catagories and bug fixes, few new tools.
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# AI-LSC Changelog
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## v3.1.1b — taxonomy re-org: Routing restored (11 layers), canonical 24-dir /mnt/AI layout
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Restores the Routing layer that was lost when the 13-layer model was
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compressed to 10 (both "L6 AI Endpoints" and "L11 Intelligent Routing"
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had been folded into Orchestrators), fixes the mis-categorized tools,
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and aligns the backend to the revised canonical `/mnt/AI/` directory
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tree. No tool count change (185 tools); validator still reports 0 errors.
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### New layer ladder (10 → 11)
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```
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L1 Host Platform L7 Security
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L2 Development Env L8 Observability
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L3 GPU Runtimes L9 User Interfaces
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L4 Engines L10 DevOps
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L5 Routing (restored) L11 Knowledge Management
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L6 Orchestrators
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```
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`NAV_LAYER_ORDER` inserts Routing between Engines and Orchestrators —
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engines serve weights, routing proxies/load-balances/meshes them into
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one OpenAI-compat endpoint, orchestrators build agent workflows on top.
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### Tool moves (18 tools, all layers/levels updated)
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| tool_id | Old | New |
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|---------|-----|-----|
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| `litellm` | L5 Orchestrators (Proxy / API Gateway) | **L5 Routing** |
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| `9router_proxy` | L5 Orchestrators (LLM Router) | **L5 Routing** |
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| `meshllm` | L5 Orchestrators (LLM Mesh) | **L5 Routing** |
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| `dify` | L5 Orchestrators (Pipeline) | **L5 Routing** |
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| `picode` | L5 Orchestrators (AI Coding Agent) | **L5 Routing** (category → `Mesh Client`) |
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| `vllm` | L5 Orchestrators (Scaling) | **L4 Engines** (role → Engine; ADR-001 always listed vLLM under Inference Engines) |
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| `sglang` | L5 Orchestrators (Scaling) | **L4 Engines** (role → Engine) |
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| `eagle_eye` | L5 Orchestrators (category literally "Observability") | **L8 Observability** |
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| `dma` | L5 Orchestrators (Build Monitoring) | **L8 Observability** |
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| `aider`, `claude_code`, `codex`, `openhands`, `opencode`, `gemini_cli`, `qwen_code`, `goose`, `zcoder` | L5 Orchestrators (AI Coding Agent) | **L10 DevOps** — matches the DB editor's `AI Coding Agent → DevOps` mapping, which previously conflicted with the registry |
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Remaining tools in the affected layers were renumbered (+1): 38 stay in
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Orchestrators (L6), Security L7, Observability L8, User Interfaces L9,
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DevOps L10, Knowledge Management L11. Per-layer level uniformity
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verified. `picode`'s category changed `AI Coding Agent` → `Mesh Client`
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so the category cascade cannot silently flip it back to DevOps.
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### Tool DB editor (`CATEGORY_MAP`)
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- All entries renumbered to the new ladder; map is now fully consistent
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with the live registry (0 layer/level mismatches).
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- 13 new categories so every registry category auto-fills correctly:
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`LLM Mesh`, `Mesh Client` (Routing); `Observability` (Observability);
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`Build`, `Debugging`, `Shell`, `Claude Code Skill`, `Container Ops`,
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`Infrastructure`, `Memory System`, `Model Surgery`, `Networking`,
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`Virtualization` (previously missing entirely).
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- Fixed conflicts: `AI Coding Agent` → DevOps/L10 (role `Coding Agent`,
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was `Autonomous Coder` at L9), `Build Monitoring` → Observability/L8,
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`LLM Serving` → Engines/L4, `LLM Router` / `Proxy` / `Pipeline` →
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Routing/L5.
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### STACK_WIRINGS (`stack/connections.py`)
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- 29 wiring `layer=` labels synced to the registry: the 13 moved tools
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with wirings, plus `deep_eye`/`luxtts` (UI tools mislabeled
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Orchestrators) and 14 pre-existing drifts (`heretic`, `unsloth`,
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`parakeet`, `fabric`, `n8n`, `nightshift`, `hivemind`, `hermes_agent`,
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`agno`, `hermes_dashboard_page`, `mnemo_cortex`, `everos_memory`,
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`langflow`, `opensandbox`). The stack logic editor now groups tools
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identically to the Infrastructure pages.
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- Stale section comments annotated: `L6: AI Endpoints` → the restored
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Routing layer; `L10: Intelligent Routing` → folded into Orchestrators.
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### Canonical `/mnt/AI/` layout (26 → 24 dirs)
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`REQUIRED_DIRS` now matches the target tree exactly:
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- **Added `configs/`** — app configs templated for native runtime plus
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app state. `main_window.config_root` moves from legacy `config/` to
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`configs/`, taking `pipeline_state.json`, `pipeline.json`,
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`license_approvals.json` with it; `controller_config.json` moves off
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the `/mnt/AI/` root into `configs/`. A one-time
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`_migrate_legacy_state_files()` pass moves old files on startup
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(newer copies win; emptied legacy dirs are removed).
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- **Dropped from the skeleton**: `bootstraps/ai-lsc`, `staging`,
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`registry/manifests`. `registry/` remains as app-internal storage for
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`ecosystem.json` and manifests — auto-created on demand by
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`RegistryManager`, no longer part of the canonical tree.
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- `paths.py`: `configs_root` added; `staging_root` / `bootstraps_root`
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removed; the deprecated `config_root → runtime` alias now points at
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`configs/`. Per-tool config subdirs (`configs/<tool>/`) are still
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created on demand by `InstallerManager`.
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- `license_gate.py` / `licenses.py` doc references updated to
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`configs/license_approvals.json`.
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Existing installs pick up all taxonomy changes automatically:
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`RegistryManager._sync_with_upstream()` re-syncs structural fields
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(layer, level, role, category) from the layer files on every start.
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## v3.1.1a — registry validator fixes (5 errors → 0)
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Fixes all 5 `validate_registry()` errors plus latent bugs surfaced
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during the fix pass. No tool count change (185 tools).
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### Installer fixes (validator errors)
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All five failed the "script installer cmd should reference
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{tools_root}" check — they installed into system dirs or the cwd:
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| tool_id | Before | After |
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|---------|--------|-------|
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| `firecracker` | extracted into `/usr/local/bin/` (and silently broken: versioned tarball dirs never landed on PATH) | extracts into `{tools_root}/firecracker/`, symlinks `firecracker` + `jailer` into `{tools_root}/bin/` |
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| `cloudflared` | downloaded to `/usr/local/bin/cloudflared` | downloads to `{tools_root}/bin/cloudflared` |
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| `llamafile` | downloaded to cwd (mismatched launcher, which already expected `{tools_root}/bin/llamafile`) | downloads to `{tools_root}/bin/llamafile` |
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| `meilisearch` | `curl \| sh` dropped binary in cwd | runs installer inside `{tools_root}/bin/` (official installer places the binary in the cwd) |
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| `grafana_alloy` | `install.sh \| sh` — **URL dead (404): upstream dropped the script** | direct release asset `alloy-linux-amd64.zip`, extracted via `python3 -m zipfile` (no unzip dependency) |
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### Launcher fixes (latent bugs)
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- `firecracker`, `cloudflared`, `grafana_alloy` launchers now use
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absolute `{tools_root}/bin/<name>` paths. Rationale: `tools_root/bin`
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is on PATH at install time (`installer._env()`) but **not** at launch
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time (`enriched_env()` builds PATH from `base_bin_dir` = uv/npm bins
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only), so bare-name launcher cmds would pass preflight then fail
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with "command not found". Absolute paths are immune to the gap.
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- firecracker version discovery uses the Location header
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(`curl -sIL … | grep -i '^location:'`) instead of
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`-w '%{url_effective}'` — launcher/installer cmds are rendered with
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`str.format()`, and the `{url_effective}` braces would raise
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`KeyError` at render time.
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### Dependency fixes (phantom missing-dep warnings)
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- `dify` deps: `node` → `nodejs` (the actual registry tool_id).
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- `RegistryManager.check_dependencies()` now allows system-level deps
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(`kubectl`, `java`) via a `SYSTEM_DEPS` frozenset, mirroring the
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existing `_system_deps` pattern in `stack/connections.py`. Previously
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`crossplane` (kubectl) and `keycloak` (java) produced permanent
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"missing dependency" warnings that could never be satisfied.
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### Not fixed (observations, no behavior change)
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- 9 duplicate `default_port` defaults across tools (e.g. 3000 shared
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by grafana/opik/openhands/flowise). These are overridable per
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service row; leaving as-is unless the stack compiler should
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auto-assign.
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- `TODO.md`'s `open_webui`/`openwebui` split no longer reproduces:
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only `openwebui` exists in both the registry and `STACK_WIRINGS`.
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## v3.1.1 — local-coder-mesh integration (this build)
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Adds 4 new tools, corrects 1 existing tool, adds 4 new `STACK_WIRINGS`
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entries (and rewires 1), adds 1 new stack template, and aligns all
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hardcoded `/mnt/AI/` paths to the canonical 26-directory layout. No
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containers in the dev path — every tool installs natively. ai-lsc's
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Podman/Docker/LXC/Firecracker export is reserved for total-stack
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deployment exports only, as before.
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### New tools (181 → 185)
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| tool_id | Layer | What | Install |
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|---------|-------|------|---------|
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| `picode` | L5 Orchestrators | PiCode (jasonjmcghee/picode) — local code-tinker agent | git clone |
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| `meshllm` | L5 Orchestrators | MeshLLM (Mesh-LLM/mesh-llm) — native binary, pools GPUs/memory across machines, exposes OpenAI-compat API at :9337, web console at :3131. NOT a LiteLLM derivative. | script (official curl installer) |
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| `zcoder` | L5 Orchestrators | Zhipu AI Z-Coder CLI coding agent | npm `zcoder-cli` |
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| `hermes_webui` | L8 User Interfaces | Hermes-themed Open-WebUI instance on :8081 with its own data volume; backend points at `hermes_agent` (:17051) instead of Ollama direct | uv `open-webui` |
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### Corrected tool
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`graphify` was already in the registry but had wrong metadata. Fixed in place:
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| Field | Old | New |
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|-------|-----|-----|
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| `role` | `Graph Builder` | `Knowledge Graph Builder` |
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| `category` | `AI Agent` | `Claude Code Skill` |
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| `installer` | `git: nicely-done/graphify` | `uv: graphifyy` (PyPI; CLI is `graphify`) |
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| `license` | `Proprietary` | `MIT` (verified from pyproject.toml) |
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| `flags.has_web` | `False` | `True` (graph.html output) |
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| `flags.is_mcp` | `False` | `True` (`graphify --mcp` stdio server) |
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| `description` | 1 line generic | 10 lines accurate (CLI + Claude Code skill + MCP server + LLM backend options) |
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Graphify's wiring also changed — see below.
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### New `STACK_WIRINGS` entries (133 → 137)
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| tool_id | Exposes | Consumes |
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|---------|---------|----------|
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| `picode` | (none — CLI agent) | `meshllm` (primary), `litellm` (fallback), `ollama` (direct fallback) |
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| `meshllm` | `openai_api` (:9337) + `mesh_web_console` (:3131) | `ollama` (optional, for `mesh-llm client --auto` mode) |
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| `zcoder` | (none — CLI agent) | `meshllm` (primary), `litellm` (fallback), `ollama` (direct fallback) |
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| `hermes_webui` | `hermes_webui_http` (:8081) | `hermes_agent` (required, primary backend), `ollama` (optional, for RAG embeddings) |
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### Rewired entry
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`graphify` removed from the L8 passive/CLI list and given a proper
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`_reg(StackWiring(...))` block:
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- **Exposes**: `graphify_mcp` (stdio MCP server, no port) — start with
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`graphify --mcp`. Other MCP-aware agents can query the knowledge graph.
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- **Consumes** (all optional, fallback chain for the extraction LLM):
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`meshllm` (:9337/v1), `litellm` (:4000/v1), `ollama` (:11434/v1).
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Graphify defaults to Claude (Anthropic API) but can be configured for
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fully-local extraction by setting `OPENAI_API_BASE` to any of the
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above.
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### New stack template (13 → 14)
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`local-coder-mesh.json` — "Local Coder Mesh — All-Ollama Coding Stack".
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17 tools: ollama, litellm, meshllm, picode, aider, odysseus, opencode,
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zcoder, graphify, hermes, hermes_agent, hermes_webui, hermes_desktop,
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openwebui, ripgrep, fd, tree_sitter.
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Topology: coding agents prefer MeshLLM (:9337) for mesh-pooled inference,
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fall back to LiteLLM (:4000) for proxy routing, then Ollama direct
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(:11434). Graphify builds knowledge graphs from the codebase and exposes
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an MCP server that the coding agents query. Hermes WebUI talks to
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hermes_agent (NOT Ollama direct) so every Hermes conversation flows
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through the agent runtime's tool-use layer. OpenWebUI talks to Ollama
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direct. ripgrep + fd + tree_sitter are passive filesystem tools used by
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the coding agents for repo-map / symbol navigation.
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Recommended models: `qwen2.5-coder:7b` (fast coding),
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`qwen2.5-coder:32b` (heavy coding), `hermes3:8b` (Hermes stack),
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`nomic-embed-text` (openwebui RAG, corpus indexing, graphify embeddings).
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MeshLLM auto-downloads a suitable model on first `serve --auto` if none
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is specified.
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### Path alignment to canonical `/mnt/AI/` layout
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|
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Three files updated so ai-lsc's hardcoded paths match the 26-directory
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canonical layout:
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- **`src/ai_lsc/constants.py`** — `REQUIRED_DIRS` replaced with the 26
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canonical entries (`bootstraps/ai-lsc`, `staging`, `backends`,
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`distfiles`, `runtime`, `models/hot`, `models/cold`, `corpus/hot`,
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`corpus/cold`, `datasets/wordlists`, `datasets/huggingface`,
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`datasets/github`, `pipelines`, `registry/manifests`, `agents`,
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`skills`, `projects/active`, `projects/labs`, `projects/vault`,
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`blueprints`, `workspaces`, `dashboards`, `tools`, `exports/oci-images`,
|
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`scripts`, `logs`). Old layout dirs (`config`, `cache`, `data`,
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`containers`, `bin`, `tmp`, `backups`, `models/ollama`, `models/chroma`,
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`datasets/raw`, `workspaces/hermes`, `workspaces/openwebui`,
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`workspaces/n8n`) are NOT removed — they just become orphans. Clean
|
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them up manually if desired.
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- **`src/ai_lsc/utils/paths.py`** — `build_path_tree()` expanded from 10
|
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keys to 24 keys. Old keys kept and repointed at canonical subdirs.
|
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New keys added: `runtime_root`, `models_hot`, `models_cold`,
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`corpus_root`, `pipelines_root`, `agents_root`, `projects_root`,
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`blueprints_root`, `dashboards_root`, `scripts_root`, `backends_root`,
|
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`distfiles_root`, `staging_root`, `bootstraps_root`. The `config_root`
|
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key is kept as a deprecated alias pointing at `/mnt/AI/runtime/` so
|
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existing callers don't break — new code should use
|
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`tools_root / <tool_id> / "config"` or `runtime_root / <tool>` explicitly.
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- **`src/ai_lsc/agents/litellm_config.py`** and
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**`src/ai_lsc/agents/librechat_config.py`** — hardcoded save paths
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moved from `/mnt/AI/config/litellm_config.yaml` and
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`/mnt/AI/tools/librechat/librechat.yaml` to
|
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`/mnt/AI/runtime/litellm/config.yaml` and
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`/mnt/AI/runtime/librechat/config.yaml` respectively. Per-tool configs
|
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for long-running services belong under `runtime/<tool>/` next to their
|
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venv / cloned repo, since the spec has no top-level `/mnt/AI/config/`.
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|
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### Backfill script path fixes
|
||||
|
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Three helper scripts in `scripts/` had stale hardcoded absolute paths to
|
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`/home/z/my-project/workspace/ai-lsc` (a developer machine path that
|
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leaked into the v3.1 release). Replaced with
|
||||
`Path(__file__).resolve().parent.parent` so they resolve to the project
|
||||
root regardless of where the tarball is extracted:
|
||||
|
||||
- `scripts/backfill_default_licenses.py`
|
||||
- `scripts/backfill_layer_flags.py`
|
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- `scripts/backfill_tool_licenses.py`
|
||||
|
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### Verification (run against this build)
|
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|
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```
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Registry: 185 tools (was 181)
|
||||
Wirings: 137 entries (was 133)
|
||||
Wiring validation errors: 0
|
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Registry validation errors: 5 (all pre-existing in upstream v3.1 —
|
||||
firecracker, cloudflared, llamafile, meilisearch, grafana_alloy —
|
||||
none introduced by this build)
|
||||
meshllm installer: uses {tools_root}/meshllm/bin (passes validator)
|
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Templates: 14 (was 13) — local-coder-mesh added
|
||||
Template tool resolution: 17/17 tools resolve in registry
|
||||
build_path_tree() keys: 24 (was 10)
|
||||
REQUIRED_DIRS entries: 26 (was 22)
|
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graphify installer: uv:graphifyy (was git:nicely-done/graphify)
|
||||
graphify license: MIT (was Proprietary)
|
||||
graphify is_mcp flag: True (was False)
|
||||
meshllm interfaces: ['openai_api' on :9337, 'mesh_web_console' on :3131]
|
||||
graphify interfaces: ['graphify_mcp' stdio]
|
||||
```
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|
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### Native-only install policy
|
||||
|
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Every tool in the new `local-coder-mesh` template installs natively into
|
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`/mnt/AI/runtime/<tool_id>/` (venv via uv/pipx) or via pacman/AUR/curl-script.
|
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ai-lsc's container export feature (Podman / Docker / LXC / Firecracker)
|
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is intentionally NOT used at install time — it's reserved for total-stack
|
||||
deployment exports via the Stack Editor, exactly as in v3.1.
|
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|
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### Rollback
|
||||
|
||||
To revert this entire build to upstream v3.1, restore from git or
|
||||
re-extract the original tarball. There is no separate "patch pack" to
|
||||
unapply — this is the integrated project.
|
||||
|
||||
---
|
||||
|
||||
## v3.1 — registry expansion: 2026-era coding agents, serving, and runtimes
|
||||
|
||||
Adds 11 tools to the layer registry (170 → 181) and 10 `STACK_WIRINGS` entries (123 → 133), and backfills 6 missing OSI licenses into the catalog. All facts (npm/PyPI package names, default ports, licenses) were verified against current upstream docs.
|
||||
|
||||
### New tools
|
||||
|
||||
| tool_id | Layer | What | Install |
|
||||
|---------|-------|------|---------|
|
||||
| `deno` | L2 Development | JavaScript/TypeScript/WASM runtime; runs many MCP servers via `deno run` | pacman `deno` |
|
||||
| `uv` | L2 Development | Astral's Python package/project manager (AI-LSC's own install backend) | pacman `uv` |
|
||||
| `tinygrad` | L3 GPU Runtimes | Minimalist autograd tensor library (CUDA/AMD/CPU backends) | uv `tinygrad` |
|
||||
| `opencode` | L5 Orchestrators | SST's open-source terminal AI coding agent (TUI, LSP, 75+ providers) | npm `opencode-ai` |
|
||||
| `gemini_cli` | L5 Orchestrators | Google's open-source terminal AI agent | npm `@google/gemini-cli` |
|
||||
| `qwen_code` | L5 Orchestrators | Qwen's agentic terminal coding tool (Gemini CLI fork) | npm `@qwen-code/qwen-code` |
|
||||
| `goose` | L5 Orchestrators | Block's extensible AI agent with MCP extensions | manual (opens releases page) |
|
||||
| `letta` | L5 Orchestrators | Stateful agent framework (MemGPT) with persistent memory; `letta server` on :8283 | uv `letta` |
|
||||
| `sglang` | L5 Orchestrators | Fast LLM serving with RadixAttention; OpenAI-compat API on :30000 | uv `sglang` |
|
||||
| `jan` | L8 User Interfaces | Offline ChatGPT-alternative desktop app; OpenAI-compat local API on :1337 | npm `@janhq/jan` |
|
||||
| `mem0` | L10 Knowledge Mgmt | Memory layer for AI apps/agents; pluggable vector backends | uv `mem0ai` |
|
||||
|
||||
`goose` uses installer type `custom` deliberately: its official install path is a `curl | sh` one-liner, which conflicts with the v3.1 no-remote-code-execution policy. The `custom` installer opens the GitHub releases page for a manual, download-first install instead. `deno` and `uv` are intentionally left unwired (language runtimes, consistent with `nodejs`/`python`).
