The tooling exists. Ollama, Open WebUI, AnythingLLM, Continue.dev, Aider, Flowise — five years ago this stack didn’t exist at all. The problem in 2026 isn’t finding open-source AI tools; it’s figuring out which ones compose into a coherent workflow and which combinations quietly waste your weekend.

The short answer: almost any combination works, because all modern local AI tools speak the OpenAI HTTP API. The longer answer: there are real failure modes — port conflicts, silent context truncation, embedding model mismatches — that the tutorials skip. This is the guide that covers them.

Versions verified: Ollama v0.24.0 (May 14, 2026), Open WebUI v0.9.5 (May 2026), vLLM v0.21.0 (May 2026), Continue.dev v1.3.34 (early 2026), Aider v0.86.0.

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The API layer that makes it all compose

Every tool in this stack exposes or consumes an OpenAI-compatible HTTP API. This one design decision is the reason cross-tool compatibility is almost automatic.

Ollama’s REST API at localhost:11434 accepts the same request format as OpenAI’s /v1/chat/completions. Open WebUI detects a local Ollama instance without configuration. AnythingLLM treats Ollama as a selectable LLM provider. Continue.dev has a first-party "provider": "ollama" setting in its config. Aider accepts --api-base http://localhost:11434 to redirect to any OpenAI-compatible server. Flowise has an Ollama LLM node baked in.

The practical implication: swapping the LLM runner underneath a UI or code tool is a URL change, not an integration project. Replace localhost:11434 with localhost:8000 and you’re pointed at vLLM instead. Every tool described below works against any compliant backend.

Where it gets complicated is not the protocol — it’s the port assignments, context window defaults, and embedding pipeline isolation. Those are the actual failure modes.

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Layer 1 — The LLM Runner

Ollama v0.24.0 (MIT license, github.com/ollama/ollama) is the right starting point for single-developer and home-lab setups. One installer, model downloads by name, background daemon, hot-swap between models. The May 2026 release reworked the MLX sampler for Apple Silicon and added Codex App support. It stores models as GGUF and loads them into GPU memory on first request.

# Pull and run a model
ollama pull qwen3:14b
ollama run qwen3:14b

# Check running models and VRAM allocation
ollama ps

# Increase context window (default is often 2048)
OLLAMA_NUM_CTX=16384 ollama serve

vLLM v0.21.0 (Apache 2.0, github.com/vllm-project/vllm) is the answer when Ollama’s single-user throughput ceiling isn’t enough. It runs on port 8000, exposes the identical OpenAI API, and every other tool in this stack points at it with a URL change. The tradeoffs: Linux-only, requires CUDA, no Apple Silicon support, and takes 30–90 seconds to load a model before serving.

# Serve a Qwen3 14B model with vLLM
vllm serve Qwen/Qwen3-14B \
  --max-model-len 32768 \
  --port 8000

The decision rule is simple: Ollama for development, evaluation, and solo use; vLLM when you’re serving more than one person, running a shared team endpoint, or benchmarking batch throughput. For a detailed breakdown of when the switch is worth the ops cost, see Ollama vs vLLM 2026.

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Layer 2 — Chat UIs

Open WebUI v0.9.5 (MIT, github.com/open-webui/open-webui) is built first for Ollama. Docker installation detects a local Ollama instance automatically — no manual endpoint configuration. It runs on port 3000 and covers daily chat, model management, basic RAG via document upload, and in v0.9.5, a native desktop app for Mac, Windows, and Linux that removes the Docker requirement entirely for personal setups.

The v0.9.5 release added redirect-based SSRF protection and configurable iframe content security policy, which matter if you’re exposing the interface on a local network to other users.

AnythingLLM (MIT, github.com/Mintplex-Labs/anything-llm) is a different tool with a similar surface. The distinction is architectural: AnythingLLM was designed around “workspaces” where document collections, embedding pipelines, and chat history are managed independently. It runs on port 3001 by default, which means Open WebUI and AnythingLLM can run simultaneously against the same Ollama instance without port conflict.

Both tools support the same Ollama backend. The routing decision: Open WebUI for general chat, model exploration, and multi-modal tasks; AnythingLLM when your primary workflow is interrogating documents. For deeper reviews, see Open WebUI review and AnythingLLM review.

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Layer 3 — RAG

Both chat UIs include built-in RAG, but they handle embeddings and persistence differently. Knowing which to use before you ingest a large document corpus saves a painful rebuild later.

Open WebUI’s RAG stores embeddings in SQLite-vec (since v0.9.x). Upload a document via the chat interface, and it becomes queryable in that conversation. The setup time is near zero. Configuration is limited — you pick an embedding model in admin settings, and that’s about it. Good for ad-hoc document queries; not designed for managing multiple independent knowledge bases.

