Reorder chat client layers so function invocation wraps chat middleware, and chat middleware stays outside telemetry while still running for each inner model call. Add middleware pipeline caching, refresh docs and samples, and split Anthropic into raw and public clients to match the standard layering model. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Tighten typing ignores in ancillary modules Add targeted typing ignores in workflow visualization and lab modules so pyright stays clean alongside the middleware refactor work. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix categorize_middleware to unpack tuple/Sequence and use relative MRO assertions - Broaden isinstance check in categorize_middleware from list to Sequence so tuples and other Sequence types are properly unpacked instead of being appended as a single item. - Replace fragile hardcoded MRO index assertions in anthropic test with relative ordering via mro.index(). - Add regression tests for categorize_middleware with tuple, list, and None inputs. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix middleware string decomposition, add middleware param to FunctionInvocationLayer, and add tests (#4710) - Guard categorize_middleware Sequence check against str/bytes to prevent character-by-character decomposition of accidentally passed strings - Add explicit middleware parameter to FunctionInvocationLayer.get_response and merge it into client_kwargs before categorization, fixing the inconsistency where only OpenAIChatClient supported this parameter - Add assertions that RawAnthropicClient does not inherit convenience layers - Add chat middleware cache test with non-empty base middleware - Add tests for single unwrapped middleware item and string input Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Apply pre-commit auto-fixes * Apply pre-commit auto-fixes * Address review feedback for #4710: review comment fixes --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: Copilot <copilot@github.com>
0cd40f8354
ยท
2026-03-20 00:43:37 +00:00
History
Agent Framework Orchestrations
Orchestration patterns for Microsoft Agent Framework. This package provides high-level builders for common multi-agent workflow patterns.
Installation
pip install agent-framework-orchestrations --pre
Orchestration Patterns
SequentialBuilder
Chain agents/executors in sequence, passing conversation context along:
from agent_framework.orchestrations import SequentialBuilder
workflow = SequentialBuilder(participants=[agent1, agent2, agent3]).build()
ConcurrentBuilder
Fan-out to multiple agents in parallel, then aggregate results:
from agent_framework.orchestrations import ConcurrentBuilder
workflow = ConcurrentBuilder(participants=[agent1, agent2, agent3]).build()
HandoffBuilder
Decentralized agent routing where agents decide handoff targets:
from agent_framework.orchestrations import HandoffBuilder
workflow = (
HandoffBuilder()
.participants([triage, billing, support])
.with_start_agent(triage)
.build()
)
GroupChatBuilder
Orchestrator-directed multi-agent conversations:
from agent_framework.orchestrations import GroupChatBuilder
workflow = GroupChatBuilder(
participants=[agent1, agent2],
selection_func=my_selector,
).build()
MagenticBuilder
Sophisticated multi-agent orchestration using the Magentic One pattern:
from agent_framework.orchestrations import MagenticBuilder
workflow = MagenticBuilder(
participants=[researcher, writer, reviewer],
manager_agent=manager_agent,
).build()
Documentation
For more information, see the Agent Framework documentation.