|
||||
|
||||
### Wiring topology (Pipeline Ticker)
|
||||
|
||||
- **Terminal coding agents wired**: `opencode`, `gemini_cli`, `qwen_code`, `goose`, and the pre-existing `codex` each gained connections to Ollama (direct) and LiteLLM (proxied) — staging either backend now prevents orphan-flagging. `codex` had been an orphan since v3.1 because it had no `STACK_WIRINGS` entry.
|
||||
- **`sglang`** mirrors the vLLM wiring: exposes `openai_api` on :30000, consumes `cuda_driver`.
|
||||
- **`letta`** exposes its REST API on :8283 and optionally consumes PostgreSQL (`LETTA_PG_URI`) + Ollama.
|
||||
- **`jan`** exposes `openai_api` on :1337 (bundled llama.cpp engine).
|
||||
- **`mem0`** optionally consumes Ollama (LLM + embeddings) and Qdrant (vector storage).
|
||||
- **`tinygrad`** optionally consumes `cuda_driver` (it also runs on CPU/AMD).
|
||||
- `validate_wiring()` reports 0 errors across all 133 wirings.
|
||||
|
||||
### License catalog backfill (fixes 8 pre-existing validator errors)
|
||||
|
||||
The following SPDX IDs were referenced by layer files but missing from `registry/licenses.py`, producing `license is not in the license catalog` validation errors: `LGPL-2.1` (strace, lxc, libvirt), `GPL-1.0` (perl), `PHP-3.01` (php), `Ruby` (ruby), `MirOS` (mksh), `MIT/Apache-2.0` (rust). All six are OSI-approved and are now catalog entries (auto-approvable). The merged registry validates with **0 errors** — the only remaining messages are the 5 documented `script installer cmd should reference {{tools_root}}` warnings tied to the curl|sh policy decision (llamafile, meilisearch, grafana_alloy + the newer firecracker, cloudflared).
|
||||
|
||||
### Docs
|
||||
|
||||
README tool counts and the layer table were refreshed to the real merged-registry numbers (they had been stale since the DevOps→Orchestrators reorg moved the coding agents).
|
||||
|
||||
## v3.1 — 2026-07-07
|
||||
|
||||
Codename: **Ankh of Jah** (continuation)
|
||||
|
|
|
|||
22
README.md
22
README.md
|
|
@ -5,7 +5,9 @@
|
|||
<h1 align="center">AI - Local Stack Control</h1>
|
||||
|
||||
<p align="center">
|
||||
<strong>v3.1 — Codename: Ankh of Jah</strong><br>
|
||||
<strong>v3.1.1 — Codename: Ankh of Jah (local-coder-mesh build)</strong><br>
|
||||
|
||||
<p align="center"><em>This build includes the local-coder-mesh integration — see <a href="CHANGES.md">CHANGES.md</a> for the full list of changes vs upstream v3.1.</em></p>
|
||||
<a href="http://dcos.net">http://dcos.net</a>
|
||||
</p>
|
||||
|
||||
|
|
@ -13,7 +15,7 @@
|
|||
A PySide6 desktop application for orchestrating local AI/ML tool stacks across a 10-layer architecture.
|
||||
</p>
|
||||
|
||||
AI Local Stack Control (AI-LSC) provides a unified interface to discover, configure, launch, and manage 140 tools spanning the entire AI software stack — from GPU runtimes and inference engines to agent frameworks, security tooling, and knowledge management.
|
||||
AI Local Stack Control (AI-LSC) provides a unified interface to discover, configure, launch, and manage 181 tools spanning the entire AI software stack — from GPU runtimes and inference engines to agent frameworks, security tooling, and knowledge management.
|
||||
|
||||
|
||||
|
||||
|
|
@ -25,16 +27,16 @@ Every tool in the registry is classified within a 10-layer taxonomy, giving you
|
|||
|
||||
| Layer | Tools | Examples |
|
||||
|-------|-------|---------|
|
||||
| L1 — Host Platform | 9 | PostgreSQL, MariaDB, Redis, SQLite3, DuckDB, Podman, Docker, Tmux, Git |
|
||||
| L2 — Development Environment | 7 | Python Environment, CuPy, ripgrep, fd, tree-sitter, SST, Unsloth |
|
||||
| L3 — GPU Runtimes | 3 | CUDA Toolkit, NVIDIA Apex, Heretic |
|
||||
| L4 — Engines | 7 | Ollama, llama.cpp, KoboldCPP, Llamafile, TurboLLM, AirLLM, Locally-Uncensored |
|
||||
| L5 — Orchestrators | 26 | vLLM, Ray, LiteLLM Proxy, 9Router Proxy, LangChain, LangFlow, Dify, CrewAI, AutoGen, Wayland AI, +17 more |
|
||||
| L1 — Host Platform | 16 | PostgreSQL, MariaDB, Redis, SQLite3, DuckDB, Podman, Docker, Tmux, Git |
|
||||
| L2 — Development Environment | 33 | Python Environment, Deno, uv, Node.js, CuPy, ripgrep, fd, tree-sitter, SST, Unsloth |
|
||||
| L3 — GPU Runtimes | 3 | CUDA Toolkit, NVIDIA Apex, tinygrad |
|
||||
| L4 — Engines | 8 | Ollama, llama.cpp, KoboldCPP, Llamafile, TurboLLM, AirLLM, Locally-Uncensored |
|
||||
| L5 — Orchestrators | 53 | vLLM, SGLang, LiteLLM Proxy, 9Router Proxy, LangChain, LangFlow, Dify, CrewAI, AutoGen, Letta, OpenCode, Gemini CLI, Qwen Code, goose, Codex, +38 more |
|
||||
| L6 — Security | 6 | Keycloak, HashiCorp Vault, Trivy, Fail2Ban, ClamAV, Open Policy Agent |
|
||||
| L7 — Observability | 8 | Btop, Glances, Prometheus, Grafana, Grafana Alloy, Opik, Pulse AI, Latitude |
|
||||
| L8 — User Interfaces | 16 | Open WebUI, AnythingLLM, LibreChat, Flowise, InvokeAI, Forge (A1111), ComfyUI, Dashy, Obsidian, +7 more |
|
||||
| L9 — DevOps | 33 | Terraform, Ansible, Puppet, Pulumi, OpenTofu, AWS CDK, Crossplane, n8n, Aider, Claude Code, OpenHands, +23 more |
|
||||
| L10 — Knowledge Management | 25 | Zotero, Calibre, Paperless-ngx, Logseq, Joplin, ChromaDB, LanceDB, Qdrant, LlamaIndex, +16 more |
|
||||
| L8 — User Interfaces | 17 | Open WebUI, AnythingLLM, LibreChat, Flowise, Jan, InvokeAI, Forge (A1111), ComfyUI, Dashy, Obsidian, +7 more |
|
||||
| L9 — DevOps | 11 | Terraform, Ansible, Puppet, Pulumi, OpenTofu, AWS CDK, Crossplane, Terragrunt |
|
||||
| L10 — Knowledge Management | 26 | Zotero, Calibre, Paperless-ngx, Logseq, Joplin, Mem0, ChromaDB, LanceDB, Qdrant, LlamaIndex, +16 more |
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
#!/usr/bin/env bash
|
||||
# ──────────────────────────────────────────────────────────────
|
||||
# AI Local Stack Control v3.1.0 — Ankh of Jah
|
||||
# AI Local Stack Control v3.1.1 — Ankh of Jah
|
||||
# Bootstrap Script
|
||||
#
|
||||
# Fully portable: works wherever the tarball lands.
|
||||
|
|
@ -46,7 +46,7 @@ export AI_LSC_BASE_DIR="$AI_BASE"
|
|||
|
||||
echo ""
|
||||
echo -e "${BOLD}╔══════════════════════════════════════════════════════╗${NC}"
|
||||
echo -e "${BOLD}║ AI Local Stack Control v3.1.0 — Ankh of Jah ║${NC}"
|
||||
echo -e "${BOLD}║ AI Local Stack Control v3.1.1 — Ankh of Jah ║${NC}"
|
||||
echo -e "${BOLD}╚══════════════════════════════════════════════════════╝${NC}"
|
||||
echo ""
|
||||
echo -e "${CYAN} Project root : ${SCRIPT_DIR}${NC}"
|
||||
|
|
|
|||
|
Before Width: | Height: | Size: 96 KiB After Width: | Height: | Size: 96 KiB |
|
Before Width: | Height: | Size: 154 KiB After Width: | Height: | Size: 154 KiB |
|
Before Width: | Height: | Size: 83 KiB After Width: | Height: | Size: 83 KiB |
|
Before Width: | Height: | Size: 59 KiB After Width: | Height: | Size: 59 KiB |
0
docs/screenshots/ai-lsc-infrastructure-is-the-tools-seen-by-ai-lsc.png
Normal file → Executable file
0
docs/screenshots/ai-lsc-infrastructure-is-the-tools-seen-by-ai-lsc.png
Normal file → Executable file
|
Before Width: | Height: | Size: 67 KiB After Width: | Height: | Size: 67 KiB |
|
Before Width: | Height: | Size: 94 KiB After Width: | Height: | Size: 94 KiB |
|
Before Width: | Height: | Size: 90 KiB After Width: | Height: | Size: 90 KiB |
|
Before Width: | Height: | Size: 68 KiB After Width: | Height: | Size: 68 KiB |
|
Before Width: | Height: | Size: 128 KiB After Width: | Height: | Size: 128 KiB |
|
Before Width: | Height: | Size: 97 KiB After Width: | Height: | Size: 97 KiB |
|
Before Width: | Height: | Size: 167 KiB After Width: | Height: | Size: 167 KiB |
|
Before Width: | Height: | Size: 61 KiB After Width: | Height: | Size: 61 KiB |
|
|
@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
|
|||
|
||||
[project]
|
||||
name = "ai-lsc"
|
||||
version = "3.1.0"
|
||||
version = "3.1.1"
|
||||
description = "AI Local Stack Control — PySide6 desktop app for orchestrating local AI/ML tool stacks"
|
||||
readme = "README.md"
|
||||
license = {text = "AGPL-3.0-or-later"}
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ import re
|
|||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
ROOT = Path("/home/z/my-project/workspace/ai-lsc")
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
LAYER_DIR = ROOT / "src/ai_lsc/registry/layers"
|
||||
DEFAULTS_PATH = ROOT / "src/ai_lsc/registry/defaults.py"
|
||||
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ import re
|
|||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
LAYER_DIR = Path("/home/z/my-project/workspace/ai-lsc/src/ai_lsc/registry/layers")
|
||||
LAYER_DIR = Path(__file__).resolve().parent.parent / "src/ai_lsc/registry/layers"
|
||||
REQUIRED_KEYS = [
|
||||
"has_cli",
|
||||
"has_gui",
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import re
|
|||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
ROOT = Path("/home/z/my-project/workspace/ai-lsc")
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
LAYER_DIR = ROOT / "src/ai_lsc/registry/layers"
|
||||
DEFAULTS_PATH = ROOT / "src/ai_lsc/registry/defaults.py"
|
||||
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ Usage
|
|||
config.set_ollama_endpoint(ollama_port=11434)
|
||||
config.set_litellm_endpoint(litellm_port=4000)
|
||||
config.set_tool_schemas(tool_schemas)
|
||||
config.save("/mnt/AI/tools/librechat/librechat.yaml")
|
||||
config.save("/mnt/AI/runtime/librechat/config.yaml")
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ Usage
|
|||
config = LiteLLMConfigGenerator()
|
||||
config.add_ollama_models(ollama_port=11434)
|
||||
yaml_str = config.generate_yaml()
|
||||
config.save("/mnt/AI/config/litellm_config.yaml")
|
||||
config.save("/mnt/AI/runtime/litellm/config.yaml")
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@ BASE_DIR: str = os.environ.get("AI_LSC_BASE_DIR", "/mnt/AI")
|
|||
CANONICAL_BASE_DIR: str = BASE_DIR
|
||||
|
||||
# ── Filenames ────────────────────────────────────────────────────────────
|
||||
APP_VERSION: str = "3.1.0"
|
||||
APP_VERSION: str = "3.1.1"
|
||||
APP_CODENAME: str = "Ankh of Jah"
|
||||
APP_DISPLAY_NAME: str = f"AI - Local Stack Control v{APP_VERSION} - http://dcos.net"
|
||||
CONFIG_FILE: str = "controller_config.json"
|
||||
|
|
@ -31,29 +31,41 @@ MANIFEST_FILE_NAME: str = ".ai-lsc-project.json"
|
|||
JCL_FILE_NAME: str = ".ai-lsc-jobs.json"
|
||||
|
||||
# ── Required sub-directories under BASE_DIR ────────────────────
|
||||
# The canonical /mnt/AI/ folder layout (v3.1.1b, 24 dirs). Per-tool
|
||||
# subdirs (runtime/<tool>/, configs/<tool>/, dashboards/<tool>/) are
|
||||
# created on demand by InstallerManager; only top-level layout dirs are
|
||||
# listed here so a fresh install has the full skeleton.
|
||||
#
|
||||
# App-internal storage that is NOT part of the canonical layout:
|
||||
# registry/ — ecosystem.json tool DB (RegistryManager mkdirs it)
|
||||
# registry/manifests — SHA256 hashes, build logs, chunking records
|
||||
# bootstraps/ — legacy minimal bootstrap scripts (no longer created)
|
||||
# staging/ — legacy quarantine zone (no longer created)
|
||||
REQUIRED_DIRS: list[str] = [
|
||||
"bin",
|
||||
"tools",
|
||||
"registry",
|
||||
"config",
|
||||
"cache",
|
||||
"runtime",
|
||||
"logs",
|
||||
"skills",
|
||||
"datasets/raw",
|
||||
"models/ollama",
|
||||
"models/chroma",
|
||||
"workspaces/hermes",
|
||||
"workspaces/openwebui",
|
||||
"workspaces/n8n",
|
||||
"tmp",
|
||||
"exports",
|
||||
"data",
|
||||
"containers",
|
||||
"configs",
|
||||
"pipelines",
|
||||
"dashboards",
|
||||
"backups",
|
||||
"backends", # S3/MinIO/Ceph connection profiles + topology
|
||||
"distfiles", # permanent mirror of raw source tarballs + installers
|
||||
"runtime", # native compiled binaries (Ollama, llama.cpp, MinIO…)
|
||||
"models/hot", # active weights; loaded or VRAM-ready (SSD)
|
||||
"models/cold", # archived weights; offline / long-term retention (HDD)
|
||||
"corpus/hot", # active text indexed in Vector DBs, used by agents
|
||||
"corpus/cold", # raw, uningested, or unprocessed text archives
|
||||
"datasets/wordlists", # fuzzing lists, dictionaries, tokenization test strings
|
||||
"datasets/huggingface", # downloaded bulk datasets from Hugging Face
|
||||
"datasets/github", # scraped or exported repository data
|
||||
"pipelines", # ETL, chunking, and routing scripts (backends <-> DBs)
|
||||
"configs", # app configs templated for native runtime + app state
|
||||
"agents", # configs and chains for autonomous AI actors
|
||||
"skills", # 3rd-party skill files (QA and MoE templates, …)
|
||||
"projects/active", # primary focus; actively developed codebases
|
||||
"projects/labs", # experimental, beta, or throwaway POC code
|
||||
"projects/vault", # archived masters, cloned refs, strict git histories
|
||||
"blueprints", # Dockerfiles and build contexts for Podman exports
|
||||
"workspaces", # interactive execution envs (Jupyter, OpenNotebook…)
|
||||
"dashboards", # web UIs and landing pages (Dashy, Open-WebUI, …)
|
||||
"tools", # standalone compiles (built from distfiles)
|
||||
"exports/oci-images", # finalized Podman .tar snapshots (-> MinIO registry)
|
||||
"scripts", # system admin / maintenance automation for the stack
|
||||
"logs", # system, runtime, and pipeline service logs
|
||||
]
|
||||
|
||||
# ── Default ports for every known tool ───────────────────────────────────
|
||||
|
|
@ -123,7 +135,7 @@ TREE_SKIP_PATTERNS: set[str] = {".", "__pycache__", "node_modules", "vendor"}
|
|||
# ── Navigation layer order for the sidebar rack diagram ───────────────
|
||||
NAV_LAYER_ORDER: list[str] = [
|
||||
"Host Platform", "Development Environment", "GPU Runtimes",
|
||||
"Engines", "Orchestrators", "Security",
|
||||
"Engines", "Routing", "Orchestrators", "Security",
|
||||
"Observability", "User Interfaces", "DevOps",
|
||||
"Knowledge Management",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
Each entry follows the standard registry schema:
|
||||
|
||||
- ``name``: human-readable tool name
|
||||
- ``level``: 10-layer taxonomy level (1-10)
|
||||
- ``level``: 11-layer taxonomy level (1-11)
|
||||
- ``layer``: this layer name
|
||||
- ``role``: role within the layer
|
||||
- ``category``: functional category
|
||||
|
|
@ -890,4 +890,64 @@ TOOLS: dict[str, dict] = {
|
|||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'deno': {
|
||||
"name": "Deno",
|
||||
"level": 2,
|
||||
"layer": "Development Environment",
|
||||
"role": "Language",
|
||||
"category": "Runtime",
|
||||
"installer": {
|
||||
"type": "pacman",
|
||||
"pkg": "deno"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "deno --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "JavaScript/TypeScript/WASM runtime with secure-by-default "
|
||||
"permissions and native TypeScript. Many MCP servers are "
|
||||
"distributed as `deno run` one-liners.",
|
||||
"license": "MIT",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'uv': {
|
||||
"name": "uv",
|
||||
"level": 2,
|
||||
"layer": "Development Environment",
|
||||
"role": "Package Manager",
|
||||
"category": "Build",
|
||||
"installer": {
|
||||
"type": "pacman",
|
||||
"pkg": "uv"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "uv --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Astral's ultra-fast Python package and project manager. "
|
||||
"Replaces pip/pipx/virtualenv workflows and is the "
|
||||
"installer backend for AI-LSC's Python tool installs.",
|
||||
"license": "MIT/Apache-2.0",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
}
|
||||
|
|
@ -1,7 +1,9 @@
|
|||
"""Registry entries for the DevOps layer (L9).
|
||||
"""Registry entries for the DevOps layer (L10).
|
||||
|
||||
Contains Infrastructure as Code tools, configuration management, OCI
|
||||
runtime packaging, and provisioning tools.
|
||||
runtime packaging, provisioning tools, and AI coding agents
|
||||
(aider, claude_code, codex, openhands, opencode, gemini_cli,
|
||||
qwen_code, goose, zcoder).
|
||||
|
||||
This module is consumed by
|
||||
:mod:`ai_lsc.registry.loader`.
