# Release 2026.7
July 2026 brings updates to how agent pipelines handle chat history, adds more control over pipeline versions, and new models. And most exciting - an AI assistant ready to help you fix issues with your pipelines and indexes.
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# Traces
You can now investigate pipeline execution traces directly in , without leaving the platform. The new **Traces** tab on the Pipeline Details page shows a list of all executions for your pipeline, with status, query text, trace ID, source, duration, and timestamp for each one.
Use the search, status filter, and duration filter to find the traces you care about. Click any trace to open the Trace Detail page, where a three-panel view shows the full span tree, a waterfall timeline, and a details sidebar with tabs for overview, logs, tags, input, output, failure information, and more.
You can also navigate between traces and Search History: jump from a trace to its Search History entry, or open the Trace Detail page directly from a query's detailed view in Search History.
For details, see [Monitor Pipeline Performance](/docs/how-to-guides/productionizing-your-pipeline/pipeline-performance.mdx)
July 2026 brings updates to how agent pipelines handle chat history, adds more control over pipeline versions, and new models. And most exciting - an AI assistant ready to help you fix issues with your pipelines and indexes.
## New OpenAI GPT-5.6 Models
Three new GPT-5.6 model variants are now available on the platform: **GPT-5.6 Sol**, **GPT-5.6 Terra**, and **GPT-5.6 Luna**. You can select them in the LLM or Agent component configuration in Builder.
GPT-5.6 models support a `reasoning_effort` parameter (`none`, `low`, `medium`, `high`, `xhigh`, `max`) and a `reasoning_summary` parameter (`auto`, `concise`, `detailed`) to control the depth and verbosity of the model's reasoning process. GPT-5.5 and GPT-5.5 Pro have also been updated with `reasoning_effort` (up to `xhigh`) and `reasoning_summary` support. You can set these parameters on the **Advanced** tab of the component configuration panel.
## More Control Over Pipeline Versions
Two new actions are now available in the pipeline versions list in Builder:
- **Finalize** — converts a draft version into a permanent, numbered version. Use this when you want to lock in a draft without deploying it.
- **Set as default** — marks a non-draft version as the default. The default version is used by endpoints when a request identifies a pipeline by name or ID but does not specify a version. Default versions appear with a gold star **Default** tag in the versions list.
For details, see [Manage Pipeline Versions](/docs/how-to-guides/designing-your-pipeline/manage-pipeline-versions.mdx).
## AI Assistant in Builder
Builder now has a built-in AI assistant you can chat with directly in the pipeline editor. Click **AI Assistant** to open the chat panel and start a conversation.
The AI assistant was designed with debugging in mind — you can describe a pipeline issue, paste error messages, or ask it to help you understand unexpected behavior. But it's not limited to debugging: you can also use it to get help with pipeline design, understand how components work, or explore other use cases as you build.
When you have issues listed in the Issues panel, you can also click **Fix with AI** next to any issue. The AI assistant opens, reasons through the fix, and can apply it for you.
For details, see [Debug with Builder](/docs/how-to-guides/productionizing-your-pipeline/debug-with-pipeline-builder.mdx).
## Bedrock Prompt Caching
Amazon Bedrock models now support prompt caching for system prompts. You can set the `system_cachepoint_config_ttl` for models hosted on Amazon Bedrock to cache your system prompt for 5 minutes (`"5m"`) or 1 hour (`"1h"`), reducing latency and cost for repeated requests that share the same system prompt. The system prompt must stay static, with no parameters, for the cache to work. For details, see [AmazonBedrockChatGenerator](/docs/reference/pipeline-components/legacy-components/AmazonBedrockChatGenerator.mdx) and [Use Amazon Bedrock Models](/docs/how-to-guides/designing-your-pipeline/use-hosted-models-and-services/using-amazon-bedrock-models.mdx).
## Full Message History for Agent Pipelines
You can now control how much chat history is replayed to agent pipelines on each turn using the `chat_history_granularity` setting. By default, the platform sends only user query and final answer pairs to the pipeline (`QUERY_ANSWER`). Setting granularity to `ALL_MESSAGES` sends the complete message trace from the previous turn — including tool calls, tool results, and reasoning steps — so agents retain full context across follow-up requests.
You can set this per request through the API or as a pipeline-level default in the pipeline YAML under `history.granularity`:
```yaml
history:
granularity: all_messages
```
The API field `chat_history_granularity` accepts `ALL_MESSAGES` or `QUERY_ANSWER` (uppercase). The YAML field accepts lowercase values. If both are set, the API request value takes priority over the YAML default.
For details, see [Agent Memory](/docs/concepts/ai-agents/agent-memory.mdx).
