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Core Package (agent-framework-core)

The foundation package containing all core abstractions, types, and built-in OpenAI/Azure OpenAI support.

Module Structure

agent_framework/
├── __init__.py          # Public API exports
├── security.py          # Public security primitives, middleware, and tools
├── _agents.py           # Agent implementations
├── _clients.py          # Chat client base classes and protocols
├── _types.py            # Core types (Message, ChatResponse, Content, etc.)
├── _tools.py            # Tool definitions and function invocation
├── _middleware.py       # Middleware system for request/response interception
├── _sessions.py         # AgentSession and context provider abstractions
├── _skills.py           # Agent Skills system (models, executors, provider)
├── _mcp.py              # Model Context Protocol support
├── _workflows/          # Workflow orchestration (sequential, concurrent, handoff, etc.)
├── openai/              # Built-in OpenAI client
├── azure/               # Lazy-loading entry point for Azure integrations
└── <provider>/          # Other lazy-loading provider folders

Core Classes

Agents (_agents.py)

  • SupportsAgentRun - Protocol defining the agent interface
  • BaseAgent - Abstract base class for agents
  • Agent - Main agent class wrapping a chat client with tools, instructions, and middleware

Chat Clients (_clients.py)

  • SupportsChatGetResponse - Protocol for chat client implementations
  • BaseChatClient - Abstract base class with middleware support; subclasses implement _inner_get_response() and _inner_get_streaming_response()

Types (_types.py)

  • Message - Represents a chat message with role, content, and metadata
  • ChatResponse - Response from a chat client containing messages and usage
  • ChatResponseUpdate - Streaming response update
  • AgentResponse / AgentResponseUpdate - Agent-level response wrappers
  • Content - Base class for message content (text, function calls, images, etc.)
  • ChatOptions - TypedDict for chat request options

Tools (_tools.py)

  • ToolProtocol - Protocol for tool definitions
  • FunctionTool - Wraps Python functions as tools with JSON schema generation
  • @tool decorator - Converts functions to tools
  • use_function_invocation() - Decorator to add automatic function calling to chat clients

Middleware (_middleware.py)

  • AgentMiddleware - Intercepts agent run() calls
  • ChatMiddleware - Intercepts chat client get_response() calls
  • FunctionMiddleware - Intercepts function/tool invocations
  • AgentContext / ChatContext / FunctionInvocationContext - Context objects passed through middleware. A tool can declare a FunctionInvocationContext parameter to receive it; context.tools is the live, mutable tools list for the run, and context.add_tools(...) / context.remove_tools(...) enable progressive tool exposure (changes apply on the next function-calling iteration).

Sessions (_sessions.py)

  • AgentSession - Manages conversation state and session metadata
  • SessionContext - Context object for session-scoped data during agent runs
  • ContextProvider - Base class for context providers (RAG, memory systems)
  • HistoryProvider - Base class for conversation history storage
  • InMemoryHistoryProvider - Built-in session-state history provider for local runs
  • FileHistoryProvider - JSON Lines file-backed history provider storing one file per session with one message record per line

Skills (_skills.py)

  • Skill - Abstract base for a skill definition bundling instructions (content) with frontmatter metadata, resources, and scripts. Concrete subclasses (InlineSkill, FileSkill, ClassSkill) accept a frontmatter=SkillFrontmatter(...) argument carrying the spec fields. Adding new spec fields is done in one place — on SkillFrontmatter — keeping the subclass constructors stable.
  • SkillFrontmatter - L1 discovery metadata for a skill (name, description, license, compatibility, allowed_tools, metadata). All fields are mutable plain attributes; the constructor validates name, description, and compatibility against the spec but post-construction assignments are not re-validated. Spec fields are reachable on every skill via skill.frontmatter.
  • SkillResource - Named supplementary content attached to a skill; holds either static content or a dynamic function (sync or async). Exactly one must be provided.
  • SkillScript - An executable script attached to a skill; holds either an inline function (code-defined, runs in-process) or a path to a file on disk (file-based, delegated to a runner). Exactly one must be provided.
  • SkillScriptRunner - Protocol for file-based script execution. Any callable matching (skill, script, args) -> Any satisfies it. Code-defined scripts do not use a runner.
  • SkillsProvider - Context provider (extends ContextProvider) that discovers file-based skills from SKILL.md files and/or accepts code-defined Skill instances. Follows progressive disclosure: advertise → load → read resources / run scripts.

