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Haystack 2.13.0

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⭐️ Highlights

Enhanced Agent Tracing and Async Support

Haystack’s Agent got several improvements!

Agent Tracing
Agent tracing now provides deeper visibility into the agent’s execution. For every call, the inputs and outputs of the ChatGenerator and ToolInvoker are captured and logged using dedicated child spans. This makes it easier to debug, monitor, and analyze how an agent operates step-by-step.

Below is an example of what the trace looks like in Langfuse:

Langfuse UI for tracing 

# pip install langfuse-haystack
from haystack_integrations.components.connectors.langfuse.langfuse_connector import LangfuseConnector
from haystack.components.agents import Agent 
from haystack.components.generators.chat import OpenAIChatGenerator

tracer = LangfuseConnector("My Haystack Agent")
agent = Agent(
    system_prompt="You help provide the weather for cities" 
    chat_generator=OpenAIChatGenerator(),
    tools=[weather_tool],
)

Async Support
Additionally, there’s a new run_async method to enable built-in async support for Agent. Just use run_async instead of the run method. Here’s an example of an async web search agent:

# set `SERPERDEV_API_KEY` and `OPENAI_API_KEY` as env variables
from haystack.components.agents import Agent 
from haystack.components.generators.chat import OpenAIChatGenerator 
from haystack.components.websearch import SerperDevWebSearch 
from haystack.dataclasses import ChatMessage 
from haystack.tools.component_tool import ComponentTool 

web_tool = ComponentTool(component=SerperDevWebSearch()) 

web_search_agent = Agent(     
    chat_generator=OpenAIChatGenerator(),
    tools=[web_tool],
) 

result = await web_search_agent.run_async(
    messages=[ChatMessage.from_user("Find information about Haystack by deepset")]
)

New Toolset for Enhanced Tool Management

The new Toolset groups multiple Tool instances into a single manageable unit. It simplifies the passing of tools to components like ChatGenerator, ToolInvoker, or Agent, and supports filtering, serialization, and reuse. Check out the MCPToolset for dynamic tool discovery from an MCP server.

from haystack.tools import Toolset
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator

math_toolset = Toolset([tool_one, tool_two, ...])
agent = Agent(
    chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
    tools=math_toolset
)

@super_component decorator and new ready-made SuperComponents

Creating a custom SuperComponents just got even simpler. Now, all you need to do is define a class with a pipeline attribute and decorate it with @super_component. Haystack takes care of the rest!

Here’s an example of building a custom HybridRetriever using the @super_component decorator:

# pip install haystack-ai datasets "sentence-transformers>=3.0.0"

from haystack import Document, Pipeline, super_component
from haystack.components.joiners import DocumentJoiner
from haystack.components.embedders import SentenceTransformersTextEmbedder
from haystack.components.retrievers import InMemoryBM25Retriever, InMemoryEmbeddingRetriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from datasets import load_dataset

@super_component
class HybridRetriever:
    def __init__(self, document_store: InMemoryDocumentStore, embedder_model: str = "BAAI/bge-small-en-v1.5"):
        embedding_retriever = InMemoryEmbeddingRetriever(document_store)
        bm25_retriever = InMemoryBM25Retriever(document_store)
        text_embedder = SentenceTransformersTextEmbedder(embedder_model)
        document_joiner = DocumentJoiner(join_mode="reciprocal_rank_fusion")

        self.pipeline = Pipeline()
        self.pipeline.add_component("text_embedder", text_embedder)
        self.pipeline.add_component("embedding_retriever", embedding_retriever)
        self.pipeline.add_component("bm25_retriever", bm25_retriever)
        self.pipeline.add_component("document_joiner", document_joiner)

        self.pipeline.connect("text_embedder", "embedding_retriever")
        self.pipeline.connect("bm25_retriever", "document_joiner")
        self.pipeline.connect("embedding_retriever", "document_joiner")

dataset = load_dataset("HaystackBot/medrag-pubmed-chunk-with-embeddings", split="train")
docs = [Document(content=doc["contents"], embedding=doc["embedding"]) for doc in dataset]
document_store = InMemoryDocumentStore()
document_store.write_documents(docs)

query = "What treatments are available for chronic bronchitis?"
result = HybridRetriever(document_store).run(text=query, query=query)
print(result)

New ready-made SuperComponents: MultiFileConverter, DocumentPreprocessor There are also two ready-made SuperComponents, MultiFileConverter and DocumentPreprocessor, that encapsulate widely used common logic for indexing pipelines.

