MCP Servers

gossipcat

Enables multi-agent code review with cross-verification of findings against source code, catching hallucinations and improving agent accuracy over time.

README

<p align="center"> <em>weightless in-context RL for code review — agents that learn from grounded signals, no weights touched.</em> </p>

<p align="center"> <a href="#quickstart"><strong>Install</strong></a> · <a href="#first-run--5-minutes"><strong>First Run</strong></a> · <a href="#how-to-use-it-day-to-day"><strong>Daily Use</strong></a> · <a href="#reading-the-dashboard"><strong>Dashboard</strong></a> · <a href="#troubleshooting"><strong>Troubleshooting</strong></a> · <a href="#configuration"><strong>Config</strong></a> · <a href="#for-ai-agents"><strong>For AI Agents</strong></a> </p>

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<p align="center"> <a href="#reading-the-dashboard"> <img src="https://raw.githubusercontent.com/gossipcat-ai/gossipcat-ai/master/packages/dashboard-v2/public/assets/dashboard-overview.png" alt="Gossipcat dashboard — live fleet view with per-agent vortexes, accuracy rings, signal volume, and recent hallucination catches" width="900" /> </p> <p align="center"> <em>Live dashboard at <code>http://localhost:63007/dashboard</code> — fleet vortex, signal stream, skill graduation grid, and consensus flow, all in real time.</em> </p> </p>

<p align="center"> <a href="#reading-the-dashboard"> <img src="https://raw.githubusercontent.com/gossipcat-ai/gossipcat-ai/master/packages/dashboard-v2/public/assets/dashboard-team.png" alt="Gossipcat Team page — per-agent accuracy, weight, signals, hallucinations, and last task" width="900" /> </p> <p align="center"> <em>Per-agent leaderboard — accuracy, uniqueness, impact, hallucinations caught, and dispatch weight. Updated after every consensus round.</em> </p> </p>

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The single-reviewer failure mode: a solo AI reviewer ships hallucinated bugs as critical findings 5–10% of the time in our internal usage. Gossipcat's cross-review drops that to under 1%. That delta is what the whole system exists to produce.

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Multi-agent consensus code review that catches hallucinations before you act on them — and gets smarter every session.

Gossipcat is an MCP server for Claude Code that runs 3+ AI agents in parallel to review your code. They independently find bugs, then cross-review each other's findings. Confirmed = real. Caught = hallucination, penalized. Over time, agents accumulate accuracy profiles and the system routes tasks to whoever is most reliable for that category. No weights updated — the "policy" is a markdown skill file.

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.

It's weightless in-context reinforcement learning

Most RL pipelines update model weights. Gossipcat doesn't touch weights — it learns by updating the prompt layer.

Every finding an agent produces must cite a real file:line. Peers verify those citations against actual source code. Verified findings (and caught hallucinations) become grounded reward signals — no judge model, no subjective grade, just mechanical checks against ground truth. Those signals update per-agent competency scores, which steer future dispatch. When an agent keeps failing in a category, a targeted skill file is auto-generated from its own failure history and injected into future prompts.

flowchart LR
    A([agent review]) -->|cites file:line| B([peer cross-review])
    B -->|verifies against code| C{verdict}
    C -->|confirmed| D[reward signal]
    C -->|hallucination| E[penalty signal]
    D --> F[competency score]
    E --> F
    F -->|steer dispatch| G([next agent pick])
    E -->|≥3 in category| H[auto-generate skill]
    H -->|inject into prompt| A
    G --> A
    style A fill:#0ea5e9,stroke:#0369a1,color:#fff
    style H fill:#f59e0b,stroke:#b45309,color:#fff
    style D fill:#10b981,stroke:#047857,color:#fff
    style E fill:#ef4444,stroke:#b91c1c,color:#fff

The "policy update" is a markdown file under .gossip/agents/<id>/skills/. No fine-tuning, no RLHF infrastructure, no labelling pipeline. The reward signal is grounded in source code rather than a judge model, which is the piece that makes the loop trustworthy enough to automate. When agents disagree, we check the code — not another LLM's opinion.

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How Gossipcat compares

	What you get	Hallucination filtering	Agents improve over time
Gossipcat	3+ agents cross-review each other's findings; confirmed bugs only	Yes — peers catch and penalize hallucinations mechanically	Yes — accuracy signals steer dispatch; skill files fix repeat failures
Single-agent review (Claude Code built-in, Cursor review)	One model reviews your diff	No — hallucinations ship as findings	No — no feedback loop
LLM-as-judge cross-review (most multi-agent frameworks)	One model grades another model's output	Partial — judge can hallucinate too; no ground truth	No — judge scores aren't wired to dispatch
Traditional review tools (CodeRabbit, PR-Agent)	Pattern-match + one LLM pass	No	No

The core difference: gossipcat verifies findings against actual file:line citations in your codebase. That ground truth is what makes the reward signal trustworthy enough to automate.