AnythingLLM’s RAG uses Chroma by default, supports multiple embedding backends (including nomic-embed-text via Ollama), and lets you create isolated workspaces with separate document collections. You can inspect embedding status per document, rescan sources after updates, and configure retrieval parameters per workspace. It’s more to configure but significantly more reliable for ongoing document-heavy workflows.

Flowise (Apache 2.0) handles the cases neither built-in solution covers: multi-step retrieval, reranking, conditional routing based on document metadata, or custom pre-processing pipelines. It talks to Ollama through a standard LLM node and has a visual interface for building chains. For setup, see Flowise local setup guide.

One rule that applies to all three options: embedding vectors are model-specific. Documents embedded with nomic-embed-text cannot be queried with mxbai-embed-large or Open WebUI’s default embedding model. If you switch RAG tools or embedding models mid-project, you re-embed everything from scratch. Choose your embedding model before ingesting production data.

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Layer 4 — Code Tooling

Both major code tools in this stack are OpenAI-API consumers. Neither requires Ollama specifically — they accept any compatible endpoint.

Continue.dev v1.3.34 (Apache 2.0, github.com/continuedev/continue, 2.4M VS Code installs as of early 2026) is the IDE-integrated option. Configuration lives in a single JSON file:

{
  "models": [
    {
      "title": "Qwen3 14B — chat",
      "provider": "ollama",
      "model": "qwen3:14b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Deepseek Coder V2 — autocomplete",
    "provider": "ollama",
    "model": "deepseek-coder-v2:16b"
  }
}

The two-model setup is standard practice on Tier 2 hardware: a 14B model for chat and edits, a faster smaller model for inline autocomplete that needs to respond in under a second. Both pull from the same Ollama daemon, so no additional ports or processes.

To point Continue.dev at vLLM instead, change "provider": "openai" and add "apiBase": "http://localhost:8000". The model names change to match whatever vLLM is serving, but everything else is identical.

Aider v0.86.0 (Apache 2.0, github.com/Aider-AI/aider) is the terminal alternative. It maps your repository structure, generates diffs, and commits with atomic git commits. Pointing at local Ollama:

aider --model ollama/qwen3:14b \
      --api-base http://localhost:11434 \
      --api-key ollama

The --api-key ollama is a required dummy — Ollama ignores API key validation, but Aider requires the parameter to be non-empty. Without it, the request fails before reaching Ollama.

Cline (MIT, formerly Claude Dev) is the VS Code agentic option. It accepts a custom base URL and model name, can execute terminal commands, read and write files, and navigate the codebase autonomously. Unlike Continue.dev’s edit-and-confirm model, Cline operates more like an autonomous agent — useful for refactors that span many files, less appropriate for quick inline suggestions.

For detailed coverage of the code tools: Continue.dev review, Aider review.

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Compatibility matrix

Tool	Ollama v0.24.0	vLLM v0.21.0	LM Studio	OpenAI API
Open WebUI	Native (auto-detect)	Via OpenAI endpoint	Via OpenAI endpoint	✓
AnythingLLM	Native provider	Via OpenAI endpoint	Via OpenAI endpoint	✓
Continue.dev	Native (provider: ollama)	provider: openai + apiBase	provider: openai + apiBase	✓
Aider	--api-base + dummy key	--api-base + dummy key	--api-base + dummy key	✓
Cline	Custom endpoint settings	Custom endpoint settings	Custom endpoint settings	✓
Flowise	Ollama LLM node	OpenAI LLM node	OpenAI LLM node	✓

“Native” means first-party integration with named provider support or auto-discovery. “Via endpoint” means you manually set the base URL — otherwise the behavior is identical to native.

LM Studio is intentionally included here as a reference point. It’s not open-source, but it speaks the same API and a lot of developers evaluate both. If you want a fully FOSS stack, replace LM Studio with Ollama or vLLM — nothing else in the stack changes.

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Hardware tiers for the full stack

Tier	Hardware	Usable model sizes	What runs well
Minimal	16 GB RAM, 12 GB VRAM (RTX 3060 12 GB)	7B–8B	Ollama + Open WebUI + one code tool. One model loaded at a time.
Solid	32 GB RAM, 24 GB VRAM (RTX 3090 or 4090)	Up to 13B–14B	Full stack simultaneously: Ollama, Open WebUI, AnythingLLM, Continue.dev chat + autocomplete.
Power	64 GB RAM, 48 GB+ VRAM (2× RTX 4090 or A100)	70B, multi-user	Switch to vLLM for throughput. Serve multiple users or run Flowise under real load.

The constraint at Tier 1 is simultaneous model loading. Ollama lazy-loads models and evicts them after the OLLAMA_KEEP_ALIVE timeout (default: 5 minutes). On 12 GB VRAM, loading a 7B chat model plus a code autocomplete model at the same time will overflow GPU memory — one model offloads to CPU and generation speed drops by 5–10×. The fix is to use a single smaller model for both purposes, or increase the VRAM budget.

