Local AI Agents: Goose, Observer AI, AnythingLLM in 2026
We spent three days mapping the ecosystem of local AI agents that run autonomously on personal hardware without depending on external APIs or cloud services. Our analysis categorizes the leading solutions into five key areas, based on hands-on testing across developer agents, automation tools, productivity assistants, frameworks, and local runtimes.
Local AI agent categorization
Category | Tools/Frameworks | Primary use cases (Local / Offline) |
|---|---|---|
Developer & system agents | Goose, Localforge, Devika, Roo Code (Boomerang Mode), Continue.dev, Cursor, CodeGenie, SuperCoder, Aider, Cline, Kilo Code | Local coding, debugging, file/process automation, local DevOps tasks |
Local automation & control agents | Observer AI, Browser-Use, DeepBrowser | Local browser control, file automation, app interaction, on-device workflows |
Knowledge & productivity agents | AnythingLLM (Desktop), LocalGPT (Single-User), PrivateGPT | Offline document Q&A, summarization, local search/RAG |
Frameworks | LangGraph, LangChain, LlamaIndex, CrewAI, SuperAGI, CamelAI | Building offline agent workflows, reasoning loops, tool calling |
Local runtimes & infrastructure | Ollama, LM Studio, Llamafile, LocalAI, Text-Generation WebUI, vLLM, ExLlama V2, Flowise, Langflow, n8n (Self-Hosted) | Running local models, offline inference, self-hosted automation |
1. Developer & system agents
*Execution types:
- Fully local: The tool runs natively on personal hardware using local runtimes. Tools capable of operating entirely offline.
- Hybrid local: The core model or task execution happens locally, but some features, such as IDE integration, context indexing, synchronization, or reasoning, still rely on cloud services or APIs.
** Explanation for on-machine column:
- Fully on-device: Complete offline operation inference, reasoning, and execution all run locally.
- Local inference, cloud-assisted: Core model runs locally, but IDE or management features use online services.
- Local execution, remote reasoning: Code runs locally, but external APIs power reasoning or planning steps.
Goose
An open-source, on-machine development agent that plans, writes, and tests code autonomously using local runtimes. Goose works with any compatible LLM and integrates seamlessly with MCP servers. It’s available as both a desktop app and a CLI tool. You can check it out on GitHub: Goose1
Core capabilities:
- Generates, edits, and tests source code autonomously within a local repository.
- Integrates with local LLM runtimes to perform reasoning and code generation.
- Supports multi-step task execution, including debugging and file management.
- Works with standard developer tools and file systems without internet dependency.
Roo Code
Roo Code is a local desktop AI coding assistant that focuses on self-correction and continuous refinement of its outputs. The Boomerang Mode enables local execution, allowing Roo Code to run fully on your machine without relying on cloud services.
Roo Code supports AI providers such as Anthropic, OpenAI, Google Gemini, AWS Bedrock, and local models via Ollama. It can be installed through the VS Code extensions.2
After installing Roo Code, restart your editor, whether it’s VS Code, Cursor, or Windsurf.
Once reopened, the Roo Code icon will appear in the left sidebar. Click on it to begin the guided setup process, which walks you through account configuration and initial model setup.
Local AI agent configuration in Roo Code:
Roo Code allows developers to create custom configuration profiles that define how it connects to different AI models, including locally hosted LLMs.
From Settings → Providers, you can add profiles through OpenRouter or other supported providers, then choose a local model running via Ollama or LM Studio.
Each configuration profile can store its own parameters, including temperature, reasoning depth, and token limits. This lets you switch between lightweight cloud models and fully local runtimes for on-device inference.
Cursor
Offline functionality is not yet supported in Cursor, but it is possible to run a local LLM while keeping the IDE connected online. This configuration enables local inference, but the overall agent workflow is not fully local, since some data is still sent to Cursor’s servers for supporting functionality.
In this setup, Cursor continues to use its API for features such as indexing and applying edits, while the local LLM handles the primary inference tasks.
Developer agents integrated into IDEs, such as Cursor IDE, can be configured to use a local model by installing Ollama, setting up Enrock, and linking Cursor to the Enrock URL and API key.
How to use a local LLM within Cursor:
Source:Logan Hallucinates3
2. Local automation & control agents
Observer AI
Observer AI is an open-source framework for automating screen-based and system-level tasks through autonomous agents that run directly on a user’s local machine. It processes all data locally, without relying on external dependencies.
Core functions:
- Runs agents powered by local LLMs through Ollama or any v1 chat-completions API.
- Observes the user’s screen via OCR or screenshots.
- Executes Python code through an integrated Jupyter server.
- Operates with zero cloud connectivity, keeping computation and data confined to the user’s environment.
Browser-Use
Browser-Use is a Python-based framework that lets AI agents interact with a browser through Playwright.
One method to install it is to use pip install browser-use command, which sets up both the Python interface and local browser control on the same machine.