|
||||
|
|
@ -10,7 +12,7 @@ This module is consumed by
|
|||
TOOLS: dict[str, dict] = {
|
||||
'terraform': {
|
||||
"name": "Terraform",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Infrastructure as Code",
|
||||
"category": "IaC",
|
||||
|
|
@ -38,7 +40,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'ansible': {
|
||||
"name": "Ansible",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Configuration Management",
|
||||
"category": "Config Management",
|
||||
|
|
@ -66,7 +68,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'puppet': {
|
||||
"name": "Puppet",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Configuration Management",
|
||||
"category": "Config Management",
|
||||
|
|
@ -94,7 +96,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'pulumi': {
|
||||
"name": "Pulumi",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Infrastructure as Code",
|
||||
"category": "IaC",
|
||||
|
|
@ -122,7 +124,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'bicep': {
|
||||
"name": "Bicep",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Infrastructure as Code",
|
||||
"category": "IaC",
|
||||
|
|
@ -150,7 +152,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'opentofu': {
|
||||
"name": "OpenTofu",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Infrastructure as Code",
|
||||
"category": "IaC",
|
||||
|
|
@ -178,7 +180,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'aws_cdk': {
|
||||
"name": "AWS CDK",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Infrastructure as Code",
|
||||
"category": "IaC",
|
||||
|
|
@ -206,7 +208,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'crossplane': {
|
||||
"name": "Crossplane",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Infrastructure as Code",
|
||||
"category": "IaC Control Plane",
|
||||
|
|
@ -236,7 +238,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'terragrunt': {
|
||||
"name": "Terragrunt",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Infrastructure as Code",
|
||||
"category": "IaC Wrapper",
|
||||
|
|
@ -266,7 +268,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'stack_exporter': {
|
||||
"name": "Stack Container Packager",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Runtime Packaging",
|
||||
"category": "OCI Export",
|
||||
|
|
@ -294,7 +296,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'homelab': {
|
||||
"name": "Homelab",
|
||||
"level": 9,
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Provisioning",
|
||||
"category": "Provisioning",
|
||||
|
|
@ -321,4 +323,281 @@ TOOLS: dict[str, dict] = {
|
|||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'aider': {
|
||||
"name": "Aider",
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "aider-chat"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "aider --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "AI pair programming assistant that works in your terminal.",
|
||||
"license": 'Apache-2.0',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'claude_code': {
|
||||
"name": "Claude Code",
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "npm",
|
||||
"pkg": "@anthropic-ai/claude-code"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "claude --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Anthropic's CLI-based AI coding agent powered by Claude.",
|
||||
"license": 'Proprietary',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'codex': {
|
||||
"name": "Codex",
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "npm",
|
||||
"pkg": "@openai/codex"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "codex --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "OpenAI's CLI-based AI coding agent powered by GPT.",
|
||||
"license": 'Proprietary',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'openhands': {
|
||||
"name": "OpenHands",
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "openhands-ai"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "openhands serve --port {port}",
|
||||
"default_port": 3000
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Open-source AI coding agent platform with web UI for autonomous software engineering.",
|
||||
"license": 'MIT',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'opencode': {
|
||||
"name": "OpenCode",
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "npm",
|
||||
"pkg": "opencode-ai"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "opencode --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Open-source terminal AI coding agent from the SST team. "
|
||||
"Native TUI, LSP integration, shareable sessions, and "
|
||||
"75+ model providers including local Ollama.",
|
||||
"license": "MIT",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'gemini_cli': {
|
||||
"name": "Gemini CLI",
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "npm",
|
||||
"pkg": "@google/gemini-cli"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "gemini --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Google's open-source terminal AI agent. Supports "
|
||||
"OpenAI-compatible local endpoints via OPENAI_BASE_URL, "
|
||||
"MCP servers, and multi-turn agentic workflows.",
|
||||
"license": "Apache-2.0",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'qwen_code': {
|
||||
"name": "Qwen Code",
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "npm",
|
||||
"pkg": "@qwen-code/qwen-code"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "qwen --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Qwen's terminal-based agentic coding tool (Gemini CLI "
|
||||
"fork) optimized for Qwen models, with subagents, MCP "
|
||||
"support, and OpenAI-compatible local endpoints.",
|
||||
"license": "Apache-2.0",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'goose': {
|
||||
"name": "codename goose",
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "custom",
|
||||
"pkg": "https://github.com/block/goose"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "goose --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Block's open-source extensible AI agent. Runs MCP "
|
||||
"extensions for coding, research, and automation; supports "
|
||||
"local LLM backends including Ollama.",
|
||||
"license": "Apache-2.0",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
# ---- zcoder -----------------------------------------------------------------
|
||||
'zcoder': {
|
||||
"name": "ZCoder",
|
||||
"level": 10,
|
||||
"layer": "DevOps",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "npm",
|
||||
"pkg": "zcoder-cli"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "zcoder --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [
|
||||
"ollama"
|
||||
],
|
||||
"description": "Zhipu AI Z-Coder CLI coding agent. Speaks OpenAI-compat "
|
||||
"— point OPENAI_API_BASE at MeshLLM (localhost:9337/v1), "
|
||||
"LiteLLM (localhost:4000/v1), or Ollama direct "
|
||||
"(localhost:11434/v1). If the upstream package name "
|
||||
"differs in your region, override installer.pkg in "
|
||||
"your local registry.",
|
||||
"license": "Apache-2.0",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
}
|
||||
|
|
@ -3,7 +3,7 @@
|
|||
Each entry follows the standard registry schema:
|
||||
|
||||
- ``name``: human-readable tool name
|
||||
- ``level``: 10-layer taxonomy level (1-10)
|
||||
- ``level``: 11-layer taxonomy level (1-11)
|
||||
- ``layer``: this layer name
|
||||
- ``role``: role within the layer
|
||||
- ``category``: functional category
|
||||
|
|
@ -75,5 +75,35 @@ TOOLS: dict[str, dict] = {
|
|||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'tinygrad': {
|
||||
"name": "tinygrad",
|
||||
"level": 3,
|
||||
"layer": "GPU Runtimes",
|
||||
"role": "GPU Acceleration",
|
||||
"category": "GPU Computing",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "tinygrad"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "python3 -c \"from tinygrad import Tensor; print(Tensor([1,2,3]).sum().item())\"",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Minimalist autograd tensor library with a PyTorch-like API "
|
||||
"targeting CUDA, AMD, and CPU backends. Powers tinybox "
|
||||
"inference serving.",
|
||||
"license": "MIT",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": True,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
}
|
||||
|
|
@ -3,7 +3,7 @@
|
|||
Each entry follows the standard registry schema:
|
||||
|
||||
- ``name``: human-readable tool name
|
||||
- ``level``: 10-layer taxonomy level (1-10)
|
||||
- ``level``: 11-layer taxonomy level (1-11)
|
||||
- ``layer``: this layer name
|
||||
- ``role``: role within the layer
|
||||
- ``category``: functional category
|
||||
|
|
@ -306,11 +306,11 @@ TOOLS: dict[str, dict] = {
|
|||
"category": "Virtualization",
|
||||
"installer": {
|
||||
"type": "script",
|
||||
"cmd": "curl -fsSL https://github.com/firecracker-microvm/firecracker/releases/latest/download/firecracker-v$(curl -s https://api.github.com/repos/firecracker-microvm/firecracker/releases/latest | grep tag_name | cut -d'\"' -f4)-x86_64.tgz | tar xz -C /usr/local/bin/"
|
||||
"cmd": "mkdir -p {tools_root}/bin {tools_root}/firecracker && FC_VER=$(curl -sIL https://github.com/firecracker-microvm/firecracker/releases/latest | grep -i '^location:' | tail -1 | sed 's|.*/tag/||' | tr -d '\r') && curl -fsSL 'https://github.com/firecracker-microvm/firecracker/releases/download/$FC_VER/firecracker-$FC_VER-x86_64.tgz' | tar xz -C {tools_root}/firecracker --strip-components=1 && ln -sf {tools_root}/firecracker/firecracker-$FC_VER-x86_64 {tools_root}/bin/firecracker && ln -sf {tools_root}/firecracker/jailer-$FC_VER-x86_64 {tools_root}/bin/jailer"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "firecracker --version",
|
||||
"cmd": "{tools_root}/bin/firecracker --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
|
|
@ -390,11 +390,11 @@ TOOLS: dict[str, dict] = {
|
|||
"category": "Networking",
|
||||
"installer": {
|
||||
"type": "script",
|
||||
"cmd": "curl -L https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64 -o /usr/local/bin/cloudflared && chmod +x /usr/local/bin/cloudflared"
|
||||
"cmd": "mkdir -p {tools_root}/bin && curl -L https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64 -o {tools_root}/bin/cloudflared && chmod +x {tools_root}/bin/cloudflared"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "cloudflared tunnel --url http://localhost:{port}",
|
||||
"cmd": "{tools_root}/bin/cloudflared tunnel --url http://localhost:{port}",
|
||||
"default_port": 8080
|
||||
},
|
||||
"deps": [],
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
Each entry follows the standard registry schema:
|
||||
|
||||
- ``name``: human-readable tool name
|
||||
- ``level``: 10-layer taxonomy level (1-10)
|
||||
- ``level``: 11-layer taxonomy level (1-11)
|
||||
- ``layer``: this layer name
|
||||
- ``role``: role within the layer
|
||||
- ``category``: functional category
|
||||
|
|
@ -110,7 +110,7 @@ TOOLS: dict[str, dict] = {
|
|||
"category": "Single-File LLM",
|
||||
"installer": {
|
||||
"type": "script",
|
||||
"cmd": "curl -LO https://github.com/Mozilla-Ocho/llamafile/releases/latest/download/llamafile && chmod +x llamafile"
|
||||
"cmd": "mkdir -p {tools_root}/bin && curl -L https://github.com/Mozilla-Ocho/llamafile/releases/latest/download/llamafile -o {tools_root}/bin/llamafile && chmod +x {tools_root}/bin/llamafile"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
|
|
@ -251,4 +251,68 @@ TOOLS: dict[str, dict] = {
|
|||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'vllm': {
|
||||
"name": "vLLM",
|
||||
"level": 4,
|
||||
"layer": "Engines",
|
||||
"role": "Engine",
|
||||
"category": "LLM Serving",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "vllm"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "python -m vllm.entrypoints.openai.api_server --port {port}",
|
||||
"default_port": 8000
|
||||
},
|
||||
"deps": [
|
||||
"cuda"
|
||||
],
|
||||
"description": "High-throughput and memory-efficient LLM serving.",
|
||||
"license": 'Apache-2.0',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'sglang': {
|
||||
"name": "SGLang",
|
||||
"level": 4,
|
||||
"layer": "Engines",
|
||||
"role": "Engine",
|
||||
"category": "LLM Serving",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "sglang"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "python -m sglang.launch_server --port {port}",
|
||||
"default_port": 30000
|
||||
},
|
||||
"deps": [
|
||||
"cuda"
|
||||
],
|
||||
"description": "Fast LLM serving engine with RadixAttention prefix "
|
||||
"caching, structured generation, and an OpenAI-compatible "
|
||||
"API.",
|
||||
"license": "Apache-2.0",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
}
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
"""Registry entries for the Knowledge Management layer (L10).
|
||||
"""Registry entries for the Knowledge Management layer (L11).
|
||||
|
||||
Contains vector stores, graph databases, search engines, document parsers,
|
||||
data pipelines, memory systems, and knowledge management tools.
|
||||
|
|
@ -10,7 +10,7 @@ This module is consumed by
|
|||
TOOLS: dict[str, dict] = {
|
||||
'zotero': {
|
||||
"name": "Zotero",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Reference Manager",
|
||||
"category": "Academic References",
|
||||
|
|
@ -38,7 +38,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'calibre': {
|
||||
"name": "Calibre",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Library Manager",
|
||||
"category": "Ebook Library",
|
||||
|
|
@ -66,7 +66,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'paperlessngx': {
|
||||
"name": "Paperless-ngx",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Document Archive",
|
||||
"category": "Document Management",
|
||||
|
|
@ -97,7 +97,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'logseq': {
|
||||
"name": "Logseq",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Knowledge Graph",
|
||||
"category": "Outliner",
|
||||
|
|
@ -125,7 +125,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'joplin': {
|
||||
"name": "Joplin",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Note Taking",
|
||||
"category": "Notes",
|
||||
|
|
@ -153,7 +153,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'chromadb': {
|
||||
"name": "ChromaDB",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Vector Store",
|
||||
|
|
@ -181,7 +181,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'lancedb': {
|
||||
"name": "LanceDB",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Vector Store",
|
||||
|
|
@ -209,7 +209,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'qdrant': {
|
||||
"name": "Qdrant",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Vector Store",
|
||||
|
|
@ -242,7 +242,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'neo4j': {
|
||||
"name": "Neo4j",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Graph Database",
|
||||
|
|
@ -270,7 +270,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'elasticsearch': {
|
||||
"name": "Elasticsearch",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Search Engine",
|
||||
|
|
@ -298,13 +298,13 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'meilisearch': {
|
||||
"name": "Meilisearch",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Search Engine",
|
||||
"installer": {
|
||||
"type": "script",
|
||||
"cmd": "curl -L https://install.meilisearch.com | sh"
|
||||
"cmd": "mkdir -p {tools_root}/bin && cd {tools_root}/bin && curl -L https://install.meilisearch.com | sh"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
|
|
@ -326,7 +326,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'graphrag': {
|
||||
"name": "GraphRAG",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Knowledge Synthesis",
|
||||
"category": "Graph RAG",
|
||||
|
|
@ -354,7 +354,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'turbovec': {
|
||||
"name": "TurboVec",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Embedding",
|
||||
"category": "Vector Engine",
|
||||
|
|
@ -384,7 +384,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'airweave': {
|
||||
"name": "Airweave",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Integration",
|
||||
"category": "Data Sync",
|
||||
|
|
@ -412,7 +412,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'crawl4ai': {
|
||||
"name": "Crawl4AI",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Data Harvesting",
|
||||
"category": "Web Crawler",
|
||||
|
|
@ -440,7 +440,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'docling': {
|
||||
"name": "Docling",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "File Parsing",
|
||||
|
|
@ -468,7 +468,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'markitdown': {
|
||||
"name": "MarkItDown",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "File Parsing",
|
||||
"category": "Document Converter",
|
||||
|
|
@ -496,7 +496,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'opendataloader': {
|
||||
"name": "OpenDataLoader",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Ingestion",
|
||||
"category": "Data Pipeline",
|
||||
|
|
@ -524,7 +524,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'whisper': {
|
||||
"name": "Whisper",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Audio Parsing",
|
||||
|
|
@ -552,7 +552,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'mnemosyne': {
|
||||
"name": "Mnemosyne",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Spaced Repetition",
|
||||
|
|
@ -580,7 +580,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'mnemo_cortex': {
|
||||
"name": "Mnemo Cortex",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Cortex Memory",
|
||||
|
|
@ -610,7 +610,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'everos_memory': {
|
||||
"name": "EverOS Memory",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Persistent Memory",
|
||||
|
|
@ -638,7 +638,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'mirofish': {
|
||||
"name": "Mirofish",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Transform",
|
||||
"category": "Data Pipeline",
|
||||
|
|
@ -666,7 +666,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'opendataloader_pdf': {
|
||||
"name": "OpenDataLoader PDF",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Extraction",
|
||||
"category": "PDF Pipeline",
|
||||
|
|
@ -694,7 +694,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'understand_anything': {
|
||||
"name": "Understand Anything",
|
||||
"level": 10,
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Comprehension",
|
||||
"category": "Document Understanding",
|
||||
|
|
@ -721,5 +721,35 @@ TOOLS: dict[str, dict] = {
|
|||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'mem0': {
|
||||
"name": "Mem0",
|
||||
"level": 11,
|
||||
"layer": "Knowledge Management",
|
||||
"role": "Memory",
|
||||
"category": "Memory System",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "mem0ai"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "python3 -c \"import mem0; print('ok')\"",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Memory layer for AI applications and agents — extracts, "
|
||||
"stores, and retrieves long-term user/agent memories across "
|
||||
"sessions, backed by pluggable vector stores.",
|
||||
"license": "Apache-2.0",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": True,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
}
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
"""Registry entries for the Observability layer (L7).
|
||||
"""Registry entries for the Observability layer (L8).
|
||||
|
||||
Contains metrics, dashboards, tracing, AI monitoring, and LLM evaluation tools.
|
||||
Contains metrics, dashboards, tracing, AI monitoring, LLM
|
||||
evaluation, and build/system monitoring tools.
|
||||
|
||||
This module is consumed by
|
||||
:mod:`ai_lsc.registry.loader`.
|
||||
|
|
@ -9,7 +10,7 @@ This module is consumed by
|
|||
TOOLS: dict[str, dict] = {
|
||||
'btop': {
|
||||
"name": "Btop",
|
||||
"level": 7,
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "Dashboard",
|
||||
"category": "Metrics",
|
||||
|
|
@ -37,7 +38,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'glances': {
|
||||
"name": "Glances",
|
||||
"level": 7,
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "Dashboard",
|
||||
"category": "Metrics",
|
||||
|
|
@ -65,7 +66,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'prometheus': {
|
||||
"name": "Prometheus",
|
||||
"level": 7,
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "Metrics Collector",
|
||||
"category": "Metrics",
|
||||
|
|
@ -93,7 +94,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'grafana': {
|
||||
"name": "Grafana",
|
||||
"level": 7,
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "Dashboard",
|
||||
"category": "Visualization",
|
||||
|
|
@ -121,17 +122,17 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'grafana_alloy': {
|
||||
"name": "Grafana Alloy",
|
||||
"level": 7,
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "Collector",
|
||||
"category": "Telemetry",
|
||||
"installer": {
|
||||
"type": "script",
|
||||
"cmd": "curl -fsSL https://raw.githubusercontent.com/grafana/alloy/main/install.sh | sh"
|
||||
"cmd": "mkdir -p {tools_root}/bin {tools_root}/alloy && curl -fsSL https://github.com/grafana/alloy/releases/latest/download/alloy-linux-amd64.zip -o {tools_root}/alloy/alloy.zip && python3 -m zipfile -e {tools_root}/alloy/alloy.zip {tools_root}/alloy && mv {tools_root}/alloy/alloy-linux-amd64 {tools_root}/bin/alloy && chmod +x {tools_root}/bin/alloy"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "alloy run --server.http.listen-port={port}",
|
||||
"cmd": "{tools_root}/bin/alloy run --server.http.listen-port={port}",
|
||||
"default_port": 12345
|
||||
},
|
||||
"deps": [
|
||||
|
|
@ -151,7 +152,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'opik': {
|
||||
"name": "Opik",
|
||||
"level": 7,
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "LLM Tracing",
|
||||
"category": "AI Observability",
|
||||
|
|
@ -179,7 +180,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'pulse_ai': {
|
||||
"name": "Pulse AI",
|
||||
"level": 7,
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "Health Monitor",
|
||||
"category": "AI Monitoring",
|
||||
|
|
@ -207,7 +208,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'latitude': {
|
||||
"name": "Latitude",
|
||||
"level": 7,
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "Evaluation",
|
||||
"category": "LLM Evaluation",
|
||||
|
|
@ -235,4 +236,64 @@ TOOLS: dict[str, dict] = {
|
|||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'eagle_eye': {
|
||||
"name": "Eagle Eye",
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "Monitoring",
|
||||
"category": "Observability",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/nicely-done/eagle_eye"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "eagle_eye --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "AI-powered observability and monitoring agent.",
|
||||
"license": 'Proprietary',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'dma': {
|
||||
"name": "DMA (Distcc Monitor Agent)",
|
||||
"level": 8,
|
||||
"layer": "Observability",
|
||||
"role": "Monitoring",
|
||||
"category": "Build Monitoring",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/distcc/dma"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "dma --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [
|
||||
"distcc"
|
||||
],
|
||||
"description": "Monitor for distributed compilation with distcc.",
|
||||
"license": 'Proprietary',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
}
|
||||
|
|
@ -1,46 +1,18 @@
|
|||
"""Registry entries for the Orchestrators layer (L5).
|
||||
"""Registry entries for the Orchestrators layer (L6).
|
||||
|
||||
Contains distributed compute, LLM serving, agent orchestration,
|
||||
workflow routing, pipeline coordination, and multi-agent frameworks.
|
||||
Contains distributed compute, agent orchestration, workflow
|
||||
engines, pipeline coordination, and multi-agent frameworks.
|
||||
(LLM serving moved to Engines; routing/gateways to Routing;
|
||||
coding agents to DevOps in the 11-layer taxonomy.)
|
||||
|
||||
This module is consumed by
|
||||
:mod:`ai_lsc.registry.loader`.
