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Python: Implement annotation-based context compaction (#4469)

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* Implement annotation-based context compaction

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Handle missing compaction attributes in BaseChatClient

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Fix CI typing and bandit issues

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Optimize incremental compaction annotation pass

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* refinement

* Python: add ToolResultCompactionStrategy and CompactionProvider

Add ToolResultCompactionStrategy that collapses older tool-call groups
into short summary messages (e.g. [Tool calls: get_weather]) while
keeping the most recent groups verbatim. This mirrors the .NET
ToolResultCompactionStrategy from PR #4533.

Add CompactionProvider as a context-provider that auto-applies compaction
before each agent turn and stores compacted history in session state
after each turn.

Includes tests and samples for both features.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* refinement and alignment with dotnet PR

* updated tool result compaction

* updated tool result compaction

* Python: add ToolResultCompactionStrategy, CompactionProvider, and skip_excluded

- ToolResultCompactionStrategy collapses older tool-call groups into
  [Tool results: func_name: result] summaries with bidirectional tracing
  (same pattern as SummarizationStrategy).
- CompactionProvider as BaseContextProvider with separate before_strategy
  and after_strategy parameters. before_strategy compacts loaded context;
  after_strategy compacts stored history via history_source_id.
- InMemoryHistoryProvider gains skip_excluded flag to filter out messages
  marked as excluded by compaction strategies.
- Tests, samples, and exports updated.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fixed checks

* fix mypy

* Fix: ensure summary messages from both strategies get full compaction annotations

SummarizationStrategy was not calling annotate_message_groups after
inserting its summary message, so the summary lacked core group
annotations (id, kind, index, has_reasoning, _excluded). Added the
missing call. ToolResultCompactionStrategy already had it.

Added tests verifying both strategies produce fully annotated summaries.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* updated propagation

