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SmartAgent implementation with LLM providers, MCP client, HTTP server, and CLI.","maintainers":[{"name":"fr0ster","email":"oleksij.kyslytsja@gmail.com"}],"readme":"# @mcp-abap-adt/llm-agent-server\n\n[![Stand With Ukraine](https://raw.githubusercontent.com/vshymanskyy/StandWithUkraine/main/badges/StandWithUkraine.svg)](https://stand-with-ukraine.pp.ua)\n[![License: GPL v3](https://img.shields.io/badge/License-GPL_v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)\n\nRunnable distribution of SmartAgent (CLI + HTTP server). **Binary-only.**\n\n## Library imports are not supported\n\nImporting from `@mcp-abap-adt/llm-agent-server` as a library is not supported as of 12.0.1. Composition surface lives elsewhere:\n\n- `@mcp-abap-adt/llm-agent-libs` — `SmartAgentBuilder`, `SessionManager`, `LlmAdapter`, `InMemoryMetrics`, etc.\n- `@mcp-abap-adt/llm-agent-mcp` — `MCPClientWrapper`, `McpClientAdapter`, connection strategies.\n- `@mcp-abap-adt/llm-agent-rag` — `makeRag`, `resolveEmbedder`, prefetch helpers.\n- `@mcp-abap-adt/llm-agent` — interfaces and DTOs (`IMetrics`, `IRag`, `Message`, etc.).\n\n(... existing binary documentation continues below ...)\n\n## CLI shipped\n\n- `llm-agent` — the runtime (`llm-agent --config smart-server.yaml`). It is the only command a\n  global install (`npm i -g @mcp-abap-adt/llm-agent-server`) puts on your PATH.\n\n### Repository tools (not published)\n\nTwo helpers live in the source repository and run from a checkout only:\n\n| From the repo root | Does |\n|---|---|\n| `npm run models:check` | reports which SAP AI Core models work for chat and which for embeddings |\n| `npm run claude:via-agent` | starts `llm-agent` and points the Claude CLI at it (`npm run build` first) |\n\n`models:check` probes through the server's own providers (`SapCoreAIProvider`,\n`SapAiCoreEmbedder`) and reads the account by the server's credential rule, so a model it marks ✓\nworks in the server with the same settings. Every model is probed in both modes; the output is a\ncapability matrix:\n\n```text\n  Model                                  Chat               Embed\n  gpt-5                                  ✓ 2007ms           ✗\n  text-embedding-3-small                 ✗                  ✓ 1536 dimensions\n  nvidia--llama-3.2-nv-embedqa-1b        ✗                  ✗\n    ↳ embed declared, but failed: HTTP 400: Embedding Module: Model 'nvidia--llama-3.2-nv-embedqa-1b' requires 'type' …\n  Models: 3  Chat: 1/3  Embed: 1/3  Failed: 1\n```\n\n| Option (after `npm run models:check --`) | Effect |\n|---|---|\n| *(none)* | every model in the AI Core `foundation-models` catalog |\n| `gpt-4o text-embedding-3-small` | only the named models |\n| `--chat` / `--embed` | one mode only |\n| `--config <yaml>` | the SAP AI Core models a server config uses, each in the mode of its role, with that role's `credentialRef` and the `temperature` / `maxTokens` the server would send (a classifier derived from `main` at `classifierTemperature` is its own row); non-SAP roles are listed as not checked |\n| `--credential-ref <REF>` | the account: reads `<REF>_SERVICE_KEY` (default `LLM` → `LLM_SERVICE_KEY`) |\n| `--env-path <file>` | the env file to load (default `.env` in the current directory) |\n| `--resource-group <rg>` | AI Core resource group (default `$SAP_AI_RESOURCE_GROUP`, else `default`) |\n| `--embed-scenario <s>` | `orchestration` (default) or `foundation-models` |\n| `--timeout <ms>` / `--delay <ms>` | per-call timeout (default 60000) / pause between calls (default 2000) |\n| `--version` / `--help` | package version / usage |\n\nA ✗ in a mode the catalog does not declare for the model (an embedder under **Chat**) is expected\nand printed without a reason. A reason line appears when a declared mode fails, or for a model the\ncatalog does not list. The exit code is `1` when such a failure exists. Unknown flags are rejected,\nand a missing account is an error naming the variable to set.\n\nSee the repo docs for architecture, pipeline configuration and deployment:\n[`docs/ARCHITECTURE.md`](https://github.com/fr0ster/llm-agent/blob/main/docs/ARCHITECTURE.md), [`docs/PIPELINES.md`](https://github.com/fr0ster/llm-agent/blob/main/docs/PIPELINES.md), [`docs/DEPLOYMENT.md`](https://github.com/fr0ster/llm-agent/blob/main/docs/DEPLOYMENT.md).