haylanandClaude-Bot
1eaa2a0d2e
Resolves #27 (part of #26). Findings from opencode.ai/docs (fetched 2026-09-03) plus a fresh clone of anomalyco/opencode @ b578b72 (v1.18.27): - No percentage-threshold config key exists; compaction.{auto,prune, reserved,tail_turns,preserve_recent_tokens} is the full global config surface (opencode.json top-level, not per-provider/model). - Trigger is usedTokens >= context - reservedBuffer, not a hardcoded 75%/95% cutoff — contradicts an unverified claim in a closed GitHub feature request (#11314). - Reasoning tokens (Qwen3's reasoning_content) are counted via the provider's usage.total_tokens in the normal path, but excluded from the fallback sum if a provider ever omits total_tokens. - No per-model/per-agent threshold override exists (confirmed by several closed-not-planned feature requests); the only per-model lever is each model's own limit.context/limit.output. - Mechanism is provider-agnostic: applies identically to a hand- declared @ai-sdk/openai-compatible provider (this repo's llamacpp setup) as to hosted providers, provided limit.context is set. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01FCAUsjGNSoJTtK8hyLKg5m
LLM-Server
Local AI inference stack: llama.cpp (ROCm) serving Qwen3.8-27B on an AMD Radeon AI PRO R9700, fronted by Open WebUI (RAG + Memory via Qdrant), with Lazytainer auto-suspending the inference container when idle.
See the wayfinder map (issue #1) for the full architecture rationale and open questions.
Quickstart
./scripts/update.sh
update.sh creates .env from .env.example if missing, fills in every secret and per-workload virtual key it can generate itself (random secrets via openssl, OPENWEBUI_LITELLM_KEY/LITELLM_PGVECTOR_EMBEDDING_KEY minted through LiteLLM's own /key/generate API, SEARXNG_LAN_IP resolved from search.home on this host), downloads both model GGUFs into the models volume if they're not there yet, then pulls/builds/brings up the whole stack. Safe to re-run any time — it only fills in what's still blank, skips models already downloaded, and only recreates what changed. See docs/proxy-key-onboarding.md if a key mint fails and needs doing by hand.
- Open WebUI:
http://<this-machine>:3000locally, orai.home/ai.haylan.chonce routed through Nginx Proxy Manager — seedocs/network-access.md. First signup becomes the admin account (WEBUI_AUTHis on). - llama.cpp's own API is internal-only now — everything routes through the AI proxy below.
Pointing Claude Code CLI, Kimi CLI, or OpenCode CLI at the local endpoint: see docs/coding-cli-setup.md.
Known risk: Qwen3.8-27B's tool-calling reliability against llama.cpp's Anthropic shim is not yet verified (open upstream parser bugs against its model lineage) — see docs/research/qwen3.8-27b-tool-calling.md.
AI proxy (LiteLLM)
An AI gateway/proxy fronts llama.cpp: per-workload virtual keys, usage tracking, and a shadow cost estimate ("what this would have cost on Claude Sonnet 5"). ./scripts/update.sh handles LITELLM_MASTER_KEY/LITELLM_SALT_KEY and every other secret (see .env.example).
- Proxy API:
http://<this-machine>:4000/v1locally, orproxy.ai.home/proxy.ai.haylan.chonce routed through NPM — seedocs/network-access.md. - Admin UI (
/ui, key/budget management): LAN-only — seedocs/network-access.md. - Issuing a key for a new workload:
docs/proxy-key-onboarding.md. - Request priority across workloads:
docs/proxy-request-priority.md.
Open WebUI and the coding CLIs (see docs/coding-cli-setup.md) route through the proxy now — llama-server has no published host port anymore. Not yet verified: none of this has been smoke-tested on real hardware (LiteLLM's priority scheduler in particular is beta — see docs/proxy-request-priority.md) — see issue #17.
Web search, knowledgebase, and memory
The gateway also fronts SearXNG-backed web search and a pgvector-backed knowledgebase (loaded with data/memory.md / data/claude-legacy-memory.md), wired at the LiteLLM layer so every client gets them, not just Open WebUI — see docs/memory-knowledgebase.md. Not yet verified on real hardware — see issue #24.