Local Ai
Give any Ollama-compatible client session memory + a shared knowledge wiki by swapping the chat URL
Hey folks — I built ContextMemory, an open-source agentic context gateway for apps that already talk to LLMs. The idea is simple: keep your existing POST /api/chat client (Ollama wire format), point i
Hey folks — I built ContextMemory, an open-source agentic context gateway for apps that already talk to LLMs. The idea is simple: keep your existing POST /api/chat client (Ollama wire format), point it at ContextMemory instead of raw Ollama, and get memory + optional tools without rewriting your chat stack. What it actually does Most “memory” demos are either: stuffing the whole history into the prompt, or bolting on a separate RAG service with a new API surface. ContextMemory sits in front of your LLM as a drop-in proxy: Session memory — a per-session markdown wiki (Karpathy-style) maintained across turns and injected automatically. Global Wiki — an app-scoped knowledge base (docs from Jira, Confluence, SQL, files, pipelines…). The model pulls facts on demand via a wiki_search tool — it does not dump the whole corpus into every prompt. Same /api/chat — Ollama-compatible request/response (message.content / done). Not OpenAI choices[]. Optional agentic loop — tools (sandbox, outbound MCP, HITL) on that same chat endpoint when enabled per app. Multi-app / multi-tenant — API keys + X-App-Id, per-app prompts, models, and feature flags. LLM backends can be local Ollama, or OpenAI / Azure / Anthropic as providers behind the gateway; the client still speaks Ollama schema. Why this shape If you already have a UI, bot, or agent that calls Ollama, you shouldn’t need a second protocol to get memory. Swap the base URL, keep parsing the same JSON, and the gateway handles: compiling session context optional Global Wiki retrieval optional web search / tools …then calls your model. There’s also a hosted path (Kortexio Cloud) with the same chat body/response if you don’t want to self-host — BYOK, no token markup. Self-host and cloud are meant to be interchangeable at the wire level. Global Wiki (the part people usually ask about) Ingest structured markdown with stable documentIds (upsert / batch). Query by keywords with a character budget. In chat, when Global Wiki is enabled for the app, the model uses wiki_search only when it needs documented facts — good for org knowledge without turning every turn into a RAG megaprompt. Quick self-host vibe Your app → POST http://localhost:5100/api/chat → ContextMemory → Ollama / other LLM + session wiki + optional wiki_search (Global Wiki) Auth is typically Authorization: Bearer … + X-App-Id / X-User-Id / X-Session-Id for self-host. Repo Open source (AGPL): https://github.com/Kortexio/ContextMemory Looking for feedback from this community Especially interested in: How you currently bolt memory onto local models (what sucks?) Whether Ollama-compatible wire format is the right “universal client” bet vs going all-in on OpenAI schema Global Wiki as tool-calling vs always-on retrieval — what would you default to? Anything missing for production self-host (ops, eval, multi-user UX) Happy to answer questions or dive into architecture. If you try it with a local model + a small wiki ingest, I’d love to hear what breaks first. submitted by /u/Miserable_Candy_1503 [link] [comments]
Related
- Give your local Ollama models a personal knowledge bank (graph-based, not just vector search)
- Big update: my local Ollama chat now has persistent JSON memory
- karpathy’s “llm wiki” idea got me thinking. what would you want ai to surface from your saved stuff?
Source: r/ollama | 2026-07-27