At Tier 2, the RTX 3090 and 4090 both carry 24 GB of VRAM at very different price points. A used 3090 on the secondary market fits many home-lab budgets at a fraction of the 4090 price, with the same VRAM capacity for model loading. The difference is raw throughput — the 4090 is noticeably faster on inference due to higher compute density, though the gap is smaller than spec sheets suggest because LLM inference at low batch sizes is memory-bandwidth-bound, and both cards carry 24 GB of GDDR6X.

At Tier 3, the calculus changes. Two 4090s or an A100 opens up 70B models, but the hardware investment is substantial. Before committing, it’s worth testing 70B inference on rented capacity — RunPod offers A100 and H100 instances by the hour, which is the cheapest way to validate whether 70B throughput actually improves your workflow before buying hardware. For GPU purchasing decisions, runaihome.com covers the hardware comparison side in depth.

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What actually breaks

Port conflicts: Flowise and Open WebUI both default to port 3000. This is the most common collision in a full local stack. Fix it before starting Flowise:

# Flowise: set a non-conflicting port
PORT=3010 npx flowise start
# Or in .env
PORT=3010

AnythingLLM defaults to 3001. vLLM to 8000. Ollama to 11434. These rarely conflict with each other.

Context window truncation: Ollama models have a context length configured in their Modelfile — often 2048 or 4096 tokens. Continue.dev will silently truncate any file context that exceeds this without an error message. The symptom is the model appearing to ignore parts of the file you’re editing. Fix:

# Set a higher default context window for all Ollama models
OLLAMA_NUM_CTX=16384 ollama serve

Or create a custom Modelfile for the model you use most:

FROM qwen3:14b
PARAMETER num_ctx 16384

Missing API key in Aider: Aider validates that --api-key is non-empty before sending any request. Ollama ignores the key value entirely, but if you omit the parameter, Aider exits before reaching the network. Use any non-empty placeholder.

vLLM startup latency: vLLM loads the full model into GPU memory before the HTTP server starts accepting requests. A 13B model takes 20–40 seconds; a 70B model on an A100 can take 90 seconds. This is expected behavior — not a crash. The health endpoint at /health returns 200 once the model is ready.

Embedding model lock-in: This is the sneaky one. AnythingLLM and Open WebUI default to different embedding models. If you’ve ingested documents in AnythingLLM using nomic-embed-text and then try to query them from Open WebUI, the retrieval will fail silently — it’s comparing vectors from incompatible embedding spaces. Each tool’s RAG is isolated by default. Treat RAG data as belonging to a specific tool + embedding model combination.

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The minimum viable stack

For most solo developers evaluating local AI, the right starting configuration is:

1. Ollama as the model runner — pull whatever you want to test, swap freely
2. Open WebUI for daily chat
3. Continue.dev for IDE integration, with two models: one larger for chat, one smaller for autocomplete
4. AnythingLLM added later, only when you have a real document-querying workflow that justifies the extra setup

The Flowise and vLLM layers come when the basic stack proves useful and you’re hitting specific limitations: complex pipelines (Flowise), or more than one concurrent user (vLLM).

Starting with all six tools simultaneously is a mistake. The debugging surface is too large when something doesn’t work.

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When local is the wrong answer

A local AI stack makes sense when: you have Tier 2 hardware minimum, your data is sensitive enough to warrant keeping it off cloud APIs, or per-token costs at your usage volume actually add up.

It does not make sense when:

• Your machine has 8 GB of total RAM — model loading and normal development tools compete for the same memory
• You need 70B+ model quality with latency under 500ms for interactive users
• You’re building a product with reliability requirements — a local daemon is not a production service
• The developer-hours spent on ops exceed the API cost you’d otherwise pay

The quality gap between local 14B models and frontier cloud APIs has narrowed significantly in 2026. Qwen3 14B and Llama 3.3 70B cover the majority of coding and document tasks that used to require GPT-4. For solo development workflows on Tier 2 hardware, the local stack is genuinely competitive.

Where cloud still wins: frontier-model reasoning tasks, multimodal at production scale, and anything requiring predictable low latency under concurrent load. The local stack is excellent for development; it is not a managed service.

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Sources

• Ollama v0.24.0 release notes — GitHub
• Open WebUI v0.9.5 release — GitHub
• vLLM OpenAI-compatible server documentation
• Continue.dev GitHub repository and changelog
• Aider v0.86.0 release — GitHub
• AnythingLLM GitHub repository (Mintplex-Labs)
• Flowise GitHub repository
• Local LLM hardware guide 2026 — promptquorum.com
• How to build a local AI stack: Ollama to vLLM — MindStudio

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Recommended Gear

The hardware mentioned in this guide, with current prices on Amazon (affiliate links — at no extra cost to you, purchases help support this site):

• RTX 3060
• RTX 3090
• RTX 4090