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.
📖 Read the full source: r/openclaw
👀 See Also
Non-technical user builds and deploys website using Claude AI from phone
A user with no coding experience built and deployed a full website from their phone in one hour using Claude AI. They created a fake Portal 3 loading screen for an April Fools' prank by describing requirements in plain language and having Claude generate specifications and code.
Using Claude with MCPs for Automated B2B Outbound Campaigns
A Reddit user shares their workflow using Claude with Model Context Protocol (MCP) servers to automate B2B outbound campaigns, replacing Clay with custom API integrations for lead discovery, enrichment, verification, and email sending.
Developer builds flight comparison tool with Claude Code using BDMA approach
A non-developer built easyscape.eu using Claude Code with a BDMA (build/debug/measure/adjust) loop approach. The tool compares multiple departure airports, integrates real costs like road tolls and parking, and shows the most economical departure option, not just the cheapest ticket.
OpenClaw Agent Structure: 5 Core Files and 3 Practical Use Cases
An OpenClaw user found that all agents are built from five core files: User, Soul, Agent, Tools, and Identity. They shared three working agents including a daily AI briefing aggregator, a math coach for children, and a YouTube Shorts generator.