When later run (for example, with python -m browser_use), it will open and control a browser instance locally, executing actions and reasoning either through a local LLM (e.g., via Ollama) or through connected APIs:
Setting Browser-Use up locally4
For those who want to see the complete setup in action, here’s a step-by-step video guide showing how to install and run Browser-Use on a local machine:
The walkthrough covers everything from installing dependencies like Playwright and LangChain to connecting Browser-Use with a local model via Ollama.5
For more, check our benchmark on Browser-use tool use capabilities.
3. Knowledge & productivity agents
AnythingLLM
We tested AnythingLLM Desktop to see how a local, on-device agent works from setup to final output.
1. Setting up the workspace
We opened the workspace settings and went to Agent Configuration.
There, we chose an LLM provider and selected the mistral-medium-2505 model.
After clicking Update Workspace Agent, the workspace confirmed that the setup was complete.
2. Enabling agent skills
Next, we opened the Configure Agent Skills panel.
This menu allows you to enable built-in agent capabilities with a single click. No coding is required.
3. Testing the “Save Files” skill
We enabled the Save Files skill, allowing the agent to write outputs directly to the local machine.
After turning it on and saving the change, the agent was ready.
To test it, we went back to the chat window and used one of the sample prompts from the documentation.
This confirmed that the agent could generate a file and prepare it for local saving.
4. Running the agent in chat
We asked the agent to summarize a historical topic and invoked it using @agent.
We modified the command to save the output as a simple text file instead of a PDF.
The system confirmed that Agent Chat Mode was active and showed how to exit the loop.
The agent produced the summary and prepared the file for saving.
5. Saving the file locally
To save the output, we used the example command from the AnythingLLM docs:
“@agent can save this information as a PDF on my desktop folder?”
We ran the same structure in chat, but for a text file.
A file browser window opened, and we saved the output on the device.
The file appeared in the Downloads folder, indicating that the full process, reasoning, execution, and saving were all performed entirely on-device.
4. Frameworks
*Role in local AI system:
- Local reasoning & collaboration frameworks: Form the cognitive core where agents reason, plan, and collaborate locally.
- Workflow orchestration platforms: Manage and automate how those agents interact and execute tasks on-device.
LangGraph
By combining LangGraph for agent logic with Ollama for local model hosting, developers can build fully offline AI agents that reason, search, and respond autonomously without any cloud dependency.
Building a local AI agent with LangGraph and Ollama:
1. Check your GPU (Optional)
A dedicated GPU enables faster local inference.
On Windows, open Task Manager → Performance → GPU to see your graphics card and memory.
If you don’t have a GPU, Ollama still works on a CPU, but it will run slower.
For smoother performance, a GPU with 6–8 GB VRAM is recommended.
2. Install LangGraph and required packages
LangGraph is a framework for building reasoning workflows. It can be installed through PyPI and runs independently.6
Run the following commands in your terminal or PowerShell:
3. Install Ollama
Ollama hosts and runs local models. After downloading it, check that it’s installed:7
Pull a model for offline use:
Ollama stores the model on your machine and uses it without any internet connection.
You can switch models at any time by changing the name in your script.
4. Write and run a local agent
The example below shows how to create a simple offline agent.
It utilizes LangGraph for reasoning and Ollama to run a local model, such as Llama 3.
Create a file named agent.py and add:
Adapted from Digital Ocean8
Run the script:
The agent will reply with a clear explanation generated entirely on your device.
This example shows how LangGraph and Ollama work together to create a fully local AI agent with no cloud access needed.
5. Local runtimes & infrastructure
Role in local AI system:
- On-machine inference engines: Run models directly on desktops or edge devices, enabling completely offline AI use.
- Self-hosted runtimes: Provide scalable, high-performance inference for private or team deployments within secure local infrastructure.
Local AI agent category descriptions
- Developer & system agents (action layer): Agents that run directly on your device to perform coding, system, and workflow automation tasks locally.
- Local automation & control agents: Agents that automate real-world actions on your machine by controlling the browser, UI, or OS.
- Knowledge & productivity agents: Local assistants for chat, summarization, and document handling without sending data to the cloud.
- Frameworks (agent reasoning/control layer): Libraries that provide reasoning, planning, and coordination for building and running local AI agents.
- Local runtimes & infrastructure (model execution layer): Engines that execute LLMs on local hardware, enabling fully offline inference.
How to approach the local AI agent stack
Start with the smallest set of layers your use case requires. If your agent needs offline reasoning, begin with a local runtime like Ollama or LM Studio. If it needs to understand your files, add a knowledge layer such as AnythingLLM or LocalGPT. For agents that must take actions (opening apps, controlling the browser, managing files) add a local automation layer. Only use frameworks like LangGraph or LlamaIndex when you need multi-step workflows, planning loops, or complex toolchains.
FAQ
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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