Nexus Agents

OpenSSF Best Practices OpenSSF Scorecard

Governance substrate for your AI coding agents — adversarial review, drift-detected rules, immutable audit, closed-loop telemetry

npm version License: MIT Node.js Version

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Why Nexus Agents?

Nexus-agents is a governance layer that sits above your AI coding agents — Claude Code, Codex, Gemini, and OpenCode. The agents do the engineering; nexus-agents enforces the rules they have to follow, reviews their work adversarially before it ships, audits everything they touch, and routes the next task based on what actually worked.

What it gives you:

• Adversarial PR review — pr_review runs 5 voter roles (architect, security, devex, catfish, scope_steward) with a 4-point verification gate. On the v5 evaluation set (10 PRs): 100% bug-catch and 50% raw false-positive rate; manual triage reclassified most "FPs" as legitimate findings the dataset had mislabeled. Full numbers: docs/research/pr-review-experiment-results-v5.md
• Drift-detected charter — CLAUDE.md + governance:check + blocking CI gates fail the build when documented rules drift from registered behavior (model registry, MCP tools, expert types, skills)
• Immutable audit trail — every tool call, every voter decision, every routing choice flows through AuditTrail with structured logging and hash-chained append-only storage; integrity is verifiable via the verify_audit_chain MCP tool
• Closed-loop routing — OutcomeStore feeds production telemetry back into LinUCB + TOPSIS scoring so the system actually learns from what shipped vs what regressed. A second, bounded loop runs by default: a signal.swarm_unhealthy (adapter circuit-breaker / swarm-health) applies a small, capped, auto-decaying routing demotion via TuneAdjustmentStore — demotion-only, never zeroes a CLI, every adjustment audited, opt-out with NEXUS_TUNE_ENFORCE=false
• Multi-voter consensus — consensus_vote runs a default 7-role panel (architect, security, devex, ai_ml, pm, catfish, scope_steward; --quick uses 3). Six strategies: simple/super-majority, unanimous, higher-order Bayesian, opinion-wise, proof-of-learning

You:               "Review this PR / orchestrate this task / vote on this proposal"
                    ↓
nexus-agents:       enforce rules → route → adversarial review → audit → learn from outcome
                    ↓
Engineering agents: Claude Code · Codex · Gemini · OpenCode
                    ↓
Code:               actual edits, tests, PRs, issues

What this is NOT:

• Not another autonomous coding agent. OpenHands, SWE-agent, AutoGen, Devin, Factory — those are agents. Nexus-agents is the layer above them. Use whichever agents fit; we govern them
• Not a chat framework. Nothing here orchestrates conversations. It orchestrates real CLI tool invocations with real file I/O and outcome tracking
• Not a model API proxy. The value is the rules, the gates, the audit, and the learning. Routing is a side effect of the governance work, not the product

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Where nexus-agents sits in your stack

   Human / IDE / CLI
   (Claude Code, Cursor, VS Code, terminal)
            │ MCP Protocol
            ▼
  ┌─────────────────────────────────────────────────────┐
  │  GOVERNANCE SUBSTRATE — what nexus-agents provides   │
  │                                                       │
  │   Charter (drift-checked)   Adversarial PR review    │
  │   Role registry             Multi-voter consensus    │
  │   Immutable audit trail     Closed-loop telemetry    │
  │                                                       │
  │   46 MCP tools · multi-stage CompositeRouter         │
  └────────────────────────┬────────────────────────────┘
                           │
                           ▼ delegates execution to
  ┌─────────────────────────────────────────────────────┐
  │  ENGINEERING AGENTS — what does the actual work      │
  │                                                       │
  │   Claude Code · Codex · Gemini · OpenCode            │
  └────────────────────────┬────────────────────────────┘
                           │
                           ▼ produces
                   Code, tests, PRs, issues

The governance substrate is the layer that catches the mistakes engineering agents would otherwise make — bad code shipped, rules drifting from intent, audit gaps, telemetry-free routing — and routes the next task based on what actually worked the last time.