📚 Learn more about SuperComponents and get the full code example in the Tutorial: Creating Custom SuperComponents

⬆️ Upgrade Notes

  • The deprecated api, api_key, and api_params parameters for LLMEvaluator, ContextRelevanceEvaluator, and FaithfulnessEvaluator have been removed. By default, these components will continue to use OpenAI in JSON mode. To customize the LLM, use the chat_generator parameter with a ChatGenerator instance configured to return a response in JSON format. For example:
chat_generator=OpenAIChatGenerator(generation_kwargs={"response_format": {"type": "json_object"}})
  • The deprecated generator_api and generator_api_params initialization parameters of LLMMetadataExtractor and the LLMProvider enum have been removed. Use chat_generator instead to configure the underlying LLM. In order for the component to work, the LLM should be configured to return a JSON object. For example, if using OpenAI, you should initialize the LLMMetadataExtractor with
chat_generator=OpenAIChatGenerator(generation_kwargs={"response_format": {"type": "json_object"}})

🚀 New Features

  • Add run_async for OpenAITextEmbedder.
  • Add run_async method to HuggingFaceAPIDocumentEmbedder. This method enriches Documents with embeddings. It supports the same parameters as the run method. It returns a coroutine that can be awaited.
  • Support custom HTTP client configuration via http_client_kwargs (proxy, SSL) for:
    • AzureOpenAIGenerator, OpenAIGenerator and DALLEImageGenerator
    • OpenAIDocumentEmbedder and OpenAITextEmbedder
    • RemoteWhisperTranscriber
  • OpenAIChatGenerator and AzureOpenAIChatGenerator now support custom HTTP client config via http_client_kwargs, enabling proxy and SSL setup.
  • Introduced the Toolset class, allowing for the grouping and management of related tool functionalities. This new abstraction supports dynamic tool loading and registration.
  • We have added internal tracing support to Agent. It is now possible to track the internal loops within the agent by viewing the inputs and outputs each time the ChatGenerator and ToolInvoker is called.
  • The HuggingFaceAPITextEmbedder now also has support for a run() method in an asynchronous way, i.e., run_async.
  • Add a run_async to the Agent, which calls the run_async of the underlying ChatGenerator if available.
  • SuperComponents now support mapping nonleaf pipelines outputs to the SuperComponents output when specifying them in output_mapping.
  • AzureOpenAITextEmbedder and AzureOpenAIDocumentEmbedder now support custom HTTP client config via http_client_kwargs, enabling proxy and SSL setup.
  • The AzureOpenAIDocumentEmbedder component now inherits from the OpenAIDocumentEmbedder component, enabling asynchronous usage.
  • The AzureOpenAITextEmbedder component now inherits from the OpenAITextEmbedder component, enabling asynchronous usage.
  • Added async support to the OpenAIDocumentEmbedder component.
  • Agent now supports a List of Tools or a Toolset as input.

⚡️ Enhancement Notes

  • Added component_name and component_type attributes to PipelineRuntimeError.
    • Moved error message creation to within PipelineRuntimeError
    • Created a new subclass of PipelineRuntimeError called PipelineComponentsBlockedError for the specific case where the pipeline cannot run since no components are unblocked.
  • The ChatGenerator Protocol no longer requires to_dict and from_dict methods.

⚠️ Deprecation Notes

  • The utility function deserialize_tools_inplace has been deprecated and will be removed in Haystack 2.14.0. Use deserialize_tools_or_toolset_inplace instead.

🐛 Bug Fixes

  • OpenAITextEmbedder no longer replaces newlines with spaces in the text to embed. This was only required for the discontinued v1 embedding models.
  • OpenAIDocumentEmbedder and AzureOpenAIDocumentEmbedder no longer replace newlines with spaces in the text to embed. This was only required for the discontinued v1 embedding models.
  • Fix ChatMessage.from_dict to handle cases where optional fields like name and meta are missing.
  • Make Document’s first-level fields to take precedence over meta fields when flattening the dictionary representation.
  • In Agent we make sure state_schema is always initialized to have ‘messages’. Previously this was only happening at run time which is why pipeline.connect failed because output types are set at init time. Now the Agent correctly sets everything in state_schema (including messages by default) at init time. Now, when you call an Agent without tools, it acts like a ChatGenerator, which means it returns a ChatMessage based on user input.
  • In AsyncPipeline, the span tag name is updated from hasytack.component.outputs to haystack.component.output. This matches the tag name used in Pipeline and is the tag name expected by our tracers.
  • The batch_size parameter has now been added to to_dict function of TransformersSimilarityRanker. This means serialization of batch_size now works as expected.