Model Context Protocol (_mcp.py)

  • MCPTool - Base wrapper that owns the MCP ClientSession and exposes the remote server's tools as FunctionTools.
  • MCPStdioTool / MCPStreamableHTTPTool / MCPWebsocketTool - Transport-specific subclasses.
  • Argument allowlist (_prepare_call_kwargs) - Before each tools/call, kwargs are filtered to an allowlist built from the tool's declared parameters (inputSchema.properties) plus any user-configured extras. Framework runtime kwargs injected through the function-invocation pipeline (e.g. thread, conversation_id, chat_options, options, response_format) are stripped by default rather than forwarded. A tool that declares no usable properties (including schemas with additionalProperties: true) forwards only the configured extras. The _MCP_FRAMEWORK_DENYLIST is a safety net for framework-named params a server declares in its schema (those are dropped); names explicitly opted in via additional_tool_argument_names always win. The reserved _meta key is extracted as MCP request metadata, never forwarded as an argument.
  • additional_tool_argument_names (constructor arg on all MCPTool subclasses) - Opt extra argument names back into the allowlist. Accepts a Sequence[str] (applied to every tool) or a Mapping[str, Sequence[str]] keyed by remote tool name, where the reserved key "*" denotes global extras. It is configured only in user code at construction; there is no per-call/runtime override, so a model-issued tool call cannot change which names pass through. To use a server that accepts additionalProperties: true, list the extra names here and then either (1) manually extend that tool's inputSchema (via the .functions list after connecting) so the model is prompted to supply them, or (2) supply the values yourself via function_invocation_kwargs. If a name is supplied by both the model and function_invocation_kwargs, the model-supplied value wins.
  • Sampling guardrails (sampling_callback) - Passing client= advertises SamplingCapability so the server can send sampling/createMessage. Because remote servers are untrusted (confused-deputy risk), the default sampling_callback is deny-by-default and applies, in order: a per-session rate limit (sampling_max_requests, default _DEFAULT_SAMPLING_MAX_REQUESTS), an approval gate (sampling_approval_callback), and a maxTokens cap (sampling_max_tokens, default _DEFAULT_SAMPLING_MAX_TOKENS). The approval callback (constructor arg on all subclasses; exported type alias SamplingApprovalCallback) receives the raw CreateMessageRequestParams, may be sync or async, and must return truthy to approve. When it is None (the default) every sampling request is denied; pass lambda params: True to restore legacy auto-approve as an explicit opt-in. Requests and denials are logged at WARNING (content is not logged). The per-session counter resets in _reset_session_state.
  • MCPTaskOptions (experimental, MCP_LONG_RUNNING_TASKS feature, frozen) - Per-tool-instance options controlling the SEP-2663 long-running task lifecycle. When the server advertises a tool with execution.taskSupport == "required", MCPTool.call_tool transparently routes through call_tool_as_task, which sends an augmented tools/call, polls tasks/get until terminal, and reinterprets tasks/result as a normal CallToolResult. Instances are immutable; replace via MCPTool.task_options = MCPTaskOptions(...). Fields:
    • default_ttl: timedelta | None — forwarded to the server as params.task.ttl (milliseconds). When None, the server's default applies.
    • cancel_remote_task_on_local_cancellation: bool = True — only gates the CancelledError path. Abandonment paths (see below) always cancel.
    • max_task_wait: timedelta | None — client-side deadline for the whole post-create lifecycle (poll + result fetch). When exceeded, raises ToolExecutionException and fires a best-effort tasks/cancel. None (default) means no client-side bound. Bounds sleeps, sends, AND reconnects via asyncio.wait_for.
  • Permissive fallback: servers that ignore the augmentation (return CallToolResult directly) or reject the unknown task field with METHOD_NOT_FOUND / INVALID_PARAMS fall back to the plain session.call_tool(...) path so legacy servers keep working. An unparseable success response (server accepted the augmented call but returned a payload that is neither CreateTaskResult nor CallToolResult) does not fall back — it raises ToolExecutionException to avoid double-executing a side-effecting tool.
  • Submit-vs-track reconnect policy: a dropped connection before a task_id is known raises ToolExecutionException("connection lost; task state unknown") without re-issuing the augmented tools/call, so a server that accepted the request but lost the response cannot be made to start the same operation twice; once a task_id exists, tasks/get / tasks/result reconnect once and retry against the same id (a shared _send_with_one_reconnect helper).
  • Cancel-on-abandonment vs terminal failure: any path where the remote task may still be running (max-wait exceeded, hard McpError in poll, malformed tasks/get, second connection loss in poll/fetch, reconnect failure) fires best-effort tasks/cancel before raising. Terminal failures (failed/cancelled/input_required server-side, completed+isError, malformed tasks/result after server completed) do not cancel — the server is already done. _MCPTaskAbandoned is the private marker distinguishing the two.
  • Transient poll retry: a slow tasks/get that surfaces as McpError(code=408 REQUEST_TIMEOUT) is retried (bounded by max_task_wait). All other non-connection McpErrors during poll are treated as abandonment. tasks/result does not get transient retry — the server has already completed, so a slow payload fetch is anomalous.