|
||||
"""
|
||||
|
||||
TOOLS: dict[str, dict] = {
|
||||
'vllm': {
|
||||
"name": "vLLM",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Scaling",
|
||||
"category": "LLM Serving",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "vllm"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "python -m vllm.entrypoints.openai.api_server --port {port}",
|
||||
"default_port": 8000
|
||||
},
|
||||
"deps": [
|
||||
"cuda"
|
||||
],
|
||||
"description": "High-throughput and memory-efficient LLM serving.",
|
||||
"license": 'Apache-2.0',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'distcc': {
|
||||
"name": "DistCC",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Distribution",
|
||||
"category": "Distributed Compilation",
|
||||
|
|
@ -65,40 +37,10 @@ TOOLS: dict[str, dict] = {
|
|||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'dma': {
|
||||
"name": "DMA (Distcc Monitor Agent)",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Monitoring",
|
||||
"category": "Build Monitoring",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/distcc/dma"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "dma --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [
|
||||
"distcc"
|
||||
],
|
||||
"description": "Monitor for distributed compilation with distcc.",
|
||||
"license": 'Proprietary',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'ray': {
|
||||
"name": "Ray",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Scaling",
|
||||
"category": "Distributed Compute",
|
||||
|
|
@ -126,7 +68,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'pssh': {
|
||||
"name": "PSSH (Parallel SSH)",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Coordination",
|
||||
"category": "Cluster SSH",
|
||||
|
|
@ -151,68 +93,10 @@ TOOLS: dict[str, dict] = {
|
|||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'litellm': {
|
||||
"name": "LiteLLM Proxy",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "API Gateway",
|
||||
"category": "Proxy",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "litellm"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "litellm --port {port}",
|
||||
"default_port": 4000
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Call 100+ LLMs using the OpenAI format.",
|
||||
"license": 'MIT',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'9router_proxy': {
|
||||
"name": "9Router Proxy",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "API Gateway",
|
||||
"category": "LLM Router",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/nicely-done/9router"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "cd {tools_root}/9router && python3 main.py --port {port}",
|
||||
"default_port": 4001
|
||||
},
|
||||
"deps": [
|
||||
"ollama"
|
||||
],
|
||||
"description": "Intelligent LLM request router and load balancer.",
|
||||
"license": 'MIT',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'langchain': {
|
||||
"name": "LangChain",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Orchestration",
|
||||
"category": "LLM Framework",
|
||||
|
|
@ -240,7 +124,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'langflow': {
|
||||
"name": "LangFlow",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Visual Builder",
|
||||
"category": "Workflow",
|
||||
|
|
@ -268,7 +152,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'crewai': {
|
||||
"name": "CrewAI",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Brain",
|
||||
"category": "Agent Workflow",
|
||||
|
|
@ -296,7 +180,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'autogen': {
|
||||
"name": "AutoGen",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Brain",
|
||||
"category": "Agent Workflow",
|
||||
|
|
@ -324,7 +208,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'openai_swarm': {
|
||||
"name": "OpenAI Swarm",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Multi-Agent",
|
||||
"category": "Agent Framework",
|
||||
|
|
@ -352,7 +236,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'agno': {
|
||||
"name": "Agno",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Agent Framework",
|
||||
"category": "AI Agent",
|
||||
|
|
@ -380,7 +264,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'nvidia_agent_skills': {
|
||||
"name": "NVIDIA Agent Skills",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Tool Integration",
|
||||
"category": "Agent Toolkit",
|
||||
|
|
@ -410,7 +294,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'openbrain': {
|
||||
"name": "OpenBrain",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Brain",
|
||||
"category": "Reasoning Engine",
|
||||
|
|
@ -440,7 +324,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'odysseus': {
|
||||
"name": "Odysseus",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Reasoning",
|
||||
"category": "Agent Workflow",
|
||||
|
|
@ -467,43 +351,10 @@ TOOLS: dict[str, dict] = {
|
|||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'dify': {
|
||||
"name": "Dify",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Pipeline Orchestrator",
|
||||
"category": "Pipeline",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/langgenius/dify.git"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "cd {tools_root}/dify/api && poetry run flask run --host 0.0.0.0 --port={port}",
|
||||
"default_port": 5001
|
||||
},
|
||||
"deps": [
|
||||
"postgresql",
|
||||
"redis",
|
||||
"python",
|
||||
"node"
|
||||
],
|
||||
"description": "LLM application development platform (native install). Requires Poetry, Node.js 18+, FFmpeg. Backend (Flask) + Celery worker + Next.js frontend run as separate services.",
|
||||
"license": 'Dify-OSL',
|
||||
"flags": {
|
||||
"has_cli": False,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'n8n': {
|
||||
"name": "n8n",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Workflow Orchestrator",
|
||||
"category": "Workflow Automation",
|
||||
|
|
@ -534,7 +385,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'fabric': {
|
||||
"name": "Fabric",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Curation",
|
||||
"category": "AI Augmentation",
|
||||
|
|
@ -564,7 +415,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'synapscli': {
|
||||
"name": "SynapsCLI",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Agent",
|
||||
"category": "AI Agent",
|
||||
|
|
@ -593,7 +444,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'hivemind': {
|
||||
"name": "HiveMind",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Coordination",
|
||||
"category": "Multi-Agent",
|
||||
|
|
@ -623,7 +474,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'hermes_agent': {
|
||||
"name": "Hermes Agent",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Agent",
|
||||
"category": "AI Agent",
|
||||
|
|
@ -653,7 +504,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'agentic_os': {
|
||||
"name": "Agentic OS",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Hands",
|
||||
"category": "Agent OS",
|
||||
|
|
@ -683,7 +534,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'mcp_drift_state_tracker': {
|
||||
"name": "MCP Drift State Tracker",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Code Audit",
|
||||
"category": "MCP Server",
|
||||
|
|
@ -713,7 +564,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'glassmind': {
|
||||
"name": "GlassMind",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Reasoning",
|
||||
"category": "Reasoning Engine",
|
||||
|
|
@ -740,122 +591,10 @@ TOOLS: dict[str, dict] = {
|
|||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'aider': {
|
||||
"name": "Aider",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "aider-chat"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "aider --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "AI pair programming assistant that works in your terminal.",
|
||||
"license": 'Apache-2.0',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'claude_code': {
|
||||
"name": "Claude Code",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "npm",
|
||||
"pkg": "@anthropic-ai/claude-code"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "claude --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Anthropic's CLI-based AI coding agent powered by Claude.",
|
||||
"license": 'Proprietary',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'codex': {
|
||||
"name": "Codex",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "npm",
|
||||
"pkg": "@openai/codex"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "codex --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "OpenAI's CLI-based AI coding agent powered by GPT.",
|
||||
"license": 'Proprietary',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'openhands': {
|
||||
"name": "OpenHands",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Coding Agent",
|
||||
"category": "AI Coding Agent",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "openhands-ai"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "openhands serve --port {port}",
|
||||
"default_port": 3000
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Open-source AI coding agent platform with web UI for autonomous software engineering.",
|
||||
"license": 'MIT',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'honcho': {
|
||||
"name": "Honcho",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Process Manager",
|
||||
"category": "Workflow Automation",
|
||||
|
|
@ -880,44 +619,16 @@ TOOLS: dict[str, dict] = {
|
|||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'eagle_eye': {
|
||||
"name": "Eagle Eye",
|
||||
"level": 5,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Monitoring",
|
||||
"category": "Observability",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/nicely-done/eagle_eye"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "eagle_eye --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "AI-powered observability and monitoring agent.",
|
||||
"license": 'Proprietary',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'graphify': {
|
||||
"name": "Graphify",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Graph Builder",
|
||||
"category": "AI Agent",
|
||||
"role": "Knowledge Graph Builder",
|
||||
"category": "Claude Code Skill",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/nicely-done/graphify"
|
||||
"type": "uv",
|
||||
"pkg": "graphifyy"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
|
|
@ -925,21 +636,31 @@ TOOLS: dict[str, dict] = {
|
|||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Graph-based knowledge and workflow visualization agent.",
|
||||
"license": 'Proprietary',
|
||||
"description": "Knowledge graph builder for code, docs, PDFs, and "
|
||||
"images. Installs as a Claude Code skill (type "
|
||||
"`/graphify .` in Claude Code) or runs standalone "
|
||||
"via CLI. Builds interactive graph.html, Obsidian "
|
||||
"vault, Wikipedia-style wiki, and persistent "
|
||||
"graph.json from any folder. MCP stdio server mode "
|
||||
"(`graphify --mcp`) lets other agents query the "
|
||||
"graph. Uses Claude vision by default; can be "
|
||||
"configured to use any OpenAI-compat endpoint "
|
||||
"(MeshLLM, LiteLLM, Ollama) for extraction. PyPI "
|
||||
"package is `graphifyy` (CLI is `graphify`).",
|
||||
"license": "MIT",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_mcp": True,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'headroom': {
|
||||
"name": "Headroom",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Resource Manager",
|
||||
"category": "Infrastructure",
|
||||
|
|
@ -967,7 +688,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'loop_engineering': {
|
||||
"name": "Loop Engineering",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Engineering Loop",
|
||||
"category": "Workflow Automation",
|
||||
|
|
@ -995,7 +716,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'nightshift': {
|
||||
"name": "Nightshift",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Night Ops",
|
||||
"category": "Workflow Automation",
|
||||
|
|
@ -1023,7 +744,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'opensandbox': {
|
||||
"name": "OpenSandbox",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Sandbox",
|
||||
"category": "Container Ops",
|
||||
|
|
@ -1051,7 +772,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'ponytail': {
|
||||
"name": "Ponytail",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "CI/CD",
|
||||
"category": "Workflow Automation",
|
||||
|
|
@ -1079,7 +800,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'promptops': {
|
||||
"name": "PromptOps",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Prompt Ops",
|
||||
"category": "AI Agent",
|
||||
|
|
@ -1107,7 +828,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'agent_reach': {
|
||||
"name": "Agent Reach",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Agent Discovery",
|
||||
"category": "Multi-Agent",
|
||||
|
|
@ -1135,7 +856,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'algory': {
|
||||
"name": "Algory",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Algorithm",
|
||||
"category": "AI Agent",
|
||||
|
|
@ -1163,7 +884,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'atlas_os': {
|
||||
"name": "Atlas OS",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "OS Framework",
|
||||
"category": "Agent OS",
|
||||
|
|
@ -1191,7 +912,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'career_ops': {
|
||||
"name": "Career Ops",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Career Ops",
|
||||
"category": "Workflow Automation",
|
||||
|
|
@ -1219,7 +940,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'container_tool': {
|
||||
"name": "Container Tool",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Containerization",
|
||||
"category": "Container Ops",
|
||||
|
|
@ -1247,7 +968,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'pm_skills': {
|
||||
"name": "PM Skills",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Project Management",
|
||||
"category": "Workflow Automation",
|
||||
|
|
@ -1275,7 +996,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'skillspector': {
|
||||
"name": "Skillspector",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Skill Inspector",
|
||||
"category": "AI Agent",
|
||||
|
|
@ -1303,7 +1024,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'spec_kit': {
|
||||
"name": "Spec Kit",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Specification",
|
||||
"category": "AI Agent",
|
||||
|
|
@ -1331,7 +1052,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'wayland_ai': {
|
||||
"name": "Wayland AI",
|
||||
"level": 5,
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Agent Orchestrator",
|
||||
"category": "AI Agent",
|
||||
|
|
@ -1358,4 +1079,35 @@ TOOLS: dict[str, dict] = {
|
|||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'letta': {
|
||||
"name": "Letta",
|
||||
"level": 6,
|
||||
"layer": "Orchestrators",
|
||||
"role": "Agent Framework",
|
||||
"category": "Agent Framework",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "letta"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "letta server --port {port}",
|
||||
"default_port": 8283
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Stateful agent framework (MemGPT) with persistent memory, "
|
||||
"self-editing agents, and a REST API server for agent "
|
||||
"management.",
|
||||
"license": "Apache-2.0",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,187 @@
|
|||
"""Registry entries for the Routing layer (L5).
|
||||
|
||||
Restored layer (v3.1.1b): model gateways, LLM proxies, request routers,
|
||||
mesh transport, and the model-routing tier that sits between the Engines
|
||||
(who serve weights) and the Orchestrators (who build agent workflows on
|
||||
top of a single OpenAI-compatible endpoint). This re-unites the old
|
||||
13-layer model's "AI Endpoints" tier (LiteLLM, model routers, API
|
||||
gateways) that had been folded into Orchestrators during the 13-to-10
|
||||
reorg, plus the mesh-aware clients that ride on it.
|
||||
|
||||
This module is consumed by :mod:`ai_lsc.registry.loader`.
|
||||
"""
|
||||
|
||||
TOOLS: dict[str, dict] = {
|
||||
'litellm': {
|
||||
"name": "LiteLLM Proxy",
|
||||
"level": 5,
|
||||
"layer": "Routing",
|
||||
"role": "API Gateway",
|
||||
"category": "Proxy",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "litellm"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "litellm --port {port}",
|
||||
"default_port": 4000
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Call 100+ LLMs using the OpenAI format.",
|
||||
"license": 'MIT',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'9router_proxy': {
|
||||
"name": "9Router Proxy",
|
||||
"level": 5,
|
||||
"layer": "Routing",
|
||||
"role": "API Gateway",
|
||||
"category": "LLM Router",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/nicely-done/9router"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "cd {tools_root}/9router && python3 main.py --port {port}",
|
||||
"default_port": 4001
|
||||
},
|
||||
"deps": [
|
||||
"ollama"
|
||||
],
|
||||
"description": "Intelligent LLM request router and load balancer.",
|
||||
"license": 'MIT',
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
# ---- meshllm ----------------------------------------------------------------
|
||||
# MeshLLM is its own native binary — NOT a LiteLLM re-skin. It pools GPUs
|
||||
# and memory across machines, exposes one OpenAI-compat API at :9337, and
|
||||
# has a web console at :3131. Install via the official curl installer.
|
||||
'meshllm': {
|
||||
"name": "MeshLLM",
|
||||
"level": 5,
|
||||
"layer": "Routing",
|
||||
"role": "API Gateway",
|
||||
"category": "LLM Mesh",
|
||||
"installer": {
|
||||
"type": "script",
|
||||
"cmd": "mkdir -p {tools_root}/meshllm/bin && curl -fsSL https://raw.githubusercontent.com/Mesh-LLM/mesh-llm/main/install.sh | MESH_LLM_INSTALL_DIR={tools_root}/meshllm/bin bash"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "mesh-llm serve --auto --port {port}",
|
||||
"default_port": 9337
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Native binary that pools GPUs and memory across "
|
||||
"machines and exposes the result as one OpenAI-"
|
||||
"compatible API at http://localhost:9337/v1. Start "
|
||||
"one node, add more nodes later — the mesh decides "
|
||||
"whether a model runs locally, routes to a peer, or "
|
||||
"uses Skippy stage splits for models too large for "
|
||||
"one box. Web console on :3131. Has subcommands "
|
||||
"(`mesh-llm goose`, `mesh-llm opencode`, `mesh-llm "
|
||||
"claude`, `mesh-llm pi`) that wrap other coding agents "
|
||||
"to use the mesh. QUIC-encrypted peer transport via "
|
||||
"Iroh relays. NOT a LiteLLM derivative — distinct "
|
||||
"project at https://github.com/Mesh-LLM/mesh-llm.",
|
||||
"license": "MIT",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'dify': {
|
||||
"name": "Dify",
|
||||
"level": 5,
|
||||
"layer": "Routing",
|
||||
"role": "Pipeline Orchestrator",
|
||||
"category": "Pipeline",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/langgenius/dify.git"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "cd {tools_root}/dify/api && poetry run flask run --host 0.0.0.0 --port={port}",
|
||||
"default_port": 5001
|
||||
},
|
||||
"deps": [
|
||||
"postgresql",
|
||||
"redis",
|
||||
"python",
|
||||
"nodejs"
|
||||
],
|
||||
"description": "LLM application development platform (native install). Requires Poetry, Node.js 18+, FFmpeg. Backend (Flask) + Celery worker + Next.js frontend run as separate services.",
|
||||
"license": 'Dify-OSL',
|
||||
"flags": {
|
||||
"has_cli": False,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
'picode': {
|
||||
"name": "PiCode",
|
||||
"level": 5,
|
||||
"layer": "Routing",
|
||||
"role": "Coding Agent",
|
||||
"category": "Mesh Client",
|
||||
"installer": {
|
||||
"type": "git",
|
||||
"pkg": "https://github.com/jasonjmcghee/picode.git"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "picode --version",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [
|
||||
"ollama"
|
||||
],
|
||||
"description": "Local code-tinker agent. Speaks OpenAI-compat — point "
|
||||
"OPENAI_API_BASE at the mesh (localhost:9337 for MeshLLM, "
|
||||
"localhost:4000 for LiteLLM) or directly at Ollama "
|
||||
"(localhost:11434/v1).",
|
||||
"license": "MIT",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": False,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
}
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
"""Registry entries for the Security layer (L6).
|
||||
"""Registry entries for the Security layer (L7).
|
||||
|
||||
Contains identity management, secrets management, container scanning,
|
||||
intrusion prevention, antivirus, and policy enforcement tools for
|
||||
|
|
@ -11,7 +11,7 @@ This module is consumed by
|
|||
TOOLS: dict[str, dict] = {
|
||||
'keycloak': {
|
||||
"name": "Keycloak",
|
||||
"level": 6,
|
||||
"level": 7,
|
||||
"layer": "Security",
|
||||
"role": "Identity",
|
||||
"category": "Auth",
|
||||
|
|
@ -39,7 +39,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'vault': {
|
||||
"name": "HashiCorp Vault",
|
||||
"level": 6,
|
||||
"level": 7,
|
||||
"layer": "Security",
|
||||
"role": "Secrets",
|
||||
"category": "Secrets Management",
|
||||
|
|
@ -67,7 +67,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'trivy': {
|
||||
"name": "Trivy",
|
||||
"level": 6,
|
||||
"level": 7,
|
||||
"layer": "Security",
|
||||
"role": "Scanner",
|
||||
"category": "Container Security",
|
||||
|
|
@ -95,7 +95,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'fail2ban': {
|
||||
"name": "Fail2Ban",
|
||||
"level": 6,
|
||||
"level": 7,
|
||||
"layer": "Security",
|
||||
"role": "IDS",
|
||||
"category": "Intrusion Prevention",
|
||||
|
|
@ -123,7 +123,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'clamav': {
|
||||
"name": "ClamAV",
|
||||
"level": 6,
|
||||
"level": 7,
|
||||
"layer": "Security",
|
||||
"role": "Scanner",
|
||||
"category": "Antivirus",
|
||||
|
|
@ -151,7 +151,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'opa': {
|
||||
"name": "Open Policy Agent",
|
||||
"level": 6,
|
||||
"level": 7,
|
||||
"layer": "Security",
|
||||
"role": "Policy",
|
||||
"category": "Policy Engine",
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
"""Registry entries for the User Interfaces layer (L8).
|
||||
"""Registry entries for the User Interfaces layer (L9).
|
||||
|
||||
Contains frontends, dashboards, chat UIs, image generation interfaces,
|
||||
sensory interfaces (vision, speech, voice), and knowledge graph tools.
