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Evan Mattson
3a463b8bf6
* Python: bump package versions for 1.2.1 release PATCH bump (1.2.0 -> 1.2.1) for the released cohort. The release window covers two PRs, no new public APIs: - agent-framework-core: prevent inner_exception from being lost in AgentFrameworkException (#5167) - samples: add requirements.txt and .env.example to the a2a/ hosting sample for pip-based setup (#5510) Per lockstep convention, all 21 beta packages stamp 1.0.0b260428 and all 3 alpha packages stamp 1.0.0a260428, regardless of per-package code churn. Every non-core package floor on agent-framework-core is raised to >=1.2.1 to keep cohort signaling consistent. Date stamp reflects the local (Asia) cut date 2026-04-28. * Python: silence pyright unknown-type warnings in hosted-env detection `azure.ai.agentserver.core` is probed at runtime via `importlib.util.find_spec` and is not a declared dependency. The existing `# pyright: ignore[reportMissingImports]` suppresses the missing-import warning, but at `lowest-direct` resolution pyright still reports the imported symbol (`AgentConfig`) and its members (`from_env`, `is_hosted`) as unknown, breaking `validate-dependency-bounds-test` for `packages/core`. Extend the existing ignore to cover `reportUnknownVariableType` on the import and `reportUnknownMemberType` on the call site so the bounds check returns to green. Behavior is unchanged. Latent since #5455 (shipped in 1.2.0). * Python: raise agent-framework-gemini lower bound to google-genai>=1.65.0 The Gemini chat client references several `google.genai.types` symbols (`FileSearch`, `ThinkingLevel`, `SearchTypes`, `McpServer`, `StreamableHttpTransport`, plus call-site keyword args `mcp_servers` and `search_types`) that are not present at the lower bound of `google-genai>=1.0.0`. At `lowest-direct` resolution this caused `validate-dependency-bounds-test` to fail for `packages/gemini` with eleven `reportAttributeAccessIssue` / `reportUnknownVariableType` errors. Walking the upstream `google.genai.types` API: - `GoogleMaps`, `AuthConfig`: present from 1.40.0 - `FileSearch`: introduced in 1.49.0 - `ThinkingLevel`: introduced in 1.55.0 - `SearchTypes`, `McpServer`, `StreamableHttpTransport`: introduced in 1.65.0 Bump the lower bound to 1.65.0 — the minimum version that exposes every symbol the package actually uses. Keep the `<2.0.0` upper cap unchanged. With this bump `validate-dependency-bounds-test` passes for both lower and upper resolution scenarios across all 27 workspace packages. Latent since #4847 (Gemini package introduction in 1.1.0); aggravated by subsequent feature additions that pulled in newer `types.*` symbols. * Python: add dependabot bumps to 1.2.1 CHANGELOG Catalog the 15 dependabot dependency updates that merged on `upstream/main` between python-1.2.0 and the 1.2.1 cut window under a new Changed section: - Workspace dev/runtime deps: `rich`, `prek`, `python-multipart`, `pyasn1`, `pytest` (ag-ui, devui, lab), `uv` (lab) - Frontend deps: `vite` (devui, chatkit), `postcss` (devui, chatkit, handoff), `picomatch` (devui, handoff) CHANGELOG-only — no source or pyproject.toml changes. PRs themselves merged upstream independently of this release branch and will be brought in via the PR merge.
3a463b8bf6
·
2026-04-28 18:23:26 +09:00
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.