* fix mypy

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Eduard van Valkenburg
2026-03-11 20:23:00 +01:00
committed by GitHub
Unverified
parent 565c0b1623
commit 3e03a305f6
29 changed files with 4397 additions and 205 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/packages/azure-ai-search/tests/test_aisearch_context_provider.py
+7
View File
@@ -16,6 +16,13 @@ from agent_framework_azure_ai_search._context_provider import AzureAISearchConte
# -- Helpers -------------------------------------------------------------------
@pytest.fixture(autouse=True)
def clear_azure_search_environment(monkeypatch: pytest.MonkeyPatch) -> None:
for key in tuple(os.environ):
if key.startswith("AZURE_SEARCH_"):
monkeypatch.delenv(key, raising=False)
class MockSearchResults:
"""Async-iterable mock for Azure SearchClient.search() results."""
python/packages/core/agent_framework/__init__.py
+54
View File
@@ -29,6 +29,34 @@ from ._clients import (
SupportsMCPTool,
SupportsWebSearchTool,
)
from ._compaction import (
COMPACTION_STATE_KEY,
EXCLUDE_REASON_KEY,
EXCLUDED_KEY,
GROUP_ANNOTATION_KEY,
GROUP_HAS_REASONING_KEY,
GROUP_ID_KEY,
GROUP_INDEX_KEY,
GROUP_KIND_KEY,
GROUP_TOKEN_COUNT_KEY,
SUMMARIZED_BY_SUMMARY_ID_KEY,
SUMMARY_OF_GROUP_IDS_KEY,
SUMMARY_OF_MESSAGE_IDS_KEY,
CharacterEstimatorTokenizer,
CompactionProvider,
CompactionStrategy,
SelectiveToolCallCompactionStrategy,
SlidingWindowStrategy,
SummarizationStrategy,
TokenBudgetComposedStrategy,
TokenizerProtocol,
ToolResultCompactionStrategy,
TruncationStrategy,
annotate_message_groups,
apply_compaction,
included_messages,
included_token_count,
)
from ._mcp import MCPStdioTool, MCPStreamableHTTPTool, MCPWebsocketTool
from ._middleware import (
AgentContext,
@@ -196,7 +224,19 @@ from .exceptions import (
__all__ = [
"AGENT_FRAMEWORK_USER_AGENT",
"APP_INFO",
"COMPACTION_STATE_KEY",
"DEFAULT_MAX_ITERATIONS",
"EXCLUDED_KEY",
"EXCLUDE_REASON_KEY",
"GROUP_ANNOTATION_KEY",
"GROUP_HAS_REASONING_KEY",
"GROUP_ID_KEY",
"GROUP_INDEX_KEY",
"GROUP_KIND_KEY",
"GROUP_TOKEN_COUNT_KEY",
"SUMMARIZED_BY_SUMMARY_ID_KEY",
"SUMMARY_OF_GROUP_IDS_KEY",
"SUMMARY_OF_MESSAGE_IDS_KEY",
"USER_AGENT_KEY",
"USER_AGENT_TELEMETRY_DISABLED_ENV_VAR",
"Agent",
@@ -218,6 +258,7 @@ __all__ = [
"BaseEmbeddingClient",
"BaseHistoryProvider",
"Case",
"CharacterEstimatorTokenizer",
"ChatAndFunctionMiddlewareTypes",
"ChatContext",
"ChatMiddleware",
@@ -227,6 +268,8 @@ __all__ = [
"ChatResponse",
"ChatResponseUpdate",
"CheckpointStorage",
"CompactionProvider",
"CompactionStrategy",
"Content",
"ContinuationToken",
"Default",
@@ -273,6 +316,7 @@ __all__ = [
"Runner",
"RunnerContext",
"SecretString",
"SelectiveToolCallCompactionStrategy",
"SessionContext",
"SingleEdgeGroup",
"Skill",
@@ -280,8 +324,10 @@ __all__ = [
"SkillScript",
"SkillScriptRunner",
"SkillsProvider",
"SlidingWindowStrategy",
"SubWorkflowRequestMessage",
"SubWorkflowResponseMessage",
"SummarizationStrategy",
"SupportsAgentRun",
"SupportsChatGetResponse",
"SupportsCodeInterpreterTool",
@@ -294,8 +340,12 @@ __all__ = [
"SwitchCaseEdgeGroupCase",
"SwitchCaseEdgeGroupDefault",
"TextSpanRegion",
"TokenBudgetComposedStrategy",
"TokenizerProtocol",
"ToolMode",
"ToolResultCompactionStrategy",
"ToolTypes",
"TruncationStrategy",
"TypeCompatibilityError",
"UpdateT",
"UsageDetails",
@@ -322,12 +372,16 @@ __all__ = [
"__version__",
"add_usage_details",
"agent_middleware",
"annotate_message_groups",
"apply_compaction",
"chat_middleware",
"create_edge_runner",
"detect_media_type_from_base64",
"executor",
"function_middleware",
"handler",
"included_messages",
"included_token_count",
"load_settings",
"map_chat_to_agent_update",
"merge_chat_options",
python/packages/core/agent_framework/_agents.py
+45 -3
View File
@@ -74,6 +74,7 @@ else:
from typing_extensions import Self, TypedDict # pragma: no cover
if TYPE_CHECKING:
from ._compaction import CompactionStrategy, TokenizerProtocol
from ._types import ChatOptions
logger = logging.getLogger("agent_framework")
@@ -177,6 +178,8 @@ class _RunContext(TypedDict):
session_messages: Sequence[Message]
agent_name: str
chat_options: MutableMapping[str, Any]
compaction_strategy: CompactionStrategy | None
tokenizer: TokenizerProtocol | None
filtered_kwargs: Mapping[str, Any]
finalize_kwargs: Mapping[str, Any]
@@ -665,6 +668,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
default_options: OptionsCoT | None = None,
context_providers: Sequence[BaseContextProvider] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> None:
"""Initialize a Agent instance.
@@ -688,6 +693,10 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
Note: response_format typing does not flow into run outputs when set via default_options.
These can be overridden at runtime via the ``options`` parameter of ``run()``.
tools: The tools to use for the request.
compaction_strategy: Optional agent-level in-run compaction.
If both this and a compaction_strategy on the underlying client are set, this one is used.
tokenizer: Optional agent-level tokenizer.
If both this and a tokenizer on the underlying client are set, this one is used.
kwargs: Any additional keyword arguments. Will be stored as ``additional_properties``.
"""
opts = dict(default_options) if default_options else {}
@@ -705,6 +714,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
**kwargs,
)
self.client = client
self.compaction_strategy = compaction_strategy
self.tokenizer = tokenizer
# Get tools from options or named parameter (named param takes precedence)
tools_ = tools if tools is not None else opts.pop("tools", None)
@@ -799,6 +810,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
session: AgentSession | None = None,
tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
options: ChatOptions[ResponseModelBoundT],
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[AgentResponse[ResponseModelBoundT]]: ...
@@ -811,6 +824,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
session: AgentSession | None = None,
tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
options: OptionsCoT | ChatOptions[None] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[AgentResponse[Any]]: ...
@@ -823,6 +838,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
session: AgentSession | None = None,
tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
options: OptionsCoT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> ResponseStream[AgentResponseUpdate, AgentResponse[Any]]: ...
@@ -834,6 +851,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
session: AgentSession | None = None,
tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
options: OptionsCoT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[AgentResponse[Any]] | ResponseStream[AgentResponseUpdate, AgentResponse[Any]]:
"""Run the agent with the given messages and options.
@@ -857,8 +876,14 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
``Agent[OpenAIChatOptions]``, this enables IDE autocomplete for
provider-specific options including temperature, max_tokens, model_id,
tool_choice, and provider-specific options like reasoning_effort.
kwargs: Additional keyword arguments for the agent.
Will only be passed to functions that are called.
compaction_strategy: Optional per-run compaction override passed to
``client.get_response()``. When omitted, the agent-level override
is used, falling back to the client default.
tokenizer: Optional per-run tokenizer override passed to
``client.get_response()``. When omitted, the agent-level override
is used, falling back to the client default.
kwargs: Additional keyword arguments for the agent. These are only
passed to functions that are called.
Returns:
When stream=False: An Awaitable[AgentResponse] containing the agent's response.
@@ -873,6 +898,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
session=session,
tools=tools,
options=options,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
kwargs=kwargs,
)
response = cast(
@@ -881,6 +908,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
messages=ctx["session_messages"],
stream=False,
options=ctx["chat_options"], # type: ignore[reportArgumentType]
compaction_strategy=ctx["compaction_strategy"],
tokenizer=ctx["tokenizer"],
**ctx["filtered_kwargs"],
),
)
@@ -954,6 +983,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
session=session,
tools=tools,
options=options,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
kwargs=kwargs,
)
ctx: _RunContext = ctx_holder["ctx"] # type: ignore[assignment] # Safe: we just assigned it
@@ -961,6 +992,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
messages=ctx["session_messages"],
stream=True,
options=ctx["chat_options"], # type: ignore[reportArgumentType]
compaction_strategy=ctx["compaction_strategy"],
tokenizer=ctx["tokenizer"],
**ctx["filtered_kwargs"],
)
@@ -1047,6 +1080,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
session: AgentSession | None,
tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None,
options: Mapping[str, Any] | None,
compaction_strategy: CompactionStrategy | None,
tokenizer: TokenizerProtocol | None,
kwargs: dict[str, Any],
) -> _RunContext:
opts = dict(options) if options else {}
@@ -1081,9 +1116,10 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
options=opts,
)
agent_name = self._get_agent_name()
# Normalize tools
normalized_tools = normalize_tools(tools_)
agent_name = self._get_agent_name()
# Resolve final tool list (runtime provided tools + local MCP server tools)
final_tools: list[FunctionTool | Callable[..., Any] | dict[str, Any] | Any] = []
@@ -1153,6 +1189,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
"session_messages": session_messages,
"agent_name": agent_name,
"chat_options": co,
"compaction_strategy": compaction_strategy or self.compaction_strategy,
"tokenizer": tokenizer or self.tokenizer,
"filtered_kwargs": filtered_kwargs,
"finalize_kwargs": finalize_kwargs,
}
@@ -1408,6 +1446,8 @@ class Agent(
default_options: OptionsCoT | None = None,
context_providers: Sequence[BaseContextProvider] | None = None,
middleware: Sequence[MiddlewareTypes] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> None:
"""Initialize a Agent instance."""
@@ -1421,5 +1461,7 @@ class Agent(
default_options=default_options,
context_providers=context_providers,
middleware=middleware,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
**kwargs,
)
python/packages/core/agent_framework/_clients.py
+130 -6
View File
@@ -52,6 +52,7 @@ else:
if TYPE_CHECKING:
from ._agents import Agent
from ._compaction import CompactionStrategy, TokenizerProtocol
from ._middleware import (
MiddlewareTypes,
)
@@ -134,6 +135,8 @@ class SupportsChatGetResponse(Protocol[OptionsContraT]):
*,
stream: Literal[False] = ...,
options: ChatOptions[ResponseModelBoundT],
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[ResponseModelBoundT]]: ...
@@ -144,6 +147,8 @@ class SupportsChatGetResponse(Protocol[OptionsContraT]):
*,
stream: Literal[False] = ...,
options: OptionsContraT | ChatOptions[None] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]]: ...
@@ -154,6 +159,8 @@ class SupportsChatGetResponse(Protocol[OptionsContraT]):
*,
stream: Literal[True],
options: OptionsContraT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> ResponseStream[ChatResponseUpdate, ChatResponse[Any]]: ...
@@ -163,6 +170,8 @@ class SupportsChatGetResponse(Protocol[OptionsContraT]):
*,
stream: bool = False,
options: OptionsContraT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]] | ResponseStream[ChatResponseUpdate, ChatResponse[Any]]:
"""Send input and return the response.
@@ -171,6 +180,8 @@ class SupportsChatGetResponse(Protocol[OptionsContraT]):
messages: The sequence of input messages to send.
stream: Whether to stream the response. Defaults to False.
options: Chat options as a TypedDict.
compaction_strategy: Optional per-call compaction override.
tokenizer: Optional per-call tokenizer override.
**kwargs: Additional chat options.
Returns:
@@ -252,7 +263,13 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
"""
OTEL_PROVIDER_NAME: ClassVar[str] = "unknown"
DEFAULT_EXCLUDE: ClassVar[set[str]] = {"additional_properties"}
compaction_strategy: CompactionStrategy | None = None
tokenizer: TokenizerProtocol | None = None
DEFAULT_EXCLUDE: ClassVar[set[str]] = {
"additional_properties",
"compaction_strategy",
"tokenizer",
}
STORES_BY_DEFAULT: ClassVar[bool] = False
"""Whether this client stores conversation history server-side by default.
@@ -267,15 +284,21 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
self,
*,
additional_properties: dict[str, Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> None:
"""Initialize a BaseChatClient instance.
Keyword Args:
additional_properties: Additional properties for the client.
compaction_strategy: Optional compaction strategy to apply before model calls.
tokenizer: Optional tokenizer used by token-aware compaction strategies.
kwargs: Additional keyword arguments (merged into additional_properties).
"""
self.additional_properties = additional_properties or {}
self.compaction_strategy = compaction_strategy
self.tokenizer = tokenizer
super().__init__(**kwargs)
def to_dict(self, *, exclude: set[str] | None = None, exclude_none: bool = True) -> dict[str, Any]:
@@ -337,6 +360,46 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
finalizer=lambda updates: self._finalize_response_updates(updates, response_format=response_format),
)
async def _prepare_messages_for_model_call(
self,
messages: Sequence[Message],
*,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
) -> list[Message]:
prepared_messages = list(messages)
if compaction_strategy is None:
if tokenizer is None:
return prepared_messages
from ._compaction import annotate_message_groups
annotate_message_groups(prepared_messages, tokenizer=tokenizer)
return prepared_messages
from ._compaction import apply_compaction
return await apply_compaction(
prepared_messages,
strategy=compaction_strategy,
tokenizer=tokenizer,
)
def _resolve_compaction_overrides(
self,
*,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
) -> dict[str, Any]:
current_compaction_strategy = getattr(self, "compaction_strategy", None)
current_tokenizer = getattr(self, "tokenizer", None)
ret: dict[str, Any] = {}
if current_compaction_strategy is not None or compaction_strategy is not None:
ret["compaction_strategy"] = (
current_compaction_strategy if compaction_strategy is None else compaction_strategy
)
if current_tokenizer is not None or tokenizer is not None:
ret["tokenizer"] = current_tokenizer if tokenizer is None else tokenizer
return ret
# region Internal method to be implemented by derived classes
@abstractmethod
@@ -374,6 +437,8 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
*,
stream: Literal[False] = ...,
options: ChatOptions[ResponseModelBoundT],
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[ResponseModelBoundT]]: ...
@@ -384,6 +449,8 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
*,
stream: Literal[False] = ...,
options: OptionsCoT | ChatOptions[None] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]]: ...
@@ -394,6 +461,8 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
*,
stream: Literal[True],
options: OptionsCoT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> ResponseStream[ChatResponseUpdate, ChatResponse[Any]]: ...
@@ -403,6 +472,8 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
*,
stream: bool = False,
options: OptionsCoT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]] | ResponseStream[ChatResponseUpdate, ChatResponse[Any]]:
"""Get a response from a chat client.
@@ -411,17 +482,62 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
messages: The message or messages to send to the model.
stream: Whether to stream the response. Defaults to False.
options: Chat options as a TypedDict.
compaction_strategy: Optional per-call override for in-run compaction.
When omitted, the client-level default is used.
tokenizer: Optional per-call tokenizer override. When omitted, the
client-level default is used.
**kwargs: Other keyword arguments, can be used to pass function specific parameters.
Returns:
When streaming a response stream of ChatResponseUpdates, otherwise an Awaitable ChatResponse.
"""
return self._inner_get_response(
messages=messages,
stream=stream,
options=options or {}, # type: ignore[arg-type]
**kwargs,
compaction_overrides = self._resolve_compaction_overrides(
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
)
if not compaction_overrides:
return self._inner_get_response(
messages=messages,
stream=stream,
options=options or {},
**kwargs,
)
if stream:
async def _get_stream() -> ResponseStream[ChatResponseUpdate, ChatResponse[Any]]:
prepared_messages = await self._prepare_messages_for_model_call(
messages,
**compaction_overrides,
)
stream_response = self._inner_get_response(
messages=prepared_messages,
stream=True,
options=options or {},
**kwargs,
)
if isinstance(stream_response, ResponseStream):
return stream_response # type: ignore[reportUnknownVariableType]
awaited_stream_response = await stream_response
if isinstance(awaited_stream_response, ResponseStream):
return awaited_stream_response
raise ValueError("Streaming responses must return a ResponseStream.")
return ResponseStream.from_awaitable(_get_stream()) # type: ignore[reportUnknownVariableType]
async def _get_response() -> ChatResponse[Any]:
prepared_messages = await self._prepare_messages_for_model_call(
messages,
**compaction_overrides,
)
return await self._inner_get_response(
messages=prepared_messages,
stream=False,
options=options or {},
**kwargs,
)
return _get_response()
def service_url(self) -> str:
"""Get the URL of the service.
@@ -446,6 +562,8 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
context_providers: Sequence[Any] | None = None,
middleware: Sequence[MiddlewareTypes] | None = None,
function_invocation_configuration: FunctionInvocationConfiguration | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Agent[OptionsCoT]:
"""Create a Agent with this client.
@@ -468,6 +586,10 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
