task-orchestrator
by jpicklyk
Persistent AI memory for coding assistants - MCP server providing context persistence across sessions for Claude, Cursor, Windsurf. MCP Tools for task tracking, workflow automation, and AI memory. Eliminates context loss between sessions.
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17forks
Kotlin
Added 12/27/2025
AI Agentsai-coding-assistantai-developmentai-memoryai-toolsclaude
Installation
# Add to your Claude Code skills
git clone https://github.com/jpicklyk/task-orchestratorMCP Task Orchestrator
Stop losing context. Start building faster.
An orchestration framework for AI coding assistants that solves context pollution and token exhaustion - enabling your AI to work on complex projects without running out of memory.
The Problem
AI assistants suffer from context pollution - a well-documented challenge where model accuracy degrades as token count increases. This "context rot" stems from transformer architecture's quadratic attention mechanism, where each token must maintain pairwise relationships with all others.
The Impact: As your AI works on complex features, it accumulates conversation history, tool outputs, and code examples. By task 10-15, the context window fills with 200k+ tokens. The model loses focus, forgets earlier decisions, and eventually fails. You're forced to restart sessions and spend 30-60 minutes rebuilding context just to continue.
Industry Validation: Anthropic's research on context management confirms production AI agents "exhaust their effective context windows" on long-running tasks, requiring active intervention to prevent failure.
Traditional approaches treat context windows like unlimited memory. Task Orchestrator recognizes they're a finite resource that must be managed proactively.
The Solution
Task Orchestrator implements from Anthropic's : persistent external memory, summary-based context passing, and sub-agent architectures with clean contexts.