File Access Harness (_harness/_file_access.py)

  • AgentFileStore - Abstract async store backing the file-access harness. Implementations expose write_file, read_file, delete_file, list_files, list_directories, file_exists, search_files, and create_directory over forward-slash relative paths. list_files/list_directories return only direct children; search_files accepts a keyword-only recursive flag (default False) and, when recursive=True, walks all descendants and returns file_name values relative to the search directory.
  • InMemoryAgentFileStore - Dict-backed store suitable for tests and lightweight scenarios.
  • FileSystemAgentFileStore - Disk-backed store rooted under a configurable directory. Enforces relative-path normalization, root containment, and rejects symlink/reparse-point segments to prevent escape.
  • FileSearchResult / FileSearchMatch - SerializationMixin DTOs returned by search_files, carrying the matching file name, a context snippet, and the matching lines with 1-based line numbers.
  • FileAccessProvider - ContextProvider that adds shared file-access tools (file_access_save_file, file_access_read_file, file_access_delete_file, file_access_list_files, file_access_list_subdirectories, file_access_search_files) plus default usage instructions to each invocation. file_access_list_files/file_access_list_subdirectories enumerate direct children (files / subdirectories) so the agent can walk the tree level by level; file_access_search_files searches recursively from the store root and returns store-root-relative file_name paths, scoped via an fnmatch glob (where * crosses /, e.g. *.md, reports/*). Unlike MemoryContextProvider, the store is intentionally shared across sessions and agents.

Tool Approval Harness (_harness/_tool_approval.py)

  • ToolApprovalMiddleware - Experimental opt-in agent middleware that coordinates session-backed approval rules, heuristic auto_approval_rules, queued approval requests, collected approval responses, and streaming/non-streaming approval prompts. Heuristic callbacks receive the underlying function_call content.
  • ToolApprovalRule / ToolApprovalState - Serializable state models for standing approvals and queued approval flow. ToolApprovalRule.arguments is None means a tool-wide rule; an empty dict {} means an exact no-argument call for create_always_approve_tool_with_arguments_response.
  • create_always_approve_tool_response / create_always_approve_tool_with_arguments_response - Helpers that return normal function_approval_response content with additional_properties metadata consumed by ToolApprovalMiddleware. Standing rules for hosted tools include the server_label boundary, so same-named tools on different hosted servers do not share approvals.
  • Mixed tool-call batches use a default .NET-style bypass in the function invocation loop: when a session is available, approval requests for known non-approval-required tools are treated as already approved, hidden, stored in session state keyed to the visible approval request ids from that batch, and reinjected only when that visible approval flow resumes.

Agent Loop (_harness/_loop.py)