context_providers: Context providers to include during agent invocation.
middleware: List of middleware to intercept agent and function invocations.
function_invocation_configuration: Optional function invocation configuration override.
compaction_strategy: Optional agent-level compaction override. When omitted,
client-level compaction defaults remain in effect for each call.
tokenizer: Optional agent-level tokenizer override. When omitted,
client-level tokenizer defaults remain in effect for each call.
kwargs: Any additional keyword arguments. Will be stored as ``additional_properties``.
Returns:
@@ -504,6 +626,8 @@ class BaseChatClient(SerializationMixin, ABC, Generic[OptionsCoT]):
context_providers=context_providers,
middleware=middleware,
function_invocation_configuration=function_invocation_configuration,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
**kwargs,
)
python/packages/core/agent_framework/_compaction.py
+1310
View File
File diff suppressed because it is too large Load Diff
python/packages/core/agent_framework/_middleware.py
+43 -1
View File
@@ -37,6 +37,7 @@ if TYPE_CHECKING:
from ._agents import SupportsAgentRun
from ._clients import SupportsChatGetResponse
from ._compaction import CompactionStrategy, TokenizerProtocol
from ._sessions import AgentSession
from ._tools import FunctionTool
from ._types import ChatOptions, ChatResponse, ChatResponseUpdate
@@ -101,6 +102,8 @@ class AgentContext:
session: The agent session for this invocation, if any.
options: The options for the agent invocation as a dict.
stream: Whether this is a streaming invocation.
compaction_strategy: Optional per-run compaction override.
tokenizer: Optional per-run tokenizer override.
metadata: Metadata dictionary for sharing data between agent middleware.
result: Agent execution result. Can be observed after calling ``call_next()``
to see the actual execution result or can be set to override the execution result.
@@ -139,6 +142,8 @@ class AgentContext:
session: AgentSession | None = None,
options: Mapping[str, Any] | None = None,
stream: bool = False,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
metadata: Mapping[str, Any] | None = None,
result: AgentResponse | ResponseStream[AgentResponseUpdate, AgentResponse] | None = None,
kwargs: Mapping[str, Any] | None = None,
@@ -158,6 +163,8 @@ class AgentContext:
session: The agent session for this invocation, if any.
options: The options for the agent invocation as a dict.
stream: Whether this is a streaming invocation.
compaction_strategy: Optional per-run compaction override.
tokenizer: Optional per-run tokenizer override.
metadata: Metadata dictionary for sharing data between agent middleware.
result: Agent execution result.
kwargs: Additional keyword arguments passed to the agent run method.
@@ -170,6 +177,8 @@ class AgentContext:
self.session = session
self.options = options
self.stream = stream
self.compaction_strategy = compaction_strategy
self.tokenizer = tokenizer
self.metadata: dict[str, Any] = dict(metadata) if metadata is not None else {}
self.result = result
self.kwargs: dict[str, Any] = dict(kwargs) if kwargs is not None else {}
@@ -969,6 +978,8 @@ class ChatMiddlewareLayer(Generic[OptionsCoT]):
*,
stream: Literal[False] = ...,
options: ChatOptions[ResponseModelBoundT],
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[ResponseModelBoundT]]: ...
@@ -979,6 +990,8 @@ class ChatMiddlewareLayer(Generic[OptionsCoT]):
*,
stream: Literal[False] = ...,
options: OptionsCoT | ChatOptions[None] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]]: ...
@@ -989,6 +1002,8 @@ class ChatMiddlewareLayer(Generic[OptionsCoT]):
*,
stream: Literal[True],
options: OptionsCoT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> ResponseStream[ChatResponseUpdate, ChatResponse[Any]]: ...
@@ -998,11 +1013,18 @@ class ChatMiddlewareLayer(Generic[OptionsCoT]):
*,
stream: bool = False,
options: OptionsCoT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]] | ResponseStream[ChatResponseUpdate, ChatResponse[Any]]:
"""Execute the chat pipeline if middleware is configured."""
super_get_response = super().get_response # type: ignore[misc]
if compaction_strategy is not None:
kwargs["compaction_strategy"] = compaction_strategy
if tokenizer is not None:
kwargs["tokenizer"] = tokenizer
call_middleware = kwargs.pop("middleware", [])
middleware = categorize_middleware(call_middleware)
kwargs["function_middleware"] = middleware["function"]
@@ -1091,6 +1113,8 @@ class AgentMiddlewareLayer:
session: AgentSession | None = None,
middleware: Sequence[MiddlewareTypes] | None = None,
options: ChatOptions[ResponseModelBoundT],
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[AgentResponse[ResponseModelBoundT]]: ...
@@ -1103,6 +1127,8 @@ class AgentMiddlewareLayer:
session: AgentSession | None = None,
middleware: Sequence[MiddlewareTypes] | None = None,
options: ChatOptions[None] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[AgentResponse[Any]]: ...
@@ -1115,6 +1141,8 @@ class AgentMiddlewareLayer:
session: AgentSession | None = None,
middleware: Sequence[MiddlewareTypes] | None = None,
options: ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> ResponseStream[AgentResponseUpdate, AgentResponse[Any]]: ...
@@ -1126,6 +1154,8 @@ class AgentMiddlewareLayer:
session: AgentSession | None = None,
middleware: Sequence[MiddlewareTypes] | None = None,
options: ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[AgentResponse[Any]] | ResponseStream[AgentResponseUpdate, AgentResponse[Any]]:
"""MiddlewareTypes-enabled unified run method."""
@@ -1150,7 +1180,15 @@ class AgentMiddlewareLayer:
# Execute with middleware if available
if not pipeline.has_middlewares:
return super().run(messages, stream=stream, session=session, options=options, **combined_kwargs) # type: ignore[misc, no-any-return]
return super().run( # type: ignore[misc, no-any-return]
messages,
stream=stream,
session=session,
options=options,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
**combined_kwargs,
)
context = AgentContext(
agent=self, # type: ignore[arg-type]
@@ -1158,6 +1196,8 @@ class AgentMiddlewareLayer:
session=session,
options=options,
stream=stream,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
kwargs=combined_kwargs,
)
@@ -1195,6 +1235,8 @@ class AgentMiddlewareLayer:
stream=context.stream,
session=context.session,
options=context.options,
compaction_strategy=context.compaction_strategy,
tokenizer=context.tokenizer,
**context.kwargs,
)
python/packages/core/agent_framework/_sessions.py
+11 -1
View File
@@ -547,6 +547,7 @@ class InMemoryHistoryProvider(BaseHistoryProvider):
store_context_messages: bool = False,
store_context_from: set[str] | None = None,
store_outputs: bool = True,
skip_excluded: bool = False,
) -> None:
"""Initialize the in-memory history provider.
@@ -558,6 +559,11 @@ class InMemoryHistoryProvider(BaseHistoryProvider):
store_context_messages: Whether to store context from other providers.
store_context_from: If set, only store context from these source_ids.
store_outputs: Whether to store response messages.
skip_excluded: When True, ``get_messages`` omits messages whose
``additional_properties["_excluded"]`` is truthy. This is
useful when a ``CompactionProvider`` marks messages as excluded
in stored history and you want the loaded context to reflect
those exclusions. Defaults to False (load all messages).
"""
super().__init__(
source_id=source_id or self.DEFAULT_SOURCE_ID,
@@ -567,6 +573,7 @@ class InMemoryHistoryProvider(BaseHistoryProvider):
store_context_from=store_context_from,
store_outputs=store_outputs,
)
self.skip_excluded = skip_excluded
async def get_messages(
self, session_id: str | None, *, state: dict[str, Any] | None = None, **kwargs: Any
@@ -574,7 +581,10 @@ class InMemoryHistoryProvider(BaseHistoryProvider):
"""Retrieve messages from session state."""
if state is None:
return []
return list(state.get("messages", []))
messages = list(state.get("messages", []))
if self.skip_excluded:
messages = [m for m in messages if not m.additional_properties.get("_excluded", False)]
return messages
async def save_messages(
self,
python/packages/core/agent_framework/_skills.py
+3 -7
View File
@@ -196,9 +196,7 @@ class SkillScript:
self._accepts_kwargs: bool = False
if function is not None:
sig = inspect.signature(function)
self._accepts_kwargs = any(
p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
)
self._accepts_kwargs = any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values())
@property
def parameters_schema(self) -> dict[str, Any] | None:
@@ -454,9 +452,7 @@ class SkillScriptRunner(Protocol):
satisfies this protocol.
"""
def __call__(
self, skill: Skill, script: SkillScript, args: dict[str, Any] | None = None
) -> Any:
def __call__(self, skill: Skill, script: SkillScript, args: dict[str, Any] | None = None) -> Any:
"""Run a skill script.
The :class:`SkillsProvider` resolves skill and script names
@@ -677,7 +673,7 @@ class SkillsProvider(BaseContextProvider):
self._instructions = _create_instructions(
prompt_template=instruction_template,
skills=self._skills,
include_script_runner_instructions=has_file_scripts or has_code_scripts
include_script_runner_instructions=has_file_scripts or has_code_scripts,
)
self._tools = self._create_tools(
python/packages/core/agent_framework/_tools.py
+13
View File
@@ -59,6 +59,7 @@ else:
if TYPE_CHECKING:
from ._clients import SupportsChatGetResponse
from ._compaction import CompactionStrategy, TokenizerProtocol
from ._mcp import MCPTool
from ._middleware import FunctionMiddlewarePipeline, FunctionMiddlewareTypes
from ._types import (
@@ -1811,6 +1812,8 @@ class FunctionInvocationLayer(Generic[OptionsCoT]):
*,
stream: Literal[False] = ...,
options: ChatOptions[ResponseModelBoundT],
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[ResponseModelBoundT]]: ...
@@ -1821,6 +1824,8 @@ class FunctionInvocationLayer(Generic[OptionsCoT]):
*,
stream: Literal[False] = ...,
options: OptionsCoT | ChatOptions[None] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]]: ...
@@ -1831,6 +1836,8 @@ class FunctionInvocationLayer(Generic[OptionsCoT]):
*,
stream: Literal[True],
options: OptionsCoT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> ResponseStream[ChatResponseUpdate, ChatResponse[Any]]: ...
@@ -1841,6 +1848,8 @@ class FunctionInvocationLayer(Generic[OptionsCoT]):
stream: bool = False,
options: OptionsCoT | ChatOptions[Any] | None = None,
function_middleware: Sequence[FunctionMiddlewareTypes] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]] | ResponseStream[ChatResponseUpdate, ChatResponse[Any]]:
from ._middleware import FunctionMiddlewarePipeline
@@ -1869,6 +1878,10 @@ class FunctionInvocationLayer(Generic[OptionsCoT]):
middleware_pipeline=function_middleware_pipeline,
)
filtered_kwargs = {k: v for k, v in kwargs.items() if k != "session"}
if compaction_strategy is not None:
filtered_kwargs["compaction_strategy"] = compaction_strategy
if tokenizer is not None:
filtered_kwargs["tokenizer"] = tokenizer
# Make options mutable so we can update conversation_id during function invocation loop
mutable_options: dict[str, Any] = dict(options) if options else {}
python/packages/core/agent_framework/_types.py
+37 -8
View File
@@ -277,6 +277,17 @@ def _serialize_value(value: Any, exclude_none: bool) -> Any:
return value
def _restore_compaction_annotation_in_additional_properties(
additional_properties: MutableMapping[str, Any] | None,
*,
allow_none: bool = False,
) -> dict[str, Any] | None:
if additional_properties is None:
return None if allow_none else {}
return dict(additional_properties)
# endregion
# region Constants and types
@@ -509,7 +520,9 @@ class Content:
"""
self.type = type
self.annotations = annotations
self.additional_properties: dict[str, Any] = additional_properties or {} # type: ignore[assignment]
self.additional_properties: dict[str, Any] = (
_restore_compaction_annotation_in_additional_properties(additional_properties) or {}
)
self.raw_representation = raw_representation
# Set all content-specific attributes
@@ -1638,7 +1651,9 @@ class Message(SerializationMixin):
self.contents = parsed_contents
self.author_name = author_name
self.message_id = message_id
self.additional_properties = additional_properties or {}
self.additional_properties = (
_restore_compaction_annotation_in_additional_properties(additional_properties) or {}
)
self.raw_representation = raw_representation
@property
@@ -1989,7 +2004,9 @@ class ChatResponse(SerializationMixin, Generic[ResponseModelT]):
self._value: ResponseModelT | None = value
self._response_format: type[BaseModel] | None = response_format
self._value_parsed: bool = value is not None
self.additional_properties = additional_properties or {}
self.additional_properties = (
_restore_compaction_annotation_in_additional_properties(additional_properties) or {}
)
self.continuation_token = continuation_token
self.raw_representation: Any | list[Any] | None = raw_representation
@@ -2239,7 +2256,10 @@ class ChatResponseUpdate(SerializationMixin):
self.created_at = created_at
self.finish_reason = finish_reason
self.continuation_token = continuation_token
self.additional_properties = additional_properties
self.additional_properties = _restore_compaction_annotation_in_additional_properties(
additional_properties,
allow_none=True,
)
self.raw_representation = raw_representation
@property
@@ -2352,7 +2372,9 @@ class AgentResponse(SerializationMixin, Generic[ResponseModelT]):
self._value: ResponseModelT | None = value
self._response_format: type[BaseModel] | None = response_format
self._value_parsed: bool = value is not None
self.additional_properties = additional_properties or {}
self.additional_properties = (
_restore_compaction_annotation_in_additional_properties(additional_properties) or {}
)
self.continuation_token = continuation_token
self.raw_representation = raw_representation
@@ -2582,7 +2604,10 @@ class AgentResponseUpdate(SerializationMixin):
self.message_id = message_id
self.created_at = created_at
self.continuation_token = continuation_token
self.additional_properties = additional_properties
self.additional_properties = _restore_compaction_annotation_in_additional_properties(
additional_properties,
allow_none=True,
)
self.raw_representation: Any | list[Any] | None = raw_representation
@property
@@ -3381,7 +3406,9 @@ class Embedding(Generic[EmbeddingT]):
self._dimensions = dimensions
self.model_id = model_id
self.created_at = created_at
self.additional_properties = additional_properties or {}
self.additional_properties = (
_restore_compaction_annotation_in_additional_properties(additional_properties) or {}
)
@property
def dimensions(self) -> int | None:
@@ -3439,7 +3466,9 @@ class GeneratedEmbeddings(list[Embedding[EmbeddingT]], Generic[EmbeddingT, Embed
super().__init__(embeddings or [])
self.options = options
self.usage = usage
self.additional_properties = additional_properties or {}
self.additional_properties = (
_restore_compaction_annotation_in_additional_properties(additional_properties) or {}
)
# endregion
python/packages/core/agent_framework/observability.py
+39 -2
View File
@@ -49,6 +49,7 @@ if TYPE_CHECKING: # pragma: no cover
from ._agents import SupportsAgentRun
from ._clients import SupportsChatGetResponse
from ._compaction import CompactionStrategy, TokenizerProtocol
from ._sessions import AgentSession
from ._tools import FunctionTool
from ._types import (
@@ -1122,6 +1123,8 @@ class ChatTelemetryLayer(Generic[OptionsCoT]):
*,
stream: Literal[False] = ...,
options: ChatOptions[ResponseModelBoundT],
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[ResponseModelBoundT]]: ...
@@ -1132,6 +1135,8 @@ class ChatTelemetryLayer(Generic[OptionsCoT]):
*,
stream: Literal[False] = ...,
options: OptionsCoT | ChatOptions[None] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]]: ...
@@ -1142,6 +1147,8 @@ class ChatTelemetryLayer(Generic[OptionsCoT]):
*,
stream: Literal[True],
options: OptionsCoT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> ResponseStream[ChatResponseUpdate, ChatResponse[Any]]: ...
@@ -1151,6 +1158,8 @@ class ChatTelemetryLayer(Generic[OptionsCoT]):
*,
stream: bool = False,
options: OptionsCoT | ChatOptions[Any] | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[ChatResponse[Any]] | ResponseStream[ChatResponseUpdate, ChatResponse[Any]]:
"""Trace chat responses with OpenTelemetry spans and metrics."""
@@ -1160,7 +1169,14 @@ class ChatTelemetryLayer(Generic[OptionsCoT]):
super_get_response = super().get_response # type: ignore[misc]
if not OBSERVABILITY_SETTINGS.ENABLED:
return super_get_response(messages=messages, stream=stream, options=options, **kwargs) # type: ignore[no-any-return]
return super_get_response( # type: ignore[no-any-return]
messages=messages,
stream=stream,
options=options,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
**kwargs,
)
opts: dict[str, Any] = options or {} # type: ignore[assignment]
provider_name = str(getattr(self, "otel_provider_name", "unknown"))
@@ -1178,7 +1194,14 @@ class ChatTelemetryLayer(Generic[OptionsCoT]):
if stream:
result_stream = cast(
ResponseStream[ChatResponseUpdate, ChatResponse[Any]],
super_get_response(messages=messages, stream=True, options=opts, **kwargs),
super_get_response(
messages=messages,
stream=True,
options=opts,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
**kwargs,
),
)
# Create span directly without trace.use_span() context attachment.
@@ -1266,6 +1289,8 @@ class ChatTelemetryLayer(Generic[OptionsCoT]):
messages=messages,
stream=False,
options=opts,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
**kwargs,
),
)
@@ -1393,6 +1418,8 @@ class AgentTelemetryLayer:
*,
stream: Literal[False] = ...,
session: AgentSession | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[AgentResponse[Any]]: ...
@@ -1403,6 +1430,8 @@ class AgentTelemetryLayer:
*,
stream: Literal[True],
session: AgentSession | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> ResponseStream[AgentResponseUpdate, AgentResponse[Any]]: ...
