OpenClaw Multi-Agent Workflow Issues: Stalling, Context Loss, and Token Inefficiency
OpenClaw Multi-Agent Workflow Challenges
A developer has detailed significant issues with OpenClaw's multi-agent workflow system, specifically around maintaining autonomy during complex project analysis tasks. The user is moving back to other agentic frameworks due to these problems.
Technical Setup
The configuration tested included:
- Models: Gemini 3 Pro and Codex
- Structure: 1 COO Agent (Orchestrator) plus multiple specialized task agents
- Configuration: Custom SOUL.md, IDENTITY.md, and USER.md files for context
- Integration: Various Clawhub.ai skills
Reported Issues
Workflow Stalling
Agents frequently hang during operation. The Orchestrator (COO) assumes agents are still processing, but the Dashboard shows zero activity after the initial 10 minutes. Implementing a "check-in" loop did not solve the communication breakdown between agents.
Context Leakage/Loss
Despite providing custom documentation files, agents require constant re-prompting for basic project facts. The system appears to struggle with long-term task state management.
Token Inefficiency
In one run, over 400M tokens were consumed with no tangible output. This was primarily due to agents looping or re-analyzing the same steps without progressing to "Action" phases.
User Assessment
The developer questions whether OpenClaw is currently just a "cool UI" for manual prompting rather than a stable autonomous system. They note it feels significantly less stable than Claude Code or even basic AutoGPT setups for long-running tasks.
The user specifically asks: Are there specific configurations or "Clawhub" skills that actually fix the autonomy issue, or is the architecture currently too fragile for multi-agent loops?
📖 Read the full source: r/openclaw
👀 See Also
ClawPy: Minimal Single-File Python Implementation of OpenClaw with Experience Memory
A developer built ClawPy, a stripped-down Python script that implements OpenClaw's autonomous task execution mechanics with a persistent experience system that learns from past errors and successes.
Lobster Cage: Dockerized Security Environment for Self-Hosting OpenClaw on Raspberry Pi
A developer built Lobster Cage, a Docker Compose environment with restricted outbound access and proxy-based routing to run OpenClaw securely on a Raspberry Pi for experimentation.
CC-Canary: Detect Regressions in Claude Code with Local JSONL Analysis
CC-Canary reads Claude Code session logs and produces a forensic report on model drift, including read:edit ratio, reasoning loops, cost trends, and auto-detected inflection dates.
Using a Local LLM as a Claude Code Subagent to Reduce Context Usage
A developer shares a method to use Claude Code to delegate tasks to a local LLM via LM Studio's API, keeping file content out of Claude's context. The approach uses a ~120-line Python script with tool-calling to read files locally and return summaries.