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Real-world session

What a typical gossipcat session looks like in practice (2026-05-22, v0.4.30 ship):

1 feature shipped end-to-end — consensus auto-verify (PR #448, master commit 4b28a1c, 1255+ LOC, 50/50 new tests, zero regressions). Full design ↔ ship arc through gossipcat itself: 6 consensus rounds on the spec before any code was written.
6 consensus rounds on the spec caught 21+ HIGH-severity defects across rev-1 → rev-6 — including a double-dispatch bug on the run() path (rev-3, opus-implementer), a phantom AgentTeam type the implementer would have invented if shipped (rev-5, sonnet-reviewer grep-grounded against live AgentConfig), and a metadata field that didn't exist on ConsensusSignal at all (rev-4, sonnet — metadata lives only on MetaSignal / PipelineSignal). Each round produced a measurable rev: rev-1 had 4 HIGHs, rev-6 had 0.
1 post-merge bug caught by a pre-existing drift test — signal-allowlist-drift.test.ts:108 (which exists precisely to catch this — same failure mode as PR #329's silent-drop of transport_failure) flagged that the implementer added the 2 new signals to KNOWN_SIGNALS + the type union + OPERATIONAL_SIGNAL_NAMES but missed VALID_CONSENSUS_SIGNALS in performance-writer.ts. 7-line fix landed in the same PR before merge.
Methodology lesson recorded — sonnet-reviewer's habit of grepping cited file:line against live code (rather than trusting prose claims) is what ultimately closed the spec. Recorded as a citation_grounding agreement signal so the agent's pattern compounds across sessions.

Nothing landed without cross-review. Two agents got +/- score adjustments based on what they caught vs. what they missed. The spec is now usable as a worked example of what 6 rounds of multi-agent design review looks like — docs/superpowers/specs/2026-05-21-consensus-auto-verify-design.md.

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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

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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

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Features

<table> <tr> <td align="center" width="33%"> <h3>Consensus Review</h3> 3+ agents review independently, then cross-review each other. Findings tagged as CONFIRMED, DISPUTED, or UNIQUE. </td> <td align="center" width="33%"> <h3>Adaptive Dispatch</h3> Agent accuracy is tracked per-category. Dispatch weights adjust automatically — the best agent for the job gets picked. </td> <td align="center" width="33%"> <h3>Skill Development</h3> 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. </td> </tr> <tr> <td align="center"> <h3>Multi-Provider</h3> Mix Anthropic, Google, OpenAI, and OpenClaw agents in one team. Each brings different strengths. Native agents need no API key. 🦞 Lobster friendly. </td> <td align="center"> <h3>Live Dashboard</h3> Real-time view of tasks, consensus reports, agent scores, and activity feed. Terminal Amber theme. WebSocket updates. </td> <td align="center"> <h3>Agent Memory</h3> Per-agent cognitive memory persists across sessions. Agents remember past findings, patterns, and project context. </td> </tr> <tr> <td align="center" colspan="3"> <h3>Auto-Verify (v0.4.30)</h3> Opt-in. Every UNVERIFIED finding gets <code>file_read</code>-checked by a verifier agent before the report is returned. <code>tag</code> stays <code>'unverified'</code> — auto-verify is metadata, not state transition. Flag: <code>GOSSIP_CONSENSUS_AUTO_VERIFY_UNVERIFIED=1</code>. </td> </tr> </table>

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<div align="center"> <table> <tr> <td align="center"><strong>Works<br/>with</strong></td> <td align="center"> <img src="https://img.shields.io/badge/Claude%20Code-supported-orange?style=flat&logo=anthropic&logoColor=white" alt="Claude Code" /><br/><sub>Full support</sub> </td> <td align="center"><strong>Cursor</strong><br/><sub>Not yet</sub></td> <td align="center"><strong>Windsurf</strong><br/><sub>Not yet</sub></td> <td align="center"><strong>VS Code</strong><br/><sub>Not yet</sub></td> </tr> </table>

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<table> <tr> <td align="center"><strong>Provider<br/>gateways</strong></td> <td align="center"> <img src="https://img.shields.io/badge/OpenClaw-gateway-4A90D9?style=flat" alt="OpenClaw" /><br/><sub>HTTP gateway ✅</sub> </td> <td align="center"> <img src="https://img.shields.io/badge/Ollama-local-gray?style=flat" alt="Ollama" /><br/><sub>Local models ✅</sub> </td> <td align="center"> <img src="https://img.shields.io/badge/OpenAI--compatible-any-green?style=flat" alt="OpenAI-compatible" /><br/><sub>Any base_url ✅</sub> </td> </tr> </table> </div>

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How it works

The Mermaid diagram above shows the loop end-to-end. Here's the per-step definition:

Step	What happens
Dispatch	Tasks routed to agents based on dispatch weights (accuracy history per category)
Parallel review	Agents work independently, each producing findings with confidence scores
Cross-review	Each agent reviews peers' findings: agree, disagree, unverified, or new finding
Consensus	Findings deduplicated and tagged: CONFIRMED, DISPUTED, UNVERIFIED, UNIQUE
Signals	You verify findings against code and record accuracy signals
Skill development	Agents with repeated failures get targeted skill files injected into future prompts

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Two types of agents

	Native	Relay
Runs as	Claude Code subagent (`Agent()` tool)	WebSocket worker on relay server
Providers	Anthropic (Claude)	Google (Gemini), OpenAI, any provider
API key	None — uses your Claude Code subscription	Required per provider
Defined in	`.claude/agents/*.md`	`.gossip/config.json`
Consensus	Yes	Yes
Memory & Skills	Yes	Yes

Both types participate equally in consensus, cross-review, and skill development. Native subagents get skill files injected into their system prompts and can call gossip_remember for memory recall. Relay workers call the equivalent memory_query tool and get file_read + file_grep during cross-review so their verification parity matches natives.