@@ -1412,6 +1441,8 @@ class AgentTelemetryLayer:
*,
stream: bool = False,
session: AgentSession | None = None,
compaction_strategy: CompactionStrategy | None = None,
tokenizer: TokenizerProtocol | None = None,
**kwargs: Any,
) -> Awaitable[AgentResponse[Any]] | ResponseStream[AgentResponseUpdate, AgentResponse[Any]]:
"""Trace agent runs with OpenTelemetry spans and metrics."""
@@ -1430,6 +1461,8 @@ class AgentTelemetryLayer:
messages=messages,
stream=stream,
session=session,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
**kwargs,
)
@@ -1452,6 +1485,8 @@ class AgentTelemetryLayer:
messages=messages,
stream=True,
session=session,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
**kwargs,
)
if isinstance(run_result, ResponseStream):
@@ -1541,6 +1576,8 @@ class AgentTelemetryLayer:
messages=messages,
stream=False,
session=session,
compaction_strategy=compaction_strategy,
tokenizer=tokenizer,
**kwargs,
)
except Exception as exception:
python/packages/core/agent_framework/openai/_responses_client.py
-1
View File
@@ -1164,7 +1164,6 @@ class RawOpenAIResponsesClient( # type: ignore[misc]
"type": "function_call",
"name": content.name,
"arguments": content.arguments,
"status": None,
}
case "function_result":
shell_output_type = (
python/packages/core/tests/core/test_agents.py
+132
View File
@@ -10,6 +10,8 @@ import pytest
from pytest import raises
from agent_framework import (
GROUP_ANNOTATION_KEY,
GROUP_TOKEN_COUNT_KEY,
Agent,
AgentResponse,
AgentResponseUpdate,
@@ -21,14 +23,24 @@ from agent_framework import (
Content,
FunctionTool,
Message,
SlidingWindowStrategy,
SupportsAgentRun,
SupportsChatGetResponse,
TruncationStrategy,
tool,
)
from agent_framework._agents import _get_tool_name, _merge_options, _sanitize_agent_name
from agent_framework._mcp import MCPTool
class _FixedTokenizer:
def __init__(self, token_count: int) -> None:
self.token_count = token_count
def count_tokens(self, text: str) -> int:
return self.token_count
def test_agent_session_type(agent_session: AgentSession) -> None:
assert isinstance(agent_session, AgentSession)
@@ -217,6 +229,30 @@ async def test_prepare_session_does_not_mutate_agent_chat_options(
assert len(agent.default_options["tools"]) == 1
async def test_prepare_run_context_keeps_compaction_overrides_out_of_kwargs(
chat_client_base: SupportsChatGetResponse,
) -> None:
strategy = SlidingWindowStrategy(keep_last_groups=2)
tokenizer = _FixedTokenizer(13)
agent = Agent(client=chat_client_base)
ctx = await agent._prepare_run_context( # type: ignore[reportPrivateUsage]
messages=[Message(role="user", text="Hello")],
session=None,
tools=None,
options=None,
compaction_strategy=strategy,
tokenizer=tokenizer,
kwargs={"custom_flag": True},
)
assert ctx["compaction_strategy"] is strategy
assert ctx["tokenizer"] is tokenizer
assert ctx["filtered_kwargs"].get("custom_flag") is True
assert "compaction_strategy" not in ctx["filtered_kwargs"]
assert "tokenizer" not in ctx["filtered_kwargs"]
async def test_chat_client_agent_run_with_session(
chat_client_base: SupportsChatGetResponse,
) -> None:
@@ -1128,6 +1164,102 @@ async def test_chat_agent_tool_choice_none_at_run_preserves_agent_level(chat_cli
assert captured_options[0]["tool_choice"] == "auto"
async def test_chat_agent_compaction_overrides_client_defaults(chat_client_base: Any) -> None:
captured_roles: list[list[str]] = []
captured_token_counts: list[list[int | None]] = []
original_inner = chat_client_base._inner_get_response
async def capturing_inner(
*, messages: MutableSequence[Message], options: dict[str, Any], **kwargs: Any
) -> ChatResponse:
captured_roles.append([message.role for message in messages])
captured_token_counts.append([
group.get(GROUP_TOKEN_COUNT_KEY) if isinstance(group, dict) else None
for group in (message.additional_properties.get(GROUP_ANNOTATION_KEY) for message in messages)
])
return await original_inner(messages=messages, options=options, **kwargs)
chat_client_base._inner_get_response = capturing_inner
chat_client_base.function_invocation_configuration["enabled"] = False
chat_client_base.compaction_strategy = TruncationStrategy(max_n=1, compact_to=1)
chat_client_base.tokenizer = _FixedTokenizer(5)
agent = Agent(
client=chat_client_base,
compaction_strategy=SlidingWindowStrategy(keep_last_groups=2),
tokenizer=_FixedTokenizer(9),
)
await agent.run([
Message(role="user", text="Hello"),
Message(role="assistant", text="Previous response"),
])
assert captured_roles == [["user", "assistant"]]
assert captured_token_counts == [[9, 9]]
async def test_chat_agent_uses_client_compaction_defaults_when_agent_unset(chat_client_base: Any) -> None:
captured_roles: list[list[str]] = []
original_inner = chat_client_base._inner_get_response
async def capturing_inner(
*, messages: MutableSequence[Message], options: dict[str, Any], **kwargs: Any
) -> ChatResponse:
captured_roles.append([message.role for message in messages])
return await original_inner(messages=messages, options=options, **kwargs)
chat_client_base._inner_get_response = capturing_inner
chat_client_base.function_invocation_configuration["enabled"] = False
chat_client_base.compaction_strategy = TruncationStrategy(max_n=1, compact_to=1)
agent = Agent(client=chat_client_base)
await agent.run([
Message(role="user", text="Hello"),
Message(role="assistant", text="Previous response"),
])
assert captured_roles == [["assistant"]]
async def test_chat_agent_run_level_compaction_and_tokenizer_override_agent_defaults(chat_client_base: Any) -> None:
captured_roles: list[list[str]] = []
captured_token_counts: list[list[int | None]] = []
original_inner = chat_client_base._inner_get_response
async def capturing_inner(
*, messages: MutableSequence[Message], options: dict[str, Any], **kwargs: Any
) -> ChatResponse:
captured_roles.append([message.role for message in messages])
captured_token_counts.append([
group.get(GROUP_TOKEN_COUNT_KEY) if isinstance(group, dict) else None
for group in (message.additional_properties.get(GROUP_ANNOTATION_KEY) for message in messages)
])
return await original_inner(messages=messages, options=options, **kwargs)
chat_client_base._inner_get_response = capturing_inner
chat_client_base.function_invocation_configuration["enabled"] = False
agent = Agent(
client=chat_client_base,
compaction_strategy=SlidingWindowStrategy(keep_last_groups=2),
tokenizer=_FixedTokenizer(9),
)
await agent.run(
[
Message(role="user", text="Hello"),
Message(role="assistant", text="Previous response"),
],
compaction_strategy=TruncationStrategy(max_n=1, compact_to=1),
tokenizer=_FixedTokenizer(23),
)
assert captured_roles == [["assistant"]]
assert captured_token_counts == [[23]]
# region Test _merge_options
python/packages/core/tests/core/test_clients.py
+197
View File
@@ -1,21 +1,34 @@
# Copyright (c) Microsoft. All rights reserved.
from typing import Any
from unittest.mock import patch
from agent_framework import (
GROUP_ANNOTATION_KEY,
GROUP_TOKEN_COUNT_KEY,
BaseChatClient,
ChatResponse,
Message,
SlidingWindowStrategy,
SupportsChatGetResponse,
SupportsCodeInterpreterTool,
SupportsFileSearchTool,
SupportsImageGenerationTool,
SupportsMCPTool,
SupportsWebSearchTool,
TruncationStrategy,
)
class _FixedTokenizer:
def __init__(self, token_count: int) -> None:
self.token_count = token_count
def count_tokens(self, text: str) -> int:
return self.token_count
def test_chat_client_type(client: SupportsChatGetResponse):
assert isinstance(client, SupportsChatGetResponse)
@@ -48,6 +61,190 @@ async def test_base_client_get_response_streaming(chat_client_base: SupportsChat
assert update.text == "update - Hello" or update.text == "another update"
async def test_base_client_applies_compaction_before_non_streaming_inner_call(
chat_client_base: SupportsChatGetResponse,
):
chat_client_base.function_invocation_configuration["enabled"] = False # type: ignore[attr-defined]
chat_client_base.compaction_strategy = TruncationStrategy(max_n=1, compact_to=1) # type: ignore[attr-defined]
captured_roles: list[list[str]] = []
original = chat_client_base._get_non_streaming_response # type: ignore[attr-defined]
async def _capture(
*,
messages: list[Message],
options: dict[str, Any],
**kwargs: Any,
) -> ChatResponse:
captured_roles.append([message.role for message in messages])
return await original(messages=messages, options=options, **kwargs)
chat_client_base._get_non_streaming_response = _capture # type: ignore[attr-defined,method-assign]
await chat_client_base.get_response([
Message(role="user", text="Hello"),
Message(role="assistant", text="Previous response"),
])
assert captured_roles == [["assistant"]]
async def test_base_client_applies_compaction_before_streaming_inner_call(
chat_client_base: SupportsChatGetResponse,
):
chat_client_base.function_invocation_configuration["enabled"] = False # type: ignore[attr-defined]
chat_client_base.compaction_strategy = TruncationStrategy(max_n=1, compact_to=1) # type: ignore[attr-defined]
captured_roles: list[list[str]] = []
original = chat_client_base._get_streaming_response # type: ignore[attr-defined]
def _capture(
*,
messages: list[Message],
options: dict[str, Any],
**kwargs: Any,
):
captured_roles.append([message.role for message in messages])
return original(messages=messages, options=options, **kwargs)
chat_client_base._get_streaming_response = _capture # type: ignore[attr-defined,method-assign]
async for _ in chat_client_base.get_response(
[
Message(role="user", text="Hello"),
Message(role="assistant", text="Previous response"),
],
stream=True,
):
pass
assert captured_roles == [["assistant"]]
async def test_base_client_per_call_compaction_override_applies_before_inner_call(
chat_client_base: SupportsChatGetResponse,
) -> None:
chat_client_base.function_invocation_configuration["enabled"] = False # type: ignore[attr-defined]
captured_roles: list[list[str]] = []
original = chat_client_base._get_non_streaming_response # type: ignore[attr-defined]
async def _capture(
*,
messages: list[Message],
options: dict[str, Any],
**kwargs: Any,
) -> ChatResponse:
captured_roles.append([message.role for message in messages])
return await original(messages=messages, options=options, **kwargs)
chat_client_base._get_non_streaming_response = _capture # type: ignore[attr-defined,method-assign]
await chat_client_base.get_response(
[
Message(role="user", text="Hello"),
Message(role="assistant", text="Previous response"),
],
compaction_strategy=TruncationStrategy(max_n=1, compact_to=1),
)
assert captured_roles == [["assistant"]]
async def test_base_client_per_call_tokenizer_override_annotates_messages(
chat_client_base: SupportsChatGetResponse,
) -> None:
chat_client_base.function_invocation_configuration["enabled"] = False # type: ignore[attr-defined]
captured_token_counts: list[list[int | None]] = []
original = chat_client_base._get_non_streaming_response # type: ignore[attr-defined]
async def _capture(
*,
messages: list[Message],
options: dict[str, Any],
**kwargs: Any,
) -> ChatResponse:
captured_token_counts.append([
group.get(GROUP_TOKEN_COUNT_KEY) if isinstance(group, dict) else None
for group in (message.additional_properties.get(GROUP_ANNOTATION_KEY) for message in messages)
])
return await original(messages=messages, options=options, **kwargs)
chat_client_base._get_non_streaming_response = _capture # type: ignore[attr-defined,method-assign]
await chat_client_base.get_response(
[
Message(role="user", text="Hello"),
Message(role="assistant", text="Previous response"),
],
compaction_strategy=SlidingWindowStrategy(keep_last_groups=2),
tokenizer=_FixedTokenizer(17),
)
assert captured_token_counts == [[17, 17]]
async def test_base_client_per_call_tokenizer_override_without_strategy_annotates_messages(
chat_client_base: SupportsChatGetResponse,
) -> None:
chat_client_base.function_invocation_configuration["enabled"] = False # type: ignore[attr-defined]
captured_token_counts: list[list[int | None]] = []
original = chat_client_base._get_non_streaming_response # type: ignore[attr-defined]
async def _capture(
*,
messages: list[Message],
options: dict[str, Any],
**kwargs: Any,
) -> ChatResponse:
captured_token_counts.append([
group.get(GROUP_TOKEN_COUNT_KEY) if isinstance(group, dict) else None
for group in (message.additional_properties.get(GROUP_ANNOTATION_KEY) for message in messages)
])
return await original(messages=messages, options=options, **kwargs)
chat_client_base._get_non_streaming_response = _capture # type: ignore[attr-defined,method-assign]
await chat_client_base.get_response(
[
Message(role="user", text="Hello"),
Message(role="assistant", text="Previous response"),
],
tokenizer=_FixedTokenizer(17),
)
assert captured_token_counts == [[17, 17]]
async def test_base_client_default_tokenizer_without_strategy_annotates_messages(
chat_client_base: SupportsChatGetResponse,
) -> None:
chat_client_base.function_invocation_configuration["enabled"] = False # type: ignore[attr-defined]
chat_client_base.tokenizer = _FixedTokenizer(19) # type: ignore[attr-defined]
captured_token_counts: list[list[int | None]] = []
original = chat_client_base._get_non_streaming_response # type: ignore[attr-defined]
async def _capture(
*,
messages: list[Message],
options: dict[str, Any],
**kwargs: Any,
) -> ChatResponse:
captured_token_counts.append([
group.get(GROUP_TOKEN_COUNT_KEY) if isinstance(group, dict) else None
for group in (message.additional_properties.get(GROUP_ANNOTATION_KEY) for message in messages)
])
return await original(messages=messages, options=options, **kwargs)
chat_client_base._get_non_streaming_response = _capture # type: ignore[attr-defined,method-assign]
await chat_client_base.get_response([
Message(role="user", text="Hello"),
Message(role="assistant", text="Previous response"),
])
assert captured_token_counts == [[19, 19]]
def test_base_client_as_agent_does_not_copy_client_compaction_defaults(
chat_client_base: SupportsChatGetResponse,
) -> None:
strategy = TruncationStrategy(max_n=1, compact_to=1)
tokenizer = _FixedTokenizer(11)
chat_client_base.compaction_strategy = strategy # type: ignore[attr-defined]
chat_client_base.tokenizer = tokenizer # type: ignore[attr-defined]
agent = chat_client_base.as_agent(name="shared-client-agent")
assert agent.compaction_strategy is None # type: ignore[attr-defined]
assert agent.tokenizer is None # type: ignore[attr-defined]
async def test_chat_client_instructions_handling(chat_client_base: SupportsChatGetResponse):
instructions = "You are a helpful assistant."
python/packages/core/tests/core/test_compaction.py
+954
View File
@@ -0,0 +1,954 @@
# Copyright (c) Microsoft. All rights reserved.
from __future__ import annotations
import logging
from typing import Any
from agent_framework import (
EXCLUDED_KEY,
GROUP_ANNOTATION_KEY,
GROUP_HAS_REASONING_KEY,
GROUP_ID_KEY,
GROUP_KIND_KEY,
GROUP_TOKEN_COUNT_KEY,
SUMMARIZED_BY_SUMMARY_ID_KEY,
SUMMARY_OF_GROUP_IDS_KEY,
SUMMARY_OF_MESSAGE_IDS_KEY,
CharacterEstimatorTokenizer,
ChatResponse,
CompactionProvider,
Content,
Message,
SelectiveToolCallCompactionStrategy,
SlidingWindowStrategy,
SummarizationStrategy,
TokenBudgetComposedStrategy,
ToolResultCompactionStrategy,
TruncationStrategy,
annotate_message_groups,
apply_compaction,
included_messages,
included_token_count,
)
from agent_framework._compaction import (
append_compaction_message,
extend_compaction_messages,
)
def _assistant_function_call(call_id: str) -> Message:
return Message(
role="assistant",
contents=[Content.from_function_call(call_id=call_id, name="tool", arguments='{"value":"x"}')],
)
def _assistant_reasoning_and_function_calls(*call_ids: str) -> Message:
contents: list[Content] = [Content.from_text_reasoning(text="thinking")]
for call_id in call_ids:
contents.append(
Content.from_function_call(
call_id=call_id,
name="tool",
arguments='{"value":"x"}',
)
)
return Message(role="assistant", contents=contents)
def _tool_result(call_id: str, result: str) -> Message:
return Message(
role="tool",
contents=[Content.from_function_result(call_id=call_id, result=result)],
)
def _group_id(message: Message) -> str | None:
annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
if not isinstance(annotation, dict):
return None
value = annotation.get(GROUP_ID_KEY)
return value if isinstance(value, str) else None
def _group_kind(message: Message) -> str | None:
annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
if not isinstance(annotation, dict):
return None
value = annotation.get(GROUP_KIND_KEY)
return value if isinstance(value, str) else None
def _group_has_reasoning(message: Message) -> bool | None:
annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
if not isinstance(annotation, dict):
return None
value = annotation.get(GROUP_HAS_REASONING_KEY)
return value if isinstance(value, bool) else None
def _token_count(message: Message) -> int | None:
annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
if not isinstance(annotation, dict):
return None
value = annotation.get(GROUP_TOKEN_COUNT_KEY)
return value if isinstance(value, int) else None
def _group_unknown_value(message: Message, key: str) -> Any:
annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
if not isinstance(annotation, dict):
return None
return annotation.get(key)
def test_group_annotations_keep_tool_call_and_tool_result_atomic() -> None:
messages = [
Message(role="user", text="hello"),
_assistant_function_call("c1"),
_tool_result("c1", "ok"),
Message(role="assistant", text="final"),
]
annotate_message_groups(messages)
call_group = _group_id(messages[1])
assert call_group is not None
assert call_group == _group_id(messages[2])
assert _group_id(messages[1]) != _group_id(messages[0])
def test_group_annotations_include_reasoning_in_tool_call_group() -> None:
messages = [
_assistant_reasoning_and_function_calls("c2"),
_tool_result("c2", "ok"),
]
annotate_message_groups(messages)
first_group = _group_id(messages[0])
assert first_group is not None
assert _group_id(messages[1]) == first_group
assert _group_has_reasoning(messages[0]) is True
assert _group_kind(messages[0]) == "tool_call"
def test_group_annotations_handle_same_message_reasoning_and_function_calls() -> None:
