Agent-Reach vs Contextberg: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Agent-Reach and Contextberg — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
A
Agent-Reach
Agent-Reach
Agent-Reach is a free CLI and library that gives AI agents read and search access to 16 web platforms like Twitter, Reddit, YouTube, and GitHub.
Key features
- Unified Platform Access: Read and search 16 platforms including Twitter/X, Reddit, YouTube, GitHub, Bilibili, and LinkedIn through one interface.
- Zero API Fees: Uses open-source upstream tools so agents browse without paid API keys.
- One-Command Install: pip install agent-reach then 'agent-reach install' wires the tools into the agent.
- Broad Agent Compatibility: Works with Claude Code, Cursor, OpenClaw, Windsurf, Codex, and more.
- Search & Read Modes: Supports both searching for content and reading specific URLs across supported platforms.
Best for
- Market & Social Research: Let an agent gather posts and discussions across Twitter, Reddit, and XiaoHongShu.
- Content Monitoring: Track YouTube, podcasts, and RSS feeds programmatically from within an agent.
- Developer Research: Pull GitHub and forum content into an agent's context for engineering tasks.
- Web Automation: Give a coding assistant the ability to read arbitrary URLs during a task.
Contextberg
Contextberg
Surfaces your active work as persistent agent memory and serves it to agents via the Model Context Protocol (MCP).
Key features
- Work-to-Memory Conversion: Extracts contextual signals from a user's workspace (files, tabs, app state and activity) and converts them into structured memory artifacts usable by agents.
- MCP Serving: Exposes collected memory via the Model Context Protocol (MCP) so any MCP-compatible agent or tool can query and consume context in a standard way.
- Long-Term Persistence: Stores and indexes historical context across sessions to provide agents with continuity and long-term state for multi-step or recurring tasks.
- Interoperability with Agent Tooling: Designed to plug into developer workflows and agent infrastructures, enabling multiple agents and platforms to reuse the same context artifacts.
- Context Enrichment: Organizes and surfaces relevant snippets of work history so agents receive concise, actionable context rather than raw logs or bulk files.
- Serve work history and artifacts as agent-readable memory via the Model Context Protocol (MCP).
- Index and persist long-term context so agents can access historical state across sessions.
- Provide a standardized memory endpoint for agent frameworks and tooling to query context.