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Quick Start (2 minutes)

1. Install

npm install -g nexus-agents

Or as a Claude Code plugin (single-command install from the official marketplace):

/plugin install nexus-agents

See docs/getting-started/PLUGIN_INSTALL.md for plugin-specific setup, or llms-install.md for the short install guide an AI agent can follow.

2. Verify

nexus-agents doctor

Prints a health table — Node version, configured CLIs (claude / codex / gemini / opencode), API keys missing vs present. Read-only; safe to run any time.

3. See what success looks like (60-second smoke task — no API keys needed)

nexus-agents vote --quick --proposal "Use SQLite over JSON files for the outcome store"

You should see:

Nexus Agents Consensus Vote
============================

Collecting votes from 3 agents (timeout: 60s each)...

Proposal: Use SQLite over JSON files for the outcome store

Votes

  ✓ Software Architect: APPROVE (86%)
  ✓ Security Engineer:  APPROVE (74%)
  ✓ Scope Steward:      APPROVE (91%)

Summary

  Approve:  3
  Reject:   0
  Abstain:  0
  Approval: 100.0%
  Threshold: simple_majority

Result: APPROVED

Completed in ~30s

Three voter roles deliberate via whichever local CLIs you have (Claude, Codex, Gemini) — no API keys required. Per-voter reasoning is recorded; the terminal prints the verdict. Mixed outcomes (some approve / some reject) and graceful error handling are demonstrated on the project site hero with a real 7-voter run.

4. Wire into your editor

nexus-agents setup   # Auto-configures MCP server in Claude Code, Cursor, etc.

Restart your editor. The 46 MCP tools (orchestrate, consensus_vote, research_synthesize, verify_audit_chain, …) become available to whatever agent you're already using.

What setup configures

By default, setup writes/updates up to seven things in your environment. Each can be skipped with the corresponding --skip-* flag if you don't want it.

Configured	Where written	Opt-out flag
MCP server registration (Claude)	~/.claude/mcp.json / Claude Desktop config	--skip-mcp
Project rules	.cursor/rules/ and/or .claude/rules/	--skip-rules
Session hooks	~/.claude/hooks/ (session-start / pre-tool / etc.)	--skip-hooks
OpenCode MCP config	~/.config/opencode/opencode.json	--skip-opencode
Gemini MCP config	~/.gemini/mcp.json	--skip-gemini
Codex MCP config	~/.codex/config.toml	--skip-codex
Project config file	./nexus-agents.yaml	--skip-config

Run with --interactive (the default) for a per-step confirm flow, or --no-interactive to accept all defaults.

5. Standalone usage (no editor required)

export ANTHROPIC_API_KEY=your-key
nexus-agents orchestrate "Explain the architecture of this codebase"

Security: In default MCP mode, the server communicates only via stdio with the parent process (no network exposure). The REST API (opt-in) auto-generates an API key on first start. For network-exposed deployments, set NEXUS_AUTH_ENABLED=true. See SECURITY.md.

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Capabilities

Category	Details
Adversarial PR Review	pr_review MCP tool: 5 voter roles (architect, security, devex, catfish, scope_steward) with 4-point gate. v5 evaluation (n=10): 100% bug-catch, 50% raw FP rate; manual triage reclassified most FPs as legitimate findings (details)
Consensus Voting	6 strategies: simple_majority, supermajority, unanimous, higher_order (Bayesian correlation-aware), opinion_wise, proof_of_learning
Drift-Detected Charter	CLAUDE.md + inject-governance.ts check enforces single-source registries (model registry, MCP tools, expert types). Blocking CI gate fails build on drift
Audit Trail	Structured logging for every tool call, voter decision, and routing choice. Hash-chained immutable storage; integrity verifiable via verify_audit_chain MCP tool (#2281, #2289)
Closed-Loop Telemetry	OutcomeStore feeds LinUCB + TOPSIS scoring; a second bounded, audited self-tuning loop demotes unhealthy CLIs (capped, auto-decaying, on by default, opt-out NEXUS_TUNE_ENFORCE=false)
Security Pipeline	Sandboxing (Docker/policy), trust-tiered input handling, SARIF parsing, red-team patterns, ClawGuard access policies (audit/enforce)
Multi-Expert Orchestration	12 built-in expert types coordinated by Orchestrator. Roles bind prompt + tools + memory
Development Pipeline	Research → Plan → Vote → Decompose → Implement → QA → Security. Three modes: autonomous, harness (caller implements), dry-run
Memory & Learning	5 user-facing backends (session, belief, agentic, adaptive, typed). Cross-session persistence feeds routing decisions
Research System	9 discovery sources (arXiv, GitHub, Semantic Scholar, etc). Auto-catalog, quality scoring, synthesis into topic clusters
Graph Workflows	DAG-based workflow execution with checkpoint/resume, state reduction, and event hooks
46 MCP Tools	Agent management, workflow execution, research, memory, codebase intelligence, repo analysis, consensus, operations