messages = [
Message(role="user", text="hello"),
_assistant_reasoning_and_function_calls("c1", "c2"),
_tool_result("c1", "ok1"),
_tool_result("c2", "ok2"),
Message(role="assistant", text="final"),
]
annotate_message_groups(messages)
call_group = _group_id(messages[1])
assert call_group is not None
assert _group_id(messages[2]) == call_group
assert _group_id(messages[3]) == call_group
assert _group_kind(messages[1]) == "tool_call"
assert _group_has_reasoning(messages[1]) is True
def test_annotate_message_groups_with_tokenizer_adds_token_counts() -> None:
messages = [
Message(role="user", text="hello"),
Message(role="assistant", text="world"),
]
annotate_message_groups(
messages,
tokenizer=CharacterEstimatorTokenizer(),
)
assert isinstance(_token_count(messages[0]), int)
assert isinstance(_token_count(messages[1]), int)
def test_extend_compaction_messages_preserves_existing_annotations_and_tokens() -> None:
tokenizer = CharacterEstimatorTokenizer()
messages = [_assistant_function_call("c3")]
annotate_message_groups(messages)
old_group_id = _group_id(messages[0])
assert old_group_id is not None
old_token_count = tokenizer.count_tokens("precomputed")
annotation = messages[0].additional_properties.get(GROUP_ANNOTATION_KEY)
if isinstance(annotation, dict):
annotation[GROUP_TOKEN_COUNT_KEY] = old_token_count
extend_compaction_messages(messages, [_tool_result("c3", "ok")], tokenizer=tokenizer)
assert _group_id(messages[1]) == old_group_id
assert _token_count(messages[0]) == old_token_count
assert isinstance(_token_count(messages[1]), int)
def test_append_compaction_message_annotates_new_message() -> None:
messages = [Message(role="user", text="hello")]
annotate_message_groups(messages)
append_compaction_message(messages, Message(role="assistant", text="world"))
assert len(messages) == 2
assert isinstance(_group_id(messages[1]), str)
async def test_truncation_strategy_keeps_system_anchor() -> None:
messages = [
Message(role="system", text="you are helpful"),
Message(role="user", text="u1"),
Message(role="assistant", text="a1"),
Message(role="user", text="u2"),
Message(role="assistant", text="a2"),
]
strategy = TruncationStrategy(max_n=3, compact_to=3, preserve_system=True)
annotate_message_groups(messages)
changed = await strategy(messages)
assert changed is True
projected = included_messages(messages)
assert projected[0].role == "system"
assert len(projected) <= 3
async def test_truncation_strategy_compacts_when_token_limit_exceeded() -> None:
tokenizer = CharacterEstimatorTokenizer()
messages = [
Message(role="system", text="you are helpful"),
Message(role="user", text="u1 " * 200),
Message(role="assistant", text="a1 " * 200),
]
strategy = TruncationStrategy(
max_n=80,
compact_to=40,
tokenizer=tokenizer,
preserve_system=True,
)
annotate_message_groups(messages, tokenizer=tokenizer)
changed = await strategy(messages)
assert changed is True
projected = included_messages(messages)
assert projected[0].role == "system"
assert included_token_count(messages) <= 40
def test_truncation_strategy_validates_token_targets() -> None:
try:
TruncationStrategy(max_n=3, compact_to=4)
except ValueError as exc:
assert "compact_to must be less than or equal to max_n" in str(exc)
else:
raise AssertionError("Expected ValueError when compact_to is greater than max_n.")
async def test_selective_tool_call_strategy_excludes_older_tool_groups() -> None:
messages = [
Message(role="user", text="u"),
_assistant_function_call("call-1"),
_tool_result("call-1", "r1"),
_assistant_function_call("call-2"),
_tool_result("call-2", "r2"),
Message(role="assistant", text="done"),
]
strategy = SelectiveToolCallCompactionStrategy(keep_last_tool_call_groups=1)
annotate_message_groups(messages)
changed = await strategy(messages)
assert changed is True
assert messages[1].additional_properties.get(EXCLUDED_KEY) is True
assert messages[2].additional_properties.get(EXCLUDED_KEY) is True
assert messages[3].additional_properties.get(EXCLUDED_KEY) is not True
assert messages[4].additional_properties.get(EXCLUDED_KEY) is not True
async def test_selective_tool_call_strategy_with_zero_removes_assistant_tool_pair() -> None:
messages = [
Message(role="user", text="u"),
_assistant_function_call("call-1"),
_tool_result("call-1", "r1"),
Message(role="assistant", text="done"),
]
strategy = SelectiveToolCallCompactionStrategy(keep_last_tool_call_groups=0)
annotate_message_groups(messages)
changed = await strategy(messages)
assert changed is True
assert messages[1].additional_properties.get(EXCLUDED_KEY) is True
assert messages[2].additional_properties.get(EXCLUDED_KEY) is True
assert messages[0].additional_properties.get(EXCLUDED_KEY) is not True
assert messages[3].additional_properties.get(EXCLUDED_KEY) is not True
def test_selective_tool_call_strategy_rejects_negative_keep_count() -> None:
try:
SelectiveToolCallCompactionStrategy(keep_last_tool_call_groups=-1)
except ValueError as exc:
assert "must be greater than or equal to 0" in str(exc)
else:
raise AssertionError("Expected ValueError for negative keep_last_tool_call_groups.")
class _FakeSummarizer:
async def get_response(
self,
messages: list[Message],
*,
stream: bool = False,
options: dict[str, Any] | None = None,
**kwargs: Any,
) -> ChatResponse:
return ChatResponse(messages=[Message(role="assistant", text="summarized context")])
class _FailingSummarizer:
async def get_response(
self,
messages: list[Message],
*,
stream: bool = False,
options: dict[str, Any] | None = None,
**kwargs: Any,
) -> ChatResponse:
raise RuntimeError("summary failed")
class _EmptySummarizer:
async def get_response(
self,
messages: list[Message],
*,
stream: bool = False,
options: dict[str, Any] | None = None,
**kwargs: Any,
) -> ChatResponse:
return ChatResponse(messages=[Message(role="assistant", text=" ")])
async def test_summarization_strategy_adds_bidirectional_trace_links() -> None:
messages = [
Message(role="user", text="u1"),
Message(role="assistant", text="a1"),
Message(role="user", text="u2"),
Message(role="assistant", text="a2"),
Message(role="user", text="u3"),
Message(role="assistant", text="a3"),
]
strategy = SummarizationStrategy(client=_FakeSummarizer(), target_count=2, threshold=0)
annotate_message_groups(messages)
changed = await strategy(messages)
assert changed is True
summary_messages = [
message for message in messages if _group_unknown_value(message, SUMMARY_OF_MESSAGE_IDS_KEY) is not None
]
assert len(summary_messages) == 1
summary = summary_messages[0]
summary_id = summary.message_id
assert summary_id is not None
assert _group_unknown_value(summary, SUMMARY_OF_GROUP_IDS_KEY)
summarized_message_ids = _group_unknown_value(summary, SUMMARY_OF_MESSAGE_IDS_KEY)
assert isinstance(summarized_message_ids, list)
for message in messages:
if message.message_id in summarized_message_ids:
assert _group_unknown_value(message, SUMMARIZED_BY_SUMMARY_ID_KEY) == summary_id
assert message.additional_properties.get(EXCLUDED_KEY) is True
async def test_summarization_strategy_returns_false_when_summary_generation_fails(
caplog: Any,
) -> None:
messages = [
Message(role="user", text="u1"),
Message(role="assistant", text="a1"),
Message(role="user", text="u2"),
Message(role="assistant", text="a2"),
Message(role="user", text="u3"),
Message(role="assistant", text="a3"),
]
strategy = SummarizationStrategy(client=_FailingSummarizer(), target_count=2, threshold=0)
annotate_message_groups(messages)
with caplog.at_level(logging.WARNING, logger="agent_framework"):
changed = await strategy(messages)
assert changed is False
assert any("summary generation failed" in record.message for record in caplog.records)
assert all(message.additional_properties.get(EXCLUDED_KEY) is not True for message in messages)
async def test_summarization_strategy_returns_false_when_summary_is_empty(
caplog: Any,
) -> None:
messages = [
Message(role="user", text="u1"),
Message(role="assistant", text="a1"),
Message(role="user", text="u2"),
Message(role="assistant", text="a2"),
Message(role="user", text="u3"),
Message(role="assistant", text="a3"),
]
strategy = SummarizationStrategy(client=_EmptySummarizer(), target_count=2, threshold=0)
annotate_message_groups(messages)
with caplog.at_level(logging.WARNING, logger="agent_framework"):
changed = await strategy(messages)
assert changed is False
assert any("returned no text" in record.message for record in caplog.records)
assert all(message.additional_properties.get(EXCLUDED_KEY) is not True for message in messages)
async def test_token_budget_composed_strategy_meets_budget_or_falls_back() -> None:
messages = [
Message(role="system", text="system"),
Message(role="user", text="user " * 200),
Message(role="assistant", text="assistant " * 200),
]
strategy = TokenBudgetComposedStrategy(
token_budget=20,
tokenizer=CharacterEstimatorTokenizer(),
strategies=[SlidingWindowStrategy(keep_last_groups=1)],
)
changed = await strategy(messages)
assert changed is True
assert included_token_count(messages) <= 20
class _ExcludeOldestNonSystem:
async def __call__(self, messages: list[Message]) -> bool:
group_ids = annotate_message_groups(messages)
kinds: dict[str, str] = {}
for message in messages:
group_id = _group_id(message)
kind = _group_kind(message)
if group_id is not None and kind is not None and group_id not in kinds:
kinds[group_id] = kind
for group_id in group_ids:
if kinds.get(group_id) == "system":
continue
for message in messages:
if _group_id(message) == group_id:
message.additional_properties[EXCLUDED_KEY] = True
return True
return False
async def test_apply_compaction_projects_included_messages_only() -> None:
messages = [
Message(role="system", text="sys"),
Message(role="user", text="hello"),
Message(role="assistant", text="world"),
]
projected = await apply_compaction(messages, strategy=_ExcludeOldestNonSystem())
assert len(projected) < len(messages)
assert projected[0].role == "system"
# --- ToolResultCompactionStrategy tests ---
async def test_tool_result_compaction_collapses_old_groups_into_summary() -> None:
"""Old tool-call groups are collapsed into summary messages, newest kept."""
messages = [
Message(role="user", text="u"),
_assistant_function_call("call-1"),
_tool_result("call-1", "r1"),
_assistant_function_call("call-2"),
_tool_result("call-2", "r2"),
Message(role="assistant", text="done"),
]
strategy = ToolResultCompactionStrategy(keep_last_tool_call_groups=1)
annotate_message_groups(messages)
changed = await strategy(messages)
assert changed is True
projected = included_messages(messages)
texts = [m.text or "" for m in projected]
summary_msgs = [t for t in texts if t.startswith("[Tool results:")]
assert len(summary_msgs) == 1
assert "r1" in summary_msgs[0]
assert any(m.role == "tool" for m in projected)
async def test_tool_result_compaction_zero_collapses_all() -> None:
"""With keep=0, all tool-call groups are collapsed into summaries."""
messages = [
Message(role="user", text="u"),
_assistant_function_call("call-1"),
_tool_result("call-1", "r1"),
_assistant_function_call("call-2"),
_tool_result("call-2", "r2"),
Message(role="assistant", text="done"),
]
strategy = ToolResultCompactionStrategy(keep_last_tool_call_groups=0)
annotate_message_groups(messages)
changed = await strategy(messages)
assert changed is True
projected = included_messages(messages)
summary_msgs = [m for m in projected if (m.text or "").startswith("[Tool results:")]
assert len(summary_msgs) == 2
assert not any(m.role == "tool" for m in projected)
async def test_tool_result_compaction_no_change_when_within_limit() -> None:
"""No compaction when tool groups count does not exceed keep limit."""
messages = [
Message(role="user", text="u"),
_assistant_function_call("call-1"),
_tool_result("call-1", "r1"),
]
strategy = ToolResultCompactionStrategy(keep_last_tool_call_groups=1)
annotate_message_groups(messages)
changed = await strategy(messages)
assert changed is False
def test_tool_result_compaction_rejects_negative() -> None:
try:
ToolResultCompactionStrategy(keep_last_tool_call_groups=-1)
except ValueError as exc:
assert "must be greater than or equal to 0" in str(exc)
else:
raise AssertionError("Expected ValueError for negative keep_last_tool_call_groups.")
async def test_tool_result_compaction_preserves_tool_results_in_summary() -> None:
"""Summary text should include the tool results from the collapsed group."""
messages = [
Message(role="user", text="u"),
Message(
role="assistant",
contents=[
Content.from_function_call(call_id="c1", name="get_weather", arguments="{}"),
Content.from_function_call(call_id="c2", name="search_docs", arguments="{}"),
],
),
_tool_result("c1", "sunny"),
_tool_result("c2", "found 3 docs"),
Message(role="assistant", text="done"),
]
strategy = ToolResultCompactionStrategy(keep_last_tool_call_groups=0)
annotate_message_groups(messages)
await strategy(messages)
projected = included_messages(messages)
summary_msgs = [m for m in projected if (m.text or "").startswith("[Tool results:")]
assert len(summary_msgs) == 1
assert "sunny" in summary_msgs[0].text # type: ignore[operator]
assert "found 3 docs" in summary_msgs[0].text # type: ignore[operator]
async def test_tool_result_compaction_bidirectional_tracing() -> None:
"""Summary and originals should link to each other like SummarizationStrategy does."""
messages = [
Message(role="user", text="u"),
_assistant_function_call("call-1"),
_tool_result("call-1", "r1"),
Message(role="assistant", text="done"),
]
strategy = ToolResultCompactionStrategy(keep_last_tool_call_groups=0)
annotate_message_groups(messages)
await strategy(messages)
# Find the summary message.
summary_msgs = [m for m in messages if _group_unknown_value(m, SUMMARY_OF_MESSAGE_IDS_KEY) is not None]
assert len(summary_msgs) == 1
summary = summary_msgs[0]
summary_id = summary.message_id
assert summary_id is not None
# Forward link: summary knows which messages/groups it replaces.
assert isinstance(_group_unknown_value(summary, SUMMARY_OF_MESSAGE_IDS_KEY), list)
assert isinstance(_group_unknown_value(summary, SUMMARY_OF_GROUP_IDS_KEY), list)
# Back link: excluded originals know which summary replaced them.
for m in messages:
if m.additional_properties.get(EXCLUDED_KEY):
assert _group_unknown_value(m, SUMMARIZED_BY_SUMMARY_ID_KEY) == summary_id
# Core compaction annotations must be present on the summary message.
assert _group_id(summary) is not None
assert _group_kind(summary) is not None
assert summary.additional_properties.get(EXCLUDED_KEY) is False
async def test_tool_result_compaction_summary_has_full_annotations() -> None:
"""Summary messages inserted by ToolResultCompactionStrategy must have all compaction annotations."""
messages = [
Message(role="user", text="u"),
_assistant_function_call("c1"),
_tool_result("c1", "r1"),
Message(role="assistant", text="done"),
]
strategy = ToolResultCompactionStrategy(keep_last_tool_call_groups=0)
annotate_message_groups(messages)
await strategy(messages)
summary = next(m for m in messages if (m.text or "").startswith("[Tool results:"))
annotation = summary.additional_properties.get(GROUP_ANNOTATION_KEY)
assert isinstance(annotation, dict)
assert GROUP_ID_KEY in annotation
assert GROUP_KIND_KEY in annotation
assert GROUP_HAS_REASONING_KEY in annotation
assert SUMMARY_OF_MESSAGE_IDS_KEY in annotation
assert summary.additional_properties.get(EXCLUDED_KEY) is False
async def test_summarization_strategy_summary_has_full_annotations() -> None:
"""Summary messages inserted by SummarizationStrategy must have all compaction annotations."""
messages = [
Message(role="user", text="u1"),
Message(role="assistant", text="a1"),
Message(role="user", text="u2"),
Message(role="assistant", text="a2"),
Message(role="user", text="u3"),
Message(role="assistant", text="a3"),
]
strategy = SummarizationStrategy(client=_FakeSummarizer(), target_count=2, threshold=0)
annotate_message_groups(messages)
changed = await strategy(messages)
assert changed is True
summary = next(m for m in messages if _group_unknown_value(m, SUMMARY_OF_MESSAGE_IDS_KEY) is not None)
annotation = summary.additional_properties.get(GROUP_ANNOTATION_KEY)
assert isinstance(annotation, dict)
assert GROUP_ID_KEY in annotation
assert GROUP_KIND_KEY in annotation
assert GROUP_HAS_REASONING_KEY in annotation
assert SUMMARY_OF_MESSAGE_IDS_KEY in annotation
assert summary.additional_properties.get(EXCLUDED_KEY) is False
async def test_tool_result_compaction_multiple_groups_combined() -> None:
"""Multiple tool-call groups collapsed independently, each with its own summary.
Scenario: 3 tool-call groups, keep_last=1 → groups 1 and 2 each get a
separate summary, group 3 stays verbatim.
"""
messages = [
Message(role="user", text="Compare weather in London, Paris, and Tokyo"),
# Group 1: get_weather for London
Message(
role="assistant",
contents=[Content.from_function_call(call_id="c1", name="get_weather", arguments='{"city":"London"}')],
),
_tool_result("c1", '{"temp":12,"condition":"cloudy","wind":"NW 15km/h"}'),
Message(role="assistant", text="London is cloudy at 12°C."),
# Group 2: get_weather for Paris + search_hotels
Message(
role="assistant",
contents=[
Content.from_function_call(call_id="c2", name="get_weather", arguments='{"city":"Paris"}'),
Content.from_function_call(call_id="c3", name="search_hotels", arguments='{"city":"Paris"}'),
],
),
_tool_result("c2", '{"temp":18,"condition":"sunny"}'),
_tool_result("c3", "Grand Hotel (€120), Le Petit (€85)"),
Message(role="assistant", text="Paris is sunny at 18°C. Found 2 hotels."),
# Group 3: get_weather for Tokyo (most recent — should be kept)
Message(
role="assistant",
contents=[Content.from_function_call(call_id="c4", name="get_weather", arguments='{"city":"Tokyo"}')],
),
_tool_result("c4", '{"temp":22,"condition":"rainy"}'),
Message(role="assistant", text="Tokyo is rainy at 22°C."),
]
strategy = ToolResultCompactionStrategy(keep_last_tool_call_groups=1)
annotate_message_groups(messages)
changed = await strategy(messages)
assert changed is True
projected = included_messages(messages)