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Quickstart

Requirements: Node.js 22+ and Claude Code.

One-liner

npm install -g gossipcat && claude mcp add gossipcat -s user -- gossipcat

Restart Claude Code. Then in any project, ask:

"Set up a gossipcat team for this project"

<details> <summary><strong>Manual MCP config</strong> (if <code>claude mcp add</code> doesn't work for your setup)</summary>

Add to ~/.claude/mcp_settings.json:

{
  "mcpServers": {
    "gossipcat": {
      "command": "gossipcat"
    }
  }
}

Or project-local in .mcp.json:

{
  "mcpServers": {
    "gossipcat": {
      "command": "npx",
      "args": ["gossipcat"]
    }
  }
}

</details>

Claude Code will call gossip_setup() to scaffold .gossip/config.json and your agent team. First-run bootstrap also writes the dispatch rules and tool catalog so Claude Code knows how to use gossipcat — no manual config needed.

Gossipcat is on npm and GitHub Releases — both carry the same bundle. npm install -g gossipcat pulls from the registry and is the shortest path; the GitHub release URL is useful when you want to pin to a specific tarball (see Alternative install paths below). Either way, npm drops a gossipcat binary on your PATH.

What the install ships

	What you get
MCP server	Bundled binary at `dist-mcp/mcp-server.js`, wired as the `gossipcat` command on `PATH`
Dashboard	Prebuilt static assets in `dist-dashboard/` — launches automatically on a dynamic port (ask Claude Code "what's my gossipcat dashboard URL?"). Override with `GOSSIPCAT_PORT=24420` if you want a stable port.
Default skills + rules + archetypes	16 bundled skill templates, operational rules, and project archetypes copied into the install
Postinstall wizard	Writes `.mcp.json` with correct absolute paths for your machine

Alternative install paths

Pin to a specific npm version:

npm install -g gossipcat@0.5.2

Pin to a specific GitHub release tarball (version-locked, bypasses npm registry):

npm install -g https://github.com/gossipcat-ai/gossipcat-ai/releases/download/v0.5.2/gossipcat-0.5.2.tgz

Project-local install (each project gets its own gossipcat):

cd your-project
npm install --save-dev gossipcat

The postinstall writes .mcp.json to your project root. Open Claude Code in that directory and gossipcat connects automatically — no claude mcp add needed.

From source (contributors):

git clone https://github.com/gossipcat-ai/gossipcat-ai.git
cd gossipcat-ai
npm install
npm run build:mcp
claude mcp add gossipcat -s user -- node "$PWD/dist-mcp/mcp-server.js"

Upgrading

Re-run the install — npm will fetch the latest version and replace the installed binary:

npm install -g gossipcat@latest

Or in-session, ask Claude Code: "Check for gossipcat updates" — the gossip_update tool fetches the latest release notes and applies the upgrade with your confirmation.

3. API keys

Add env vars for the providers you want to use. Pass them with -e when registering, or set them in your shell environment.

Provider	Env var	Notes
Native (Claude Code)	—	Dispatches through your active Claude Code subscription. No key needed.
Anthropic API	`ANTHROPIC_API_KEY`	Direct API access if you don't want to go through Claude Code.
Google Gemini	`GOOGLE_API_KEY`	Gemini Pro / Flash relay agents.
OpenAI	`OPENAI_API_KEY` (+ optional `OPENAI_BASE_URL`)	GPT-4 / GPT-4o relay agents. `OPENAI_BASE_URL` lets you point at OpenAI-compatible gateways (Azure, Together, Groq, etc.).
OpenClaw	— (local gateway)	OpenAI-compatible, defaults to `http://127.0.0.1:18789/v1`. No API key — auth handled by your local OpenClaw daemon.
Ollama (local)	—	Runs locally via `http://localhost:11434`. No key. Pull your model first with `ollama pull llama3.1:8b`.

Examples — registering gossipcat with each provider

Native only (zero API keys — everything runs through Claude Code):

claude mcp add gossipcat -s user -- gossipcat

Then in session ask for a team built from sonnet-reviewer / haiku-researcher / opus-implementer. Native agents dispatch through Agent() and relay back. Good zero-config starting point.

Anthropic API (direct, bypasses Claude Code):

claude mcp add gossipcat -s user \
  -e ANTHROPIC_API_KEY=sk-ant-... \
  -- gossipcat

Use this if you want relay agents running Claude models without going through the Claude Code subscription path — e.g. for parallelism beyond Claude Code's concurrency cap, or for running long background reviews while you keep working.

Google Gemini:

claude mcp add gossipcat -s user \
  -e GOOGLE_API_KEY=AIza... \
  -- gossipcat

Enables gemini-reviewer, gemini-tester, gemini-implementer on the relay. Watch the quota — gossipcat has a built-in 429 watcher that falls back to native agents when Gemini is cooling down.

OpenAI (and OpenAI-compatible gateways):

claude mcp add gossipcat -s user \
  -e OPENAI_API_KEY=sk-... \
  -- gossipcat

For Azure / Together / Groq / OpenRouter, add OPENAI_BASE_URL:

claude mcp add gossipcat -s user \
  -e OPENAI_API_KEY=your-key \
  -e OPENAI_BASE_URL=https://api.groq.com/openai/v1 \
  -- gossipcat

OpenClaw (local gateway):

# Start the OpenClaw daemon first (see openclaw docs), default port 18789
claude mcp add gossipcat -s user -- gossipcat

No env vars. Configure an agent with provider: "openclaw" in .gossip/config.json and gossipcat talks to the local gateway automatically. Override the port with base_url in the agent config if your daemon runs elsewhere.

