Not yet and that is really key feature. I asked the online AI about it and apparently its already possible. I will need to try this. Short answer: Ollama’s local models that can read the web and be used in self-improving/agentic workflows are the tool-enabled conversational models like qwen3 (and qwen3:4b/other sizes) and gpt-oss when paired with Ollama’s web_search/web_fetch tools and an agent framework (e.g., MCP/web-search agent examples). Notes: Ollama added a web search API and web_fetch that lets local models call the web as a tool. To build self-improving or agentic behavior you combine a capable model (qwen3 or gpt-oss are recommended) with Ollama’s tool integrations (web_search/web_fetch, MCP) or an external agent orchestration layer (Open WebUI pipelines, custom MCP server). Step-by-step: Run a local Ollama model that reads the web and supports agentic/self-improving workflows Prerequisites (assume macOS/Linux; adapt for Windows/WSL): Install Ollama (https://ollama.com/install) and confirm ollama CLI runs. Install Python ≥3.10 and pip; create a venv. Choose and pull a tool-capable model Recommended: qwen3 (or qwen3.5/qwen3.5-small) or a Llama3 variant that supports tools. Pull and run locally: ollama pull qwen3:8bollama run qwen3:8b This exposes the Ollama local API at http://localhost:11434/v1. Enable Ollama web tools Ollama provides web_search / web_fetch (tool endpoints). Confirm availability in your Ollama version and enable any feature flags per Ollama docs. Test the API manually: curl http://localhost:11434/api/web_search -d '{"query":"latest X"}' curl http://localhost:11434/api/web_fetch -d '{"url":"https://example.com"}' Build an agent loop that gives the model tool access Use an agent framework (examples: Qwen-Agent, LangGraph, LangChain-style orchestration, or custom MCP client). Core idea: call Ollama chat API with the model and supply tool definitions so the model can call web_search/web_fetch as functions. Python minimal pattern (outline): Install client libs: pip install requests qwen-agent langgraph (optional) Open a chat session to Ollama: POST /api/chat with: model: "qwen3:8b" messages: system + user tools: include function schemas for web_search and web_fetch (name, description, parameters) When the model returns a tool_call in its response, execute the requested tool (call Ollama web_search/web_fetch or your own fetcher), then feed the tool result back to the model as a new message (assistant -> tool result), continuing the loop until final answer. Example flow (decision + tool-call + feedback) User asks: “Find recent research on X and summarize.” Model returns: tool_call web_search with query -> you call web_search -> return results. Feed results into model: assistant message containing tool output. Model may call web_fetch for a chosen URL -> call web_fetch to obtain full content -> feed back. Repeat until model emits a final textual response. Add memory & self-improvement steps Persistence: store successful prompts, chain-of-thought, tool results, and the model’s final output in a local DB (SQLite/JSON). Reuse: on similar future tasks, retrieve past prompts/responses as context (RAG) so the agent learns effective strategies. Automated iteration (careful, human oversight required): Implement a test-suite (set of evaluation queries + expected metrics). Periodically run the agent on tests, record performance. If candidate prompt/template or tool-usage pattern improves metrics, update the system prompt or tool-handling heuristics automatically (version and log changes). Keep checkpoints and require human approval before deploying any automatic prompt/template changes into production. Use MCP or tool frameworks for richer integrations Option A — MCP: run an MCP server (filesystem, web, calendar tools), implement a small MCP client that converts MCP tools into Ollama tool schemas and proxies calls. Option B — Qwen-Agent / LangGraph: bind Python functions as tools and use their agent runtime to orchestrate tool-calls, memory, and UI. Safety, reliability, and best practices Always validate and sanitize web_fetch results (strip scripts, limit size). Cross-validate claims across multiple sources before accepting them as truth. Log all tool calls and model decisions for auditing. Rate-limit and sandbox any code-execution or external actions. Keep human-in-the-loop control for any automatic “self-improvement” changes. Example resources & next steps Look for sample projects: Qwen-Agent + Ollama examples, LangGraph + Ollama tutorials, MCP client examples (search for “Ollama MCP tutorial”, “Qwen-Agent Ollama”). Start small: build a single-task agent (search + summarize), add persistent memory, then add automated evaluation and controlled prompt updates. If you want, I can produce a concise runnable Python example that: starts a chat with Ollama qwen3, defines web_search/web_fetch tools, executes tool calls and feeds results back, and stores exchanges in SQLite for simple memory and iterative prompt updates. Which model size should I target?