summary_msgs = [m for m in projected if (m.text or "").startswith("[Tool results:")]
# Two summaries: one for group 1, one for group 2.
assert len(summary_msgs) == 2
# Group 1 summary: London weather result.
g1_text = summary_msgs[0].text or ""
assert "12" in g1_text
assert "cloudy" in g1_text
# Group 2 summary: Paris weather + hotel results combined.
g2_text = summary_msgs[1].text or ""
assert "18" in g2_text
assert "Grand Hotel" in g2_text
# Group 3 (Tokyo) stays verbatim — tool role messages still present.
verbatim_tool_msgs = [m for m in projected if m.role == "tool"]
assert len(verbatim_tool_msgs) == 1
assert "rainy" in (verbatim_tool_msgs[0].contents[0].result or "")
# All text assistant messages should still be present.
text_msgs = [m for m in projected if m.role == "assistant" and m.text and not m.text.startswith("[Tool results:")]
texts = [m.text for m in text_msgs]
assert "London is cloudy at 12°C." in texts
assert "Paris is sunny at 18°C. Found 2 hotels." in texts
assert "Tokyo is rainy at 22°C." in texts
# Final projected shape: 8 messages in order.
assert len(projected) == 8
assert projected[0].role == "user" # original user message
assert projected[1].text == '[Tool results: get_weather: {"temp":12,"condition":"cloudy","wind":"NW 15km/h"}]'
assert projected[2].text == "London is cloudy at 12°C."
expected_g2 = (
'[Tool results: get_weather: {"temp":18,"condition":"sunny"};'
" search_hotels: Grand Hotel (€120), Le Petit (€85)]"
)
assert projected[3].text == expected_g2
assert projected[4].text == "Paris is sunny at 18°C. Found 2 hotels." # group 2 assistant text
assert projected[5].role == "assistant" # group 3 function_call (verbatim)
assert projected[6].role == "tool" # group 3 tool result (verbatim)
assert projected[7].text == "Tokyo is rainy at 22°C." # group 3 assistant text
# --- CompactionProvider tests ---
class _MockSessionContext:
"""Minimal mock for SessionContext used in CompactionProvider tests."""
def __init__(self) -> None:
self.context_messages: dict[str, list[Message]] = {}
self.input_messages: list[Message] = []
self._response: Any = None
@property
def response(self) -> Any:
return self._response
def extend_messages(self, provider: Any, messages: list[Message]) -> None:
source_id = getattr(provider, "source_id", "unknown")
self.context_messages.setdefault(source_id, []).extend(messages)
def get_messages(self) -> list[Message]:
result: list[Message] = []
for msgs in self.context_messages.values():
result.extend(msgs)
return result
async def test_compaction_provider_compacts_existing_context_messages() -> None:
"""CompactionProvider.before_run compacts messages already in context from earlier providers."""
provider = CompactionProvider(
before_strategy=SlidingWindowStrategy(keep_last_groups=2, preserve_system=True),
)
context = _MockSessionContext()
context.context_messages["history"] = [
Message(role="system", text="sys"),
Message(role="user", text="u1"),
Message(role="assistant", text="a1"),
Message(role="user", text="u2"),
Message(role="assistant", text="a2"),
Message(role="user", text="u3"),
Message(role="assistant", text="a3"),
]
await provider.before_run(agent=None, session=None, context=context, state={})
remaining = context.context_messages["history"]
assert len(remaining) == 3
assert remaining[0].role == "system"
assert remaining[1].text == "u3"
assert remaining[2].text == "a3"
async def test_compaction_provider_noop_when_no_context_messages() -> None:
"""before_run with no context messages does nothing."""
provider = CompactionProvider(
before_strategy=SlidingWindowStrategy(keep_last_groups=2),
)
context = _MockSessionContext()
await provider.before_run(agent=None, session=None, context=context, state={})
assert context.context_messages == {}
async def test_compaction_provider_preserves_messages_from_multiple_sources() -> None:
"""CompactionProvider correctly filters across multiple provider sources."""
provider = CompactionProvider(
before_strategy=SlidingWindowStrategy(keep_last_groups=2, preserve_system=True),
)
context = _MockSessionContext()
context.context_messages["history"] = [
Message(role="system", text="sys"),
Message(role="user", text="old_user"),
Message(role="assistant", text="old_assistant"),
]
context.context_messages["rag"] = [
Message(role="user", text="recent_rag_context"),
Message(role="assistant", text="recent_rag_answer"),
]
await provider.before_run(agent=None, session=None, context=context, state={})
all_remaining = context.get_messages()
assert any(m.role == "system" for m in all_remaining)
assert len(all_remaining) < 5
class _MockSession:
"""Minimal mock for AgentSession used in CompactionProvider after_run tests."""
def __init__(self) -> None:
self.state: dict[str, Any] = {}
async def test_compaction_provider_after_run_compacts_stored_history() -> None:
"""after_run annotates exclusions on stored messages without removing them."""
provider = CompactionProvider(
after_strategy=SelectiveToolCallCompactionStrategy(keep_last_tool_call_groups=0),
history_source_id="in_memory_history",
)
session = _MockSession()
session.state["in_memory_history"] = {
"messages": [
Message(role="user", text="old question"),
Message(role="assistant", text="old answer"),
_assistant_function_call("c1"),
_tool_result("c1", "result"),
Message(role="assistant", text="final answer"),
]
}
context = _MockSessionContext()
await provider.after_run(agent=None, session=session, context=context, state={})
stored = session.state["in_memory_history"]["messages"]
# All messages are kept; tool-call group is excluded via annotation.
assert len(stored) == 5
excluded = [m for m in stored if m.additional_properties.get("_excluded", False)]
assert len(excluded) == 2 # assistant function_call + tool result
assert any(m.text == "final answer" for m in stored if not m.additional_properties.get("_excluded", False))
async def test_compaction_provider_after_run_noop_without_history() -> None:
"""after_run does nothing when there is no history state."""
provider = CompactionProvider(
after_strategy=SlidingWindowStrategy(keep_last_groups=2),
history_source_id="in_memory_history",
)
session = _MockSession()
context = _MockSessionContext()
await provider.after_run(agent=None, session=session, context=context, state={})
assert "in_memory_history" not in session.state
async def test_compaction_provider_both_strategies() -> None:
"""Both before_strategy and after_strategy work independently."""
provider = CompactionProvider(
before_strategy=SlidingWindowStrategy(keep_last_groups=2, preserve_system=True),
after_strategy=SelectiveToolCallCompactionStrategy(keep_last_tool_call_groups=0),
history_source_id="history",
)
# before_run: compact loaded context
context = _MockSessionContext()
context.context_messages["history"] = [
Message(role="system", text="sys"),
Message(role="user", text="u1"),
Message(role="assistant", text="a1"),
Message(role="user", text="u2"),
Message(role="assistant", text="a2"),
]
await provider.before_run(agent=None, session=None, context=context, state={})
assert len(context.get_messages()) == 3
# after_run: compact stored history
session = _MockSession()
session.state["history"] = {
"messages": [
Message(role="user", text="q"),
_assistant_function_call("c1"),
_tool_result("c1", "ok"),
Message(role="assistant", text="done"),
]
}
await provider.after_run(agent=None, session=session, context=_MockSessionContext(), state={})
stored = session.state["history"]["messages"]
excluded = [m for m in stored if m.additional_properties.get("_excluded", False)]
assert len(excluded) == 2 # tool-call group excluded
async def test_compaction_provider_none_strategies_are_noop() -> None:
"""When both strategies are None, before_run and after_run are no-ops."""
provider = CompactionProvider()
context = _MockSessionContext()
context.context_messages["history"] = [
Message(role="user", text="hello"),
Message(role="assistant", text="hi"),
]
await provider.before_run(agent=None, session=None, context=context, state={})
assert len(context.get_messages()) == 2
session = _MockSession()
await provider.after_run(agent=None, session=session, context=context, state={})
assert "in_memory_history" not in session.state
async def test_in_memory_history_provider_skip_excluded() -> None:
"""InMemoryHistoryProvider with skip_excluded=True omits excluded messages."""
from agent_framework._compaction import EXCLUDED_KEY
from agent_framework._sessions import InMemoryHistoryProvider as _InMemoryHistoryProvider
provider = _InMemoryHistoryProvider(skip_excluded=True)
state: dict[str, Any] = {
"messages": [
Message(role="user", text="u1"),
Message(role="assistant", text="a1", additional_properties={EXCLUDED_KEY: True}),
Message(role="user", text="u2"),
Message(role="assistant", text="a2"),
]
}
loaded = await provider.get_messages(session_id="test", state=state)
assert len(loaded) == 3
assert all(m.text != "a1" for m in loaded)
async def test_in_memory_history_provider_default_loads_all() -> None:
"""InMemoryHistoryProvider with default settings loads all messages including excluded."""
from agent_framework._compaction import EXCLUDED_KEY
from agent_framework._sessions import InMemoryHistoryProvider as _InMemoryHistoryProvider
provider = _InMemoryHistoryProvider()
state: dict[str, Any] = {
"messages": [
Message(role="user", text="u1"),
Message(role="assistant", text="a1", additional_properties={EXCLUDED_KEY: True}),
Message(role="user", text="u2"),
]
}
loaded = await provider.get_messages(session_id="test", state=state)
assert len(loaded) == 3
python/packages/core/tests/core/test_function_invocation_logic.py
+139
View File
@@ -15,9 +15,27 @@ from agent_framework import (
SupportsChatGetResponse,
tool,
)
from agent_framework._compaction import (
EXCLUDED_KEY,
GROUP_ANNOTATION_KEY,
GROUP_ID_KEY,
CharacterEstimatorTokenizer,
SlidingWindowStrategy,
TokenBudgetComposedStrategy,
annotate_message_groups,
included_token_count,
)
from agent_framework._middleware import FunctionInvocationContext, FunctionMiddleware, MiddlewareTermination
def _group_id(message: Message) -> str | None:
annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
if not isinstance(annotation, dict):
return None
value = annotation.get(GROUP_ID_KEY)
return value if isinstance(value, str) else None
async def test_base_client_with_function_calling(chat_client_base: SupportsChatGetResponse):
exec_counter = 0
@@ -131,6 +149,127 @@ async def test_base_client_with_function_calling_resets(chat_client_base: Suppor
assert response.messages[3].contents[0].type == "function_result"
async def test_function_loop_applies_compaction_projection_each_model_call(chat_client_base: SupportsChatGetResponse):
@tool(name="test_function", approval_mode="never_require")
def ai_func(arg1: str) -> str:
return f"Processed {arg1}"
class _ExcludeOldestGroupAfterFirstTurn:
async def __call__(self, messages: list[Message]) -> bool:
groups = annotate_message_groups(messages)
if len(groups) <= 1:
return False
oldest_group_id = groups[0]
changed = False
for message in messages:
if _group_id(message) == oldest_group_id:
if message.additional_properties.get(EXCLUDED_KEY) is not True:
changed = True
message.additional_properties[EXCLUDED_KEY] = True
return changed
captured_roles: list[list[str]] = []
original = chat_client_base._get_non_streaming_response # type: ignore[attr-defined]
async def _capture(
*,
messages: list[Message],
options: dict[str, Any],
**kwargs: Any,
) -> ChatResponse:
captured_roles.append([message.role for message in messages])
return await original(messages=messages, options=options, **kwargs)
chat_client_base._get_non_streaming_response = _capture # type: ignore[attr-defined,method-assign]
chat_client_base.compaction_strategy = _ExcludeOldestGroupAfterFirstTurn() # type: ignore[attr-defined]
chat_client_base.run_responses = [
ChatResponse(
messages=Message(
role="assistant",
contents=[
Content.from_function_call(call_id="1", name="test_function", arguments='{"arg1": "value1"}')
],
)
),
ChatResponse(messages=Message(role="assistant", text="done")),
]
await chat_client_base.get_response(
[Message(role="user", text="hello")], options={"tool_choice": "auto", "tools": [ai_func]}
)
assert len(captured_roles) >= 2
assert "user" in captured_roles[0]
assert "user" not in captured_roles[1]
async def test_function_loop_token_budget_strategy_caps_tokens_each_iteration(
chat_client_base: SupportsChatGetResponse,
):
exec_counter = 0
token_budget = 500
tokenizer = CharacterEstimatorTokenizer()
@tool(name="test_function", approval_mode="never_require")
def ai_func(arg1: str) -> str:
nonlocal exec_counter
exec_counter += 1
return f"Processed {arg1}. " + ("result " * 120)
captured_token_counts: list[int] = []
original = chat_client_base._get_non_streaming_response # type: ignore[attr-defined]
async def _capture(
*,
messages: list[Message],
options: dict[str, Any],
**kwargs: Any,
) -> ChatResponse:
annotate_message_groups(messages, force_reannotate=True, tokenizer=tokenizer)
captured_token_counts.append(included_token_count(messages))
return await original(messages=messages, options=options, **kwargs)
chat_client_base._get_non_streaming_response = _capture # type: ignore[attr-defined,method-assign]
chat_client_base.tokenizer = tokenizer # type: ignore[attr-defined]
chat_client_base.function_invocation_configuration["max_iterations"] = 3 # type: ignore[attr-defined]
chat_client_base.compaction_strategy = TokenBudgetComposedStrategy( # type: ignore[attr-defined]
token_budget=token_budget,
tokenizer=tokenizer,
strategies=[SlidingWindowStrategy(keep_last_groups=2)],
)
chat_client_base.run_responses = [
ChatResponse(
messages=Message(
role="assistant",
contents=[
Content.from_function_call(call_id="1", name="test_function", arguments='{"arg1": "value1"}')
],
)
),
ChatResponse(
messages=Message(
role="assistant",
contents=[
Content.from_function_call(call_id="2", name="test_function", arguments='{"arg1": "value2"}')
],
)
),
ChatResponse(messages=Message(role="assistant", text="done")),
]
response = await chat_client_base.get_response(
[Message(role="user", text="hello " * 160)],
options={"tool_choice": "auto", "tools": [ai_func]},
)
assert response.messages[-1].text == "done"
assert exec_counter == 2
assert len(captured_token_counts) >= 3
assert all(token_count > 0 for token_count in captured_token_counts)
assert all(token_count <= token_budget for token_count in captured_token_counts)
async def test_base_client_with_streaming_function_calling(chat_client_base: SupportsChatGetResponse):
exec_counter = 0
python/packages/core/tests/core/test_skills.py
+6 -5
View File
@@ -35,7 +35,7 @@ from agent_framework._skills import (
async def _noop_script_runner(skill: Any, script: Any, args: Any = None) -> None:
"""No-op script runner for tests that need a SkillScriptRunner."""
return None
return
def _symlinks_supported(tmp: Path) -> bool:
@@ -1994,7 +1994,7 @@ class TestSkillScriptRunnerProtocol:
"""Tests for the SkillScriptRunner protocol."""
async def test_async_callable_satisfies_protocol(self) -> None:
from agent_framework import SkillScriptRunner, SkillScript
from agent_framework import SkillScript, SkillScriptRunner
results: list[tuple] = []
@@ -2015,7 +2015,7 @@ class TestSkillScriptRunnerProtocol:
assert results[0] == ("test-skill", "my-script", {"key": "val"})
async def test_callable_class_satisfies_protocol(self) -> None:
from agent_framework import SkillScriptRunner, SkillScript
from agent_framework import SkillScript, SkillScriptRunner
class _CustomRunner:
async def __call__(self, skill, script, args=None):
@@ -2056,7 +2056,7 @@ class TestSkillScriptRunnerProtocol:
assert result == {"exit_code": 0, "output": "ok"}
def test_sync_callable_satisfies_protocol(self) -> None:
from agent_framework import SkillScriptRunner, SkillScript
from agent_framework import SkillScript, SkillScriptRunner
results: list[tuple] = []
@@ -2077,7 +2077,7 @@ class TestSkillScriptRunnerProtocol:
assert results[0] == ("test-skill", "my-script", {"key": "val"})
def test_sync_callable_class_satisfies_protocol(self) -> None:
from agent_framework import SkillScriptRunner, SkillScript
from agent_framework import SkillScript, SkillScriptRunner
class _SyncRunner:
def __call__(self, skill, script, args=None):
@@ -2117,6 +2117,7 @@ class TestSkillScriptRunnerProtocol:
result = dict_runner(skill, script)
assert result == {"exit_code": 0, "output": "ok"}
# ---------------------------------------------------------------------------
# SkillsProvider static factory tests
# ---------------------------------------------------------------------------
python/packages/core/tests/core/test_types.py
+78
View File
@@ -28,6 +28,12 @@ from agent_framework import (
merge_chat_options,
tool,
)
from agent_framework._compaction import (
GROUP_ANNOTATION_KEY,
GROUP_HAS_REASONING_KEY,
GROUP_ID_KEY,
GROUP_TOKEN_COUNT_KEY,
)
from agent_framework._types import (
_get_data_bytes,
_get_data_bytes_as_str,
@@ -1654,6 +1660,78 @@ def test_chat_message_complex_content_serialization():
assert reconstructed.contents[2].type == "function_result"
def test_message_roundtrip_preserves_compaction_annotation_dict() -> None:
message = Message(
role="assistant",
contents=[Content.from_text("Hello")],
additional_properties={
GROUP_ANNOTATION_KEY: {
"id": "group_1",
"kind": "assistant_text",
"index": 1,
"has_reasoning": False,
"token_count": 42,
}
},
)
restored = Message.from_dict(message.to_dict())
annotation = restored.additional_properties.get(GROUP_ANNOTATION_KEY)
assert isinstance(annotation, dict)
assert annotation[GROUP_ID_KEY] == "group_1"
assert annotation[GROUP_TOKEN_COUNT_KEY] == 42
def test_content_roundtrip_preserves_compaction_annotation_dict() -> None:
content = Content.from_text(
text="Hello",
additional_properties={
GROUP_ANNOTATION_KEY: {
"id": "group_2",
"kind": "assistant_text",
"index": 2,
"has_reasoning": False,
"token_count": None,
}
},
)
restored = Content.from_dict(content.to_dict())
annotation = restored.additional_properties.get(GROUP_ANNOTATION_KEY)
assert isinstance(annotation, dict)
assert annotation[GROUP_ID_KEY] == "group_2"
