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chernistry/bernstein

v1.8.4 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 1mo AI Agents & Assistants
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

agent-framework agent-orchestrator agentic-ai ai-agents ai-coding aider
+14 more
anthropic claude-code cli-tool codex-cli coding-agent deterministic-scheduler hmac-audit llm mcp-server model-context-protocol multi-agent parallel-worktrees python swe-bench

Affected surfaces

auth rbac

Summary

AI summary

Plan-and-Execute architecture formalized, agent identity cards with capability enforcement, and built-in eval framework introduced.

Full changelog

v1.8.4

Planning, identity, and evaluation.

Features

  • Plan-and-Execute architecture formalized. Planning and execution are now explicit phases with typed interfaces, so you can swap planners without touching executors.
  • Agent identity cards with capability enforcement. Every spawn carries a signed identity card; the orchestrator refuses tool calls outside the card's declared capabilities.
  • Built-in eval framework with per-model accuracy reporting — useful for A/B-ing planners, routers, or adapter configs.
  • Canary deployments for prompt/model versions. Route a fraction of traffic to a new prompt or model; promote automatically on metric parity.
  • Opus alias upgraded to Claude Opus 4.7 — default "opus" now resolves to the 4.7 snapshot.

Docs

  • Added growth metrics breakdown.
  • Small README polish.

CI

  • npm publish: NPM_TOKEN is exported so .npmrc interpolation works under GitHub Actions.

Full changelog: https://github.com/chernistry/bernstein/compare/v1.8.3...v1.8.4

View diff on GitHub

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About chernistry/bernstein

Deterministic multi-agent orchestrator for 18 CLI coding agents (Claude Code, Codex, Cursor, Aider, Gemini CLI, OpenAI Agents SDK, and more). MCP server mode (stdio + HTTP/SSE) exposes the orchestrator to any MCP client. Git worktree isolation per agent, HMAC-chained audit trail, cost-aware model routing via contextual bandit. ~11K monthly PyPI downloads, Apache 2.0.

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Related context

Beta — feedback welcome: [email protected]