|
||||
|
|
@ -10,7 +10,7 @@ This module is consumed by
|
|||
TOOLS: dict[str, dict] = {
|
||||
'openwebui': {
|
||||
"name": "Open WebUI",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Chat Frontend",
|
||||
|
|
@ -40,7 +40,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'anythingllm': {
|
||||
"name": "AnythingLLM",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Chat",
|
||||
|
|
@ -68,7 +68,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'librechat': {
|
||||
"name": "LibreChat",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Chat Agent Platform",
|
||||
|
|
@ -105,7 +105,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'flowise': {
|
||||
"name": "Flowise",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Workflow",
|
||||
|
|
@ -133,7 +133,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'invokeai': {
|
||||
"name": "InvokeAI",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Image Generation",
|
||||
|
|
@ -163,7 +163,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'forge': {
|
||||
"name": "Forge (A1111)",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Image Generation",
|
||||
|
|
@ -193,7 +193,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'dashy': {
|
||||
"name": "Dashy",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Homepage",
|
||||
|
|
@ -221,7 +221,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'obsidian': {
|
||||
"name": "Obsidian",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Knowledge Graph Notes",
|
||||
|
|
@ -249,7 +249,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'hermes': {
|
||||
"name": "Hermes",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Ecosystem Dashboard",
|
||||
|
|
@ -279,7 +279,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'hermes_desktop': {
|
||||
"name": "Hermes Desktop",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Desktop Agent",
|
||||
|
|
@ -309,7 +309,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'hermes_dashboard_page': {
|
||||
"name": "Hermes Dashboard",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Dashboard",
|
||||
|
|
@ -339,7 +339,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'local_llm_launcher': {
|
||||
"name": "Local LLM Launcher",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "LLM GUI",
|
||||
|
|
@ -369,7 +369,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'openjarvis': {
|
||||
"name": "OpenJarvis",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Central Intelligence",
|
||||
"category": "AI Assistant Platform",
|
||||
|
|
@ -408,7 +408,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'deep_eye': {
|
||||
"name": "Deep Eye",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Vision",
|
||||
"category": "Computer Vision",
|
||||
|
|
@ -438,7 +438,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'parakeet': {
|
||||
"name": "Parakeet.cpp",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Senses",
|
||||
"category": "Speech Recognition",
|
||||
|
|
@ -468,7 +468,7 @@ TOOLS: dict[str, dict] = {
|
|||
},
|
||||
'luxtts': {
|
||||
"name": "LuxTTS",
|
||||
"level": 8,
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Voice",
|
||||
"category": "Text-to-Speech",
|
||||
|
|
@ -494,4 +494,71 @@ TOOLS: dict[str, dict] = {
|
|||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'jan': {
|
||||
"name": "Jan",
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "LLM GUI",
|
||||
"installer": {
|
||||
"type": "npm",
|
||||
"pkg": "@janhq/jan"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "desktop",
|
||||
"cmd": "jan",
|
||||
"default_port": None
|
||||
},
|
||||
"deps": [],
|
||||
"description": "Offline-capable ChatGPT-alternative desktop app with a "
|
||||
"built-in llama.cpp engine and an OpenAI-compatible local "
|
||||
"API server at 127.0.0.1:1337.",
|
||||
"license": "AGPL-3.0",
|
||||
"flags": {
|
||||
"has_cli": True,
|
||||
"has_gui": True,
|
||||
"has_web": False,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
'hermes_webui': {
|
||||
"name": "Hermes WebUI",
|
||||
"level": 9,
|
||||
"layer": "User Interfaces",
|
||||
"role": "Face",
|
||||
"category": "Chat Frontend",
|
||||
"installer": {
|
||||
"type": "uv",
|
||||
"pkg": "open-webui"
|
||||
},
|
||||
"launcher": {
|
||||
"type": "tmux",
|
||||
"cmd": "open-webui serve --port {port} --data-dir {workspaces_root}/hermes-webui --env OLLAMA_BASE_URL=http://localhost:17051",
|
||||
"default_port": 8081
|
||||
},
|
||||
"deps": [
|
||||
"hermes_agent",
|
||||
"ollama"
|
||||
],
|
||||
"description": "Hermes-themed Open-WebUI instance running on a "
|
||||
"separate port (8081) with its own data volume so "
|
||||
"users, prompts, and RAG corpus don't collide with "
|
||||
"the general openwebui instance. Backend points at "
|
||||
"hermes_agent (17051) instead of Ollama direct, so "
|
||||
"every conversation goes through the Hermes runtime.",
|
||||
"license": "MIT",
|
||||
"flags": {
|
||||
"has_cli": False,
|
||||
"has_gui": False,
|
||||
"has_web": True,
|
||||
"is_ollama": False,
|
||||
"is_passive": False,
|
||||
"is_mcp": False,
|
||||
"is_skills_collection": False
|
||||
}
|
||||
},
|
||||
|
||||
}
|
||||
|
|
@ -8,7 +8,7 @@ tool's ``license`` SPDX ID against three sources:
|
|||
tool_id is blocked, raise :class:`LicenseBlocked` immediately. No
|
||||
dialog, no acceptance, no install.
|
||||
|
||||
2. **Auto-approval registry** (``config/license_approvals.json``) — a
|
||||
2. **Auto-approval registry** (``configs/license_approvals.json``) — a
|
||||
user-editable list of OSI-approved SPDX IDs that have been
|
||||
pre-approved. If the tool's license is in this list, install
|
||||
proceeds without a dialog. Only OSI-approved licenses can appear
|
||||
|
|
@ -28,7 +28,7 @@ the install.
|
|||
|
||||
Files managed
|
||||
-------------
|
||||
* ``config/license_approvals.json`` — ``{"licenses": ["MIT", "Apache-2.0"], "updated_at": "..."}``
|
||||
* ``configs/license_approvals.json`` — ``{"licenses": ["MIT", "Apache-2.0"], "updated_at": "..."}``
|
||||
* ``config/license_acceptances.json`` — ``{"ollama": {"spdx": "MIT", "accepted_at": "...", "via": "auto-approved"}, ...}``
|
||||
"""
|
||||
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ Defines the three license categories the AI-LSC license gate recognizes:
|
|||
a prominent disclaimer warning about ToS restrictions before
|
||||
install. Example: Claude Code (Anthropic ToS).
|
||||
|
||||
The license-approvals registry (``config/license_approvals.json``) is
|
||||
The license-approvals registry (``configs/license_approvals.json``) is
|
||||
a user-editable list of SPDX IDs that have been pre-approved. Only
|
||||
OSI-approved licenses can appear in this list — the
|
||||
:func:`LicenseGate.add_auto_approval` method rejects attempts to
|
||||
|
|
@ -224,6 +224,71 @@ CATALOG: dict[str, LicenseInfo] = {
|
|||
"commercial use and modification."
|
||||
),
|
||||
),
|
||||
"LGPL-2.1": LicenseInfo(
|
||||
spdx="LGPL-2.1",
|
||||
name="GNU Lesser General Public License v2.1",
|
||||
category=Category.OSI,
|
||||
url="https://opensource.org/licenses/LGPL-2.1",
|
||||
summary=(
|
||||
"Weak copyleft license allowing linking from proprietary "
|
||||
"software, but modifications to the LGPL-licensed code itself "
|
||||
"must be shared under LGPL. Used by strace, LXC, libvirt."
|
||||
),
|
||||
),
|
||||
"GPL-1.0": LicenseInfo(
|
||||
spdx="GPL-1.0",
|
||||
name="GNU General Public License v1.0",
|
||||
category=Category.OSI,
|
||||
url="https://opensource.org/licenses/GPL-1.0",
|
||||
summary=(
|
||||
"The original 1989 GNU copyleft license. Superseded by "
|
||||
"GPL-2.0/GPL-3.0 but still applied by some legacy packages "
|
||||
"(e.g. Perl)."
|
||||
),
|
||||
),
|
||||
"PHP-3.01": LicenseInfo(
|
||||
spdx="PHP-3.01",
|
||||
name="PHP License v3.01",
|
||||
category=Category.OSI,
|
||||
url="https://opensource.org/licenses/PHP-3.01",
|
||||
summary=(
|
||||
"Permissive, PHP-specific license governing the PHP "
|
||||
"interpreter. Allows commercial use and modification with "
|
||||
"copyright notice; not derivatively-named redistribution."
|
||||
),
|
||||
),
|
||||
"Ruby": LicenseInfo(
|
||||
spdx="Ruby",
|
||||
name="Ruby License",
|
||||
category=Category.OSI,
|
||||
url="https://www.ruby-lang.org/en/about/license.txt",
|
||||
summary=(
|
||||
"Permissive dual license (Ruby's own terms or BSD-2-Clause) "
|
||||
"governing the Ruby interpreter and standard library."
|
||||
),
|
||||
),
|
||||
"MirOS": LicenseInfo(
|
||||
spdx="MirOS",
|
||||
name="MirOS License",
|
||||
category=Category.OSI,
|
||||
url="https://opensource.org/licenses/MirOS",
|
||||
summary=(
|
||||
"Permissive BSD-style license with attribution requirement, "
|
||||
"used by MirBSD and mksh."
|
||||
),
|
||||
),
|
||||
"MIT/Apache-2.0": LicenseInfo(
|
||||
spdx="MIT/Apache-2.0",
|
||||
name="MIT OR Apache-2.0 (dual license)",
|
||||
category=Category.OSI,
|
||||
url="https://opensource.org/licenses/Apache-2.0",
|
||||
summary=(
|
||||
"Dual licensing under MIT OR Apache-2.0 at the recipient's "
|
||||
"choice — the standard convention for Rust crates (and tools "
|
||||
"like uv). Either grant applies; Apache-2.0 adds a patent "
|
||||
"grant."
|
||||
),
|
||||
),
|
||||
|
||||
# ── Source-available / fair-code (NOT auto-approvable) ───────────
|
||||
"BSL-1.1": LicenseInfo(
|
||||
|
|
|
|||
|
|
@ -133,6 +133,10 @@ class RegistryManager:
|
|||
).append((t_id, meta))
|
||||
return dict(sorted(layers.items()))
|
||||
|
||||
# Host-level prerequisites referenced in deps but not managed as
|
||||
# registry tools (mirrors _system_deps in stack/connections.py).
|
||||
SYSTEM_DEPS: frozenset[str] = frozenset({"kubectl", "java"})
|
||||
|
||||
def check_dependencies(
|
||||
self, selected: list[str],
|
||||
) -> list[str]:
|
||||
|
|
@ -142,4 +146,7 @@ class RegistryManager:
|
|||
for t in selected
|
||||
if not t.startswith("skill:")
|
||||
))
|
||||
return list({d for d in all_deps if d not in selected})
|
||||
return list({
|
||||
d for d in all_deps
|
||||
if d not in selected and d not in self.SYSTEM_DEPS
|
||||
})
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
{
|
||||
"id": "local-coder-mesh",
|
||||
"name": "Local Coder Mesh — All-Ollama Coding Stack",
|
||||
"description": "Every coding agent in this stack speaks OpenAI-compat and points at localhost Ollama, MeshLLM, or LiteLLM. Includes the four tools missing from the existing hermes-ai-coder-stack template: PiCode, MeshLLM (the native GPU-pooling mesh binary from Mesh-LLM/mesh-llm, NOT a LiteLLM re-skin), Hermes WebUI, ZCoder. Also includes Graphify for knowledge-graph-aware coding. Use this template when you want the complete local coding-agent fleet wired to one Ollama instance with no cloud calls.",
|
||||
"version": "1.1",
|
||||
"author": "ai-lsc-template-pack",
|
||||
"tags": ["coding", "agent", "local-first", "ollama", "mesh", "hermes", "native", "knowledge-graph"],
|
||||
"endpoints": {
|
||||
"ollama_base": "http://localhost:11434/v1",
|
||||
"litellm_base": "http://localhost:4000",
|
||||
"meshllm_api": "http://localhost:9337/v1",
|
||||
"meshllm_console": "http://localhost:3131",
|
||||
"openwebui": "http://localhost:3000",
|
||||
"hermes_webui": "http://localhost:8081",
|
||||
"hermes_agent": "http://localhost:17051",
|
||||
"hermes_dashboard": "http://localhost:17050"
|
||||
},
|
||||
"tools": [
|
||||
"ollama",
|
||||
"litellm",
|
||||
"meshllm",
|
||||
"picode",
|
||||
"aider",
|
||||
"odysseus",
|
||||
"opencode",
|
||||
"zcoder",
|
||||
"graphify",
|
||||
"hermes",
|
||||
"hermes_agent",
|
||||
"hermes_webui",
|
||||
"hermes_desktop",
|
||||
"openwebui",
|
||||
"ripgrep",
|
||||
"fd",
|
||||
"tree_sitter"
|
||||
],
|
||||
"notes": {
|
||||
"philosophy": "One Ollama, one mesh (MeshLLM for multi-node pooling, LiteLLM for proxy routing), every coding agent speaks OpenAI-compat to localhost. No cloud calls, no containers in the dev path — ai-lsc's Podman/Docker/LXC/Firecracker export is reserved for total-stack export only.",
|
||||
"meshllm_clarification": "MeshLLM is the native binary from https://github.com/Mesh-LLM/mesh-llm. It pools GPUs and memory across machines, exposes one OpenAI-compat API at :9337, and has a web console at :3131. It is NOT a LiteLLM re-skin — the two tools coexist in this template: MeshLLM for mesh-pooled multi-node inference, LiteLLM for proxy routing to multiple backends. Coding agents prefer MeshLLM (:9337) first, fall back to LiteLLM (:4000), then Ollama direct (:11434).",
|
||||
"graphify_role": "Graphify (https://github.com/Graphify-Labs/graphify) builds knowledge graphs from code, docs, PDFs, and images. It runs as a CLI (pip install graphifyy), a Claude Code skill (/graphify .), or an MCP stdio server (graphify --mcp). Other agents in this stack can query graphify's MCP server to navigate the codebase graph without re-reading source files — 71.5x fewer tokens per query vs reading raw files. Uses Claude vision by default but can be configured to use MeshLLM/LiteLLM/Ollama for fully-local extraction.",
|
||||
"topology": "ollama (11434) <- litellm (4000, proxy mesh) + meshllm (9337, native mesh) <- {picode, aider, odysseus, opencode, zcoder}. graphify builds graphs from the codebase and exposes an MCP server that the coding agents query. hermes_webui + hermes_desktop talk to hermes (17050) -> hermes_agent (17051) -> ollama. openwebui talks to ollama directly. ripgrep + fd + tree_sitter are passive filesystem tools used by the coding agents for repo-map / symbol navigation.",
|
||||
"recommended_models": "qwen2.5-coder:7b (fast coding), qwen2.5-coder:32b (heavy coding), hermes3:8b (Hermes stack), nomic-embed-text (openwebui RAG, corpus indexing, graphify embeddings). MeshLLM auto-downloads a suitable model on first `serve --auto` if none specified.",
|
||||
"install_order": "1) ollama (already running). 2) litellm (proxy mesh) + meshllm (native mesh binary via curl installer). 3) hermes_agent -> hermes -> hermes_webui + hermes_desktop. 4) aider, picode, odysseus, opencode, zcoder (CLI agents, all native venvs/uv/npm). 5) graphify (uv install graphifyy). 6) openwebui (native uv). 7) ripgrep, fd, tree_sitter (pacman).",
|
||||
"native_only_policy": "Every tool in this template installs natively into /mnt/AI/runtime/<tool_id>/ (venv/uv) or via pacman/AUR/curl-script. ai-lsc's container export feature is intentionally not used at install time — it's reserved for total-stack deployment exports via the Stack Editor.",
|
||||
"non_destructive_policy": "Applying this template never removes existing installs. ai-lsc's InstallerManager.preflight() detects existing installs and skips them unless force=True.",
|
||||
"missing_tools_added_by_template_pack": "picode, meshllm, hermes_webui, zcoder — these four require the registry + wirings patches in this template pack before the template will compile. graphify already exists in the registry but needs the graphify-update.diff and graphify-wiring-update.diff patches to fix its URL (Graphify-Labs/graphify), license (MIT), role (Knowledge Graph Builder), installer (uv graphifyy), and wiring (MCP server + LLM backend connections). See MERGE-GUIDE.md."
|
||||
}
|
||||
}
|
||||
|
|
@ -632,7 +632,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="heretic",
|
||||
layer="GPU Runtimes",
|
||||
layer="Engines",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
|
|
@ -656,7 +656,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="unsloth",
|
||||
layer="GPU Runtimes",
|
||||
layer="Development Environment",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
|
|
@ -771,7 +771,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="vllm",
|
||||
layer="Orchestrators",
|
||||
layer="Engines",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="openai_api",
|
||||
|
|
@ -931,12 +931,12 @@ _reg(StackWiring(
|
|||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
# L6: AI Endpoints
|
||||
# L6: AI Endpoints (→ the restored "Routing" layer in the 11-layer taxonomy)
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="litellm",
|
||||
layer="Orchestrators",
|
||||
layer="Routing",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="openai_api",
|
||||
|
|
@ -1015,7 +1015,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="9router_proxy",
|
||||
layer="Orchestrators",
|
||||
layer="Routing",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="router_api",
|
||||
|
|
@ -1041,7 +1041,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="deep_eye",
|
||||
layer="Orchestrators",
|
||||
layer="User Interfaces",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="deep_eye_api",
|
||||
|
|
@ -1066,7 +1066,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="luxtts",
|
||||
layer="Orchestrators",
|
||||
layer="User Interfaces",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="tts_api",
|
||||
|
|
@ -1234,7 +1234,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="parakeet",
|
||||
layer="Knowledge Management",
|
||||
layer="User Interfaces",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="parakeet_api",
|
||||
|
|
@ -1306,7 +1306,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="fabric",
|
||||
layer="Knowledge Management",
|
||||
layer="Orchestrators",
|
||||
interfaces=[],
|
||||
connections=[],
|
||||
context=EngineeringContext(
|
||||
|
|
@ -1408,7 +1408,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="n8n",
|
||||
layer="DevOps",
|
||||
layer="Orchestrators",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="n8n_api",
|
||||
|
|
@ -1454,7 +1454,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="nightshift",
|
||||
layer="DevOps",
|
||||
layer="Orchestrators",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="nightshift_api",
|
||||
|
|
@ -1470,7 +1470,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="hivemind",
|
||||
layer="DevOps",
|
||||
layer="Orchestrators",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="hivemind_api",
|
||||
|
|
@ -1495,7 +1495,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="hermes_agent",
|
||||
layer="DevOps",
|
||||
layer="Orchestrators",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="hermes_agent_api",
|
||||
|
|
@ -1546,7 +1546,7 @@ _reg(StackWiring(
|
|||
# Passive / CLI-only tools in L8 — no interfaces or network connections
|
||||
for _tid in [
|
||||
"agent_reach", "agentic_os", "aider", "algory", "atlas_os",
|
||||
"claude_code", "eagle_eye", "graphify", "headroom", "honcho",
|
||||
"claude_code", "eagle_eye", "headroom", "honcho",
|
||||
"loop_engineering", "mcp_drift_state_tracker", "nvidia_agent_skills",
|
||||
"ponytail", "promptops", "skillspector", "spec_kit", "synapscli",
|
||||
"wayland_ai",
|
||||
|
|
@ -1595,7 +1595,7 @@ for _tid in [
|
|||
# agno has a web interface but no deps in defaults
|
||||
_reg(StackWiring(
|
||||
tool_id="agno",
|
||||
layer="DevOps",
|
||||
layer="Orchestrators",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="agno_web",
|
||||
|
|
@ -1787,7 +1787,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="hermes_dashboard_page",
|
||||
layer="Observability",
|
||||
layer="User Interfaces",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="dashboard_api",
|
||||
|
|
@ -1837,7 +1837,7 @@ _reg(StackWiring(
|
|||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
# L10: Intelligent Routing
|
||||
# L10: Intelligent Routing (folded into "Orchestrators" in the 11-layer taxonomy)
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
|
||||
_reg(StackWiring(
|
||||
|
|
@ -1892,7 +1892,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="mnemo_cortex",
|
||||
layer="Orchestrators",
|
||||
layer="Knowledge Management",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="mnemo_cortex_api",
|
||||
|
|
@ -1942,7 +1942,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="everos_memory",
|
||||
layer="Orchestrators",
|
||||
layer="Knowledge Management",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="everos_memory_api",
|
||||
|
|
@ -2049,7 +2049,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="dify",
|
||||
layer="User Interfaces",
|
||||
layer="Routing",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="dify_web",
|
||||
|
|
@ -2271,7 +2271,7 @@ _reg(StackWiring(
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="langflow",
|
||||
layer="User Interfaces",
|
||||
layer="Orchestrators",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="langflow_web",
|
||||
|
|
@ -2381,7 +2381,7 @@ for _tid in ["hermes_desktop", "local_llm_launcher"]:
|
|||
|
||||
_reg(StackWiring(
|
||||
tool_id="opensandbox",
|
||||
layer="DevOps",
|
||||
layer="Orchestrators",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="opensandbox_web",
|
||||
|
|
@ -2529,6 +2529,378 @@ for _tid in [
|
|||
))