\n\n## Credentials\n\n**TL;DR** — a config file carries no secrets, only names. Each section that authenticates may say\n`credentialRef: <REF>`; the binary reads that account from the environment by one naming rule. Omit the\nref and the section uses its role's default.\n\n### The naming rule\n\n| Set in the environment | You get |\n|---|---|\n| `<REF>_API_KEY` | an API key — OpenAI, Anthropic, DeepSeek, an OpenAI embedder, Qdrant |\n| `<REF>_SERVICE_KEY` | a SAP AI Core service key (the JSON) — the token **and** the API base URL both come from it |\n| `<REF>_USER` + `<REF>_PASSWORD` | a login — pg-vector, HANA |\n\n- Set **one** of the three per ref. Two at once is refused as ambiguous; `<REF>_USER` without\n  `<REF>_PASSWORD` (or the reverse) is refused.\n- A **named** ref must resolve. `credentialRef: OPENAI` with no `OPENAI_*` variable set fails at\n  startup, naming `OPENAI`. A ref holding the wrong kind fails, naming the kind wanted and the kind found.\n- The same ref named in several sections is **one** credential — one rate-limit bucket.\n\n### Defaults, when a section names no ref\n\n| Section | Default ref | A single-account deployment sets |\n|---|---|---|\n| each `llm:` entry | `LLM` | `LLM_API_KEY`, or `LLM_SERVICE_KEY` for SAP AI Core |\n| `rag.store`, a qdrant `skillPlugins.store` | `RAG_STORE` | `RAG_STORE_API_KEY` (Qdrant) or `RAG_STORE_USER` + `RAG_STORE_PASSWORD` |\n| `rag.embedder` | `RAG_EMBEDDER` | `RAG_EMBEDDER_API_KEY`, or `RAG_EMBEDDER_SERVICE_KEY` for SAP AI Core |\n\n**The skill store shares `RAG_STORE` with `rag.store`.** A qdrant `skillPlugins.store` with no\n`credentialRef` reads the `RAG_STORE` default too, so the two share its one credential kind:\n\n- A pg-vector or HANA `rag.store` on `RAG_STORE_USER` + `RAG_STORE_PASSWORD`, beside a qdrant skill\n  store you meant to run **anonymously**, fails at startup: the skill store finds a login where it\n  wants an API key (wrong kind). \"Anonymous\" applies only when the default is unset.\n- Fix it by naming the ref explicitly on the pg/HANA store — `rag.store.credentialRef: PG` with\n  `PG_USER` + `PG_PASSWORD` — and leaving `RAG_STORE_*` unset, so the skill store stays anonymous.\n- If the skill store has a key of its own, name that instead: `skillPlugins.store.credentialRef: SKILLS`\n  with `SKILLS_API_KEY`. A named ref must resolve, so this does not work for an anonymous store.\n\nA default is read only when the target needs a credential. Where a target can work without one (a Qdrant\nwithout auth, a pg-vector connection that needs no login), an unset default means anonymous.\n\n### Ollama and other targets that send nothing\n\n- An **Ollama LLM** gets a credential only from a `credentialRef` that names one — never the `LLM`\n  default, which usually holds a hosted provider's key.\n- An **Ollama embedder**, an **in-memory store** and an embedder **`factory`** send nothing from the\n  binary, so naming a ref for them is refused.\n\n### Coming from `AICORE_SERVICE_KEY` or `apiKey: ${…}`\n\n- The binary no longer reads `AICORE_SERVICE_KEY`. Set `LLM_SERVICE_KEY` instead (and\n  `RAG_EMBEDDER_SERVICE_KEY` for a SAP AI Core embedder) — or keep the variable and name it:\n  `credentialRef: AICORE` reads `AICORE_SERVICE_KEY`.\n- `apiKey: ${DEEPSEEK_API_KEY}` is refused in YAML. Delete the line and set `LLM_API_KEY`, or write\n  `credentialRef: DEEPSEEK`, which reads `DEEPSEEK_API_KEY`.\n- SAP AI Core's API base URL is never written in YAML: it travels inside `<REF>_SERVICE_KEY`.\n\n## License\n\n**GNU General Public License v3.0 only** (`GPL-3.0-only`) — see\n[`LICENSE`](LICENSE). This is the only package in the monorepo that is not\nLGPL: it ships no library exports, so it is the ready-to-run product rather\nthan something you embed, and it carries the full GPL.\n\nCopyright © 2025–2026 Oleksii Kyslytsia\n\n**Running this server and talking to it over HTTP places no obligation on you\nor on your client code** — the GPL has no network trigger. The libraries it\ncomposes stay `LGPL-3.0-only`, so you can still embed those in a closed-source\nprogram. What the GPL asks is that if you distribute a *modified* build of this\nserver, the corresponding source goes out under the GPL too.\n\nVersions up to and including v20.9.5 were MIT and v21.0.0 was `LGPL-3.0-only`;\na licence change is not retroactive, so those releases keep the terms they\nshipped under. Full detail:\n[docs/LICENSING.md](https://github.com/fr0ster/llm-agent/blob/main/docs/LICENSING.md).\n","readmeFilename":"README.md"}