assert annotation[GROUP_TOKEN_COUNT_KEY] is None
def test_chat_response_roundtrip_preserves_compaction_annotation_dict() -> None:
response = ChatResponse(
messages=[
Message(
role="assistant",
contents=[Content.from_text("Hello")],
additional_properties={
GROUP_ANNOTATION_KEY: {
"id": "group_3",
"kind": "assistant_text",
"index": 3,
"has_reasoning": True,
"token_count": 15,
}
},
)
]
)
restored = ChatResponse.from_dict(response.to_dict())
annotation = restored.messages[0].additional_properties.get(GROUP_ANNOTATION_KEY)
assert isinstance(annotation, dict)
assert annotation[GROUP_ID_KEY] == "group_3"
assert annotation[GROUP_HAS_REASONING_KEY] is True
def test_usage_content_serialization_with_details():
"""Test UsageContent from_dict and to_dict with UsageDetails conversion."""
python/packages/core/tests/openai/test_openai_responses_client.py
+52
View File
@@ -524,6 +524,58 @@ def test_response_content_creation_with_reasoning() -> None:
assert response.messages[0].contents[0].text == "Reasoning step"
def test_response_content_keeps_reasoning_and_function_calls_in_one_message() -> None:
"""Reasoning + function calls should parse into one assistant message."""
client = OpenAIResponsesClient(model_id="test-model", api_key="test-key")
mock_response = MagicMock()
mock_response.output_parsed = None
mock_response.metadata = {}
mock_response.usage = None
mock_response.id = "test-id"
mock_response.model = "test-model"
mock_response.created_at = 1000000000
mock_reasoning_content = MagicMock()
mock_reasoning_content.text = "Reasoning step"
mock_reasoning_item = MagicMock()
mock_reasoning_item.type = "reasoning"
mock_reasoning_item.id = "rs_123"
mock_reasoning_item.content = [mock_reasoning_content]
mock_reasoning_item.summary = []
mock_function_call_item_1 = MagicMock()
mock_function_call_item_1.type = "function_call"
mock_function_call_item_1.id = "fc_1"
mock_function_call_item_1.call_id = "call_1"
mock_function_call_item_1.name = "tool_1"
mock_function_call_item_1.arguments = '{"x": 1}'
mock_function_call_item_2 = MagicMock()
mock_function_call_item_2.type = "function_call"
mock_function_call_item_2.id = "fc_2"
mock_function_call_item_2.call_id = "call_2"
mock_function_call_item_2.name = "tool_2"
mock_function_call_item_2.arguments = '{"y": 2}'
mock_response.output = [
mock_reasoning_item,
mock_function_call_item_1,
mock_function_call_item_2,
]
response = client._parse_response_from_openai(mock_response, options={}) # type: ignore
assert len(response.messages) == 1
assert response.messages[0].role == "assistant"
assert [content.type for content in response.messages[0].contents] == [
"text_reasoning",
"function_call",
"function_call",
]
def test_response_content_creation_with_code_interpreter() -> None:
"""Test _parse_response_from_openai with code interpreter outputs."""
python/pyproject.toml
+1 -1
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@@ -222,7 +222,7 @@ samples-lint = "ruff check samples --fix --exclude samples/autogen-migration,sam
pyright = "python scripts/run_tasks_in_packages_if_exists.py pyright"
mypy = "python scripts/run_tasks_in_packages_if_exists.py mypy"
samples-syntax = "pyright -p pyrightconfig.samples.json --warnings"
typing = ["pyright", "mypy"]
typing = "python scripts/run_tasks_in_packages_if_exists.py mypy pyright"
# cleaning
clean-dist-packages = "python scripts/run_tasks_in_packages_if_exists.py clean-dist"
clean-dist-meta = "rm -rf dist"
python/samples/02-agents/compaction/README.md
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# Context Compaction Samples
This folder demonstrates context compaction patterns introduced by ADR-0019.
## Files
- `basics.py` — builds a local message list and applies each built-in strategy one at a time.
- `advanced.py` — composes multiple strategies with `TokenBudgetComposedStrategy`.
- `agent_client_overrides.py` — shows client defaults, agent-level overrides, and per-run compaction overrides.
- `custom.py` — defines a custom strategy implementing the `CompactionStrategy` protocol.
- `tiktoken_tokenizer.py` — shows a `TokenizerProtocol` implementation backed by `tiktoken`.
- `compaction_provider.py` — uses `CompactionProvider` with an agent and `InMemoryHistoryProvider`.
Run samples with:
```bash
uv run samples/02-agents/compaction/basics.py
uv run samples/02-agents/compaction/advanced.py
uv run samples/02-agents/compaction/agent_client_overrides.py
uv run samples/02-agents/compaction/custom.py
uv run samples/02-agents/compaction/tiktoken_tokenizer.py
uv run samples/02-agents/compaction/compaction_provider.py # requires OPENAI_API_KEY
```
python/samples/02-agents/compaction/advanced.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
from typing import Any
from agent_framework import (
CharacterEstimatorTokenizer,
ChatResponse,
Message,
SelectiveToolCallCompactionStrategy,
SlidingWindowStrategy,
SummarizationStrategy,
TokenBudgetComposedStrategy,
annotate_message_groups,
apply_compaction,
included_token_count,
)
"""This sample demonstrates composed in-run compaction with a token budget.
Key components:
- TokenBudgetComposedStrategy
- Sequential strategy composition
- Summarization with a SupportsChatGetResponse-compatible summarizer client
"""
class BudgetSummaryClient:
async def get_response(
self,
messages: list[Message],
*,
stream: bool = False,
options: dict[str, Any] | None = None,
**kwargs: Any,
) -> ChatResponse:
summary_text = f"Budget summary generated from {len(messages)} prompt messages."
return ChatResponse(messages=[Message(role="assistant", text=summary_text)])
def _build_long_history() -> list[Message]:
history = [Message(role="system", text="You are a migration copilot.")]
for i in range(1, 8):
history.append(
Message(
role="user",
text=f"Iteration {i}: capture migration requirements and edge cases.",
)
)
history.append(
Message(
role="assistant",
text=(
f"Iteration {i}: detailed plan with dependencies, rollback guidance, and testing details. "
"This sentence is intentionally long to create token pressure."
),
)
)
return history
async def main() -> None:
# 1. Build synthetic history representing long-running in-run growth.
messages = _build_long_history()
# 2. Configure tokenizer and measure token count before compaction.
tokenizer = CharacterEstimatorTokenizer()
annotate_message_groups(messages, tokenizer=tokenizer)
budget_before = included_token_count(messages)
# 3. Configure composed strategy stack.
composed = TokenBudgetComposedStrategy(
token_budget=200,
tokenizer=tokenizer,
strategies=[
SelectiveToolCallCompactionStrategy(keep_last_tool_call_groups=0),
SummarizationStrategy(
client=BudgetSummaryClient(),
target_count=3,
threshold=3,
),
SlidingWindowStrategy(keep_last_groups=4),
],
)
# 4. Apply compaction and inspect the budget result.
projected = await apply_compaction(messages, strategy=composed, tokenizer=tokenizer)
budget_after = included_token_count(messages)
print(f"Projected messages after compaction: {len(projected)}")
print(f"Included token count before compaction: {budget_before}")
print(f"Included token count after compaction: {budget_after}")
print("Projected roles:", [m.role for m in projected])
print("Projected messages with token counts:")
for msg in projected:
group = msg.additional_properties.get("_group")
token_count = group.get("token_count") if isinstance(group, dict) else None
text_preview = msg.text[:80] if msg.text else "<non-text>"
print(f"- [{msg.role}] {text_preview} ({token_count} tokens)")
if __name__ == "__main__":
asyncio.run(main())
"""
Sample output:
Projected messages after compaction: 3
Included token count before compaction: 793
Included token count after compaction: 144
Projected roles: ['system', 'user', 'assistant']
Projected messages with token counts:
- [system] You are a migration copilot. (35 tokens)
- [user] Iteration 7: capture migration requirements and edge cases. (43 tokens)
- [assistant] Iteration 7: detailed plan with dependencies, rollback guidance, and testing det (66 tokens)
"""
python/samples/02-agents/compaction/agent_client_overrides.py
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# Copyright (c) Microsoft. All rights reserved.
from __future__ import annotations
import asyncio
from collections.abc import Awaitable, Mapping, Sequence
from typing import Any
from agent_framework import (
GROUP_ANNOTATION_KEY,
GROUP_TOKEN_COUNT_KEY,
Agent,
BaseChatClient,
ChatResponse,
Message,
SlidingWindowStrategy,
TruncationStrategy,
)
"""This sample demonstrates client defaults, agent overrides, and run-level overrides for in-run compaction.
Key components:
- A shared client with default `compaction_strategy` and `tokenizer`
- An agent-level override that takes precedence over the shared client defaults
- A run-level override passed through `agent.run(...)`
"""
class FixedTokenizer:
"""Simple tokenizer used to make token annotations easy to inspect."""
def __init__(self, token_count: int) -> None:
self._token_count = token_count
def count_tokens(self, text: str) -> int:
return self._token_count
class InspectingChatClient(BaseChatClient[Any]):
"""Chat client that records the messages it receives after compaction."""
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.last_messages: list[Message] = []
def _inner_get_response(
self,
*,
messages: Sequence[Message],
stream: bool,
options: Mapping[str, Any],
**kwargs: Any,
) -> Awaitable[ChatResponse]:
if stream:
raise ValueError("This sample only demonstrates non-streaming responses.")
self.last_messages = list(messages)
async def _get_response() -> ChatResponse:
return ChatResponse(messages=[Message(role="assistant", text="done")])
return _get_response()
def _build_messages() -> list[Message]:
return [
Message(role="user", text="Collect the deployment requirements."),
Message(role="assistant", text="I will gather the constraints first."),
Message(role="user", text="Summarize the rollout risks."),
Message(role="assistant", text="The main risks are drift, downtime, and rollback gaps."),
]
def _token_count(message: Message) -> int | None:
group_annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
if not isinstance(group_annotation, dict):
return None
value = group_annotation.get(GROUP_TOKEN_COUNT_KEY)
return value if isinstance(value, int) else None
def _print_model_input(title: str, client: InspectingChatClient) -> None:
print(f"\n{title}")
print(f"Model receives {len(client.last_messages)} message(s):")
for message in client.last_messages:
print(f"- [{message.role}] {message.text} ({_token_count(message)} tokens)")
async def main() -> None:
# 1. Create one shared client with default compaction settings.
shared_client = InspectingChatClient(
compaction_strategy=TruncationStrategy(max_n=3, compact_to=2),
tokenizer=FixedTokenizer(7),
)
# 2. Create one agent that relies on the client defaults.
client_default_agent = Agent(client=shared_client, name="ClientDefaultAgent")
# 3. Create another agent that overrides the shared client's defaults.
agent_override = Agent(
client=shared_client,
name="AgentOverrideAgent",
compaction_strategy=SlidingWindowStrategy(keep_last_groups=3),
tokenizer=FixedTokenizer(11),
)
# 4. Run the first agent; the client defaults are applied.
await client_default_agent.run(_build_messages())
_print_model_input("1. Client default compaction", shared_client)
# 5. Run the second agent; the agent-level override wins over the client defaults.
await agent_override.run(_build_messages())
_print_model_input("2. Agent-level override", shared_client)
# 6. Override both settings for a single run; the per-run values win over both.
await agent_override.run(
_build_messages(),
compaction_strategy=TruncationStrategy(max_n=2, compact_to=1),
tokenizer=FixedTokenizer(23),
)
_print_model_input("3. Per-run override", shared_client)
if __name__ == "__main__":
asyncio.run(main())
"""
Sample output:
1. Client default compaction
Model receives 2 message(s):
- [user] Summarize the rollout risks. (7 tokens)
- [assistant] The main risks are drift, downtime, and rollback gaps. (7 tokens)
2. Agent-level override
Model receives 3 message(s):
- [assistant] I will gather the constraints first. (11 tokens)
- [user] Summarize the rollout risks. (11 tokens)
- [assistant] The main risks are drift, downtime, and rollback gaps. (11 tokens)
3. Per-run override
Model receives 1 message(s):
- [assistant] The main risks are drift, downtime, and rollback gaps. (23 tokens)
"""
python/samples/02-agents/compaction/basics.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
from typing import Any
from agent_framework import (
CharacterEstimatorTokenizer,
ChatResponse,
Content,
Message,
SelectiveToolCallCompactionStrategy,
SlidingWindowStrategy,
SummarizationStrategy,
TokenBudgetComposedStrategy,
ToolResultCompactionStrategy,
TruncationStrategy,
apply_compaction,
)
"""This sample demonstrates selecting one compaction strategy at a time.
How to use this sample:
- Keep one ``selected_strategy`` block active in ``main``.
- Comment the active block and uncomment one of the alternatives to switch strategies.
- Run again to compare behavior against the same "before" message list shown once.
"""
SUMMARY_OF_MESSAGE_IDS_KEY = "_summary_of_message_ids"
SUMMARIZED_BY_SUMMARY_ID_KEY = "_summarized_by_summary_id"
# Keep optional strategy classes imported for quick uncomment/switch in main().
AVAILABLE_STRATEGY_TYPES = (
TruncationStrategy,
CharacterEstimatorTokenizer,
SlidingWindowStrategy,
SelectiveToolCallCompactionStrategy,
ToolResultCompactionStrategy,
SummarizationStrategy,
TokenBudgetComposedStrategy,
)
class LocalSummaryClient:
"""Simple local summarizer compatible with SupportsChatGetResponse."""
async def get_response(
self,
messages: list[Message],
*,
stream: bool = False,
options: dict[str, Any] | None = None,
**kwargs: Any,
) -> ChatResponse:
return ChatResponse(messages=[Message(role="assistant", text=f"Summary for {len(messages)} messages.")])
async def main() -> None:
# 1. Build one baseline history and print it once.
messages = [
Message(role="system", text="You are a helpful assistant."),
Message(role="user", text="Plan a data migration."),
Message(role="assistant", text="I will gather requirements."),
Message(
role="assistant",
contents=[
Content.from_function_call(
call_id="call_1",
name="list_tables",
arguments='{"db":"legacy"}',
)
],
),
Message(
role="tool",
contents=[
Content.from_function_result(
call_id="call_1",
result="users, orders, events",
)
],
),
Message(role="assistant", text="I found three core tables."),
Message(role="user", text="Estimate effort and risks."),
Message(role="assistant", text="Primary risk is schema drift."),
]
print("\n--- Before compaction ---")
print(f"Message count: {len(messages)}")
for index, message in enumerate(messages, start=1):
message_text = message.text or ", ".join(content.type for content in message.contents)
print(f"{index:02d}. [{message.role}] {message_text}")
# 2. Select exactly one strategy (default shown below).
# Truncate when included history exceeds 5 messages, then keep 4.
# System remains anchored, so the oldest non-system messages are removed first.
# selected_strategy_name = "TruncationStrategy"
# selected_strategy = TruncationStrategy(max_n=5, compact_to=4, preserve_system=True)
# Keep the most recent 4 non-system groups and preserve the system anchor.
# A group represents a user turn (and related assistant/tool follow-up).
# selected_strategy_name = "SlidingWindowStrategy"
# selected_strategy = SlidingWindowStrategy(keep_last_groups=4, preserve_system=True)
# This means all tool-call groups are removed (assistant function_call message
# plus matching tool result messages). In this example, setting to 0 removes
# the single assistant+tool pair.
selected_strategy_name = "SelectiveToolCallCompactionStrategy"
selected_strategy = SelectiveToolCallCompactionStrategy(keep_last_tool_call_groups=0)
# Collapse older tool-call groups into short "[Tool results: tool_name]" summaries
# while keeping the most recent group verbatim. Unlike SelectiveToolCallCompactionStrategy
# which fully excludes groups, this preserves a readable trace of tool usage.
# selected_strategy_name = "ToolResultCompactionStrategy"
# selected_strategy = ToolResultCompactionStrategy(keep_last_tool_call_groups=0)
# Summarize older messages so only recent context remains, and attach summary
# trace metadata linking summary -> originals and originals -> summary.
# summary_client = LocalSummaryClient()
# selected_strategy_name = "SummarizationStrategy"
# selected_strategy = SummarizationStrategy(
# client=summary_client, target_count=3, threshold=2
# )
# tokenizer = CharacterEstimatorTokenizer()
# selected_strategy_name = "TokenBudgetComposedStrategy"
# selected_strategy = TokenBudgetComposedStrategy(
# token_budget=150,
# tokenizer=tokenizer,
# strategies=[
# SelectiveToolCallCompactionStrategy(keep_last_tool_call_groups=0),
# SlidingWindowStrategy(keep_last_groups=2),
# ],
# )
# 3. Apply the selected strategy and print projected output.
projected = await apply_compaction(messages, strategy=selected_strategy)
print(f"\n--- After compaction ({selected_strategy_name}) ---")
print(f"Message count: {len(projected)}")
for index, message in enumerate(projected, start=1):
message_text = message.text or ", ".join(content.type for content in message.contents)
print(f"{index:02d}. [{message.role}] {message_text}")
summaries = []
summarized = []
for message in messages:
group_annotation = message.additional_properties.get("_group")
if not isinstance(group_annotation, dict):
continue
if group_annotation.get(SUMMARY_OF_MESSAGE_IDS_KEY):
summaries.append(message)
if group_annotation.get(SUMMARIZED_BY_SUMMARY_ID_KEY):
summarized.append(message)
if summaries or summarized:
print("Summary trace metadata present:")
for message in summaries:
group_annotation = message.additional_properties.get("_group")
summarized_ids = (
group_annotation.get(SUMMARY_OF_MESSAGE_IDS_KEY) if isinstance(group_annotation, dict) else None
)
print(f" summary_id={message.message_id} summarizes={summarized_ids}")