|
||||
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
# L5 additions: terminal coding agents (registry expansion pass)
|
||||
# (coding agents now live in DevOps L10; meshllm/picode in Routing L5)
|
||||
# opencode / gemini_cli / qwen_code / goose / codex — all multi-provider
|
||||
# CLI agents that consume an OpenAI-compatible endpoint. Wired to both
|
||||
# Ollama (direct) and LiteLLM (proxied) so staging either backend
|
||||
# prevents orphan-flagging in the Pipeline Ticker.
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
|
||||
_CODING_AGENT_CTX = EngineeringContext(
|
||||
decision="Terminal coding agents consume an OpenAI-compatible "
|
||||
"chat endpoint for LLM inference.",
|
||||
observation="Every modern coding agent (opencode, Gemini CLI, "
|
||||
"Qwen Code, goose, Codex) speaks the OpenAI wire "
|
||||
"format for at least one provider slot.",
|
||||
alternatives="Native provider SDKs (Anthropic, Google) or a "
|
||||
"custom proxy.",
|
||||
constraints="The endpoint must be reachable and expose "
|
||||
"/v1/chat/completions.",
|
||||
reasoning="OpenAI-compat is the de-facto agent ↔ LLM contract; "
|
||||
"pointing the agents at localhost keeps inference "
|
||||
"local per AI-LSC policy.",
|
||||
verification="Set the agent's base-URL env var to "
|
||||
"http://127.0.0.1:11434/v1 and list models.",
|
||||
lineage="Mirrors the OpenAI API spec; adopted by Ollama, vLLM, "
|
||||
"SGLang, LiteLLM.",
|
||||
assumptions="At least one model is served by the target backend.",
|
||||
)
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="opencode",
|
||||
layer="DevOps",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Local LLM inference for terminal coding sessions.",
|
||||
config_key="OPENAI_BASE_URL (ollama provider, opencode.json)",
|
||||
required=False,
|
||||
context=_CODING_AGENT_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="litellm",
|
||||
interface_id="openai_api",
|
||||
purpose="Multi-model routing via LiteLLM proxy.",
|
||||
config_key="OPENAI_BASE_URL",
|
||||
required=False,
|
||||
context=_LITELLM_CONSUMER_CTX,
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="gemini_cli",
|
||||
layer="DevOps",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Local LLM inference via the openai-compat provider.",
|
||||
config_key="OPENAI_BASE_URL",
|
||||
required=False,
|
||||
context=_CODING_AGENT_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="litellm",
|
||||
interface_id="openai_api",
|
||||
purpose="Multi-model routing via LiteLLM proxy.",
|
||||
config_key="OPENAI_BASE_URL",
|
||||
required=False,
|
||||
context=_LITELLM_CONSUMER_CTX,
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="qwen_code",
|
||||
layer="DevOps",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Local Qwen model inference via openai-compat provider.",
|
||||
config_key="OPENAI_BASE_URL",
|
||||
required=False,
|
||||
context=_CODING_AGENT_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="litellm",
|
||||
interface_id="openai_api",
|
||||
purpose="Multi-model routing via LiteLLM proxy.",
|
||||
config_key="OPENAI_BASE_URL",
|
||||
required=False,
|
||||
context=_LITELLM_CONSUMER_CTX,
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="goose",
|
||||
layer="DevOps",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Local LLM backend for goose sessions and MCP extensions.",
|
||||
config_key="GOOSE_PROVIDER / OPENAI_BASE_URL",
|
||||
required=False,
|
||||
context=_CODING_AGENT_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="litellm",
|
||||
interface_id="openai_api",
|
||||
purpose="Multi-model routing via LiteLLM proxy.",
|
||||
config_key="OPENAI_BASE_URL",
|
||||
required=False,
|
||||
context=_LITELLM_CONSUMER_CTX,
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
# codex existed in the registry since v3.1 but had no STACK_WIRINGS
|
||||
# entry, so it was flagged as an orphan by the Pipeline Ticker whenever
|
||||
# it was staged. This wiring closes that gap.
|
||||
_reg(StackWiring(
|
||||
tool_id="codex",
|
||||
layer="DevOps",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Local LLM inference for terminal coding sessions.",
|
||||
config_key="OPENAI_BASE_URL",
|
||||
required=False,
|
||||
context=_CODING_AGENT_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="litellm",
|
||||
interface_id="openai_api",
|
||||
purpose="Multi-model routing via LiteLLM proxy.",
|
||||
config_key="OPENAI_BASE_URL",
|
||||
required=False,
|
||||
context=_LITELLM_CONSUMER_CTX,
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="letta",
|
||||
layer="Orchestrators",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="http_api",
|
||||
protocol="HTTP",
|
||||
api_format="REST",
|
||||
port=8283,
|
||||
auth="Optional password (LETTA_SERVER_PASSWORD)",
|
||||
description="Letta agent server REST API — create/state/"
|
||||
"message agents with persistent memory.",
|
||||
context=EngineeringContext(
|
||||
decision="Expose the Letta server REST API as its "
|
||||
"primary interface.",
|
||||
observation="Letta (formerly MemGPT) manages stateful "
|
||||
"agents whose memory/context survives across "
|
||||
"sessions; the REST API is the management "
|
||||
"surface.",
|
||||
alternatives="Python SDK in-process, or Letta Cloud "
|
||||
"(SaaS — excluded by policy).",
|
||||
constraints="Requires a database — SQLite by default, "
|
||||
"PostgreSQL recommended for persistence.",
|
||||
reasoning="A local agent-memory server complements "
|
||||
"stateless coding agents in the stack.",
|
||||
verification="curl http://localhost:8283/v1/agents",
|
||||
lineage="Letta — https://docs.letta.com/",
|
||||
),
|
||||
),
|
||||
],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="LLM inference for agent reasoning and memory operations.",
|
||||
config_key="LETTA_INFERENCE_BASE_URL / model config",
|
||||
required=False,
|
||||
context=_OLLAMA_CONSUMER_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="postgresql",
|
||||
interface_id="postgresql",
|
||||
purpose="Persistent agent/state storage (SQLite fallback).",
|
||||
config_key="LETTA_PG_URI",
|
||||
required=False,
|
||||
context=EngineeringContext(
|
||||
decision="PostgreSQL as the optional durable backend for "
|
||||
"Letta's state store.",
|
||||
observation="Letta defaults to SQLite; PostgreSQL is "
|
||||
"recommended for multi-user / long-lived "
|
||||
"deployments.",
|
||||
alternatives="Built-in SQLite (zero-config).",
|
||||
reasoning="Sharing one PostgreSQL instance with other "
|
||||
"stack tools keeps state management uniform.",
|
||||
verification="letta server with LETTA_PG_URI set; "
|
||||
"inspect created tables.",
|
||||
lineage="Letta — https://docs.letta.com/",
|
||||
),
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="sglang",
|
||||
layer="Engines",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="openai_api",
|
||||
protocol="HTTP",
|
||||
api_format="OpenAI",
|
||||
port=30000,
|
||||
base_path="/v1",
|
||||
auth="None (or API key if configured)",
|
||||
description="SGLang OpenAI-compatible API: /v1/chat/completions, "
|
||||
"/v1/models, /v1/embeddings. High-throughput serving "
|
||||
"with RadixAttention prefix caching.",
|
||||
context=EngineeringContext(
|
||||
decision="Expose SGLang's OpenAI-compatible API as its "
|
||||
"primary interface.",
|
||||
observation="SGLang rivals vLLM on throughput for "
|
||||
"many-workload serving, and prefix caching "
|
||||
"benefits agentic loops that resend context.",
|
||||
alternatives="vLLM (port 8000), Ollama, llama.cpp server.",
|
||||
constraints="Requires a CUDA GPU with sufficient VRAM for "
|
||||
"the target model.",
|
||||
reasoning="OpenAI API compatibility lets every consumer in "
|
||||
"the stack use SGLang without code changes.",
|
||||
verification="curl http://localhost:30000/v1/models",
|
||||
lineage="SGLang — https://docs.sglang.ai/",
|
||||
),
|
||||
),
|
||||
],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="cuda",
|
||||
interface_id="cuda_driver",
|
||||
purpose="GPU-accelerated inference kernels.",
|
||||
config_key="CUDA_VISIBLE_DEVICES",
|
||||
required=True,
|
||||
context=EngineeringContext(
|
||||
decision="SGLang requires CUDA for its serving kernels.",
|
||||
observation="SGLang's throughput comes from custom CUDA "
|
||||
"kernels and RadixAttention.",
|
||||
reasoning="SGLang without a GPU has no advantage over "
|
||||
"llama.cpp on CPU.",
|
||||
verification="nvidia-smi confirms driver and GPU availability.",
|
||||
lineage="SGLang — https://docs.sglang.ai/",
|
||||
),
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
# L8 / L10 / L3 additions: jan, mem0, tinygrad
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="jan",
|
||||
layer="User Interfaces",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="openai_api",
|
||||
protocol="HTTP",
|
||||
api_format="OpenAI",
|
||||
port=1337,
|
||||
base_path="/v1",
|
||||
auth="None (local only)",
|
||||
description="Jan's built-in OpenAI-compatible local API server "
|
||||
"powered by its bundled llama.cpp engine — a "
|
||||
"drop-in replacement for cloud APIs.",
|
||||
context=EngineeringContext(
|
||||
decision="Expose Jan's local API server as its wiring "
|
||||
"interface.",
|
||||
observation="Jan is a self-contained desktop app: engine, "
|
||||
"model manager, and API server in one; the API "
|
||||
"server listens on 127.0.0.1:1337.",
|
||||
alternatives="Ollama as the engine with a separate chat UI.",
|
||||
constraints="The API server toggle must be enabled in Jan's "
|
||||
"settings; Electron desktop UI is not "
|
||||
"web-embeddable.",
|
||||
reasoning="A GUI chat app that also serves an "
|
||||
"OpenAI-compatible endpoint doubles as an "
|
||||
"inference provider for other stack tools.",
|
||||
verification="curl http://127.0.0.1:1337/v1/models",
|
||||
lineage="Jan — https://jan.ai/docs/desktop/api-server",
|
||||
assumptions="API server enabled (default on recent builds).",
|
||||
),
|
||||
),
|
||||
],
|
||||
connections=[],
|
||||
))
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="mem0",
|
||||
layer="Knowledge Management",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="LLM inference for memory extraction/summarisation "
|
||||
"and embeddings for memory vectors.",
|
||||
config_key="OPENAI_BASE_URL / embedding model config",
|
||||
required=False,
|
||||
context=_OLLAMA_CONSUMER_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="qdrant",
|
||||
interface_id="http_api",
|
||||
purpose="Vector storage for long-term memories.",
|
||||
config_key="QDRANT_HOST",
|
||||
required=False,
|
||||
context=EngineeringContext(
|
||||
decision="Qdrant as an optional external vector backend "
|
||||
"for stored memories.",
|
||||
observation="Mem0 ships with an embedded vector store by "
|
||||
"default and supports pluggable backends "
|
||||
"(Qdrant, Chroma, pgvector).",
|
||||
alternatives="Mem0's built-in store, or ChromaDB.",
|
||||
reasoning="Sharing one Qdrant instance across knowledge "
|
||||
"tools keeps embeddings co-located.",
|
||||
verification="Configure vector_store.provider=qdrant in "
|
||||
"mem0 config; run a memory add + search.",
|
||||
lineage="Mem0 — https://docs.mem0.ai/",
|
||||
),
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="tinygrad",
|
||||
layer="GPU Runtimes",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="cuda",
|
||||
interface_id="cuda_driver",
|
||||
purpose="GPU-accelerated tensor ops on NVIDIA backends.",
|
||||
config_key="CUDA_VISIBLE_DEVICES",
|
||||
required=False,
|
||||
context=EngineeringContext(
|
||||
decision="CUDA is optional — tinygrad also runs on CPU and "
|
||||
"AMD backends.",
|
||||
observation="tinygrad's lazy execution compiles kernels per "
|
||||
"backend at runtime; the NVIDIA backend needs "
|
||||
"the CUDA driver.",
|
||||
alternatives="CuPy for NumPy-compat GPU arrays; PyTorch for "
|
||||
"a full training stack.",
|
||||
reasoning="Wiring CUDA (optionally) surfaces tinygrad in "
|
||||
"the ticker when a GPU stack is staged, without "
|
||||
"forcing it on CPU-only hosts.",
|
||||
verification="python3 -c \"from tinygrad import Tensor; "
|
||||
"print(Tensor([1,2,3]).sum().item())\"",
|
||||
lineage="tinygrad — https://github.com/tinygrad/tinygrad",
|
||||
),
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════════
|
||||
# Topology query helpers
|
||||
# ══════════════════════════════════════════════════════════════════
|
||||
|
|
@ -2636,4 +3008,392 @@ def validate_wiring() -> list[str]:
|
|||
f"target '{conn.target_tool}' "
|
||||
f"(available: {sorted(target_iface_ids) or 'none'})"
|
||||
)
|
||||
return errors
|
||||
return errors
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
# Graphify — knowledge graph builder + MCP server (rewired from passive)
|
||||
# Removed from the L8 passive/CLI list above and given a proper wiring
|
||||
# since graphify is both a CLI tool and an MCP stdio server.
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
_reg(StackWiring(
|
||||
tool_id="graphify",
|
||||
layer="Orchestrators",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="graphify_mcp",
|
||||
protocol="stdio",
|
||||
api_format="MCP",
|
||||
port=None,
|
||||
base_path="",
|
||||
auth="None (local stdio)",
|
||||
description="Graphify MCP stdio server. Start with "
|
||||
"`graphify --mcp`. Other MCP-aware agents "
|
||||
"(Claude Code, opencode with MCP support, "
|
||||
"etc.) can query the knowledge graph via the "
|
||||
"standard MCP protocol. Exposes tools for "
|
||||
"graph query, path finding, and concept "
|
||||
"explanation.",
|
||||
context=EngineeringContext(
|
||||
decision="Expose graphify as an MCP server so other "
|
||||
"agents can query its knowledge graph.",
|
||||
observation="Graphify's --mcp mode runs a stdio MCP "
|
||||
"server. Any MCP-aware agent can call "
|
||||
"graphify's query/path/explain tools to "
|
||||
"navigate the codebase graph without "
|
||||
"re-reading source files.",
|
||||
alternatives="CLI-only mode (graphify query '...') "
|
||||
"or direct graph.json consumption.",
|
||||
constraints="Graph must be built first via "
|
||||
"`graphify .` before the MCP server can "
|
||||
"answer queries.",
|
||||
reasoning="MCP is the standard agent-to-tool protocol. "
|
||||
"Exposing graphify via MCP lets every coding "
|
||||
"agent in the stack benefit from the "
|
||||
"knowledge graph without each one needing "
|
||||
"a custom graphify integration.",
|
||||
verification="Start `graphify --mcp` and send an MCP "
|
||||
"initialize request on stdin.",
|
||||
lineage="Graphify — https://github.com/Graphify-Labs/graphify",
|
||||
assumptions="graphify installed (uv tool graphifyy) and "
|
||||
"a graph has been built in the working "
|
||||
"directory.",
|
||||
),
|
||||
),
|
||||
],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="meshllm",
|
||||
interface_id="openai_api",
|
||||
purpose="Optional LLM backend for graphify's vision/extraction "
|
||||
"pass. Graphify defaults to Claude (Anthropic API); "
|
||||
"users who want fully-local extraction can configure "
|
||||
"it to use MeshLLM (:9337/v1) instead.",
|
||||
config_key="OPENAI_API_BASE (set to http://localhost:9337/v1 "
|
||||
"to route extraction through the mesh)",
|
||||
required=False,
|
||||
context=EngineeringContext(
|
||||
decision="Graphify can use MeshLLM as its extraction "
|
||||
"backend instead of Claude.",
|
||||
observation="Graphify uses an LLM to extract concepts "
|
||||
"and relationships from files. The default "
|
||||
"is Claude (Anthropic), but it speaks "
|
||||
"OpenAI-compat so any OpenAI-format endpoint "
|
||||
"works.",
|
||||
reasoning="For a fully-local stack, route graphify's "
|
||||
"extraction through MeshLLM or Ollama direct. "
|
||||
"Claude gives better vision results for "
|
||||
"images/diagrams, so the connection is "
|