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Available Experts

Expert	Specialization
Code	Implementation, debugging, optimization
Architecture	System design, patterns, scalability
Security	Vulnerability analysis, secure coding
Testing	Test strategies, coverage, test generation
QA	Acceptance criteria, regression checks
Documentation	Technical writing, API docs
DevOps	CI/CD, deployment, infrastructure
Research	Literature review, state-of-the-art analysis
PM	Product management, requirements, priorities
UX	User experience, usability, accessibility
Infrastructure	Server management, bare metal, networking
Data Viz	Charts, dashboards, visual data presentation

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Supported CLIs & Providers

Nexus-agents routes tasks through 5 CLI adapters, each connecting to major AI providers:

CLI	Provider	Best For
claude	Anthropic (Claude)	Complex reasoning, analysis
gemini	Google (Gemini)	Long context, multimodal
codex	OpenAI (Codex CLI)	Code generation, reasoning
codex-mcp	OpenAI (Codex MCP)	MCP-native Codex integration
opencode	Custom OpenAI-compat	Custom endpoints, local models

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CLI Commands

nexus-agents                    # Start MCP server (default)
nexus-agents doctor             # Check installation health
nexus-agents setup              # Configure Claude CLI integration
nexus-agents orchestrate "..."  # Run task with experts
nexus-agents vote "proposal"    # Multi-agent consensus voting
nexus-agents review <pr-url>    # Review a GitHub PR
nexus-agents expert list        # List available experts
nexus-agents workflow list      # List workflow templates
nexus-agents config init        # Generate config file
nexus-agents init --portable    # Create workspace-local .nexus-agents/ for sandboxes
nexus-agents init --portable --mcp-config  # Also emit .mcp.json wiring Claude Code to it
nexus-agents init --portable --install --mcp-config  # …and install the binary into the workspace
nexus-agents fitness-audit      # Run fitness score audit
nexus-agents research query     # Query research registry
nexus-agents --help             # Full command list

See docs/ENTRYPOINTS.md for the complete CLI reference (28+ commands).

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MCP Tools

When running as an MCP server, the following tools are available. Start with run — the default entry point: give it a goal and the MetaOrchestrator picks (and, with execute: true, runs) the right strategy. The other pipeline tools are advanced force-strategy paths for pinning a specific one.

