Contextberg

Contextberg

Contextberg is a privacy-first local memory app for AI agents that records screen activity, inputs, browser history and agent conversations to provide contextual memory to agents (e.g., coding agents) and to summarize recent work for quick resumption.

Contextberg is ai tools software teams evaluate for software & gaming. Use this page to review pricing, integration signals, and the best alternatives before you commit.

Pricing not listed
#544 in AI Agents (544 tools)
Just launched
Data reviewed Sep 22, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: Software & Gaming

What it does

AI Tools software for decision-makers comparing workflow fit and alternatives.

Best fit

Software & Gaming

Pricing snapshot

Pricing available on request

Next step

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Contextberg

Contextberg is a desktop application (macOS & Windows) that observes your work in the background—screenshots, inputs, browsing and agent conversations—and converts that activity into structured context that AI agents can consume. It is designed to act as long-term and short-term memory for agents (including coding agents) by distilling daily activity into activity memories, daily summaries, and long-term memory of frequently used tools and patterns. The product emphasizes privacy by storing work history locally and allowing users to choose inference destinations (local models, LM Studio, Codex, OpenRouter, Contextberg Cloud, etc.). It also includes an MCP server to connect compatible agents directly without additional configuration.

Turn your work into private memory for AI agents Discussion | Link

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Key Features

Automatic context capture

Continuously records screen contents, user inputs, browser history and agent conversations in the background and makes them available to connected agents.

Three memory types

Generates activity memories (detailed logs of actions), daily memories (date-aggregated progress), and long-term memories (frequently used tools and patterns) for agent inference.

Privacy-first storage and model routing

Work history is stored locally by default; users can choose where to send inference requests (local model/LM Studio, Codex sign-in, Gemini/OpenRouter API key, Contextberg Cloud).

MCP server & agent integrations

Includes a built-in MCP server to connect supported agents (e.g., Claude Code, Cursor, OpenClaw, GitHub Copilot) without extra configuration.

Resume & summarization

When returning to work, Contextberg auto-summarizes recent activity (activity, browser, and agent history) so agents can immediately continue from where you left off; points of interest can be explored via chat.

Easy installation and no account required

Two-click install with no account or cloud lock-in required for local-only operation.

Pricing

Current pricing details are not available from the vendor source.

Use Cases

Coding with context-aware agents

Provide coding agents with full contextual history (screenshots, terminal logs, prior agent interactions) so they can operate without repeatedly asking for state or filenames.

Resume interrupted work quickly

Automatically generate summaries of recent activity so users and agents can pick up work immediately after returning from a break.

Long-term agent memory for workflows

Maintain long-term memory of frequently used tools and behavioral patterns to improve agent relevance over time.

Privacy-first experimentation

Run local models or select specific inference routes when working with sensitive or private data while keeping raw work history on-device.

Integrations

Claude Code

Supported coding agent that can consume Contextberg-provided context.

Cursor

Supported agent whose history Contextberg can record and feed.

OpenClaw

Agent integration listed as supported for context feeding.

GitHub Copilot / Codex

Support for recording and routing context to Codex/GitHub Copilot (Codex sign-in mentioned).

LM Studio / Local models

Option to use fully local models (LM Studio) so inference stays on-device.

OpenRouter / Gemini (API keys)

Can route inference to OpenRouter or Gemini via user-provided API keys.

Contextberg Cloud

Cloud inference route option (optional; not required).

Benefits

Reduces repetitive re-entry of context by feeding agents complete session history.
Improves agent usefulness by providing structured short-term and long-term memories.
Preserves privacy with local storage and flexible model routing choices.

Limitations

Currently available for macOS and Windows only (other distributions will be announced on the download page).
Requires compatible agents or agent support via MCP to feed and consume context; not all agents/tools may be supported out of the box.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Download and install the Contextberg desktop app (macOS or Windows).
  2. 2 Launch the app and connect a supported agent via the built-in MCP server or choose a model route (local model, LM Studio, Codex sign-in, Gemini/OpenRouter, Contextberg Cloud).
  3. 3 Work as usual: Contextberg will record activity in the background and begin generating activity, daily, and long-term memories; use the summary/chat features to resume or explore context.

Support

docs

Help center and documentation pages referenced on the site (サポート / ヘルプセンター).

blog

Product updates and launch posts available on the blog linked from the site.

social

Follow Contextberg on X (Twitter) as listed in the site footer.

API

Available: No

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