MakerAI Suite v3.8 — The AI Ecosystem for Delphi

🌐 Official Website: https://makerai.cimamaker.com 📖 Manual: https://www.gustavoenriquez.com/book-makerai — available in English and Spanish

Free Pascal / Lazarus port available — Full port of MakerAI Suite for FPC 3.2+ (12 LLM drivers, RAG, Agents, MCP, Embeddings). See the fpc branch.

MakerAI is more than an API wrapper

Most AI libraries for Delphi stop at wrapping REST calls. MakerAI is different.

Yes, MakerAI includes native, provider-specific components that give you direct, full-fidelity access to each provider's API — every model parameter, every response field, every streaming event, exactly as the provider defines it.

But on top of that, MakerAI is a complete AI application ecosystem that lets you build production-grade intelligent systems entirely in Delphi:

• RAG pipelines (vector and graph-based) with SQL-like query languages (VQL / GQL)
• Autonomous Agents with graph orchestration, checkpoints, and human-in-the-loop approval
• MCP Servers and Clients — expose or consume tools using the Model Context Protocol (dual-era: stateless spec 2026-07-28 + legacy handshake)
• Native ChatTools — bridge AI reasoning with deterministic real-world capabilities (PDF, Vision, Speech, Web Search, Shell, Computer Use)
• Skills — reusable instructions in the SKILL.md format (Agent Skills / PPM), loaded on demand by the model
• A2A — expose your agent graphs to other agents and call remote ones (Agent-to-Agent protocol 1.0)
• Production controls — tool-call guardrails, evals, OpenTelemetry tracing, persistent memory, and calibrated decisions with Jev
• FMX Visual Components — drop-in UI for multimodal chat interfaces
• Universal Connector — switch providers at runtime without changing your application code

Whether you need a simple one-provider integration or a multi-agent, multi-provider, retrieval-augmented production system, MakerAI covers the full stack — natively in Delphi.

🚀 What's New in v3.9

OpenAI GPT-Live — full-duplex voice with delegation

TAiOpenAiLiveChat (gpt-live-1, DriverName OpenAiLive) listens while it speaks: you can interrupt it, and it hands the heavy thinking to another model — a Responses model run by OpenAI (web search, your TAiFunctions and MCP tools executed locally) or any TAiChatConnection through DelegateChat (Claude, Ollama, an agent graph). Conversation turns are rebuilt from the session timeline, so overlapping speech no longer splits answers. Demo 092-GPTLiveVoice: microphone, speaker, local functions, RAG over MCP with a read-only guardrail, anti-echo speaker mode and a quick guide. Tested live.

Jev decisions on local models (Ollama)

Ollama 0.35+ serves the Jev contract at /v1/systemone with local decision models — nimble, tev1 and Cloudflare's clef/clef-flash. TAiJev only needs Url := 'http://localhost:11434/v1/'; the eight Jev adapters gained Url, TAiJev.Ask takes images for Clef (PNG/JPEG/WebP), and the TypeSafe price is no longer charged on other servers. Tested with nimble (routing, prompt guard, batch labeling) and clef 27B on images.

Memory leaks fixed

An MCP server leaked every tool schema on each tools/list (it grew forever in production), the MCP client leaked the tools/list response on each Initialize, and TAiRealtimeFactory never freed its dictionary.

⚠️ Behaviour changes

• OpenAI audio defaults: TAiOpenAiAudio / TAiOpenAiSpeechTool use gpt-transcribe and gpt-4o-mini-tts (OpenAI shuts down whisper-1 on 2027-02-26 and tts-1 on 2027-01-06). gpt-transcribe returns JSON only — srt/vtt/timestamps are reported in TTranscriptionResult.Warning; set tmWhisper1 to keep them until the shutdown.
• Custom MCP tools: IAiMCPTool.GetInputSchema returns a new object owned by the caller (what the built-in tools already did). A custom tool that returned a cached field must return a copy.
• OpenAI Responses driver: Max_Tokens is now sent as max_output_tokens (it was silently ignored), so a low limit can cut answers that used to be complete.

What came in v3.8

Released 2026-09-29. Six items change existing behaviour; they are marked ⚠️ below — two of them (TLS certificate checks on POSIX and the IAiMemoryStorage signature) can require code changes.