Ollama (fully local, no API):

# Pull a model once
ollama pull llama3.1:8b
# Then register gossipcat
claude mcp add gossipcat -s user -- gossipcat

Configure the agent with provider: "local" and model: "llama3.1:8b" in .gossip/config.json. Good for airgapped dev, offline work, and burning-down-test-debt sessions where you don't want to spend API credits.

Mixed setup (common production shape — Gemini cheap reviewers + Anthropic heavy implementers):

claude mcp add gossipcat -s user \
  -e GOOGLE_API_KEY=AIza... \
  -e ANTHROPIC_API_KEY=sk-ant-... \
  -- gossipcat

Then set up a team with gemini-reviewer + haiku-researcher (native) + opus-implementer (native) + sonnet-reviewer (native). Gossipcat dispatches by category strength from the signal pipeline.

Keys are stored persistently and cross-platform:

macOS — OS Keychain
Linux — Secret Service (secret-tool)
Windows / other — AES-256-GCM encrypted file

4. Initialize your team

Start a Claude Code session in any project and ask Claude to set up your team:

"Set up a gossipcat team with a Gemini reviewer and a Sonnet implementer"

Claude Code calls gossip_setup() to create your .gossip/config.json and agent definitions. You choose the providers, models, and roles — gossipcat adapts to your setup.

Available presets: reviewer, implementer, tester, researcher, debugger, architect, security, designer, planner, devops, documenter

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First Run — 5 Minutes

The fastest path from "just installed" to "first useful review". If you skip this section you'll probably get stuck on the same things everyone else gets stuck on.

Step 1 — Open Claude Code in any project

cd ~/your-project
claude

Gossipcat is registered globally now, so it boots automatically. You'll see it in the MCP server list.

Step 2 — Bootstrap once

In Claude Code, just type:

Run gossip_status

This loads gossipcat's operating rules into the current session, creates .gossip/ in your project on first run, and prints the dashboard URL + auth key. Copy the key — you'll paste it into the dashboard once.

You'll see something like:

Status:
  Host: claude-code (native agents supported)
  Relay: running :49664
  Workers: 0
  Dashboard: http://localhost:49664/dashboard (key: c3208820f8f70605fd45fa90004a2a4b)
  Quota: google — OK

Open the dashboard URL in your browser, paste the key. You're now connected.

Step 3 — Create your first team

Tell Claude what you're building:

"Set up a gossipcat team for this project — it's a TypeScript Next.js app with a Postgres backend and Stripe payments."

Claude calls gossip_setup() and proposes a team. Typical proposal:

Proposed team:
  - sonnet-reviewer    (anthropic/claude-sonnet-4-6, native)   reviewer + security
  - gemini-reviewer    (google/gemini-2.5-pro, relay)          reviewer + types
  - haiku-researcher   (anthropic/claude-haiku-4-5, native)    researcher
  - opus-implementer   (anthropic/claude-opus-4-6, native)     implementer

Approve? (y/n)

Native agents (native: true) run through your existing Claude Code subscription — no API key needed. Relay agents need a key for their provider. If you don't have a Google API key, drop gemini-reviewer from the team for now and add it later.

Once you approve, gossipcat writes .gossip/config.json and the agents are live.

Step 4 — Run your first review

In a project where you've made some changes:

"Do a consensus review of my recent changes"

What happens (typical timing):

Phase	Time	What you see
1. Decompose	1s	Claude picks agents and dispatches them in parallel
2. Independent review	30s–2min	Each agent reads your diff and reports findings
3. Cross-review	30s–1min	Each agent reviews the others' findings
4. Consensus report	<1s	Findings tagged CONFIRMED / DISPUTED / UNVERIFIED / UNIQUE
5. Verification	varies	Claude reads UNVERIFIED findings against the code, decides if they're real
6. Signal recording	<1s	Accuracy signals saved per agent

You get a report like:

Consensus round b81956b2-e0fa4ea4 — 3 agents

CONFIRMED (2):
  [critical] Race condition in tasks Map at server.ts:47 — sonnet + gemini
  [high]     Missing auth on WebSocket upgrade at server.ts:112 — sonnet + gemini

UNIQUE (1):
  [medium]   String concat in SQL query at queries.ts:88 — only sonnet caught this

DISPUTED (1):
  [low]      "Memory leak in timer" — haiku says yes, sonnet/gemini say no
             → verified, sonnet was right (not a leak — cleanup is in finally)

Final: 3 real bugs to fix, 1 false alarm caught by cross-review.

You only act on CONFIRMED + verified UNIQUE findings. The cross-review is the whole point — single-agent reviews ship hallucinated bugs as critical findings 5–10% of the time. Cross-review with verification drops that to under 1%.

Step 5 — Watch the dashboard

The dashboard shows everything live: agents, scores, active tasks, consensus reports, signals. You can leave it open in a tab while you work — every gossipcat tool call pushes an update via WebSocket.

That's the basic loop. The rest of this README covers advanced workflows, troubleshooting, and how to interpret what you're seeing.

<br/>

How to use it day-to-day

Concrete recipes for the most common workflows. Each one shows what to type, what you'll get back, and what to do with it.