for message in summarized:
group_annotation = message.additional_properties.get("_group")
summarized_by = (
group_annotation.get(SUMMARIZED_BY_SUMMARY_ID_KEY) if isinstance(group_annotation, dict) else None
)
print(f" original_id={message.message_id} summarized_by={summarized_by}")
if __name__ == "__main__":
asyncio.run(main())
"""
Sample output (always present):
--- Before compaction ---
Message count: 8
01. [system] You are a helpful assistant.
02. [user] Plan a data migration.
03. [assistant] I will gather requirements.
04. [assistant] function_call
05. [tool] function_result
06. [assistant] I found three core tables.
07. [user] Estimate effort and risks.
08. [assistant] Primary risk is schema drift.
"""
"""
Sample output (varies based on selected strategy):
--- After compaction (TruncationStrategy) ---
Message count: 4
01. [system] You are a helpful assistant.
02. [assistant] I found three core tables.
03. [user] Estimate effort and risks.
04. [assistant] Primary risk is schema drift.
--- After compaction (SlidingWindowStrategy) ---
Message count: 6
01. [system] You are a helpful assistant.
02. [assistant] function_call
03. [tool] function_result
04. [assistant] I found three core tables.
05. [user] Estimate effort and risks.
06. [assistant] Primary risk is schema drift.
--- After compaction (SelectiveToolCallCompactionStrategy) ---
Message count: 6
01. [system] You are a helpful assistant.
02. [user] Plan a data migration.
03. [assistant] I will gather requirements.
04. [assistant] I found three core tables.
05. [user] Estimate effort and risks.
06. [assistant] Primary risk is schema drift.
--- After compaction (ToolResultCompactionStrategy) ---
Message count: 7
01. [system] You are a helpful assistant.
02. [assistant] [Tool results: list_tables]
03. [user] Plan a data migration.
04. [assistant] I will gather requirements.
05. [assistant] I found three core tables.
06. [user] Estimate effort and risks.
07. [assistant] Primary risk is schema drift.
--- After compaction (SummarizationStrategy) ---
Message count: 5
01. [system] You are a helpful assistant.
02. [assistant] Summary for 2 messages.
03. [assistant] I found three core tables.
04. [user] Estimate effort and risks.
05. [assistant] Primary risk is schema drift.
Summary trace metadata present:
summary_id=summary_8 summarizes=['msg_1', 'msg_2', 'msg_3', 'msg_4']
original_id=msg_1 summarized_by=summary_8
original_id=msg_2 summarized_by=summary_8
original_id=msg_3 summarized_by=summary_8
original_id=msg_4 summarized_by=summary_8
--- After compaction (TokenBudgetComposedStrategy) ---
Message count: 3
01. [system] You are a helpful assistant.
02. [user] Estimate effort and risks.
03. [assistant] Primary risk is schema drift.
"""
python/samples/02-agents/compaction/compaction_provider.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
from collections.abc import Sequence
from typing import Any
from agent_framework import (
Agent,
ChatContext,
CompactionProvider,
InMemoryHistoryProvider,
Message,
SlidingWindowStrategy,
ToolResultCompactionStrategy,
chat_middleware,
tool,
)
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
load_dotenv()
"""
CompactionProvider with Agent Example
Demonstrates ``CompactionProvider`` as part of a real agent's context-provider
pipeline alongside ``InMemoryHistoryProvider``.
The compaction provider uses two separate strategies:
- ``before_strategy``: Applied to the loaded history before the model sees it.
Here a ``SlidingWindowStrategy`` keeps only the last 3 message groups, so
older turns get dropped as the conversation grows.
- ``after_strategy``: Applied to the stored history after each turn.
Here a ``ToolResultCompactionStrategy`` collapses all but the most recent
tool-call group into short ``[Tool results: ...]`` summaries.
A chat middleware logs the messages the model actually receives (after context
providers and compaction have run) so you can see the effect of compaction.
This sample intentionally is too aggressive in excluding content, because you can see
that the last turn actually does not have the full context any longer and is therefore
only comparing the results from Paris and Tokyo and not from London.
Run with:
uv run samples/02-agents/compaction/compaction_provider.py
"""
@tool(approval_mode="never_require")
def get_weather(city: str) -> str:
"""Get the current weather for a city."""
weather_data = {
"London": "cloudy, 12°C",
"Paris": "sunny, 18°C",
"Tokyo": "rainy, 22°C",
}
return weather_data.get(city, f"No data for {city}")
@chat_middleware
async def log_model_input(context: ChatContext, call_next: Any) -> None:
"""Chat middleware that logs the messages sent to the model (after compaction)."""
msgs: Sequence[Message] = context.messages
print(f"\n Model receives {len(msgs)} messages:")
for i, m in enumerate(msgs, 1):
text = m.text or ", ".join(c.type for c in m.contents)
print(f" {i:02d}. [{m.role}] {text[:70]}")
await call_next()
async def main() -> None:
client = OpenAIChatClient(model_id="gpt-4o-mini")
# History provider loads/stores conversation messages in session.state.
# skip_excluded=True means get_messages() will omit messages that were
# marked as excluded by the CompactionProvider's after_strategy.
history = InMemoryHistoryProvider(skip_excluded=True)
compaction = CompactionProvider(
# BEFORE each turn: SlidingWindow drops older message groups from
# the loaded context so the model's input stays bounded. With
# keep_last_groups=3, only the 3 most recent non-system groups are
# sent to the model — older turns are not shown to the model.
before_strategy=SlidingWindowStrategy(keep_last_groups=3, preserve_system=True),
# AFTER each turn: ToolResultCompaction marks older tool-call groups
# (assistant function_call + tool result messages) as excluded and
# inserts a short "[Tool results: ...]" summary. The original messages
# stay in storage with _excluded=True; skip_excluded on the history
# provider ensures they won't be loaded on the next turn.
after_strategy=ToolResultCompactionStrategy(keep_last_tool_call_groups=1),
history_source_id=history.source_id,
)
# Provider order matters:
# before_run: history loads → compaction trims (forward order)
# after_run: compaction marks exclusions → history stores (reverse order)
agent = Agent(
client=client,
name="WeatherAssistant",
instructions="You are a helpful weather assistant. Use the get_weather tool when asked about weather.",
tools=[get_weather],
context_providers=[history, compaction],
middleware=[log_model_input],
)
session = agent.create_session()
queries = [
"What is the weather in London?",
"How about Paris?",
"And Tokyo?",
"Which city is the warmest?",
]
for turn, query in enumerate(queries, 1):
print(f"\n{'=' * 60}")
print(f"Turn {turn} — User: {query}")
# ── What is in the persistent store right now? ──
# This shows ALL messages the history provider has accumulated,
# including any that were marked as excluded by the after_strategy
# on the previous turn. Messages marked ✗ are excluded and won't
# be loaded because skip_excluded=True on the history provider.
stored = session.state.get(history.source_id, {}).get("messages", [])
if stored:
excluded_count = sum(1 for m in stored if m.additional_properties.get("_excluded", False))
print(f"\n Stored history: {len(stored)} messages ({excluded_count} excluded)")
for i, m in enumerate(stored, 1):
text = m.text or ", ".join(c.type for c in m.contents)
excluded = m.additional_properties.get("_excluded", False)
reason = m.additional_properties.get("_exclude_reason", "")
if excluded:
marker = f" ✗ ({reason})"
elif (m.text or "").startswith("[Tool results:"):
marker = " ← summary"
else:
marker = ""
print(f" {i:02d}. [{m.role}]{marker} {text[:65]}")
# ── What the model actually sees ──
# The chat middleware fires AFTER the full context pipeline:
# 1. InMemoryHistoryProvider loads non-excluded stored messages
# 2. CompactionProvider.before_strategy (SlidingWindow) drops
# older groups so only the last 3 non-system groups survive
# 3. The agent prepends instructions and appends the new user input
# So this list is shorter than what's in storage.
result = await agent.run(query, session=session)
# ── What happens after the turn ──
# The agent's after_run pipeline runs in reverse provider order:
# 1. CompactionProvider.after_strategy (ToolResultCompaction) marks
# older tool-call groups as excluded in the stored messages —
# their assistant+tool messages get ✗ and a summary is inserted
# 2. InMemoryHistoryProvider appends the new input + response
# On the NEXT turn, skip_excluded=True means the ✗ messages won't load.
print(f"\n Agent: {result.text}")
print(f"\n{'=' * 60}")
print("Done.")
"""
Example output:
============================================================
Turn 1 — User: What is the weather in London?
Model receives 1 messages:
01. [user] What is the weather in London?
Agent: The weather in London is cloudy with a temperature of 12°C.
============================================================
Turn 2 — User: How about Paris?
Stored history: 4 messages (0 excluded)
01. [user] What is the weather in London?
02. [assistant] function_call
03. [tool] function_result
04. [assistant] The weather in London is cloudy with a temperature of 12°C.
Model receives 5 messages:
01. [user] What is the weather in London?
02. [assistant] function_call
03. [tool] function_result
04. [assistant] The weather in London is cloudy with a temperature of 12°C.
05. [user] How about Paris?
Agent: The weather in Paris is sunny with a temperature of 18°C.
============================================================
Turn 3 — User: And Tokyo?
Stored history: 8 messages (0 excluded)
01. [user] What is the weather in London?
02. [assistant] function_call
03. [tool] function_result
04. [assistant] The weather in London is cloudy with a temperature of 12°C.
05. [user] How about Paris?
06. [assistant] function_call
07. [tool] function_result
08. [assistant] The weather in Paris is sunny with a temperature of 18°C.
Model receives 5 messages:
01. [assistant] The weather in London is cloudy with a temperature of 12°C.
02. [assistant] function_call
03. [tool] function_result
04. [assistant] The weather in Paris is sunny with a temperature of 18°C.
05. [user] And Tokyo?
Agent: The weather in Tokyo is rainy with a temperature of 22°C.
============================================================
Turn 4 — User: Which city is the warmest?
Stored history: 13 messages (3 excluded)
01. [user] What is the weather in London?
02. [assistant] ← summary [Tool results: get_weather: cloudy, 12°C]
03. [assistant] ✗ (tool_result_compaction) function_call
04. [tool] ✗ (tool_result_compaction) function_result
05. [assistant] The weather in London is cloudy with a temperature of 12°C.
06. [user] ✗ (tool_result_compaction) How about Paris?
07. [assistant] function_call
08. [tool] function_result
09. [assistant] The weather in Paris is sunny with a temperature of 18°C.
10. [user] And Tokyo?
11. [assistant] function_call
12. [tool] function_result
13. [assistant] The weather in Tokyo is rainy with a temperature of 22°C.
Model receives 8 messages:
01. [assistant] function_call
02. [tool] function_result
03. [assistant] The weather in Paris is sunny with a temperature of 18°C.
04. [user] And Tokyo?
05. [assistant] function_call
06. [tool] function_result
07. [assistant] The weather in Tokyo is rainy with a temperature of 22°C.
08. [user] Which city is the warmest?
Agent: Tokyo is the warmest city with a temperature of 22°C, compared to Paris, which is at 18°C.
============================================================
Done.
"""
if __name__ == "__main__":
asyncio.run(main())
python/samples/02-agents/compaction/custom.py
+89
View File
@@ -0,0 +1,89 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
from agent_framework import (
Message,
annotate_message_groups,
apply_compaction,
included_messages,
)
"""This sample demonstrates authoring a custom compaction strategy.
The custom strategy keeps system messages and the most recent user turn while
excluding older non-system groups.
"""
EXCLUDED_KEY = "_excluded"
GROUP_ANNOTATION_KEY = "_group"
class KeepLastUserTurnStrategy:
async def __call__(self, messages: list[Message]) -> bool:
group_ids = annotate_message_groups(messages)
group_kinds: dict[str, str] = {}
for message in messages:
group_annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
group_id = group_annotation.get("id") if isinstance(group_annotation, dict) else None
kind = group_annotation.get("kind") if isinstance(group_annotation, dict) else None
if (
isinstance(group_id, str)
and isinstance(kind, str)
and group_id not in group_kinds
):
group_kinds[group_id] = kind
user_group_ids = [
group_id for group_id in group_ids if group_kinds.get(group_id) == "user"
]
if not user_group_ids:
return False
keep_user_group_id = user_group_ids[-1]
changed = False
for message in messages:
group_annotation = message.additional_properties.get(GROUP_ANNOTATION_KEY)
group_id = group_annotation.get("id") if isinstance(group_annotation, dict) else None
if message.role == "system":
continue
if group_id == keep_user_group_id:
continue
if message.additional_properties.get(EXCLUDED_KEY) is not True:
changed = True
message.additional_properties[EXCLUDED_KEY] = True
return changed
def _messages() -> list[Message]:
return [
Message(role="system", text="You are concise."),
Message(role="user", text="first request"),
Message(role="assistant", text="first response"),
Message(role="user", text="second request"),
Message(role="assistant", text="second response"),
]
async def main() -> None:
# 1. Build a short conversation.
messages = _messages()
print(f"Number of messages before compaction: {len(messages)}")
# 2. Apply custom strategy.
await apply_compaction(messages, strategy=KeepLastUserTurnStrategy())
# 3. Print projected messages.
projected = included_messages(messages)
print(f"Number of messages after compaction: {len(projected)}")
for msg in projected:
print(f"[{msg.role}] {msg.text}")
if __name__ == "__main__":
asyncio.run(main())
"""
Sample output:
Number of messages before compaction: 5
Number of messages after compaction: 2
[system] You are concise.
[user] second request
"""
python/samples/02-agents/compaction/tiktoken_tokenizer.py
+124
View File
@@ -0,0 +1,124 @@
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "tiktoken",
# ]
# ///
# Run with: uv run samples/02-agents/compaction/tiktoken_tokenizer.py
# Copyright (c) Microsoft. All rights reserved.
import asyncio
from typing import Any
import tiktoken
from agent_framework import (
Message,
TokenizerProtocol,
TruncationStrategy,
annotate_message_groups,
apply_compaction,
included_token_count,
)
"""This sample demonstrates a custom TokenizerProtocol implementation with tiktoken.
Key components:
- `TiktokenTokenizer` backed by `tiktoken`
- Token-based `TruncationStrategy` (`max_n` / `compact_to`)
- Inspecting projected roles and remaining included token count
"""
class TiktokenTokenizer(TokenizerProtocol):
"""TokenizerProtocol implementation backed by tiktoken's o200k_base (gpt-4.1 and up default) encoding."""
def __init__(
self, *, encoding_name: str = "o200k_base", model_name: str | None = None
) -> None:
if model_name is not None:
self._encoding = tiktoken.encoding_for_model(model_name)
else:
self._encoding: Any = tiktoken.get_encoding(encoding_name)
def count_tokens(self, text: str) -> int:
return len(self._encoding.encode(text))
def _build_messages() -> list[Message]:
return [
Message(role="system", text="You are a migration assistant."),
Message(
role="user",
text="List all migration risks and include detailed mitigations for each risk category.",
),
Message(
role="assistant",
text=(
"Primary risks include schema drift, missing foreign key constraints, "
"and data quality regressions. Mitigations include staged validation, "
"shadow writes, and replay-based verification."
),
),
Message(
role="user",
text=(
"Now provide a detailed checklist with owners, rollback "
"gates, and validation criteria."
),
),
Message(
role="assistant",
text=(
"Checklist: baseline snapshots, migration dry-run, production "
"canary, progressive deployment, automated integrity checks, and "
"post-migration reconciliation."
),
),
]
async def main() -> None:
# 1. Create a tokenizer implementation that uses tiktoken.
tokenizer = TiktokenTokenizer()
# 2. Configure token-based truncation.
strategy = TruncationStrategy(
max_n=250,
compact_to=150,
tokenizer=tokenizer,
preserve_system=True,
)
# 3. Build conversation and measure token count before compaction.
messages = _build_messages()
annotate_message_groups(messages, tokenizer=tokenizer)
token_count_before = included_token_count(messages)
# 4. Apply compaction and measure token count after compaction.
projected = await apply_compaction(messages, strategy=strategy, tokenizer=tokenizer)
token_count_after = included_token_count(messages)
# 5. Print before/after token counts and projected conversation.
print(f"Projected messages: {len(projected)}")
print(f"Included token count before compaction: {token_count_before}")
print(f"Included token count after compaction: {token_count_after}")
print("Projected roles:", [message.role for message in projected])
for message in projected:
token_count = message.additional_properties.get("_group", {}).get("token_count")
print(f"- [{message.role}] {message.text} ({token_count} tokens)")
if __name__ == "__main__":
asyncio.run(main())
"""
Projected messages: 3
Included token count before compaction: 263
Included token count after compaction: 149
Projected roles: ['system', 'user', 'assistant']
- [system] You are a migration assistant. (40 tokens)
- [user] Now provide a detailed checklist with owners, rollback gates, and validation criteria. (49 tokens)
- [assistant] Checklist: baseline snapshots, migration dry-run, production canary,
progressive deployment, automated integrity checks, and post-migration reconciliation. (60 tokens)
"""
python/uv.lock
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