Devswarm
High-performance MCP server, code graph engine & evolutionary algorithm platform in Zig. 33 tools: GitHub project management, agent swarm orchestration, iterative review-fix loops, blast radius analysis, and code navigation via Model Context Protocol.
Install / Use
/learn @justrach/DevswarmQuality Score
Category
Project & Program ManagementSupported Platforms
README
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
<details> <summary><strong>Claude Code</strong></summary>Add to ~/.claude.json:
{
"mcpServers": {
"devswarm": {
"command": "/path/to/devswarm",
"args": ["--mcp"],
"env": { "REPO_PATH": "/path/to/your/repo" }
}
}
}
Then run /mcp to verify — you'll see 37 tools added to your assistant.
Add to ~/.codex/config.toml:
[mcp_servers.devswarm]
command = "/path/to/devswarm"
args = ["--mcp"]
env = { REPO_PATH = "/path/to/your/repo" }
</details>
<details>
<summary><strong>Amp</strong></summary>
Add to your Amp MCP config:
{
"mcpServers": {
"devswarm": {
"command": "/path/to/devswarm",
"args": ["--mcp"],
"env": { "REPO_PATH": "/path/to/your/repo" }
}
}
}
</details>
🚀 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:
| Preset | Pipeline |
|--------|---------|
| finder_fixer | find the issue → fix it |
| reviewer_fixer | review → fix reported issues |
| explore_report | deep exploration → structured report |
| architect_build | design → implement |
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:
| Role | Model | Does |
|------|-------|------|
| finder | Sonnet | Search and locate |
| reviewer | Sonnet | Review for correctness |
| fixer | Sonnet | Apply fixes (writable) |
| explorer | Sonnet | Deep codebase exploration |
| architect | Opus | System design decisions |
| orchestrator | Opus | Decomposes swarm tasks |
| synthesizer | Sonnet | Combines agent outputs |
| monitor | Haiku | Lightweight checks |
| Mode | Use when |
|------|---------|
| smart | Most tasks |
| rush | Quick answers |
| deep | Hard problems, architecture |
| free | Minimize cost |
🔧 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
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:
- Resolves — picks backend (Claude or Codex), model tier, system prompt, and tool preamble based on role + mode + what's available on your PATH
- Dispatches — spawns the agent on the right backend, falls back automatically if one isn't available
- 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