Tool	Description
orchestrate	Task orchestration with Orchestrator coordination
create_expert	Create a specialized expert agent
execute_expert	Run a task through a previously-created expert (by expertId)
run_workflow	Run a linear workflow template (use run_graph_workflow for DAGs)
delegate_to_model	Pick the best-fit existing model for a task (no registry change)
list_experts	Inventory of expert ROLES for create_expert
list_workflows	Inventory of multi-step TEMPLATES for run_workflow
consensus_vote	Multi-model consensus voting on proposals
research_query	Query research registry (status, overlap, stats, search)
research_add	Add an arXiv PAPER to the registry (for non-paper sources use research_add_source)
research_add_source	Add a NON-PAPER source (repo/tool/blog) — for arXiv papers use research_add
research_discover	Discover papers/repos from external sources
research_analyze	Analyze registry for gaps, trends, coverage
research_catalog_review	Review auto-cataloged research references
research_synthesize	Synthesize registry into topic clusters with themes
survey_oss_landscape	Transient OSS project search (license, stars, last-commit) via GitHub
vendor_publishing_audit	Look up a vendor's signing infrastructure (GPG keys, URL patterns, signature shape)
compare_data_feeds	Diff two YAML/JSON feeds: coverage + per-field axes
memory_query	Query across all memory backends
memory_stats	Memory system statistics dashboard
memory_write	Write to typed memory backends
weather_report	Multi-CLI performance weather report
issue_triage	Triage GitHub issues with trust classification
run_graph_workflow	Run a DAG workflow with per-node checkpoints + audit trail (linear → run_workflow)
execute_spec	Execute AI software factory spec pipeline
registry_import	Draft YAML for a NEW model entry (for picking existing models use delegate_to_model)
query_trace	Query execution traces for observability
query_task_state	Query the structured task-state log for a task ID
get_job_result	Read result of an async-mode dispatch by jobId (#3042 / #2631)
list_jobs	List async-mode jobs across all tools — cross-session discovery (#3046 / #2631)
cancel_job	Mark an async-mode job as cancelled — idempotent (#3042 Stage 1b)
ci_health_check	CI infrastructure health — composes GitHub status + recent-runs activity (#3076)
verify_audit_chain	Verify hash chain of a FileAuditStorage audit log directory
repo_analyze	Analyze GitHub repository structure
repo_security_plan	Generate security scanning pipeline for a repo
extract_symbols	Tree-sitter AST symbols from a SINGLE file (functions/classes/types)
search_codebase	Cross-file ripgrep search for patterns or text (not an AST parser)
run_dev_pipeline	Full dev pipeline: research, plan, vote, implement, QA
run_pipeline	Execute a pipeline plugin by name with typed input
pr_review	Multi-voter PR review with verification gate (experimental)
supply_chain_tradeoff_panel	Per-axis tradeoff vote for build-vs-buy / supply-chain decisions
improvement_review	Threshold-gated observability loop — surfaces routing/tech-debt/bug/security signals from outcome+fitness data; files candidate issues
run_quality_gate	Run the QA quality gate (typecheck/lint/tests/build/security) over a project dir; returns structured pass/fail verdict + feedback
suggest_research_tasks	SUGGEST-ONLY: candidate pipeline tasks from research_discover findings for review — files/executes nothing (#1715)
list_available_models	Probe all model-discovery transports (OpenRouter API + opencode/claude/codex/gemini CLIs) and report per-transport health — validates the CLIs/APIs are reachable (#3406)
run	Default entry point — give a goal, MetaOrchestrator picks the strategy; returns the routing decision (execute:false, read-only) or runs it inline (execute:true; dev-pipeline+pipeline+research+consensus wired) (#3548)

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Configuration

Environment Variables:

Variable	Description
ANTHROPIC_API_KEY	Claude API key
OPENAI_API_KEY	OpenAI API key
GOOGLE_AI_API_KEY	Gemini API key
NEXUS_LOG_LEVEL	Log level (debug/info/warn/error)

Generate config file:

nexus-agents config init   # Creates nexus-agents.yaml

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Documentation

Topic	Link
Full CLI Reference	docs/ENTRYPOINTS.md
Architecture	docs/architecture/README.md
Contributing	CONTRIBUTING.md
Coding Standards	CODING_STANDARDS.md
Quick Start Guide	QUICK_START.md

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Development

git clone https://github.com/nexus-substrate/nexus-agents.git
cd nexus-agents
pnpm install
pnpm build
pnpm test

Requirements: Node.js 22.x LTS, pnpm 9.x

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Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feat/amazing-feature)
3. Commit with conventional commits (feat(scope): add feature)
4. Open a Pull Request

See CONTRIBUTING.md for details.

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License

MIT - See LICENSE

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Built with Claude Code