Recipe 1: Review a diff before committing

Type:

"Review my staged changes"

What you'll get: A consensus report (1–3 minutes) with findings tagged CONFIRMED / UNIQUE / DISPUTED. Claude verifies UNVERIFIED findings against the code and tells you which are real.

What to do with it: Fix the CONFIRMED + verified-real findings. Ignore disputed-but-falsified findings. If a finding looks important but you disagree, ask Claude "verify finding f3 against the code yourself" — it'll re-check and either back you up or push back.

When NOT to use it: Tiny diffs (under 20 lines) — overhead exceeds value. Just eyeball them.

Recipe 2: Catch security issues before shipping a feature

Type:

"Security audit the payment handler at lib/stripe/webhook.ts"

What you'll get: Each security-skilled agent reviews from a different angle (OWASP, input validation, auth, secrets). Findings get cross-validated. Real vulns surface; theoretical ones get caught and dropped.

What to do with it: Fix critical/high findings before merge. Bookmark medium/low findings for the next pass.

Tip: Be specific about the file or module. "Security audit the codebase" is too broad and produces noisy results. "Security audit lib/stripe/webhook.ts" produces actionable findings.

Recipe 3: Understand a piece of code before changing it

Type:

"Research how the WebSocket connection lifecycle works in this project before I touch it"

What you'll get: A research agent (haiku-researcher by default — fast and cheap) reads the code, traces call paths, and writes a summary. The summary is saved to that agent's cognitive memory so the next time you ask about the same area it remembers.

What to do with it: Use the summary to plan your change. The agent will reference it next time you ask anything related — no re-discovery cost.

Recipe 4: Verify your own assumption

Type:

"I think there's a race condition in the tasks Map at server.ts:47 — check if I'm right"

What you'll get: Two agents independently check the specific claim and either confirm or push back. Author self-review is optimistic — this isn't.

What to do with it: If both agree with you, fix it. If they push back, read their reasoning before defending your hypothesis. They might be right.

Recipe 5: See which agents you can actually trust

Type:

"Show me agent scores"

What you'll get: A table of agents sorted by reliability with per-category accuracy and dispatch weights. Categories include trust_boundaries, injection_vectors, concurrency, error_handling, data_integrity, type_safety, etc.

What to do with it: If gemini-reviewer is sitting at 30% accuracy on concurrency, you know not to trust its concurrency findings without cross-review. If sonnet-reviewer is at 90% on trust_boundaries, you can ship its findings on auth/session bugs with high confidence.

Recipe 6: Improve an agent that keeps making the same mistake

Type:

"gemini-reviewer keeps hallucinating about concurrency — develop a skill for it"

What you'll get: Gossipcat reads gemini-reviewer's failure data, generates a targeted skill file with concrete anti-patterns, and injects it into the agent's prompt for all future concurrency-related reviews. Effectiveness is measured statistically (z-test on post-bind signals) — it'll tell you if the skill is actually working after ~30 dispatches.

What to do with it: Nothing — it's automatic. Just keep using the agent. Over time, the failure rate drops.

Recipe 7: Set up a team for a brand-new project

Type:

"Set up a gossipcat team for a TypeScript Cloudflare Workers project with Drizzle ORM and KV storage"

What you'll get: A proposed team with archetypes matched to your stack. Worker projects need different reviewers than long-running Node services — gossipcat picks accordingly.

What to do with it: Review the proposal, drop agents you can't run (missing API keys), approve.

Things to avoid

Don't ask for "review the whole codebase" — too broad, agents will pick whatever they find first. Scope to a file, module, or diff.
Don't approve findings without reading them — even after cross-review, ~5% of findings are genuinely wrong. The reasoning matters more than the verdict.
Don't ignore the dashboard — when something feels weird (slow dispatch, repeated failures, suspicious findings), the dashboard usually shows you why before you have to ask.
Don't run consensus mode for trivial questions — gossip_run with one agent is fine for "what does this function do?"-tier queries. Save consensus for changes that touch shared state, auth, persistence, or the dispatch pipeline itself.

<br/>

Reading the dashboard

The dashboard at http://localhost:<port>/dashboard is the visual layer over everything gossipcat knows. Open it once with the auth key from gossip_status, leave the tab open while you work. Updates push live via WebSocket.

<p align="center"> <img src="https://raw.githubusercontent.com/gossipcat-ai/gossipcat-ai/master/packages/dashboard-v2/public/assets/dashboard-skills.png" alt="Skill graduation grid — per-skill effectiveness curve, 7d window, current/threshold value, and ±pp delta for graduated skills" width="900" /> </p> <p align="center"> <em>Skill graduation grid — each card is one (skill × agent) pair: post-bind effectiveness curve over a 7-day window with current value vs threshold and ±pp drift on graduated skills.</em> </p>

Panel	What it shows	When to look at it
Overview	Active agents, dispatch weights, recent finding counts	First thing in the morning — quick sanity check
Team	All agents sorted by reliability score, with category breakdowns	Picking which agent to trust for a tricky finding
Tasks	Live + historical task list with agent, duration, status	When something feels stuck — find it here first
Findings	Consensus reports paginated by round, with CONFIRMED/DISPUTED/UNVERIFIED breakdowns	Reviewing what got caught in a recent review
Agent detail	Per-agent memory entries, skills, score history, task history	Diagnosing why a specific agent keeps failing in a category
Signals	Raw signal feed (agreement / hallucination / unique_confirmed)	Auditing the scoring pipeline if scores look wrong
Logs	mcp.log content (boot, errors, warnings)	When the MCP server is misbehaving and you need raw evidence

Auth keys rotate every session. A fresh key is generated each time gossipcat boots. If the dashboard says "unauthorized", run gossip_status again to get the new key.