  • AgentLoopMiddleware - AgentMiddleware that re-runs an agent in a loop by calling call_next() repeatedly (the pipeline re-reads context.messages each time). One configurable class covers two patterns: a required user should_continue predicate (sync or async, the first positional/keyword arg), and a chat-client judge built via the .with_judge(...) factory (a second chat client decides whether the original request was answered; loops while it is not, using a JudgeVerdict structured-output response — internally just an async should_continue predicate). The constructor covers the predicate pattern directly; only the judge has a convenience classmethod factory (.with_judge(judge_client, ...)) that forwards to __init__. Supports both streaming and non-streaming runs. By default a non-streaming run returns an aggregated AgentResponse containing every iteration's messages plus the injected next_message "nudge" messages (as user messages); set return_final_only=True to return only the last iteration's response. Streaming runs always yield each iteration's updates and emit the injected nudge messages as user updates between iterations (the return_final_only flag has no effect on streaming, and the final response reflects the last iteration; MiddlewareTermination is handled cleanly). should_continue is required; other constructor args are optional: max_iterations (safety cap; defaults to DEFAULT_MAX_ITERATIONS=10, explicit None→unbounded, positive int caps; .with_judge uses DEFAULT_JUDGE_MAX_ITERATIONS=5 as its default), next_message (defaults to a short "continue" nudge), return_final_only, and additional_instructions (an extra system message injected ahead of the input before the agent runs — becomes part of the original messages so it survives fresh_context resets and persists via a session). The judge is configured only through .with_judge (judge_client/instructions/criteria), not the constructor, and its reasoning is fed back to the agent as the next iteration's input; the judge forwards the original request messages and the agent's latest response messages verbatim so multi-modal content is preserved. criteria (a list[str]) is both injected as the agent's additional_instructions and rendered into the judge instructions wherever the {{criteria}} placeholder (CRITERIA_PLACEHOLDER) appears (DEFAULT_JUDGE_INSTRUCTIONS ends with it; custom instructions may include it, and it is stripped when no criteria are given). The should_continue/next_message callables are invoked with keyword args (iteration, last_result, messages, original_messages, session, agent, progress, feedback) and may be sync or async; declare only what you need plus **kwargs. should_continue may return a plain bool or a (bool, str | None) tuple whose second item is feedback surfaced to next_message/record_feedback via the feedback kwarg (the judge uses this to relay its reasoning). Stop precedence per iteration is max_iterations → should_continue, evaluated before record_feedback so the feedback is available to it.
    • Feedback tracking - record_feedback captures a per-iteration progress entry (called with the loop kwargs; if it returns a truthy string the entry is appended, otherwise the agent's response text is used as the fallback entry). The accumulated log is exposed to every callback via the progress keyword (a per-iteration copy of prior entries) and, when inject_progress=True (default), injected into the next iteration's input as a user message (the full log without a session, only the latest entry with a session to avoid duplicating history). fresh_context=True restarts each iteration from the original task plus the progress log; when a session is attached it is snapshotted (to_dict()) before the loop and restored (from_dict + field copy) between iterations so the local transcript and any service-side conversation id reset too (in-loop working-state is discarded, pre-loop state preserved, continuity carried only by the progress log).
  • todos_remaining(provider) / background_tasks_running(provider) - Helper factories returning should_continue predicates that loop while a TodoProvider has open items, or while a BackgroundAgentsProvider's persisted state shows running tasks.

Workflows (_workflows/)

  • Workflow - Graph-based workflow definition
  • WorkflowBuilder - Fluent API for building workflows, including explicit output_from / intermediate_output_from selection for caller-facing emissions. output_from is an allow-list for Workflow Output; unselected executor payloads are hidden unless intermediate_output_from selects them as Intermediate Output. Use output_from="all" for explicit all-output behavior and intermediate_output_from="all_other" for visible progress from every output-capable executor not selected by output_from.
  • WorkflowRunResult - Non-streaming workflow result with Workflow Output get_outputs() and Intermediate Output get_intermediate_outputs() accessors
  • Orchestrators: SequentialOrchestrator, ConcurrentOrchestrator, GroupChatOrchestrator, MagenticOrchestrator, HandoffOrchestrator

Built-in Providers

OpenAI (openai/)

  • OpenAIChatClient - Chat client for the OpenAI Responses API
  • OpenAIChatCompletionClient - Chat client for the OpenAI Chat Completions API

Foundry (foundry/)

  • FoundryChatClient - Chat client for Azure AI Foundry project endpoints

Key Patterns

Creating an Agent

from agent_framework import Agent
from agent_framework.openai import OpenAIChatClient

agent = Agent(
    client=OpenAIChatClient(),
    instructions="You are helpful.",
    tools=[my_function],
)
response = await agent.run("Hello")

Using as_agent() Shorthand

agent = OpenAIChatClient().as_agent(
    name="Assistant",
    instructions="You are helpful.",
)

Middleware Pipeline

from agent_framework import Agent, AgentMiddleware, AgentContext

class LoggingMiddleware(AgentMiddleware):
    async def process(self, context: AgentContext, call_next) -> None:
        print(f"Input: {context.messages}")
        await call_next()
        print(f"Output: {context.result}")

agent = Agent(..., middleware=[LoggingMiddleware()])

Custom Chat Client

from agent_framework import BaseChatClient, ChatResponse, Message

class MyClient(BaseChatClient):
    async def _inner_get_response(self, *, messages, options, **kwargs) -> ChatResponse:
        # Call your LLM here
        return ChatResponse(messages=[Message(role="assistant", contents=["Hi!"])])

    async def _inner_get_streaming_response(self, *, messages, options, **kwargs):
        yield ChatResponseUpdate(...)