Jev — calibrated decisions before spending an LLM

TAiJev (Source/Tools/uMakerAi.Jev.pas) wraps Jev, TypeSafe AI's "System One" model. Jev does not generate text: it answers typed questions — Choice, Score, Noul (yes/no) — with calibrated probabilities and a confidence value your code can threshold. It is meant for the fast, cheap decisions that today cost a full LLM round-trip: routing a query to the right specialised agent, deciding whether that agent needs its RAG variant, classifying, gating. Several questions travel in one request (~700 input tokens at US$0.042 per million). Questions are validated locally, 429/529 are retried with backoff, and the model is pinned to jev-1.13.0 so thresholds stay valid. Demo: 084-JevRouter.

For agent graphs, TAiJevRouterTool (Source/Agents/uMakerAi.Agents.Tools.JevRouter.pas) is a node tool that writes the chosen route to Blackboard['next_route'], so an existing lmConditional link follows it — no engine change. Low confidence or a failing API falls back to NextNo instead of breaking the graph, and yes/no flags asked in the same call land in the blackboard for lmExpression or an OnRoute handler. Twenty-one new regression cases run all of it offline against a fake transport.

Two more hand-offs make Jev a drop-in for decisions the framework already takes:

• SmartDispatch without the classification LLM call. ChatTools.DispatchClassifier (new, provider-neutral) receives the tags whose tools are assigned; TAiJevDispatchClassifier answers with Jev. Tool requests skip one LLM round-trip, and CHAT replies are now generated with the conversation history (the LLM pass answered in an isolated two-message context). Unsure or failing → the usual LLM pass. 15/15 on Spanish and English requests.

• Semantic guardrails. TAiGuardrails.Classifier (new) judges the tool calls the allow/block lists let through; TAiJevGuardrailClassifier blocks when P(risk) ≥ 0.5. Lists still catch the enumerable (rm -rf) for free; Jev catches what no list anticipates — an e-mail carrying a password to an outside address, a transfer to an unknown account. Safe calls scored ≤ 0.17 and harmful ones ≥ 0.88 across 13 calibration cases. Fails closed by default. Optional permission categories (read, write, financial, system, …) are judged in the same call: block whole categories, audit each call via OnCategorized, and describe domain-named tools with ToolDescriptions (20/20 on 20 calibration calls).

• Input guardrail. ChatTools.PromptGuard (new) checks the user's message before it reaches the LLM, right after the existing regex sanitizer. TAiJevPromptGuard asks in one call about prompt injection, credentials in the message, harmful requests and — given a Scope — off-topic questions. On nine test messages the regex caught 1 of 6 problematic ones; Jev caught all 6 and let the greeting and the legitimate questions through. A blocked message never touches the network.

Demo: 085-JevDispatchGuard.

And two more for quality and retrieval:

• Calibrated eval judge. TAiEvalRunner.Scorer (new) answers ExpectScore('criterion', 0.7) with the probability that the output meets the criterion — faster and cheaper than an LLM judge, and the bar is set in code. TAiJevEvalScorer: passing answers ≥ 0.97, failing ≤ 0.02 on 10 calibration pairs.
• Semantic reranking for RAG. TAiRAGVector.Reranker (new) replaces the cosine second stage of VQL RERANK: TAiJevRAGReranker scores each passage for usable evidence and drops passages that try to instruct the model (prompt injection). No embeddings are recomputed; if the reranker fails, the search falls back to cosine. Demo: 086-JevEvalsRag.
• Bulk labeling. TAiJevBatchLabeler runs the same questions over many rows in parallel and returns labels, confidence, top-3 suggestions, total cost and the rows worth a human look; a failing row never stops the batch. Validated by reproducing the prototype: 126 accounting entries against 225 chart-of-accounts codes in 4.2 s for US$0.05 — 92.9% overall and 100% on the 61% it would book automatically (confidence ≥ 0.8). Demo: 087-JevBatchLabeling.
• Model routing. TAiJevModelRouter sends each request to the cheapest model that can handle it. Jev describes the request (task, difficulty, sensitivity); readable rules in code pick the tier. Switching provider on a TAiChatConnection used to drop the conversation — the router migrates the text history, so a chat can start on Groq and escalate to Claude and still remember the first turn. Live, with Groq → DeepSeek → Claud