Is io.github.ataberk-xyz/gossipcat safe to use?

io.github.ataberk-xyz/gossipcat has no known CVEs as of the latest MCPpedia security scan. It does not require authentication, so any local process can connect — keep this in mind in shared environments.

How do I install io.github.ataberk-xyz/gossipcat?

io.github.ataberk-xyz/gossipcat can be installed by cloning its GitHub repository and following the setup instructions in the README.

What AI clients work with io.github.ataberk-xyz/gossipcat?

io.github.ataberk-xyz/gossipcat is compatible with Claude Desktop, Cursor, Claude Code, and most MCP clients that support stdio transport. It uses stdio transport.

io.github.ataberk-xyz/gossipcat MCP Server — Score: 9/100 (F)

agentic orchestration framework — agents that learn, adapt, and get better every round.

Install · First Run · Daily Use · Dashboard · Troubleshooting · Config · For AI Agents

What is Gossipcat?

Gossipcat is an MCP server that orchestrates multiple AI agents to review your code in parallel. Agents independently review, then cross-review each other's findings. Agreements are confirmed. Hallucinations are caught and penalized. Over time, each agent builds an accuracy profile — the system learns who to trust for what.

Why multi-agent?

Without gossipcat	With gossipcat
One AI reviews your code — and hallucinates a finding you waste 20 minutes on	Multiple agents cross-check each other — hallucinations get caught before you see them
Every agent gets the same tasks regardless of track record	Dispatch weights route tasks to the agent with the best accuracy in that category
An agent keeps making the same class of mistake	Skill files are auto-generated from failure data and injected into future prompts
You don't know which agent to trust	Accuracy, uniqueness, and reliability scores are tracked per agent, per category

Gossipcat is right for you if

You want multiple AI models catching different classes of bugs
You don't trust a single agent to catch everything
You want agents to cross-check each other's findings before you act on them
You want to know which agents are actually accurate vs. hallucinating
You want agents that get better over time based on their track record

Features

Consensus Review

3+ agents review independently, then cross-review each other. Findings tagged as CONFIRMED, DISPUTED, or UNIQUE.

Adaptive Dispatch

Agent accuracy is tracked per-category. Dispatch weights adjust automatically — the best agent for the job gets picked.

Skill Development

When an agent keeps failing in a category, targeted skills are generated from failure data and injected into future prompts. Effectiveness is measured with a z-test on post-bind signals — passed, failed, or inconclusive.

Multi-Provider

Mix Anthropic, Google, OpenAI, and OpenClaw agents in one team. Each brings different strengths. Native agents need no API key. 🦞 Lobster friendly.

Live Dashboard

Real-time view of tasks, consensus reports, agent scores, and activity feed. Terminal Amber theme. WebSocket updates.

... View full README on GitHub

io.github.ataberk-xyz/gossipcat

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