#Agent orchestration (11)

Open-source orchestration for teams of AI agents: a Node.js server and React UI where you bring your own agents, set goals, and track work and spend from one dashboard. It looks like a task manager; underneath sit org charts, budgets, governance, goal alignment and agent coordination. It manages business goals, not pull requests. MIT, self-hostable.

Agent orchestrationMulti-agent

TypeScript81k15k395

stablyai/orcaMIT

Orca: the ADE that grounds parallel agents in git worktrees

Orca calls itself an ADE (Agentic Development Environment), the AI orchestrator for 100x builders (75k+ stars, MIT/TypeScript, YC-backed). Central mechanism: fan one prompt across Codex, Claude Code, OpenCode and Pi, each isolated in its own git worktree, then compare results and merge the winner. Also WebGL terminals, Design Mode (click a UI element to send its HTML/CSS/screenshot into the prompt), native GitHub and Linear integration, diff annotations routed back to the agent, SSH worktrees and mobile notifications. Not independently benchmarked; graded needs-reproduction.

Agent orchestrationAgent Harness

TypeScript76k5.0k131

multica-ai/multicaNOASSERTION

Multica: AI coding agents that show up on the board

An open-source, self-hostable workspace where you assign issues to AI coding agents the way you would to a teammate: they pick up the work, run on a runtime you control, comment as they go, and hand results back for review. Works with 26 agent CLIs and keeps intent, runs, decisions and diffs on one issue, with code never leaving your machine.

Agent orchestrationCoding Agent

Go51k6.6k184

langchain-ai/langgraphMIT

LangGraph: agents as long-running processes that must persist, interrupt and resume

A low-level stateful-graph orchestration framework from LangChain, MIT licensed, with Python and JS/TS implementations, and Klarna, Replit and Elastic named as production users in the README. Its lineage is stated in the acknowledgements - inspired by Google Pregel and Apache Beam, interface borrowing from NetworkX - which means the BSP graph-computation model applied to agents: nodes are model calls or tool executions, edges are control flow, state is an explicit object. The value is not in the loop-the-model-with-tools layer but in four runtime capabilities: durable execution (resume exactly where it stopped after a crash), human-in-the-loop interrupts (approval can stay suspended for hours or days), separate short-term and long-term memory, and LangSmith execution-path tracing. Its actual claim is that agent reliability is a runtime problem, not a model problem. Usable without LangChain; any CompiledStateGraph can be handed to Deep Agents above it as a sub-agent. 42.1k stars. Not run yet, and checkpoint-resume consistency is unverified, so this is graded as pending reproduction.

Agent orchestrationGraph Runtime

Python42k7.1k185

herdrdev/herdrApache-2.0

herdr: The Always-On Terminal Runtime for Coding Agents

A single Rust binary that runs coding agents as always-on background terminals: panes report working, blocked or idle, agents drive each other through a CLI and socket API, and sessions survive restarts.

Terminal multiplexerCoding Agent

Rust36k2.6k102

getpaseo/paseoNOASSERTION

Paseo: One Self-Hosted Cockpit for Claude Code, Codex, Copilot and More Coding Agents

Self-hosted multi-agent orchestration (20.9k stars): Claude Code, Codex, Copilot, OpenCode, Pi, Antigravity and Muse Code behind one interface on your own machines; a local daemon (WebSocket + 33 MCP tools) orchestrates them while Electron desktop, Expo iOS/Android, web and CLI clients connect; git-worktree parallel isolation, local-ONNX voice, an end-to-end encrypted relay for remote follow-up, plus a plugin registry, skills and Hub ecosystem. Apache-2.0.

AI CodingCoding Agent

TypeScript21k2.5k56

Graph Engineering with Claude: 14 Steps from Linear Agents to Agent Fleets

Most multi-agent systems are still queues. This roadmap redraws them as graphs: nodes do work, edges move data, independent nodes run in parallel, and results converge only on real dependencies.

BLOG

Multi-Agent SystemsAgent orchestration

Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning

Meta's agentic meta-reasoning harness separates control from work: a controller runs Assess, Propose, Evaluate and Dispatch over a compact state and persistent artifact memory. On ProgramBench it reaches 71.5% with GPT-5.5, versus 58.0% for Codex.

Paras Dahal, Anton Bakhtin, Taco Cohen · 623 views · Sep 29, 2026

Agent HarnessMeta-Reasoning

Sep 29, 2026

0:34

Paperclip: Turn a Bunch of AI Agents into an Actual Organization

@vicky_grok · 652 views · 2026-09-27

AI AgentMulti-Agent Orchestration

1:34

Cursor Ships Projects: One Persistent Thread, a Coordinator Agent, and Subagents

@cursor_ai · 606 views · 2026-09-10

Coding AgentAgent orchestration

Graph Engineering: The Complete Guide to Building Multi-Agent AI Systems

The hard problem in multi-agent systems is not making one agent smarter, but deciding how work itself should move. This long-form guide introduces Graph Engineering: use the dependency test to expose fake dependencies, measure latency by critical path instead of total steps, compress deterministically before reasoning, give verifiers a falsification objective, model human approval as an edge rather than a node, freeze rules that sit outside optimization, and adopt five canonical graph shapes plus a paste-ready graph spec template — along with clear criteria for when NOT to build a graph at all.