<br/>

Troubleshooting

"Dashboard says unauthorized / 401"

The auth key rotates every boot. Run gossip_status in Claude Code to get the current key, paste it into the dashboard login.

"Dashboard URL doesn't load at all"

Check ~/.gossip/mcp.log (or <your-project>/.gossip/mcp.log) for the boot log. Look for the [gossipcat] 🌐 Dashboard: line — that's the actual port. If it's missing, the relay didn't start. Common causes:

Conflicting .gossip/relay.pid from a crashed previous boot — delete it and restart Claude Code
GOSSIPCAT_PORT set to a port already in use — unset the env var or pick a free port

"Boot says 'No .gossip/config.json found' and nothing happens"

This was a critical bug in v0.1.0 — fixed in v0.1.1. Upgrade with the install one-liner above. v0.1.1+ boots in degraded mode (dashboard + relay only) so you can run gossip_setup from inside Claude Code.

"Agents keep returning empty findings"

Usually a model or quota problem. Check gossip_status — it shows Quota: google — OK (or cooling down) per provider. If you're rate-limited, gossipcat will fall back to native agents automatically, but fallback agents may not be in your team. Either wait for the cooldown or add native agents to your team.

"The same hallucinated finding keeps coming back"

Record a hallucination_caught signal: ask Claude "record a hallucination_caught signal for finding f3 in the last consensus round — it claimed X but the code shows Y". After 3 such signals, the offending agent's score drops in that category and the orchestrator stops asking it questions in that area.

"I want to use my own model / provider"

Edit .gossip/config.json directly. Any OpenAI-compatible endpoint works via provider: "openai" + base_url. Local models work via Ollama (provider: "local"). See the Configuration section.

"An agent produced output but the consensus report is empty"

The strict <agent_finding> parser drops tags whose type isn't one of finding | suggestion | insight (see invariant #8 in docs/HANDBOOK.md). When that happens, the gossip_signals receipt surfaces the drop count and a finding_dropped_format pipeline signal is emitted. Check the consensus round's droppedFindingsByType field on the dashboard — it names the offending type. If you see <agent_finding> instead of raw <agent_finding>, a transport layer is entity-encoding the output; pass agent output verbatim to gossip_relay.

"Multiple Claude Code instances all want gossipcat"

Already supported as of v0.1.1 — each instance gets its own dynamic port. If you want a stable port for one specific instance (e.g. for browser bookmarks), set GOSSIPCAT_PORT=24420 for that one project's environment.

"How do I uninstall?"

npm uninstall -g gossipcat
claude mcp remove gossipcat -s user
rm -rf ~/.gossip  # if you want to wipe global memory + signals
rm -rf <project>/.gossip  # if you want to wipe per-project state

Still stuck?

File an issue at https://github.com/gossipcat-ai/gossipcat-ai/issues. Include the contents of .gossip/mcp.log (last 100 lines) and the output of gossip_status. Or ask Claude in-session: "file a gossipcat bug report about <...>" — the gossip_bug_feedback tool packages it up automatically.

<br/>

Under the hood

Claude Code translates your natural-language requests into gossipcat MCP tool calls automatically — you don't need to type these — but if you want fine-grained control they're documented here:

gossip_run(agent_id: "auto", task: "...")        → single-agent task
gossip_dispatch(mode: "consensus", tasks: [...]) → multi-agent review with cross-review
gossip_collect(consensus: true)                  → wait for results, run consensus
gossip_signals(action: "record", signals: [...]) → record accuracy after verification
gossip_scores()                                  → view agent performance
gossip_skills(action: "develop", ...)            → improve a struggling agent
gossip_status()                                  → system status + dashboard URL
gossip_setup(...)                                → create or update your team

The dispatch rules at .claude/rules/gossipcat.md (auto-generated on first boot) teach Claude Code when to pick which mode based on what your change touches. You can edit these rules to bias the dispatch.

<br/>

For AI Agents

Currently supported orchestrator: Claude Code only. Cursor, Windsurf, and VS Code agents do not yet have native agent support and cannot run consensus cross-review. If you are reading this as a Claude Code instance with the gossipcat MCP server connected, this guide applies to you.

If you are a Claude Code instance reading this to understand how to use gossipcat, here is the complete operational guide.

You are the orchestrator. Gossipcat gives you tools to dispatch work to a team of specialized agents, collect their results, run cross-review consensus, and record accuracy signals. You do not implement code directly — you coordinate.

Step 1 — Check what you have

gossip_status()

This tells you: which agents are available, their providers/models, relay status, dashboard URL, and suggested workflows. Always call this first in a new session.

Step 2 — Dispatch work

Single agent (auto-selected):

gossip_run(agent_id: "auto", task: "Review the auth module for security issues")

The system picks the best agent for the task based on past accuracy. For relay agents, this returns the result directly. For native agents (Claude Code subagents), it returns ⚠️ EXECUTE NOW instructions — follow them immediately in the same response.

Parallel (fan-out, no consensus):

gossip_dispatch(mode: "parallel", tasks: [
  { agent_id: "gemini-reviewer", task: "..." },
  { agent_id: "sonnet-reviewer", task: "..." }
])

Consensus (cross-review):

gossip_dispatch(mode: "consensus", tasks: [
  { agent_id: "gemini-reviewer", task: "..." },
  { agent_id: "sonnet-reviewer", task: "..." },
  { agent_id: "haiku-researcher", task: "..." }
])

Step 3 — Collect results

gossip_collect(task_ids: ["id1", "id2", "id3"], consensus: true)

With consensus: true, agents cross-review each other's findings. If native agents are in the round, gossip_collect returns ⚠️ EXECUTE NOW with prompts — dispatch those Agent() calls immediately, then relay each result via gossip_relay_cross_review.

Step 4 — Verify and record signals

After consensus, verify every UNVERIFIED finding against the actual code (grep/read the cited files). Then record signals:

gossip_signals(action: "record", signals: [{
  signal: "unique_confirmed",   // or "hallucination_caught", "agreement"
  agent_id: "gemini-reviewer",
  finding: "Race condition in task map at line 47",
  finding_id: "<consensus_id>:<agent_id>:f1"   // mandatory
}])

Signals update dispatch weights. Agents that hallucinate get penalized. Agents that catch real bugs get promoted.

Key rules

Never leave UNVERIFIED findings unexamined — read the code, confirm or deny, record the signal.
finding_id is mandatory on every signal — format: <consensus_id>:<agent_id>:fN.
Use gossip_progress after reconnect — if a consensus round was in flight, it re-surfaces the pending EXECUTE NOW prompts.

When to use consensus

Use gossip_dispatch(mode: "consensus") when the change touches: shared mutable state, auth/sessions, file persistence, or the core dispatch pipeline. Use gossip_run for single-agent research, exploration, or review tasks that don't need cross-validation.

MCP Tools

These tools are called by the internal LLM (the orchestrator — Claude Code with gossipcat MCP). You don't invoke them manually; the orchestrator selects and calls them based on your requests.

Tool	Purpose
`gossip_status`	System status, dashboard URL, agent list
`gossip_run`	Single-agent dispatch with auto agent selection
`gossip_dispatch`	Multi-agent dispatch: `single`, `parallel`, or `consensus`
`gossip_collect`	Collect results with optional cross-review synthesis
`gossip_relay`	Feed native agent results back into the pipeline
`gossip_relay_cross_review`	Feed native cross-review results into consensus
`gossip_plan`	Decompose task into sub-tasks with agent assignments
`gossip_signals`	Record or retract accuracy signals
`gossip_scores`	View agent accuracy, uniqueness, and dispatch weights
`gossip_skills`	Develop, bind, unbind, or list per-agent skills
`gossip_setup`	Create or update agent team
`gossip_session_save`	Save session context for next session
`gossip_remember`	Search an agent's cognitive memory
`gossip_progress`	Check in-progress task status
`gossip_watch`	Stream signals as agents emit them, between dispatch and collect (catches pipeline drops mid-round)
`gossip_verify_memory`	Verify a memory claim against current code — FRESH / STALE / CONTRADICTED / INCONCLUSIVE — before acting on backlog items
`gossip_reload`	Self-terminate the MCP process so Claude Code respawns with a fresh bundle (dev loop after code changes)
`gossip_tools`	List all available tools
`gossip_update`	Check for or apply gossipcat updates from npm
`gossip_bug_feedback`	File a GitHub issue on the gossipcat repo from an in-session bug report

<br/>

Dashboard internals

User-facing dashboard guide is in Reading the dashboard above. This section covers the build + tech stack.

Built with React + Vite + shadcn/ui. Source lives at packages/dashboard-v2/. The bundled assets ship in dist-dashboard/ and the relay serves them as static files at http://localhost:<dynamic-port>/dashboard/. Live updates push via WebSocket — every gossipcat tool call emits an event that connected dashboard tabs receive in real time.

To rebuild from source (contributors only):

npm run build:dashboard

<br/>

Architecture

gossipcat/
  apps/
    cli/                  MCP server, native agent bridge, boot sequence
  packages/
    orchestrator/         Dispatch pipeline, consensus engine, memory, skills,
                          performance scoring, task graph, prompt assembly
    relay/                WebSocket relay server, dashboard REST/WS API
    dashboard-v2/         React + Vite frontend (Terminal Amber theme)
    client/               Lightweight WebSocket client for relay connections
    tools/                File/shell/git tool implementations for worker agents
    types/                Shared TypeScript types and message protocol

<br/>

OpenClaw Integration

Gossipcat supports OpenClaw as a provider gateway. OpenClaw runs locally and exposes an OpenAI-compatible HTTP API — gossipcat talks to it like any other relay agent, with your stored gateway token and a separate quota slot so OpenClaw rate limits never bleed into your OpenAI agents.

Wiring an OpenClaw agent

Store your gateway token once (macOS):

security add-generic-password -s gossip-mesh -a openclaw -w <your-gateway-token>

On Linux:

secret-tool store --label "Gossip Mesh openclaw" service gossip-mesh provider openclaw
# (enter token when prompted)

Then add it to your team:

"Add an OpenClaw reviewer to my team"

Or directly via gossip_setup:

gossip_setup(mode: "merge", agents: [{
  id: "openclaw-agent",
  type: "custom",
  provider: "openclaw",
  custom_model: "openclaw/default",
  role: "reviewer",
  skills: ["code_review", "typescript"]
}])

The gateway runs at http://127.0.0.1:18789/v1 by default. Override with base_url if yours is on a different port. Available models: openclaw, openclaw/default, openclaw/main.

Once added, the agent participates in consensus rounds, accumulates accuracy signals, and gets skill files generated from its failure patterns — same as any other agent in the mesh.

<br/>

Configuration

Config is searched in order: .gossip/config.json > gossip.agents.json > gossip.agents.yaml.

{
  "main_agent": {
    "provider": "google",
    "model": "gemini-2.5-pro"
  },
  "utility_model": {
    "provider": "native",
    "model": "haiku"
  },
  "consensus_judge": {
    "provider": "anthropic",
    "model": "claude-sonnet-4-6",
    "native": true
  },
  "agents": {
    "sonnet-reviewer": {
      "provider": "anthropic",
      "model": "claude-sonnet-4-6",
      "preset": "reviewer",
      "skills": ["code_review", "security_audit", "typescript"],
      "native": true
    }
  }
}

Field	Description
`main_agent`	Internal tool LLM for routing, planning, and synthesis
`utility_model`	Memory compaction, gossip, lens generation
`consensus_judge`	Model for cross-review synthesis
`agents.<id>.provider`	`anthropic`, `google`, `openai`, `openclaw`, `local`
`agents.<id>.base_url`	Custom endpoint for `openai`/`openclaw` (e.g. `http://127.0.0.1:18789/v1`)
`agents.<id>.native`	`true` = runs via Claude Code Agent(), no API key
`agents.<id>.preset`	`reviewer`, `implementer`, `tester`, `researcher`, `debugger`, `architect`, `security`, `designer`, `planner`, `devops`, `documenter`
`agents.<id>.skills`	Skill labels for dispatch matching

<br/>

Host compatibility

Gossipcat auto-detects the host environment:

Host	Native agents	Rules file
Claude Code	Yes	`.claude/rules/gossipcat.md`
Cursor	No	`.cursor/rules/gossipcat.mdc`
Windsurf	No	`.windsurfrules`
VS Code	No	—

<br/>

Roadmap

Feature	Status
Consensus code review	✅ Shipped
Adaptive dispatch weights	✅ Shipped
Per-agent skill development	✅ Shipped
Agent cognitive memory	✅ Shipped
Live dashboard	✅ Shipped
Cross-platform key storage	✅ Shipped
OpenAI-compatible gateway support (`base_url`)	✅ Shipped
OpenClaw provider integration 🦞	✅ Shipped
Local LLM support (Ollama)	✅ Shipped
Statistical skill effectiveness (z-test on per-category accuracy, auto pass/fail verdicts)	✅ Shipped
Native subagents get skill injection + cognitive memory recall	✅ Shipped
Relay cross-reviewers get `file_read` + `file_grep` (closes tool-blindness gap with natives)	✅ Shipped
Worktree-aware consensus (`resolutionRoots` + auto-discover for feature-branch reviews)	✅ Shipped
Signal pipeline observability (format-drop receipts + `finding_dropped_format` meta-signal + `gossip_watch` stream)	✅ Shipped
Consensus round retraction (`gossip_signals action: retract` with tombstones)	✅ Shipped
Worktree sandbox hardening (Layer 1+2+3 boundary enforcement + rotated audit log)	✅ Shipped
In-session bundle hot-swap (`gossip_reload`)	✅ Shipped
npm package — one-liner install with bundled MCP server + dashboard	✅ Shipped
Full implementation workflow (agents write code with scoped + worktree isolation)	✅ Shipped
Dashboard enrichment (graphs, trends, session history)	☐ Planned
Local Postgres migration (embedded Postgres for tasks/signals/consensus/memory — unblocks full task results, real queries, no more JSONL scans)	☐ Planned
Full Cursor support	☐ Planned
Windsurf / VS Code parity	☐ Planned
Standalone CLI (no IDE required)	☐ Planned
CLI parity with MCP pipeline (gossip, task graph, agent memory in chat mode)	☐ Planned

<br/>

Contributing

Gossipcat is open source and early-stage — bug reports, feature ideas, and PRs are all welcome.

Bugs / feature requests → open an issue. Or ask Claude Code directly: "File a gossipcat bug report about <...>" — the gossip_bug_feedback tool posts structured issues from your current session.
Pull requests → fork, branch, PR against master. Run npm test before pushing. Commit messages follow conventional commits (fix:, feat:, chore:, docs:).
Discussions → new ideas, design questions, "should this be a feature?" → GitHub Discussions.

See CLAUDE.md in the repo for the operational rules gossipcat's own agents follow during development — it's a useful read if you want to understand the signal pipeline and consensus workflow from the inside.

Cutting a release (maintainers)

Releases go to GitHub Releases via a two-stage script that respects branch protection — no direct commits to master.

# Stage 1 — open the version bump PR
./scripts/release.sh 0.1.2

# review + merge the PR via gh or web UI
gh pr merge <pr-number> --squash --delete-branch

# Stage 2 — build, tag, release (from master, after the PR is merged)
git checkout master && git pull
./scripts/release.sh   # no args

Stage 1 creates chore/release-X.Y.Z, bumps package.json, opens the PR, exits. Stage 2 reads the version from package.json, builds the MCP bundle + dashboard, packs the tarball, tags, pushes the tag, and creates the GitHub release with auto-generated notes from commits since the last tag.

<br/>

Star History

License

MIT

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