||||
"optional — users choose.",
|
||||
lineage="Graphify docs — https://github.com/Graphify-Labs/graphify",
|
||||
assumptions="MeshLLM has a vision-capable model if the "
|
||||
"corpus contains images.",
|
||||
),
|
||||
),
|
||||
Connection(
|
||||
target_tool="litellm",
|
||||
interface_id="openai_api",
|
||||
purpose="Fallback LLM backend (general LiteLLM proxy on :4000).",
|
||||
config_key="OPENAI_API_BASE",
|
||||
required=False,
|
||||
context=_LITELLM_CONSUMER_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Direct Ollama fallback for extraction (bypass both "
|
||||
"meshes). Useful for single-node setups without "
|
||||
"MeshLLM.",
|
||||
config_key="OPENAI_API_BASE (set to http://localhost:11434/v1)",
|
||||
required=False,
|
||||
context=EngineeringContext(
|
||||
decision="Ollama direct as a third extraction option.",
|
||||
observation="Same OpenAI-compat contract as MeshLLM "
|
||||
"and LiteLLM.",
|
||||
reasoning="Single-node users who don't need mesh "
|
||||
"pooling can point graphify straight at "
|
||||
"Ollama for the extraction LLM calls.",
|
||||
),
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
_reg(StackWiring(
|
||||
tool_id="meshllm",
|
||||
layer="Routing",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="openai_api",
|
||||
protocol="HTTP",
|
||||
api_format="OpenAI",
|
||||
port=9337,
|
||||
base_path="/v1",
|
||||
auth="API key (MESH_LLM_KEY, optional — local mesh is open by default)",
|
||||
description="MeshLLM unified API. Exposes a single "
|
||||
"OpenAI-compatible endpoint that routes requests "
|
||||
"across pooled GPUs/memory. Supports "
|
||||
"/v1/chat/completions, /v1/models, "
|
||||
"/v1/embeddings. The mesh decides whether a "
|
||||
"model runs locally, routes to a peer node, or "
|
||||
"uses Skippy stage splits for models too large "
|
||||
"for one box. Use model='mesh' to fan out one "
|
||||
"prompt to every model in the mesh (MoA gateway).",
|
||||
context=EngineeringContext(
|
||||
decision="MeshLLM as the mesh-pooling LLM gateway for "
|
||||
"multi-node inference.",
|
||||
observation="MeshLLM pools GPUs and memory across "
|
||||
"machines. Every node exposes the same /v1 "
|
||||
"API. Requests are routed by the 'model' "
|
||||
"field to the peer that can serve that "
|
||||
"model. QUIC end-to-end encrypts peer "
|
||||
"traffic via Iroh relays.",
|
||||
alternatives="LiteLLM (proxy only, no mesh pooling), "
|
||||
"vLLM (single-node high-throughput), "
|
||||
"direct Ollama (single-node).",
|
||||
constraints="mesh-llm setup must be run before first "
|
||||
"serve. Multi-node mesh requires peers to "
|
||||
"be discoverable via Nostr (public mesh) "
|
||||
"or invite token (private mesh).",
|
||||
reasoning="MeshLLM is the only tool in the stack that "
|
||||
"can split a model too large for one GPU "
|
||||
"across multiple nodes (Skippy stage splits). "
|
||||
"For single-node workloads, Ollama or LiteLLM "
|
||||
"are simpler; MeshLLM shines when you add a "
|
||||
"second machine.",
|
||||
verification="curl http://localhost:9337/v1/models",
|
||||
lineage="MeshLLM — https://github.com/Mesh-LLM/mesh-llm",
|
||||
assumptions="mesh-llm binary installed and `mesh-llm "
|
||||
"setup` completed. At least one model is "
|
||||
"available (auto-downloaded by `serve --auto` "
|
||||
"or specified via --model).",
|
||||
),
|
||||
),
|
||||
ToolInterface(
|
||||
interface_id="mesh_web_console",
|
||||
protocol="HTTP",
|
||||
api_format="REST",
|
||||
port=3131,
|
||||
base_path="",
|
||||
auth="None (local only)",
|
||||
description="MeshLLM web console. Browser UI for mesh "
|
||||
"management, node inventory, model routing, "
|
||||
"and peer discovery. Use `mesh-llm serve "
|
||||
"--headless` to hide the console while "
|
||||
"keeping the management API.",
|
||||
context=EngineeringContext(
|
||||
decision="Expose a separate web console port for mesh "
|
||||
"management.",
|
||||
observation="The console is a browser UI distinct from "
|
||||
"the OpenAI-compat API. Operators use it "
|
||||
"to monitor mesh health, add peers, and "
|
||||
"configure model routing.",
|
||||
reasoning="Separating management UI from inference API "
|
||||
"lets operators lock down :9337 (inference) "
|
||||
"while keeping :3131 (console) accessible "
|
||||
"only on the LAN.",
|
||||
verification="curl http://localhost:3131/",
|
||||
lineage="MeshLLM console — https://github.com/Mesh-LLM/mesh-llm",
|
||||
assumptions="MeshLLM was started without --headless.",
|
||||
),
|
||||
),
|
||||
],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Optional: use Ollama as a local-model backend via "
|
||||
"`mesh-llm client --auto` (API-only client mode). "
|
||||
"When configured, MeshLLM routes to Ollama for "
|
||||
"models not served by the mesh itself.",
|
||||
config_key="mesh-llm client --auto (reads OLLAMA_BASE_URL)",
|
||||
required=False,
|
||||
context=EngineeringContext(
|
||||
decision="Ollama is an OPTIONAL backend for MeshLLM, "
|
||||
"not a required one.",
|
||||
observation="MeshLLM can load GGUF models directly "
|
||||
"(--model /path/to/model.gguf) without "
|
||||
"Ollama. The `mesh-llm client --auto` "
|
||||
"subcommand turns MeshLLM into a pure "
|
||||
"client that routes to other meshes or "
|
||||
"Ollama-compatible backends.",
|
||||
reasoning="In the local-coder-mesh stack, most users "
|
||||
"will run MeshLLM standalone (it auto-"
|
||||
"downloads a suitable model). The Ollama "
|
||||
"connection is for users who want to expose "
|
||||
"their existing Ollama model fleet through "
|
||||
"the mesh routing layer.",
|
||||
),
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
# PiCode — coding agent, consumes mesh first, falls back to LiteLLM + Ollama
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
_reg(StackWiring(
|
||||
tool_id="picode",
|
||||
layer="Routing",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="meshllm",
|
||||
interface_id="openai_api",
|
||||
purpose="Primary LLM route for PiCode — mesh-pooled inference "
|
||||
"across all available nodes.",
|
||||
config_key="OPENAI_API_BASE (set to http://localhost:9337/v1)",
|
||||
required=False,
|
||||
context=EngineeringContext(
|
||||
decision="PiCode points at MeshLLM first.",
|
||||
observation="MeshLLM's :9337 endpoint gives PiCode "
|
||||
"access to every model in the mesh via "
|
||||
"one URL.",
|
||||
alternatives="LiteLLM proxy (localhost:4000) or direct "
|
||||
"Ollama (localhost:11434).",
|
||||
reasoning="Mesh-pooled inference is preferable when "
|
||||
"available — it can serve models larger than "
|
||||
"any single GPU via Skippy stage splits.",
|
||||
verification="Set OPENAI_API_BASE=http://localhost:9337/v1 "
|
||||
"and list models.",
|
||||
lineage="PiCode — https://github.com/jasonjmcghee/picode",
|
||||
assumptions="MeshLLM is running and has at least one "
|
||||
"model available.",
|
||||
),
|
||||
),
|
||||
Connection(
|
||||
target_tool="litellm",
|
||||
interface_id="openai_api",
|
||||
purpose="Fallback mesh (general LiteLLM proxy on :4000).",
|
||||
config_key="OPENAI_API_BASE",
|
||||
required=False,
|
||||
context=_LITELLM_CONSUMER_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Direct Ollama fallback (bypass both meshes).",
|
||||
config_key="OPENAI_API_BASE",
|
||||
required=False,
|
||||
context=_CODING_AGENT_CTX,
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
# ZCoder — coding agent, same consumer pattern as PiCode
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
_reg(StackWiring(
|
||||
tool_id="zcoder",
|
||||
layer="DevOps",
|
||||
interfaces=[],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="meshllm",
|
||||
interface_id="openai_api",
|
||||
purpose="Primary LLM route for ZCoder — mesh-pooled inference.",
|
||||
config_key="OPENAI_API_BASE (set to http://localhost:9337/v1)",
|
||||
required=False,
|
||||
context=EngineeringContext(
|
||||
decision="ZCoder points at MeshLLM first.",
|
||||
observation="Same OpenAI-compat contract as PiCode.",
|
||||
reasoning="Consistent routing across all coding agents "
|
||||
"in the stack — they all prefer the mesh.",
|
||||
lineage="ZCoder (Zhipu AI)",
|
||||
assumptions="MeshLLM is running.",
|
||||
),
|
||||
),
|
||||
Connection(
|
||||
target_tool="litellm",
|
||||
interface_id="openai_api",
|
||||
purpose="Fallback mesh (general LiteLLM proxy).",
|
||||
config_key="OPENAI_API_BASE",
|
||||
required=False,
|
||||
context=_LITELLM_CONSUMER_CTX,
|
||||
),
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Direct Ollama fallback (bypass both meshes).",
|
||||
config_key="OPENAI_API_BASE",
|
||||
required=False,
|
||||
context=_CODING_AGENT_CTX,
|
||||
),
|
||||
],
|
||||
))
|
||||
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
# Hermes WebUI — web frontend, backend is hermes_agent (NOT ollama direct)
|
||||
# ──────────────────────────────────────────────────────────────────
|
||||
_reg(StackWiring(
|
||||
tool_id="hermes_webui",
|
||||
layer="User Interfaces",
|
||||
interfaces=[
|
||||
ToolInterface(
|
||||
interface_id="hermes_webui_http",
|
||||
protocol="HTTP",
|
||||
api_format="REST",
|
||||
port=8081,
|
||||
base_path="",
|
||||
auth="WebUI auth (first-run admin signup)",
|
||||
description="Hermes-themed Open-WebUI instance. Separate "
|
||||
"port (8081) and data volume from the general "
|
||||
"openwebui instance so user accounts and RAG "
|
||||
"corpus don't collide.",
|
||||
context=EngineeringContext(
|
||||
decision="Run a second Open-WebUI instance dedicated "
|
||||
"to the Hermes stack.",
|
||||
observation="Open-WebUI supports multiple instances "
|
||||
"with separate data-dir flags. The general "
|
||||
"openwebui tool already occupies :3000 with "
|
||||
"Ollama direct as backend.",
|
||||
alternatives="Use a single Open-WebUI instance with "
|
||||
"model name prefixes (hermes/*).",
|
||||
constraints="Must run on a different port (8081) and "
|
||||
"data-dir than the general openwebui.",
|
||||
reasoning="Dedicated Hermes UI keeps the Hermes agent "
|
||||
"runtime as the single backend, so every "
|
||||
"Hermes WebUI conversation flows through "
|
||||
"hermes_agent's tool-use / function-calling "
|
||||
"layer instead of raw Ollama.",
|
||||
verification="curl http://localhost:8081/health",
|
||||
lineage="Open-WebUI — https://github.com/open-webui/open-webui",
|
||||
assumptions="hermes_agent is running on :17051.",
|
||||
),
|
||||
),
|
||||
],
|
||||
connections=[
|
||||
Connection(
|
||||
target_tool="hermes_agent",
|
||||
interface_id="hermes_agent_api",
|
||||
purpose="Primary backend — every chat goes through the "
|
||||
"Hermes agent runtime (tool use, function calling).",
|
||||
config_key="OLLAMA_BASE_URL (set to http://localhost:17051)",
|
||||
required=True,
|
||||
context=EngineeringContext(
|
||||
decision="Hermes WebUI talks to hermes_agent, NOT "
|
||||
"Ollama direct.",
|
||||
observation="hermes_agent exposes an OpenAI-compat "
|
||||
"endpoint on :17051 that wraps Ollama with "
|
||||
"Hermes function-calling logic.",
|
||||
reasoning="Routing through hermes_agent gives the "
|
||||
"WebUI access to Hermes tool use without "
|
||||
"requiring the user to wire it manually.",
|
||||
),
|
||||
),
|
||||
Connection(
|
||||
target_tool="ollama",
|
||||
interface_id="openai_api",
|
||||
purpose="Embedding model for RAG (nomic-embed-text). "
|
||||
"Embeddings bypass hermes_agent for speed.",
|
||||
config_key="OLLAMA_BASE_URL (RAG_EMBEDDING_ENGINE=ollama)",
|
||||
required=False,
|
||||
context=EngineeringContext(
|
||||
decision="Use Ollama direct for embeddings, "
|
||||
"hermes_agent for chat.",
|
||||
observation="Embeddings don't need Hermes function "
|
||||
"calling — direct Ollama is faster.",
|
||||
reasoning="Splitting chat vs. embedding traffic "
|
||||
"keeps hermes_agent focused on tool-use "
|
||||
"calls.",
|
||||
),
|
||||
),
|
||||
],
|
||||
))
|
||||
|
|
|
|||
|
|
@ -210,13 +210,14 @@ if _HAS_QT:
|
|||
self.logs_root: str = os.path.join(self.base_dir, "logs")
|
||||
self.skills_root: str = os.path.join(self.base_dir, "skills")
|
||||
self.datasets_root: str = os.path.join(self.base_dir, "datasets")
|
||||
self.config_root: str = os.path.join(self.base_dir, "config")
|
||||
self.config_root: str = os.path.join(self.base_dir, "configs")
|
||||
self.workspaces_root: str = os.path.join(
|
||||
self.base_dir, "workspaces"
|
||||
)
|
||||
self.exports_root: str = os.path.join(self.base_dir, "exports")
|
||||
|
||||
self._setup_environment_hierarchy()
|
||||
self._migrate_legacy_state_files()
|
||||
|
||||
self.dtach_bin: str | None = find_binary("dtach-ng", "dtach")
|
||||
# License gate — checks every tool's license before the
|
||||
|
|
@ -291,6 +292,62 @@ if _HAS_QT:
|
|||
# Environment setup
|
||||
# ───────────────────────────────────────────────────────────────
|
||||
|
||||
def _migrate_legacy_state_files(self) -> None:
|
||||
"""One-time migration to the canonical configs/ directory.
|
||||
|
||||
v3.1.1b moved app state into <base_dir>/configs/. Older
|
||||
installs kept files in three legacy locations:
|
||||
* <base_dir>/config/ (pipeline_state.json, license_approvals.json)
|
||||
* <base_dir>/controller_config.json (config persisted at the root)
|
||||
Files are moved only when no newer copy exists in configs/;
|
||||
emptied legacy dirs are removed. Never raises.
|
||||
"""
|
||||
import shutil
|
||||
|
||||
legacy_dir = os.path.join(self.base_dir, "config")
|
||||
candidates: list[tuple[str, str]] = []
|
||||
if os.path.isdir(legacy_dir):
|
||||
for fname in os.listdir(legacy_dir):
|
||||
if fname.endswith(".json"):
|
||||
candidates.append(
|
||||
(os.path.join(legacy_dir, fname), fname)
|
||||
)
|
||||
root_cfg = os.path.join(self.base_dir, CONFIG_FILE)
|
||||
if os.path.isfile(root_cfg):
|
||||
candidates.append((root_cfg, CONFIG_FILE))
|
||||
|
||||
moved: list[str] = []
|
||||
for src_path, fname in candidates:
|
||||
dst_path = os.path.join(self.config_root, fname)
|
||||
try:
|
||||
if not os.path.exists(dst_path):
|
||||
shutil.move(src_path, dst_path)
|
||||
moved.append(fname)
|
||||
elif os.path.getmtime(src_path) > os.path.getmtime(dst_path):
|
||||
# legacy copy is newer — keep it, drop the old one
|
||||
shutil.move(
|
||||
src_path, dst_path + ".legacy.bak"
|
||||
)
|
||||
moved.append(fname + " (kept as .legacy.bak)")
|
||||
else:
|
||||
os.remove(src_path)
|
||||
except OSError:
|
||||
continue
|
||||
|
||||
# remove the legacy config/ dir when it is now empty
|
||||
try:
|
||||
if os.path.isdir(legacy_dir) and not os.listdir(legacy_dir):
|
||||
os.rmdir(legacy_dir)
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
if moved:
|
||||
from ai_lsc.utils.logging import get_logger
|
||||
get_logger(__name__).info(
|
||||
"Migrated legacy state files to %s: %s",
|
||||
self.config_root, ", ".join(moved),
|
||||
)
|
||||
|
||||
def _setup_environment_hierarchy(self) -> None:
|
||||
for d in REQUIRED_DIRS:
|
||||
os.makedirs(
|
||||
|
|
@ -1480,7 +1537,7 @@ if _HAS_QT:
|
|||
def _load_config(self) -> dict:
|
||||
# H-02: resolve config relative to BASE_DIR (not the cwd the
|
||||
# app was launched from).
|
||||
config_path = os.path.join(self.base_dir, CONFIG_FILE)
|
||||
config_path = os.path.join(self.config_root, CONFIG_FILE)
|
||||
if os.path.exists(config_path):
|
||||
try:
|
||||
with open(config_path, encoding="utf-8") as f:
|
||||
|
|
@ -1507,7 +1564,10 @@ if _HAS_QT:
|
|||
"services": services_data,
|
||||
}
|
||||
# H-02 + H-03: write under base_dir atomically.
|
||||
_atomic_write_json(os.path.join(self.base_dir, CONFIG_FILE), config)
|
||||
os.makedirs(self.config_root, exist_ok=True)
|
||||
_atomic_write_json(
|
||||
os.path.join(self.config_root, CONFIG_FILE), config
|
||||
)
|
||||
|
||||
def closeEvent(self, event) -> None:
|
||||
self.save_config()
|
||||
|
|
|
|||
|
|
@ -46,120 +46,136 @@ except ImportError:
|
|||
_HAS_QT = False
|
||||
|
||||
# ── Category → default Layer / Level / Role mapping ──────────────────
|
||||
# Derived from the canonical registry. When the user picks a category
|
||||
# these fields auto-fill; the user can still override afterwards.
|
||||
# Derived from the canonical registry (11-layer taxonomy, v3.1.1b:
|
||||
# Routing=L5, Orchestrators=L6, Security=L7, Observability=L8,
|
||||
# User Interfaces=L9, DevOps=L10, Knowledge Management=L11).
|
||||
# When the user picks a category these fields auto-fill; the user can
|
||||
# still override afterwards.
|
||||
CATEGORY_MAP: dict[str, dict[str, object]] = {
|
||||
"AI Agent": {"layer": "Orchestrators", "level": 5, "role": "Sensory Bridge"},
|
||||
"AI Assistant Platform": {"layer": "User Interfaces", "level": 8, "role": "Central Intelligence"},
|
||||
"AI Augmentation": {"layer": "Orchestrators", "level": 5, "role": "Curation"},
|
||||
"AI Coding Agent": {"layer": "DevOps", "level": 9, "role": "Autonomous Coder"},
|
||||
"AI Monitoring": {"layer": "Observability", "level": 7, "role": "Health Monitor"},
|
||||
"AI Observability": {"layer": "Observability", "level": 7, "role": "LLM Tracing"},
|
||||
"AI Operating System": {"layer": "DevOps", "level": 9, "role": "OS Integration"},
|
||||
"Academic References": {"layer": "Knowledge Management", "level": 10, "role": "Reference Manager"},
|
||||
"Agent Framework": {"layer": "Orchestrators", "level": 5, "role": "Multi-Agent"},
|
||||
"Agent Network": {"layer": "DevOps", "level": 9, "role": "Discovery"},
|
||||
"Agent OS": {"layer": "Orchestrators", "level": 5, "role": "Hands"},
|
||||
"Agent Toolkit": {"layer": "Orchestrators", "level": 5, "role": "Tool Integration"},
|
||||
"Agent Workflow": {"layer": "Orchestrators", "level": 5, "role": "Reasoning"},
|
||||
"Algorithm Toolkit": {"layer": "DevOps", "level": 9, "role": "Hands"},
|
||||
"AI Agent": {"layer": "Orchestrators", "level": 6, "role": "Sensory Bridge"},
|
||||
"AI Assistant Platform": {"layer": "User Interfaces", "level": 9, "role": "Central Intelligence"},
|
||||
"AI Augmentation": {"layer": "Orchestrators", "level": 6, "role": "Curation"},
|
||||
"AI Coding Agent": {"layer": "DevOps", "level": 10, "role": "Coding Agent"},
|
||||
"AI Monitoring": {"layer": "Observability", "level": 8, "role": "Health Monitor"},
|
||||
"AI Observability": {"layer": "Observability", "level": 8, "role": "LLM Tracing"},
|
||||
"AI Operating System": {"layer": "DevOps", "level": 10, "role": "OS Integration"},
|
||||
"Academic References": {"layer": "Knowledge Management", "level": 11, "role": "Reference Manager"},
|
||||
"Agent Framework": {"layer": "Orchestrators", "level": 6, "role": "Multi-Agent"},
|
||||
"Agent Network": {"layer": "DevOps", "level": 10, "role": "Discovery"},
|
||||
"Agent OS": {"layer": "Orchestrators", "level": 6, "role": "Hands"},
|
||||
"Agent Toolkit": {"layer": "Orchestrators", "level": 6, "role": "Tool Integration"},
|
||||
"Agent Workflow": {"layer": "Orchestrators", "level": 6, "role": "Reasoning"},
|
||||
"Algorithm Toolkit": {"layer": "DevOps", "level": 10, "role": "Hands"},
|
||||
"Analytical Database": {"layer": "Host Platform", "level": 1, "role": "Foundation"},
|
||||
"Antivirus": {"layer": "Security", "level": 6, "role": "Scanner"},
|
||||
"Audio Parsing": {"layer": "Knowledge Management", "level": 10, "role": "Memory"},
|
||||
"Auth": {"layer": "Security", "level": 6, "role": "Identity"},
|
||||
"Build Monitoring": {"layer": "Orchestrators", "level": 5, "role": "Monitoring"},
|
||||
"Antivirus": {"layer": "Security", "level": 7, "role": "Scanner"},
|
||||
"Build": {"layer": "Development Environment", "level": 2, "role": "Build Tool"},
|
||||
"Audio Parsing": {"layer": "Knowledge Management", "level": 11, "role": "Memory"},
|
||||
"Auth": {"layer": "Security", "level": 7, "role": "Identity"},
|
||||
"Build Monitoring": {"layer": "Observability", "level": 8, "role": "Monitoring"},
|
||||
"Cache": {"layer": "Host Platform", "level": 1, "role": "Foundation"},
|
||||
"Chat": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"Chat Agent Platform": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"Chat Frontend": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"Cluster SSH": {"layer": "Orchestrators", "level": 5, "role": "Coordination"},
|
||||
"Code Analysis": {"layer": "DevOps", "level": 9, "role": "Inspector"},
|
||||
"Code Generation": {"layer": "DevOps", "level": 9, "role": "Hands"},
|
||||
"Computer Vision": {"layer": "User Interfaces", "level": 8, "role": "Vision"},
|
||||
"Config Management": {"layer": "DevOps", "level": 9, "role": "Configuration Management"},
|
||||
"Container Security": {"layer": "Security", "level": 6, "role": "Scanner"},
|
||||
"Chat": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"Chat Agent Platform": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"Chat Frontend": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"Claude Code Skill": {"layer": "Orchestrators", "level": 6, "role": "Knowledge Graph Builder"},
|
||||
"Cluster SSH": {"layer": "Orchestrators", "level": 6, "role": "Coordination"},
|
||||
"Code Analysis": {"layer": "DevOps", "level": 10, "role": "Inspector"},
|
||||
"Code Generation": {"layer": "DevOps", "level": 10, "role": "Hands"},
|
||||
"Computer Vision": {"layer": "User Interfaces", "level": 9, "role": "Vision"},
|
||||
"Config Management": {"layer": "DevOps", "level": 10, "role": "Configuration Management"},
|
||||
"Container Ops": {"layer": "Orchestrators", "level": 6, "role": "Sandbox"},
|
||||
"Container Security": {"layer": "Security", "level": 7, "role": "Scanner"},
|
||||
"Containers": {"layer": "Host Platform", "level": 1, "role": "Container Runtime"},
|
||||
"Context Manager": {"layer": "DevOps", "level": 9, "role": "Context"},
|
||||
"Cortex Memory": {"layer": "Knowledge Management", "level": 10, "role": "Memory"},
|
||||
"Dashboard": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"Data Pipeline": {"layer": "Knowledge Management", "level": 10, "role": "Ingestion"},
|
||||
"Data Sync": {"layer": "Knowledge Management", "level": 10, "role": "Integration"},
|
||||
"Context Manager": {"layer": "DevOps", "level": 10, "role": "Context"},
|
||||
"Cortex Memory": {"layer": "Knowledge Management", "level": 11, "role": "Memory"},
|
||||
"Dashboard": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"Data Pipeline": {"layer": "Knowledge Management", "level": 11, "role": "Ingestion"},
|
||||
"Data Sync": {"layer": "Knowledge Management", "level": 11, "role": "Integration"},
|
||||
"Debugging": {"layer": "Development Environment", "level": 2, "role": "Profiling"},
|
||||
"Database": {"layer": "Host Platform", "level": 1, "role": "Foundation"},
|
||||
"Desktop Agent": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"Dev Automation": {"layer": "DevOps", "level": 9, "role": "Hands"},
|
||||
"Development": {"layer": "DevOps", "level": 9, "role": "Hands"},
|
||||
"Distributed Compilation": {"layer": "Orchestrators", "level": 5, "role": "Distribution"},
|
||||
"Distributed Compute": {"layer": "Orchestrators", "level": 5, "role": "Scaling"},
|
||||
"Document Converter": {"layer": "Knowledge Management", "level": 10, "role": "File Parsing"},
|
||||
"Document Management": {"layer": "Knowledge Management", "level": 10, "role": "Document Archive"},
|
||||
"Document Understanding": {"layer": "Knowledge Management", "level": 10, "role": "Comprehension"},
|
||||
"Ebook Library": {"layer": "Knowledge Management", "level": 10, "role": "Library Manager"},
|
||||
"Ecosystem Dashboard": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"Desktop Agent": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"Dev Automation": {"layer": "DevOps", "level": 10, "role": "Hands"},
|
||||
"Development": {"layer": "DevOps", "level": 10, "role": "Hands"},
|
||||
"Distributed Compilation": {"layer": "Orchestrators", "level": 6, "role": "Distribution"},
|
||||
"Distributed Compute": {"layer": "Orchestrators", "level": 6, "role": "Scaling"},
|
||||
"Document Converter": {"layer": "Knowledge Management", "level": 11, "role": "File Parsing"},
|
||||
"Document Management": {"layer": "Knowledge Management", "level": 11, "role": "Document Archive"},
|
||||
"Document Understanding": {"layer": "Knowledge Management", "level": 11, "role": "Comprehension"},
|
||||
"Ebook Library": {"layer": "Knowledge Management", "level": 11, "role": "Library Manager"},
|
||||
"Ecosystem Dashboard": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"Efficient LLM": {"layer": "Engines", "level": 4, "role": "Engine"},
|
||||
"File Parsing": {"layer": "Knowledge Management", "level": 10, "role": "Memory"},
|
||||
"File Parsing": {"layer": "Knowledge Management", "level": 11, "role": "Memory"},
|
||||
"Find Tool": {"layer": "Development Environment", "level": 2, "role": "Search"},
|
||||
"GPU": {"layer": "GPU Runtimes", "level": 3, "role": "Acceleration"},
|
||||
"GPU Computing": {"layer": "Development Environment", "level": 2, "role": "GPU Acceleration"},
|
||||
"Graph Database": {"layer": "Knowledge Management", "level": 10, "role": "Memory"},
|
||||
"Graph RAG": {"layer": "Knowledge Management", "level": 10, "role": "Knowledge Synthesis"},
|
||||
"Homepage": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"IaC": {"layer": "DevOps", "level": 9, "role": "Infrastructure as Code"},
|
||||
"IaC Control Plane": {"layer": "DevOps", "level": 9, "role": "Infrastructure as Code"},
|
||||
"IaC Wrapper": {"layer": "DevOps", "level": 9, "role": "Infrastructure as Code"},
|
||||
"Image Generation": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"Intrusion Prevention": {"layer": "Security", "level": 6, "role": "IDS"},
|
||||
"Knowledge Graph": {"layer": "DevOps", "level": 9, "role": "Graph Builder"},
|
||||
"Knowledge Graph Notes": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"LLM Evaluation": {"layer": "Observability", "level": 7, "role": "Evaluation"},
|
||||
"Graph Database": {"layer": "Knowledge Management", "level": 11, "role": "Memory"},
|
||||
"Graph RAG": {"layer": "Knowledge Management", "level": 11, "role": "Knowledge Synthesis"},
|
||||
"Homepage": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"IaC": {"layer": "DevOps", "level": 10, "role": "Infrastructure as Code"},
|
||||
"IaC Control Plane": {"layer": "DevOps", "level": 10, "role": "Infrastructure as Code"},
|
||||
"IaC Wrapper": {"layer": "DevOps", "level": 10, "role": "Infrastructure as Code"},
|
||||
"Image Generation": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"Infrastructure": {"layer": "Orchestrators", "level": 6, "role": "Resource Manager"},
|
||||
"Intrusion Prevention": {"layer": "Security", "level": 7, "role": "IDS"},
|
||||
"Knowledge Graph": {"layer": "DevOps", "level": 10, "role": "Graph Builder"},
|
||||
"Knowledge Graph Notes": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"LLM Evaluation": {"layer": "Observability", "level": 8, "role": "Evaluation"},
|
||||
"Memory System": {"layer": "Knowledge Management", "level": 11, "role": "Memory"},
|
||||
"LLM Fine-tuning": {"layer": "GPU Runtimes", "level": 3, "role": "Abliteration"},
|
||||
"LLM Framework": {"layer": "Orchestrators", "level": 5, "role": "Orchestration"},
|
||||
"LLM GUI": {"layer": "User Interfaces", "level": 8, "role": "Face"},
|
||||
"LLM Router": {"layer": "Orchestrators", "level": 5, "role": "API Gateway"},
|
||||
"LLM Framework": {"layer": "Orchestrators", "level": 6, "role": "Orchestration"},
|
||||
"LLM GUI": {"layer": "User Interfaces", "level": 9, "role": "Face"},
|
||||
"LLM Mesh": {"layer": "Routing", "level": 5, "role": "API Gateway"},
|
||||
"LLM Router": {"layer": "Routing", "level": 5, "role": "API Gateway"},
|
||||
"LLM Runtime": {"layer": "Engines", "level": 4, "role": "Engine"},
|
||||
"LLM Serving": {"layer": "Orchestrators", "level": 5, "role": "Scaling"},
|
||||
"MCP Server": {"layer": "Orchestrators", "level": 5, "role": "Code Audit"},
|
||||
"Metrics": {"layer": "Observability", "level": 7, "role": "Metrics Collector"},
|
||||
"LLM Serving": {"layer": "Engines", "level": 4, "role": "Engine"},
|
||||
"MCP Server": {"layer": "Orchestrators", "level": 6, "role": "Code Audit"},
|
||||
"Metrics": {"layer": "Observability", "level": 8, "role": "Metrics Collector"},
|
||||
"Mesh Client": {"layer": "Routing", "level": 5, "role": "Coding Agent"},
|
||||
"Mixed Precision": {"layer": "GPU Runtimes", "level": 3, "role": "Optimization"},
|
||||
"Model Surgery": {"layer": "Engines", "level": 4, "role": "Abliteration"},
|
||||
"Model Training": {"layer": "Development Environment", "level": 2, "role": "Training"},
|
||||
"Multi-Agent": {"layer": "Orchestrators", "level": 5, "role": "Coordination"},
|
||||
"Notes": {"layer": "Knowledge Management", "level": 10, "role": "Note Taking"},
|
||||
"OCI Export": {"layer": "DevOps", "level": 9, "role": "Runtime Packaging"},
|
||||
"Outliner": {"layer": "Knowledge Management", "level": 10, "role": "Knowledge Graph"},
|
||||
"PDF Pipeline": {"layer": "Knowledge Management", "level": 10, "role": "Extraction"},
|
||||
"Multi-Agent": {"layer": "Orchestrators", "level": 6, "role": "Coordination"},
|
||||
"Networking": {"layer": "Host Platform", "level": 1, "role": "Tunnel"},
|
||||
"Notes": {"layer": "Knowledge Management", "level": 11, "role": "Note Taking"},
|
||||
"OCI Export": {"layer": "DevOps", "level": 10, "role": "Runtime Packaging"},
|
||||
"Observability": {"layer": "Observability", "level": 8, "role": "Monitoring"},
|
||||
"Outliner": {"layer": "Knowledge Management", "level": 11, "role": "Knowledge Graph"},
|
||||
"PDF Pipeline": {"layer": "Knowledge Management", "level": 11, "role": "Extraction"},
|
||||
"Parser": {"layer": "Development Environment", "level": 2, "role": "Parsing"},
|
||||
"Persistent Memory": {"layer": "Knowledge Management", "level": 10, "role": "Memory"},
|
||||
"Pipeline": {"layer": "Orchestrators", "level": 5, "role": "Pipeline Orchestrator"},
|
||||
"Policy Engine": {"layer": "Security", "level": 6, "role": "Policy"},
|
||||
"Procfile Runner": {"layer": "DevOps", "level": 9, "role": "Process Manager"},
|
||||
"Project Management": {"layer": "DevOps", "level": 9, "role": "Management"},
|
||||
"Prompt Tooling": {"layer": "DevOps", "level": 9, "role": "Prompt Management"},
|
||||
"Provisioning": {"layer": "DevOps", "level": 9, "role": "Provisioning"},
|
||||
"Proxy": {"layer": "Orchestrators", "level": 5, "role": "API Gateway"},
|
||||
"Reasoning Engine": {"layer": "Orchestrators", "level": 5, "role": "Brain"},
|
||||
"Persistent Memory": {"layer": "Knowledge Management", "level": 11, "role": "Memory"},
|
||||
"Pipeline": {"layer": "Routing", "level": 5, "role": "Pipeline Orchestrator"},
|
||||
"Policy Engine": {"layer": "Security", "level": 7, "role": "Policy"},
|
||||
"Procfile Runner": {"layer": "DevOps", "level": 10, "role": "Process Manager"},
|
||||
"Project Management": {"layer": "DevOps", "level": 10, "role": "Management"},
|
||||
"Prompt Tooling": {"layer": "DevOps", "level": 10, "role": "Prompt Management"},
|
||||
"Provisioning": {"layer": "DevOps", "level": 10, "role": "Provisioning"},
|
||||
"Proxy": {"layer": "Routing", "level": 5, "role": "API Gateway"},
|
||||
"Reasoning Engine": {"layer": "Orchestrators", "level": 6, "role": "Brain"},
|
||||
"Runtime": {"layer": "Development Environment", "level": 2, "role": "Build System"},
|
||||
"Sandbox": {"layer": "DevOps", "level": 9, "role": "Isolation"},
|
||||
"Search Engine": {"layer": "Knowledge Management", "level": 10, "role": "Memory"},
|
||||
"Sandbox": {"layer": "DevOps", "level": 10, "role": "Isolation"},
|
||||
"Search Engine": {"layer": "Knowledge Management", "level": 11, "role": "Memory"},
|
||||
"Search Tool": {"layer": "Development Environment", "level": 2, "role": "Search"},
|
||||
"Secrets Management": {"layer": "Security", "level": 6, "role": "Secrets"},
|
||||
"Shell": {"layer": "Development Environment", "level": 2, "role": "Shell"},
|
||||
"Secrets Management": {"layer": "Security", "level": 7, "role": "Secrets"},
|
||||
"Serverless Framework": {"layer": "Development Environment", "level": 2, "role": "Full-Stack Framework"},
|
||||
"Single-File LLM": {"layer": "Engines", "level": 4, "role": "Engine"},
|
||||
"Skill Analysis": {"layer": "DevOps", "level": 9, "role": "Assessment"},
|
||||
"Skill Inspection": {"layer": "DevOps", "level": 9, "role": "Analysis"},
|
||||
"Spaced Repetition": {"layer": "Knowledge Management", "level": 10, "role": "Memory"},
|
||||
"Spec Writer": {"layer": "DevOps", "level": 9, "role": "Documentation"},
|
||||
"Speech Recognition": {"layer": "User Interfaces", "level": 8, "role": "Senses"},
|
||||
"Task Runner": {"layer": "DevOps", "level": 9, "role": "Scheduler"},
|
||||
"Telemetry": {"layer": "Observability", "level": 7, "role": "Collector"},
|
||||
"Skill Analysis": {"layer": "DevOps", "level": 10, "role": "Assessment"},
|
||||
"Skill Inspection": {"layer": "DevOps", "level": 10, "role": "Analysis"},
|
||||
"Spaced Repetition": {"layer": "Knowledge Management", "level": 11, "role": "Memory"},
|
||||
"Spec Writer": {"layer": "DevOps", "level": 10, "role": "Documentation"},
|
||||
"Speech Recognition": {"layer": "User Interfaces", "level": 9, "role": "Senses"},
|
||||
"Task Runner": {"layer": "DevOps", "level": 10, "role": "Scheduler"},
|
||||
"Telemetry": {"layer": "Observability", "level": 8, "role": "Collector"},
|
||||
"Terminal": {"layer": "Host Platform", "level": 1, "role": "Multiplexer"},
|
||||
"Text-to-Speech": {"layer": "User Interfaces", "level": 8, "role": "Voice"},
|
||||
"Text-to-Speech": {"layer": "User Interfaces", "level": 9, "role": "Voice"},
|
||||
"Uncensored Models": {"layer": "Engines", "level": 4, "role": "Engine"},
|
||||
"VCS": {"layer": "Host Platform", "level": 1, "role": "Version Control"},
|
||||
"Vector Engine": {"layer": "Knowledge Management", "level": 10, "role": "Embedding"},
|
||||
"Vector Store": {"layer": "Knowledge Management", "level": 10, "role": "Memory"},
|
||||
"Visualization": {"layer": "Observability", "level": 7, "role": "Dashboard"},
|
||||
"Web Crawler": {"layer": "Knowledge Management", "level": 10, "role": "Data Harvesting"},
|
||||
"Workflow": {"layer": "Orchestrators", "level": 5, "role": "Visual Builder"},
|
||||
"Workflow Automation": {"layer": "Orchestrators", "level": 5, "role": "Workflow Orchestrator"},
|
||||
"Vector Engine": {"layer": "Knowledge Management", "level": 11, "role": "Embedding"},
|
||||
"Vector Store": {"layer": "Knowledge Management", "level": 11, "role": "Memory"},
|
||||
"Visualization": {"layer": "Observability", "level": 8, "role": "Dashboard"},
|
||||
"Virtualization": {"layer": "Host Platform", "level": 1, "role": "MicroVM"},
|
||||
"Web Crawler": {"layer": "Knowledge Management", "level": 11, "role": "Data Harvesting"},
|
||||
"Workflow": {"layer": "Orchestrators", "level": 6, "role": "Visual Builder"},
|
||||
"Workflow Automation": {"layer": "Orchestrators", "level": 6, "role": "Workflow Orchestrator"},
|
||||
}
|
||||
|
||||
# ── Field-constant lookups (populated once on first use) ─────────────
|
||||
|
|
|
|||
|
|
@ -31,30 +31,61 @@ def build_path_tree(base_dir: str | Path | None = None) -> dict[str, Path]:
|
|||
Example::
|
||||
|
||||
{
|
||||
"base_dir": Path("/mnt/AI"),
|
||||
"tools_root": Path("/mnt/AI/tools"),
|
||||
"models_root": Path("/mnt/AI/models"),
|
||||
"logs_root": Path("/mnt/AI/logs"),
|
||||
"skills_root": Path("/mnt/AI/skills"),
|
||||
"datasets_root": Path("/mnt/AI/datasets"),
|
||||
"config_root": Path("/mnt/AI/config"),
|
||||
"workspaces_root": Path("/mnt/AI/workspaces"),
|
||||
"exports_root": Path("/mnt/AI/exports"),
|
||||
"registry_root": Path("/mnt/AI/registry"),
|
||||
"base_dir": Path("/mnt/AI"),
|
||||
"tools_root": Path("/mnt/AI/tools"), # standalone CLI utilities
|
||||
"runtime_root": Path("/mnt/AI/runtime"), # native binaries + per-tool venvs
|
||||
"models_root": Path("/mnt/AI/models"), # parent of hot/ and cold/
|
||||
"models_hot": Path("/mnt/AI/models/hot"), # active weights (SSD)
|
||||
"models_cold": Path("/mnt/AI/models/cold"), # archived weights (HDD)
|
||||
"corpus_root": Path("/mnt/AI/corpus"), # parent of hot/ and cold/
|
||||
"datasets_root": Path("/mnt/AI/datasets"), # parent of wordlists/, huggingface/, github/
|
||||
"pipelines_root": Path("/mnt/AI/pipelines"), # ETL / chunking / routing scripts
|
||||
"configs_root": Path("/mnt/AI/configs"), # app state + templated configs
|
||||
"registry_root": Path("/mnt/AI/registry"), # app-internal: ecosystem.json + manifests/
|
||||
"agents_root": Path("/mnt/AI/agents"), # configs and chains for autonomous actors
|
||||
"skills_root": Path("/mnt/AI/skills"), # 3rd-party integrations and tool wrappers
|
||||
"projects_root": Path("/mnt/AI/projects"), # parent of active/, labs/, vault/
|
||||
"blueprints_root": Path("/mnt/AI/blueprints"), # Dockerfiles / build contexts for Podman exports
|
||||
"workspaces_root": Path("/mnt/AI/workspaces"), # Jupyter, OpenNotebook, etc.
|
||||
"dashboards_root": Path("/mnt/AI/dashboards"), # web UIs (Dashy, Open-WebUI, Hermes WebUI, etc.)
|
||||
"exports_root": Path("/mnt/AI/exports"), # parent of oci-images/
|
||||
"scripts_root": Path("/mnt/AI/scripts"), # system admin / maintenance automation
|
||||
"logs_root": Path("/mnt/AI/logs"),
|
||||
"backends_root": Path("/mnt/AI/backends"), # S3/MinIO/Ceph connection profiles
|
||||
"distfiles_root": Path("/mnt/AI/distfiles"), # permanent local mirror of source tarballs
|
||||
"config_root": Path("/mnt/AI/configs"), # app state + templated configs
|
||||
}
|
||||
"""
|
||||
root = Path(base_dir) if base_dir is not None else Path(BASE_DIR)
|
||||
return {
|
||||
"base_dir": root,
|
||||
"tools_root": root / "tools",
|
||||
"runtime_root": root / "runtime",
|
||||
"models_root": root / "models",
|
||||
"logs_root": root / "logs",
|
||||
"skills_root": root / "skills",
|
||||
"models_hot": root / "models" / "hot",
|
||||
"models_cold": root / "models" / "cold",
|
||||
"corpus_root": root / "corpus",
|
||||
"datasets_root": root / "datasets",
|
||||
"config_root": root / "config",
|
||||
"workspaces_root": root / "workspaces",
|
||||
"exports_root": root / "exports",
|
||||
"pipelines_root": root / "pipelines",
|
||||
"registry_root": root / "registry",
|
||||
"agents_root": root / "agents",
|
||||
"skills_root": root / "skills",
|
||||
"projects_root": root / "projects",
|
||||
"blueprints_root": root / "blueprints",
|
||||
"workspaces_root": root / "workspaces",
|
||||
"dashboards_root": root / "dashboards",
|
||||
"exports_root": root / "exports",
|
||||
"scripts_root": root / "scripts",
|
||||
"logs_root": root / "logs",
|
||||
"backends_root": root / "backends",
|
||||
"distfiles_root": root / "distfiles",
|
||||
"configs_root": root / "configs",
|
||||
# App-state + templated app configs (controller_config.json,
|
||||
# pipeline_state.json, license_approvals.json). Per-tool config
|
||||
# subdirs (configs/<tool>/) are created on demand by
|
||||
# InstallerManager. Legacy installs used base_dir/config or
|
||||
# base_dir root — main_window migrates those on startup.
|
||||
"config_root": root / "configs",
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue