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Change Log#

Unreleased changes targeting AI-Q v2.2.0

These entries track candidate work on release/2.2. AI-Q v2.1.0 remains the latest stable release; the candidate will be stabilized before the final v2.2.0 release.

Research and reports

  • Routed deep research now uses explicit source-router, structured planner, concurrent researcher, and writer roles, with bounded source-tool batching and no research-plan approval step

  • Report follow-up supports answers over a completed report, child-job cosmetic rewrites, and delta research that carries the parent report forward as context

  • Clarification is more targeted: it can search for context before asking the user to narrow scope or choose an output shape

Sources and integrations

  • OpenSearch is a first-class knowledge backend for self-hosted, Amazon OpenSearch Service, and Amazon OpenSearch Serverless deployments

  • Azure AI Search is a managed knowledge backend with API-key or Azure identity authentication, namespaced index ownership, and hybrid retrieval

  • Paper search adds SerpAPI and SearchAPI providers alongside Serper; the routed-research profile adds DuckDuckGo news and Polymarket sources

  • You.com adds configurable web search, page-content extraction, cited open-domain research, and finance-focused research tools

  • Nimble adds configurable web search with lite and deep modes, plus an Enterprise-only fast mode and optional focus, country, and locale controls

  • Per-user MCP OAuth adds status, connect, callback, and reconnect flows backed by a token store shared by the API and workers; disconnect and in-worker token refresh are not included

  • A standalone public MCP server exposes stateless submit, poll, and final-report tools over Streamable HTTP with PostgreSQL-backed job state

Sandboxes, artifacts, and policy

  • DeepAgents execution uses a provider-neutral sandbox contract: Modal is fresh per job, while the experimental OpenShell profile uses one shared, pre-provisioned sandbox and is not a multi-tenant isolation boundary

  • Opt-in durable artifact capture checkpoints manifest-declared files after successful sandbox execute calls, performs one final manifest-plus-directory scan on success/failure, and preserves earlier checkpoints without delaying cancellation when the provider is busy

  • Captured files store metadata in SQL and bytes in SQL or S3-compatible storage, emit metadata-only artifact.update events for live and replayed Files-tab access, and remain available through job-scoped list/content endpoints that enforce ownership when REQUIRE_AUTH=true

  • Opt-in NeMo Guardrails middleware covers selected workflow and agent input/output boundaries; defining middleware does not activate every boundary

  • Opt-in content encryption protects final async output and selected artifact event content only; it is off by default, forward-only, and does not encrypt checkpoints or most job/event metadata

  • Summary Store database logging masks URL passwords and removes query parameters so credentials and query-string secrets are not written to initialization or lifecycle logs

Deployment and observability

  • The repository source Helm chart honors helm install -n <namespace> for every namespaced resource, including GitOps-rendered deployments; chart metadata advances to aiq2-web 2.1.1 with the aiq 0.0.5 dependency

  • NAT-exported async-job traces preserve configured workflow, task/batch, named-agent, and model/tool hierarchy across concurrent researchers without copying graph-state content into structural agent spans

Agent Skills, UX, and developer workflow

  • Consumer Agent Skills now include aiq-deploy and aiq-research; maintainer skills cover workflow configuration, data sources, tools, release QA, PR preparation, prompt/model customization, and CI maintenance

  • The UI surfaces batched researcher activity and improves research-session recovery, expiry handling, and WebSocket delivery reliability

  • Contributor governance and product-level Agent Skill evaluation checks expand release and contribution tooling

  • Pinned to NeMo Agent Toolkit (NAT) v1.8.0

The eleven checked-in workflow configurations are focused profiles; no single profile enables every 2.2 capability.

Release v2.1.0

  • AI-Q REST API with pluggable auth middleware, entry-point-registered token validators, and async job ownership enforcement

  • Auth extensibility hooks (register_token_fetcher, provider lifecycle) and auth refactor eliminating the refresh race

  • Data source registry driving UI toggles, per-message filtering, and agent tool inheritance

  • New exa_web_search data source with full_text and highlights controls

  • Deep researcher consumes DeepAgents skills with a job-scoped Modal sandbox; built-in data-table-analysis skill and configs/config_skills.yml example

  • AI-Q is consumable as a portable Agent Skill (.agents/skills/aiq-research/), with .claude/skills/aiq-research/ retained as a Claude Code compatibility symlink for routed /chat and async job lifecycle against a local AI-Q server

  • Cost analysis tool with pricing configs and profiling example

  • Documented MCP client patterns scoped for 2.1: mcp_client, mcp_service_account, and user-identity tools

  • Prompt restructure across all agents for KV cache prefix reuse

  • Operability: idempotent DB init, tuned Dask/Postgres defaults, request tracing into NAT spans, UI stream-failure hardening

  • New authentication and MCP tools guides; new skills-and-sandbox example

  • Pinned to NeMo Agent Toolkit (NAT) v1.6.0; CVE bumps for Pillow, cryptography, pygments, authlib, pyopenssl, and pytest

Release v2.0.0

Ground-up rewrite of the NVIDIA AI-Q Blueprint, built on the NVIDIA NeMo Agent Toolkit (NAT).

  • Two-tier research architecture with automatic routing between shallow (fast, bounded) and deep (multi-phase, report-grade) research via a single-call Intent Classifier

  • Deep Researcher rebuilt with a three-role subagent architecture (Orchestrator, Planner, Researcher) using the deepagents library, with configurable research loops and per-role LLM assignment

  • New Shallow Researcher agent with tool-call budgets, context compaction, and synthesis anchors for citation-backed answers

  • Clarifier agent with human-in-the-loop plan generation, approval, and feedback before deep research

  • Shallow-to-deep escalation when the shallow researcher detects insufficient results

  • Async Jobs REST API (/v1/jobs/async/) with SSE streaming, event replay, reconnection support, and cooperative cancellation

  • Dask-based distributed execution with configurable workers, heartbeats, and stale job reaping

  • PostgreSQL persistence for job store, event store, LangGraph checkpoints, and document summaries

  • Pluggable Knowledge Layer with factory/registry pattern — swap between LlamaIndex (local ChromaDB) and Foundational RAG (hosted NVIDIA RAG Blueprint) without code changes

  • Multimodal document extraction (VLM-powered image captioning and chart data extraction)

  • Document summaries injected into agent prompts for file-aware research

  • Deterministic citation verification pipeline with five-level URL matching, report sanitization, and audit trail

  • New Next.js frontend with conversational UI, document upload, collection management, and real-time progress streaming

  • Optional OAuth/OIDC authentication with configurable providers

  • Multi-backend observability: Phoenix, LangSmith, W&B Weave, and OpenTelemetry Collector with privacy redaction

  • FreshQA benchmark for shallow researcher factuality evaluation via nat eval

  • Docker Compose and Helm chart deployments with distroless runtime images, non-root execution, and horizontal scaling

  • Native NAT integration — all configuration through YAML with nat run / nat serve / nat eval

  • Four pre-built configs: CLI default, Web + LlamaIndex, Web + Foundational RAG, Hybrid Frontier Model

  • uv workspace monorepo, Jupyter notebook tutorial series, and debug console at /debug

  • Pinned to NeMo Agent Toolkit (NAT) v1.4.0; Python 3.11–3.13; Node.js 22+

  • AI-Q holds top positions on both DeepResearch Bench and DeepResearch Bench II leaderboards (see drb1 and drb2 branches)

Release v1.2.1

  • Upgraded llama-3.3-70b-instruct NIM from version 1.13.1 to 1.14.0

  • Aligned Helm values and referenced Docker image tags with the new nim-llm version

  • Adopted RAG 2.3.2

  • Removed manual NIM_MODEL_PROFILE configuration from Helm values and Docker Compose to rely on automatic profile detection, updated documentation accordingly

Release v1.2.0

  • Added support for Helm deployments

  • Add support and documentation for evaluation

  • Simplified the configuration and integration with RAG, removing nginx

  • Adopted RAG 2.3.0

  • Tested for compatability with RTX Pro 6000

Release v1.1.0

  • Tested for compatability with RAG 2.2.0 release and B200

  • Adds support for NVIDIA Workbench

Release v1.0.0

Initial release of the NVIDIA AI-Q Research Assistant Blueprint featuring:

  • Multi-modal PDF document upload and processing, compatible with the NVIDIA RAG 2.1 blueprint release

  • Demo web application

  • Deep research report writing including human-in-the-loop feedback