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Changelog

All notable changes to AgentFlow are documented here.

0.7.4

Added

  • HANDLER_RESULT event from code handler nodes. Handler nodes (registered Python functions) now emit a HANDLER_RESULT event after execution, carrying the full NodeOutput (text, artifacts, metadata). Observers — such as asset collectors — can react to handler outputs the same way they react to TOOL_RESULT events from agent nodes, without parsing text.

0.7.3

Added

  • raw_result in TOOL_RESULT for local tools. LocalToolDispatcher now parses JSON results and sets last_raw_tool_result (the same ContextVar used by HTTPToolDispatcher). TOOL_RESULT events for locally dispatched tools now include raw_result with the structured dict output, enabling asset collectors to capture document URLs and other structured data from local tools.

0.7.2

Added

  • Enriched TOOL_RESULT events. HTTPToolDispatcher stashes the pre-formatted tool result dict in an asyncio-safe ContextVar. AgentExecutor reads it and includes input, result (formatted string), and raw_result (raw dict) in every TOOL_RESULT event. Downstream consumers can now capture structured tool output at call time without regex-parsing the agent's final response.

0.7.1

Fixed

  • Langfuse SDK compatibility. LangfuseEventHandler now wraps resource_attributes initialization in a try/except TypeError. Langfuse SDK 4.0.x does not support the resource_attributes parameter; older SDK versions skip it gracefully rather than raising at startup.

0.7.0

Added

  • Langfuse session and trace context. New set_trace_context() method on LangfuseEventHandler lets callers inject per-request conversation context — session_id, trace_name, user_id, tags, and metadata — before each workflow execution. Context is consumed once when WORKFLOW_STARTED fires, then cleared.
  • resource_attributes parameter. LangfuseEventHandler.__init__ now accepts resource_attributes: dict[str, str] for attaching static service metadata (e.g. service.name, service.version) to the Langfuse client instance.
  • DOMAIN_ROUTED span. LangfuseEventHandler now handles the DOMAIN_ROUTED event, recording the routing decision as a child span on the root trace with domain, target, confidence, and router metadata.

0.6.0

Added

  • Code handler nodes. Workflow nodes can now specify handler: <name> instead of agent: <name>. Handlers are registered Python async functions (async def fn(message: str, prior_outputs: dict) -> NodeOutput) passed to WorkflowExecutor(handlers={...}). Use handler nodes for deterministic processing steps that don't need an LLM call.
nodes:
  - id: transform
    handler: normalize_text
    inputs:
      message: "extract.text"

async def normalize_text(message: str, prior_outputs: dict) -> NodeOutput:
    return NodeOutput(node_id="transform", agent_id="normalize_text", text=message.lower())

executor = WorkflowExecutor(
    config=wf_config,
    runner_factory=runner_factory,
    handlers={"normalize_text": normalize_text},
)
  • Foreach iteration. Workflow nodes can specify foreach: <dotted-ref> pointing to a list artifact from a prior node. The node body executes once per item; each iteration receives loop_item, loop_index, loop_total, and loop_prior_results injected into the message. Results are collected into artifacts["results"] on the synthetic __loop__ output.
nodes:
  - id: extract
    handler: extract_items
    next: [process]
  - id: process
    agent: item_processor
    foreach: "extract.artifacts.items"

Handler nodes and foreach can be combined: a handler node with foreach iterates the handler function over the list.

  • HANDLER_RESULT event constant. Importable from agentflow.

0.5.2

Changed

  • Named inputs deliver labeled sections to agents. When a workflow node defines inputs with keys other than message, each key is now resolved in YAML-definition order and delivered as a labeled [key]\nvalue section. Previously, non-message keys were processed through _predecessors() and concatenated without labels in non-deterministic (set iteration) order.

Before (0.5.1 behavior — unlabeled, unordered):

## Section 1
Body text.

# Outline content

After (0.5.2 behavior — labeled, definition order):

[outline]
# Outline content

[sections]
## Section 1
Body text.

Migration: Agent system prompts that receive named inputs should be updated to reference the labeled sections by key name. The message key pattern is unchanged.

Fixed

  • NodeRunner._predecessors() renamed to _input_node_ids() with corrected docstring. The method is used only for scratchpad context filtering; it is no longer involved in message resolution.

Added

  • Four new tests covering named inputs: labeled section format, definition order preservation, missing upstream node handling, and end-to-end workflow integration.

0.5.1

Fixed

  • Release workflow: no longer fails if a GitHub release already exists for the current tag.

0.5.0 (alpha)

Breaking Changes

  • VectorMemory is now embedding-agnostic. The constructor requires embed_fn (an async str -> list[float] callable) and embedding_dim instead of using a hardcoded embedding model. This decouples VectorMemory from any specific embedding provider.

Changed

  • Migrated embedding from deprecated text-embedding-004 to gemini-embedding-001 in examples and tests.

0.4.0 (alpha)

Added

  • Hierarchical domain routing. New DomainRouter class implements two-tier routing: a top-level router classifies messages into domains, then per-domain routers pick specific agents or workflows.
  • New DomainConfig schema for *.domain.md files.
  • ConfigLoader now scans context/domains/ for domain definitions.
  • DOMAIN_ROUTED event constant for domain routing telemetry.
  • RoutingResult now includes a domain field.

0.3.3

Fixed

  • Subdirectory context loading: ConfigLoader now correctly loads *.context.md files from arbitrary subdirectories (not just shared/).

0.3.0

Added

  • Context profiles (*.context.md with type: profile) for conditional context loading.
  • ContextProfile and ConditionalInclude schemas.
  • ConfigLoader.get_profile() and ConfigLoader.is_profile() methods.
  • Shared context files loaded from all subdirectories, not just agents/.

0.2.0

Added

  • WorkflowExecutor with DAG-based execution.
  • NodeRunner for per-node agent execution.
  • Scratchpad for per-node working memory and summaries.
  • ArtifactStore for named artifact storage.
  • MultiUserHistory and HistoryPersistence for multi-user session support.
  • FileMemory and VectorMemory backends.
  • MemoryManager for coordinating memory operations.
  • ComplexityClassifier, DAGExecutor, Plan, PlanStep orchestration primitives.
  • LangfuseEventHandler for Langfuse telemetry integration.
  • GoogleGenAIProvider for Google Gemini models.

0.1.0

Added

  • Initial release.
  • ConfigLoader with .prompt.md, .workflow.md, .context.md parsing.
  • RouterEngine with YAML rules and LLM fallback.
  • RuleEvaluator for Python expression-based routing rules.
  • AgentExecutor with tool loop support.
  • ContextAssembler and PromptTemplate.
  • EventBus pub/sub event system.
  • ToolRegistry, LocalToolDispatcher, HTTPToolDispatcher.
  • AnthropicProvider, OpenAICompatProvider, MockLLMProvider.
  • FileSystemStorage, InMemoryStorage, S3Storage backends.
  • SessionManager and Session.
  • Core types: Message, AgentResponse, ToolCall, ToolResult, NodeOutput.
  • Protocols: LLMProvider, StorageBackend, ToolDispatcher, MemoryStore, EventHandler.