AIDB 7.6.0 release notes

Released: 26 Aug 2026

AIDB 7.6.0 introduces Agents and tools, a SQL-native framework for building in-database AI agents: an agent reasons over multiple steps — calling tools, observing their results, and repeating — instead of answering in one shot. Tools come from a unified catalog (aidb.tools ) spanning a large built-in native tool set, your own custom SQL queries, and tools imported from an external MCP server. Agents support per-call budgets (tokens, reasoning iterations, wall-clock time), delegation to other agents, and per-user conversation visibility. See Agents reference and Tools reference to get started.

AIDB 7.6.0 includes the following enhancements and bug fixes:

Type	Description
Enhancement	Agents and tools: The new SQL-native agent framework and unified tools hub described above. See Agents reference and Tools reference .
Enhancement	Native tool-calling model adapters: The new openai_responses and anthropic_messages providers (plus openai_responses_azure and anthropic_messages_azure/anthropic_messages_bedrock hosting variants) connect to OpenAI's Responses API and Anthropic's Messages API natively, emitting real tools/tool_choice request fields and parsing real tool-call responses, instead of simulating tool calls through prompt injection. See External model connections .
Enhancement	Model credentials from an environment variable: aidb.create_model() accepts a new credentials_env argument, letting credentials be read from a server-side environment variable at model-use time instead of passed inline as JSON.
Enhancement	Local reranking via llama.cpp: The new llamacpp_reranking provider reranks text locally from a GGUF file, supporting both cross-encoder models (for example, bge-reranker-v2-m3) and the Qwen3-Reranker decoder-only family — no external API required. See Local models .
Enhancement	Local OCR via llama.cpp: The new llamacpp_ocr provider runs OCR locally from a GGUF vision model, with a pre-registered default model (lightonocr-2-1b-Q8_0). See Local models .
Enhancement	Renamed `decode_text` to `generate_text`: aidb.decode_text() and aidb.decode_text_batch() are renamed to aidb.generate_text() and aidb.generate_text_batch(), matching encode_text()'s naming on the embedding side. The old names still work but are deprecated and will be removed in a future version. See Inference functions .
Enhancement	Semantic KB search across sources: The new aidb.semantic_kb_search() function fuses ranked vector search results from schema metadata and curated semantic aliases into a single result set using Reciprocal Rank Fusion.
Enhancement	Simplified semantic KB calls: kb_name (and name) are now optional on aidb.get_column_definitions(), aidb.get_metadata(), aidb.get_entity_definitions(), aidb.search_by_comment(), aidb.create_semantic_kb(), aidb.delete_semantic_kb(), aidb.refresh_semantic_kb(), aidb.update_semantic_kb_auto_processing(), and aidb.semantic_kb_stats() — when omitted, AIDB resolves the single existing semantic KB.
Change	Semantic aliases can belong to multiple KBs: aidb.create_semantic_alias() and related functions drop the model argument; an alias is now embedded once per knowledge base that owns the schema its SQL reads, instead of being bound to exactly one KB and model.
Enhancement	Pipeline metrics show last run time: aidb.get_pipeline_metrics() and the pipeline metrics views now include a last_run_completed timestamp.
Change	Leaked credential detection: aidb.create_model() now rejects a config that embeds api_key or basic_auth directly — pass them via credentials/credentials_env instead. The new aidb.audit_leaked_credentials() function reports any pre-existing models where credentials were detected embedded in their config.
Bug fix	Credentials with invalid header characters: An api_key or basic_auth value containing characters that aren't valid in an HTTP header (for example, a stray newline) now fails with a clear configuration error instead of crashing the backend.