Hacking Multi-Agent Orchestration into OpenClaw: A Developer's Experience

A developer shared their experience modifying OpenClaw to implement true multi-agent orchestration after discovering that agents were pretending to collaborate without actually calling each other.

The Problem: Fake Collaboration

The developer initially set up multiple agents (PM, planner, backend, frontend, designer) with different assigned models, expecting an orchestrator to coordinate them. While responses appeared structured with different sections and perspectives, log analysis revealed the PM agent was doing everything solo and faking the other agents' contributions. None of the other agents were actually called.

The core issue: OpenClaw treats each agent as an independent unit with no built-in way for one agent to spawn another, wait for results, and fold them back in.

The Solution: Core Runtime Modifications

To implement proper orchestration, the developer modified the core runtime (reply-Bm8VrLQh.js) to handle:

• Parent-child agent spawning via sessions_spawn / sessions_yield
• Subagent completion events bubbling up to parent
• Proper message assembly for the gateway and TUI

The sessions_yield implementation was particularly challenging, requiring about 90 minutes of continuous Codex assistance to get the async flow correct.

Results and Tradeoffs

After implementation:

• Agents now run on separate threads in parallel
• Results get aggregated by the orchestrator
• PM receives a consolidated report and formats the final output
• Each agent actually uses its assigned model (fixing a bug where they all defaulted to the base model)

Tradeoffs include:

• Full pipeline takes 30-60 seconds vs near-instant for single agent
• Cost was about $0.90 over two days of testing
• Memory sits around 10-16GB during active runs

Hardware and Initial Setup

The developer used an M4 Mac Mini (32GB) as a dedicated AI assistant for organizing messy notes and summarizing research. They initially tried running LLMs locally with a 30B model but found it painfully slow and switched to commercial APIs (OpenAI, Claude, Gemini) through OpenClaw.

Output quality with orchestration is still being evaluated. For simple tasks, a single agent is faster and cheaper, but for complex multi-step tasks, specialization may pay off with more tuning needed.