DevSwarm

Your AI coding assistant, now with a team.

Drop one MCP server into Codex, Amp, or Claude Code and get 37 tools for spawning parallel agents, running task pipelines, and doing multi-step code work — without leaving your existing workflow.

Quick Start · Features · All 37 Tools · How It Works · Contributing

The Problem

You're already using Codex, Amp, or Claude Code. It writes code, fixes bugs, answers questions. But it's still one agent doing one thing at a time.

You: "Find all the memory leaks in this codebase and fix them"

  Orchestrator decomposes the task
       │
  ┌────┼────┐
  ▼    ▼    ▼
 [W1] [W2] [W3]   ← parallel agents, each owns a subsystem
  │    │    │
  └────┼────┘
       ▼
  Synthesizer → one clean report back to you

DevSwarm is an MCP server that gives your AI assistant the ability to orchestrate itself — spawning sub-agents, running parallel workloads, and chaining multi-step task pipelines. No new UI. No new workflow.

⚡ Quick Start

Option 1: Download a binary (recommended)

Grab the latest release for your platform from GitHub Releases.

Option 2: Build from source

git clone https://github.com/justrach/codedb.git
cd codedb
zig build          # builds zig-out/bin/devswarm
zig build test     # run all tests

Requirements: Zig 0.15.x, codex and/or claude CLI on PATH, Git

Connect to your AI assistant

{
  "mcpServers": {
    "devswarm": {
      "command": "/path/to/devswarm",
      "args": ["--mcp"],
      "env": { "REPO_PATH": "/path/to/your/repo" }
    }
  }
}

[mcp_servers.devswarm]
command = "/path/to/devswarm"
args = ["--mcp"]
env = { REPO_PATH = "/path/to/your/repo" }

{
  "mcpServers": {
    "devswarm": {
      "command": "/path/to/devswarm",
      "args": ["--mcp"],
      "env": { "REPO_PATH": "/path/to/your/repo" }
    }
  }
}

🚀 What You Can Do

Swarms — parallel agents on big tasks

run_swarm("Audit the entire auth system for security issues", max_agents=5)

An orchestrator breaks the task into sub-tasks. Workers run in parallel. A synthesizer combines everything. You get one answer instead of five tabs.

Task Chains — multi-step pipelines

run_task("Fix the race condition in src/queue.zig", preset="reviewer_fixer")

Built-in presets chain agents together automatically:

Review-Fix Loops — iterate until clean

review_fix_loop("Check for memory leaks", max_iterations=3)

Runs reviewer → fixer → reviewer again, until the reviewer says NO_ISSUES_FOUND or hits the iteration cap.

Single Agents with Role + Model Routing

run_agent("Explain the PPR algorithm", role="explorer", mode="deep")

Each agent gets the right model automatically:

🔧 Full Tool List (37 tools)

Agents run_agent · run_swarm · run_task · review_fix_loop · run_reviewer · run_explorer · run_zig_infra

Planning decompose_feature · get_project_state · get_next_task · prioritize_issues

Issues create_issue · update_issue · close_issue · get_issue · create_issues_batch · close_issues_batch · link_issues

Issue-filing note: create_issue and create_issues_batch now enforce the issue template implied by CONTRIBUTING.md and the agent issue-discovery standard. Issue bodies must include: one-sentence problem, exact repro, observed result, expected result, nearby passing checks, acceptance criteria, and non-goals. create_issue(parent_issue=...) is context-only: it appends Parent issue: #N to the body. Use link_issues for explicit dependency links.

Git create_branch · get_current_branch · commit_with_context · push_branch · recently_changed · git_history_for

Pull Requests create_pr · get_pr_status · list_open_prs · merge_pr · get_pr_diff · review_pr_impact

Code Intelligence blast_radius · relevant_context · symbol_at · find_callers · find_callees · find_dependents

Repo set_repo

⚙️ How It Works

DevSwarm is a provider-agnostic runtime. When you call run_agent, it:

1. Resolves — picks backend (Claude or Codex), model tier, system prompt, and tool preamble based on role + mode + what's available on your PATH
2. Dispatches — spawns the agent on the right backend, falls back automatically if one isn't available
3. Returns — streams output back through MCP

System prompts are assembled dynamically from agency rules, role instructions, mode guidance, and auto-detected tool availability (zig tools → ripgrep → grep). No hardcoded prompts.

Telemetry

devswarm collects anonymous usage telemetry to help improve the project. This is enabled by default.

What's collected

• Agent roles used (e.g. "finder", "reviewer", "fixer")
• Model names (e.g. "claude-sonnet-4-6")
• Token counts (input/output per worker)
• Wall time and estimated cost
• Worker count and parallelism metrics

What's NEVER collected

• Your code, file contents, or diffs
• Prompts, task descriptions, or agent outputs
• Repository names, file paths, or branch names
• Any personally identifiable information

How to opt out

Edit .devswarm/config.toml:

[telemetry]
enabled = false

Or set the environment variable:

export DEVSWARM_TELEMETRY=false

You can opt out at any time. The telemetry preference is set during onboarding and stored in .devswarm/config.toml.

Contributing

Contributions are welcome. See CONTRIBUTING.md for guidelines before opening a PR.

License

MIT — see LICENSE

Full changelog: README-changelog.md