Every time I spun up a new agent session, I was back to square one — re-explaining domain rules, user context, system knowledge. It didn't matter how smart the model was. Stateless by default.

So I built ContextBook — an MCP server that lets you organise knowledge into structured Books and Pages, and lets agents pull exactly the context they need, on demand.

What makes it different from memory systems:

• Memory is pre-loaded, and ambient — agents get everything whether they need it or not
• ContextBook is surgical — agents fetch only what's relevant, right when they need it

Tech stack:

• Go monorepo, dual binaries (API + MCP server)
• Voyage AI voyage-4 embeddings + pgvector HNSW for semantic search
• OAuth 2.0 PKCE, React 19 + Vite dashboard
• Deployed on Railway

Platform-agnostic — works with Claude, GPT, Gemini, or any MCP-compatible agent. One instance, any platform.

8 MCP tools out of the box. Open-source. Deploys in minutes.

Website: https://context-book-production.up.railway.app/

Github: https://github.com/aditya201551/context-book

Dashboard

Knowledge Graph

Every time I spun up a new agent session, I was back to square one — re-explaining domain rules, user context, system knowledge. It didn't matter how smart the model was. Stateless by default.

So I built ContextBook — an MCP server that lets you organise knowledge into structured Books and Pages, and lets agents pull exactly the context they need, on demand.

What makes it different from memory systems:

• Memory is pre-loaded, and ambient — agents get everything whether they need it or not
• ContextBook is surgical — agents fetch only what's relevant, right when they need it

Tech stack:

• Go monorepo, dual binaries (API + MCP server)
• Voyage AI voyage-4 embeddings + pgvector HNSW for semantic search
• OAuth 2.0 PKCE, React 19 + Vite dashboard
• Deployed on Railway

Platform-agnostic — works with Claude, GPT, Gemini, or any MCP-compatible agent. One instance, any platform.

8 MCP tools out of the box. Open-source. Deploys in minutes.

Website: https://context-book-production.up.railway.app/

Github: https://github.com/aditya201551/context-book

Would love feedback from fellow builders — especially if you're working with agentic systems. Happy to answer any technical questions.

Dashboard

Knowledge Graph

One thing that's been quietly bothering me about working with AI agents: every session starts cold.

Your agent helped you think through something last week? Gone. You loaded a document, worked through decisions, and built up context? Gone. Next session, you're starting from scratch.

Client-level memory (Claude's, ChatGPT's) helps a little — but it's tied to one client, ambient by default, and you don't really control what gets surfaced when.

I wanted something different. So I built ContextBook.

The idea in one sentence

>Context is on-demand and surgical. Memory is ambient and pre-loaded.

ContextBook lets your agent store anything, search it semantically, and pull only what's relevant — when it decides it needs it, not pre-loaded into every prompt.

What it looks like in practice

You organise things into Books (think: a topic or project) and Pages (the actual content, chunked into meaningful pieces). Your agent can write to it, search it with natural language, and read back exactly what's relevant to the current task.

That's really it. The rest is best explored hands-on.

Links

• Dashboard: https://context-book-production.up.railway.app/
• MCP Endpoint: https://context-book-mcp-production.up.railway.app
• GitHub: https://github.com/aditya201551/context-book
• Smithery: https://smithery.ai/servers/context-book/connect
• Glama.ai: https://glama.ai/mcp/connectors/io.github.aditya201551/context-book-mcp (please add and authenticate with OAuth2.0 for it to become healthy)

Where it stands

Early stage — I've been using it for my own agent workflows, and it's been holding up well. Putting it out here because I'd rather get real usage and feedback now than polish in private.

If you're building with MCP and cross-session context is a pain point for you, give it a shot. Break things. Tell me what's missing.

How are others handling this problem right now? Curious what patterns people have landed on.