## MultiRetriever Advanced Configuration
MultiRetriever now exposes advanced options for controlling how results from multiple knowledge sources are merged and returned. On the **Advanced** tab in Builder, you can configure:
- **Join mode**: Choose `reciprocal_rank_fusion` (default) to score and rank documents across all sources, or `concatenate` to combine and deduplicate without re-scoring.
- **Result limits**: Set `top_k` to limit the final number of documents after merging, or `top_k_per_retriever` to limit how many documents each source returns.
- **Default filters**: Apply metadata filters to all configured retrievers at initialization.
You can also select which knowledge sources to query at runtime using the `active_retrievers` parameter. Pass a list of source names through the API, Playground, or pipeline YAML to run only a subset of configured retrievers on a given query.
When you set `top_k`, MultiRetriever uses reciprocal rank fusion to produce a consistent global ranking before truncating results — even if join mode is set to `concatenate`.
For details, see [MultiRetriever](/docs/reference/pipeline-components/knowledge-retrieval/MultiRetriever.mdx).
## Search History Export Improvements
### Pipeline Version ID in CSV Export
You can now filter the search history CSV export by pipeline version ID. The export also includes a `pipeline_version_id` column. Each row shows the ID of the pipeline version that served the query, so you can compare behavior across versions or investigate queries tied to a specific deployment.
### Feedback Created At Column
The Search History table and CSV export now include a **Feedback Created At** column that shows when a user submitted feedback for each query. This column is separate from **Created At**, which records when the query was made. The column appears by default in both extractive and generative pipeline history views. In the CSV, the column is named `feedback_created_at` and is empty when no feedback has been given for a query.
The search history CSV export now includes a `pipeline_version_id` column. Each row in the export shows the ID of the pipeline version that served the query, so you can compare behavior across versions or investigate queries tied to a specific deployment. For details, see [Monitor Pipeline Performance](/docs/how-to-guides/productionizing-your-pipeline/pipeline-performance.mdx).
## Search History Status Column and Feedback on Failed Runs
The Search History table now shows a **Status** column for each run, indicating whether the run succeeded or failed. Runs with a *Failed* status no longer accept new feedback — the feedback actions are hidden for those rows and in the query details, while any existing ratings remain visible. For details, see [Monitor Pipeline Performance](/docs/how-to-guides/productionizing-your-pipeline/pipeline-performance.mdx).
## SVG Logo Support for Shared Prototypes
You can now upload SVG files as brand logos in the **Style** tab of the Share prototype window, in addition to JPEG and PNG. If a logo upload fails, the prototype window now shows an error message so you can try again. For details, see [Share a Pipeline Prototype](/docs/how-to-guides/evaluating-your-pipeline/share-a-pipeline-prototype.mdx).
For details, see [Monitor Pipeline Performance](/docs/how-to-guides/productionizing-your-pipeline/pipeline-performance.mdx).
## New Integration Components
Several new Haystack integration components are now available in Pipeline Builder:
- **Audio transcription:** `LocalWhisperTranscriber`, `RemoteWhisperTranscriber`, and `FunASRTranscriber` convert audio files into documents.
- **Document classification and extraction:** `DocumentLanguageClassifier` and `TextLanguageRouter` (LangDetect), `TransformersZeroShotDocumentClassifier`, `TransformersTextRouter`, `TransformersNamedEntityExtractor`, `TransformersExtractiveReader`, and `SpacyNamedEntityExtractor`.
- **File conversion:** `AzureOCRDocumentConverter` extracts text from scanned documents and images using Azure Form Recognizer. `TikaDocumentConverter` converts a wide range of file formats using Apache Tika. `TwelveLabsVideoConverter` analyzes video content.
- **Embedders and rankers:** Sentence Transformers components compute embeddings and rank documents locally, including sparse and document-image embedders plus diversity and similarity rankers. TwelveLabs embedders support multimodal Marengo models.
- **Connectors:** `DatadogConnector`, `OAuthTokenResolver`, `OpenAPIConnector`, `OpenAPIServiceConnector`, `OpenAPIServiceToFunctions`, and `OpenTelemetryConnector` let you integrate pipelines with external services and observability platforms.
- **Cloud file sources:** `GoogleDriveFetcher`, `GoogleDriveRetriever`, `MSSharePointFetcher`, and `MSSharePointRetriever` fetch and search files from Google Drive and Microsoft SharePoint.
- **Search:** `SearchApiWebSearch` and `SerperDevWebSearch` retrieve web search results from SearchApi and Serper.
- **Generators:** `OrcaRouterChatGenerator` routes chat completions through OrcaRouter.
- **Document store:** `ArangoDocumentStore` is now supported as an external document store integration, with `ArangoEmbeddingRetriever` for vector search. See [Document Stores](/docs/concepts/document-stores/document-stores.mdx) for the full list of supported stores.