Contextkeeper
Keep shared session continuity across Claude, GPT, Gemini, and other LLMs so long agent runs do not lose decisions or repeat setup.
Overview
contextkeeper is a MCP server for the Build phase that preserves session continuity across Claude, GPT, Gemini, and other LLMs to reduce model drift.
What is this MCP server?
- Marketed for zero model drift and session continuity across Claude, GPT, Gemini, and any LLM
- Python PyPI package contextkeeper v0.2.7 with stdio MCP transport
- No API keys listed in the published server manifest—local install focused
- Fits multi-agent and multi-model workflows common in solo builder stacks
- Cross-model memory MCP server, not a project management skill or vector database replacement
- Published version 0.2.7 on PyPI package contextkeeper
- 0 required secret environment variables in the registry manifest
What problem does it solve?
Switching models or starting new agent threads wipes tacit context, so you repeat explanations and contradict earlier decisions.
Who is it for?
Solo builders juggling multiple LLM providers and long-running agent sessions who need stable handoffs without rebuilding context manually.
Skip if: Teams that already enforce all state in git and tickets only, or workflows that require cloud-hosted shared memory with enterprise SSO on day one.
What do I get? / Deliverables
Shared session continuity lets you rotate LLMs and resume work without re-teaching the repo, tradeoffs, or open tasks each time.
- Persistent session continuity across supported LLM agents
- Reduced repeated priming when swapping models mid-project
- stdio MCP configuration for contextkeeper in your agent stack
Recommended MCP Servers
Journey fit
Session continuity matters whenever you use coding agents across long Build cycles and later Operate iterations, but the catalog shelf for agent infrastructure is Build agent-tooling. Agent-tooling is where MCP servers that reduce drift and preserve context belong—this server is not a feature of your shipped app but plumbing for how you work with LLMs.
How it compares
Cross-LLM session continuity MCP server, not a single-vendor memory skill or a documentation generator.
Common Questions / FAQ
Who is Contextkeeper for?
It is for developers using several LLMs and MCP agents who want continuity when Claude, GPT, or Gemini take turns on the same project.
When should I use Contextkeeper?
Use it throughout the builder journey whenever sessions are long or you switch models—especially during Build agent-tooling and Operate iteration—rather than for one-off snippets.
How do I add Contextkeeper to my agent?
Install the PyPI Contextkeeper package, register the stdio MCP server in Claude Code, Cursor, or your client per the workbench repo docs, and restart the agent.