Show HN: Conduct, open-source guardrails for LLM and MCP tool calls

Conduct Runtime governance for AI agents — one policy enforces across every LLM call, every shell tool, every teammate's AI session. Ask Lens — natural language over your governance data Lens is Conduct's chat surface. It sits above every workspace as a Guard-enforced assistant — ask about blocked events, approvals, spend, rules, agent activity in plain English. Every tool call the assistant makes runs through Guard: same policy engine, same audit trail as a live agent. One chat for the whole platform. Guard, workflows, rules, compliance — one input, no context switching. Grounded in your data. Not a wrapper around ChatGPT. Every answer comes from your workspace's audit log, policy state, and run history. Guard-enforced. Lens's LLM calls go through the same policy engine as your agents. Same rules, same limits, same audit chain. Drilldowns built in. Ask "who got blocked today" — get a table with per-row links to the full audit record. Two product surfaces, one repo, one policy: Conduct Guard — the policy engine. Decides block / warn / audit / inject for every AI action before it executes, backed by signed configuration and a hash-chained audit log. Conduct Router — the LLM proxy. Point any provider SDK (Anthropic, OpenAI, Perplexity) at Router and every request runs through Guard on the way to the upstream provider. Governance, not observability Runtime firewalls like Straiker and Lakera tell you what an agent did. Guard controls what an agent can do — with cryptographic proof. The three-pillar moat: Signed configuration — every workspace signs its active policy set. Every Guard check verifies the signature before enforcing. A tampered pack — pushed by anyone, at any layer — is rejected before it can decide anything. Hash-chained audit — every decision appends to a SHA-256 chain rooted at workspace genesis. Any missing or altered entry breaks the chain and is caught on one-click verification. Evidence you can hand to an auditor. Policy-first, not detection-first — rules decide before the action executes, with structured reasons. Not anomaly detection after the fact. Discovery — the free wedge New here? Start with Discovery mode: read-only visibility into every AI action your team takes for 14 days. No policy to author, nothing to install upstream, no cost. When you're ready to enforce, promote a rule from what Discovery already saw. → conductai.ai/sign-up Quick start git clone https://github.com/sseshachala/conductai cd conductai docker compose up API on http://localhost:8000 (Guard + Router live at /guard/* and /proxy/*) Canvas UI on http://localhost:3000 Redis worker + Postgres come up in the same stack Point any provider SDK at Router: curl https://api.conductai.ai/proxy/anthropic/v1/messages \ -H "Authorization: Bearer cond_agt_..." \ -H "Content-Type: application/json" \ -d '{"model":"claude-sonnet-4-6","max_tokens":1024,"messages":[{"role":"user","content":"Hello"}]}' Or wrap your CLI hooks with Guard: pip install conduct-cli conduct login conduct sync # installs hook + MCP, pulls policies Now every Claude Code, Cursor, Copilot, ChatGPT, or Codex session on that machine is governed by the same active packs. What ships in this repo 20+ compliance packs ship out of the box: OWASP, SOC 2 CC7.3, HIPAA §164.312, PCI DSS 4.0, EU AI Act Art. 15/16, NIST AI RMF, ISO 42001, and framework-specific packs for Python, Node, and Terraform. 22 pre-built playbooks: Issue → PR, code review, incident response, prod deploy gate, CI/CD triage, security scanner triage, Slack digest, and more. Each is one YAML file; edit-and-run. Architecture at a glance Developer / agent Guard control plane ───────────────── ─────────────────── Claude Code ──┐ ┌── Canvas UI (Next.js) Cursor ──┤ CLI hook ────► ├── FastAPI + policy engine Copilot ──┤ (cond_cli) ├── Postgres (state, audit) Codex ──┘ ├── Redis (workers, queues) ┌──── MCP ────► └── Hash chain (SHA-256) Any SDK ────┤ (Anthropic, └── Router ────► Upstream provider (Anthropic, OpenAI, /proxy/* OpenAI, Perplexity, ...) Perplexity) Guard checks fire at three chokepoints: CLI hook — every Claude Code / Cursor / Copilot / Codex tool call. MCP layer — every MCP tool invocation. Router — every LLM call by any SDK. One policy, three enforcement surfaces. Deployment Self-host with docker compose — the command above. Runs everything locally. Self-host on Kubernetes — deployment templates ship in issue #1149. Hosted — conductai.ai. Free tier includes Discovery; paid tiers unlock enforcement + Router + hash-chain verification API. Security & Trust SECURITY.md — vulnerability reporting policy, scope, coordinated disclosure, and safe harbor. Threat model — system context, trust boundaries, attacker goals, mitigations, and residual risks. Policy decision contract — guard_check decision semantics and fail-mode behavior. Audit log verification — independent prev_hash/entry_hash chain verification procedure and example script. API versioning — proxy/MCP compatibility, deprecation windows, and OpenAPI publication guidance. License Apache License 2.0 — the entire repository, including the CLI, Guard, Router, Agent Booster, playbooks, and packs. Free for commercial and non-commercial use, modification, and redistribution. Includes an explicit patent grant from all contributors (Apache 2.0 §3). Trademark rights are not granted; see NOTICE — "Conduct", "Conduct AI", and "Conduct Guard" remain trademarks of Conduct AI. Redistribution must preserve the LICENSE and NOTICE files. The hosted control plane at conductai.ai (canvas UI, team RBAC, marketplace, managed Guard) is a commercial offering built on top of this repository. For enterprise support, indemnification, or licensing questions, email hello@conductai.ai. Contributing We accept bug reports, docs fixes, new playbooks, new packs, tests, and code. Read CONTRIBUTING.md first. Everyone participating agrees to the Code of Conduct. Security vulnerabilities: don't open a public issue. See SECURITY.md. Anything else: GitHub Discussions or SUPPORT.md. Links Product: conductai.ai Guard landing: conductai.ai/guard Router landing: conductai.ai/router Docs: conductai.ai/docs Discussions: github.com/sseshachala/conductai/discussions Changelog: CHANGELOG.md + Releases Book a demo: cal.com/sudhi-seshachala-pks7pd ⭐ If Conduct saves your team time, star it — it helps other teams find it. ⭐ If Conduct saves your team time, star it — it helps other teams find it.