Running Two Claude Code Agents on the Same Repo with Git Worktrees
Running multiple Claude Code agents on the same repository simultaneously has been achieved using git worktrees. Each agent gets its own branch and dedicated working directory on disk, eliminating file conflicts and allowing reading/writing without interference.
Workflow
- Create a git worktree for each branch.
- Open a separate Claude Code session in each worktree directory.
- Let the agents run in parallel — they are unaware of each other.
- When both finish, merge the branches via normal git merge.
Practical Example
# Create a worktree for a feature branch
git worktree add ../repo-feature feature-branch
# Create a worktree for a bugfix branch
git worktree add ../repo-bugfix bugfix-branch
In each directory, start Claude Code
cd ../repo-feature && claude-code .
cd ../repo-bugfix && claude-code .
Benefits
Speed improvement is real for parallelizable work: one agent fixing a bug while another drafts a new feature, or a refactor alongside test coverage. Not every task splits cleanly, but genuinely independent concerns benefit. User reports having one agent fixing a bug and another drafting a feature simultaneously without conflict.
Considerations
The user asks about reliability for longer-running tasks and whether merge conflicts become a problem at scale. Standard git merge resolution applies when merging worktree branches back.
Who It's For
Developers using Claude Code who want to parallelize independent tasks on the same codebase.
📖 Read the full source: r/ClaudeAI
👀 See Also
civStation: A VLM System for Playing Civilization VI via Natural Language Commands
civStation is a computer-use VLM harness that plays Civilization VI by translating high-level natural language commands into in-game actions. The system uses a 3-layer architecture separating strategy and execution, with support for human-in-the-loop intervention.
UK Sovereign LLM Inference: Relax.ai Launches Public Docs
Relax.ai released docs for UK sovereign LLM inference, redirecting to /docs/getting-started/introduction. The service was shared on HN with 104 points.
AgentWorkingMemory: A Local Memory System for AI Coding Agents
AgentWorkingMemory (AWM) is a local memory system that solves the session-to-session amnesia problem in AI coding agents. It uses a SQLite database, three local ML models (~124MB total), and integrates automatically via MCP to provide persistent, context-aware memory across Claude Code sessions.
Constrails: Early-Alpha External Governance Layer for AI Agents
Constrails is an external runtime governance layer for AI agents that places a control layer between agents and their tools, implementing capability checks, risk scoring, policy evaluation, and audit logging. The early-alpha project aims to address safety concerns by moving controls outside the agent itself.