AI Coding Agents · unohee

OpenSwarm

OpenSwarm is a TypeScript-based orchestrator that coordinates multiple AI agents (workers and reviewers) to autonomously tackle software development tasks. It integrates with Linear, Discord, and various LLM providers (OpenAI, OpenRouter, local models, Claude), storing conversation history and per-repository knowledge in a vector database for learning over time.

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Source: GitHub — github.com/unohee/OpenSwarm

816

GitHub stars

141

Forks

TypeScript

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	unohee/OpenSwarm
Owner	unohee
Primary language	TypeScript
License	MIT — OSI-approved
Stars	816
Forks	141
Open issues	0
Latest release	v0.17.5 (2026-07-05)
Last updated	2026-07-07
Source	https://github.com/unohee/OpenSwarm

What OpenSwarm is

Multi-agent agentic system written in TypeScript that picks up tasks from Linear or local SQLite, routes them to configurable LLM backends (OpenAI Codex, GPT, OpenRouter, Ollama, LM Studio, Claude), executes worker/reviewer pipelines with cost-aware model selection, and persists outcomes via LanceDB. Includes TUI, CLI, and daemon modes with pluggable notifiers and CI integration.

Quickstart

Get the OpenSwarm source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/unohee/OpenSwarm.git2cd OpenSwarm3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Autonomous Issue Triage and Fixing

Automatically pick up GitHub/Linear issues, diagnose with a frontier model, implement fixes with cost-effective lightweight models, and verify via reviewer agents — ideal for reducing manual triage and routine bug resolution overhead.

Continuous Code Quality Gates

Run `openswarm fix` in CI to auto-detect test/lint failures, fan workers across failures in parallel, re-run checks iteratively until green — saves developer context-switching on low-complexity failures.

Repository Knowledge Accumulation

Build per-repository embeddings of past task outcomes and code patterns; future agent runs recall this context to improve solution quality and reduce redundant discovery cycles in unfamiliar codebases.

Implementation considerations

Requires Node.js >= 22 and a C/C++ build toolchain for native dependencies (better-sqlite3, @lancedb/lancedb); prebuilt binaries cover common platforms but source builds needed on less common OS/arch.
LLM provider authentication must be set up before daemon start — OAuth flows for OpenAI, OpenRouter, Linear supported via `openswarm auth`; local inference (Ollama/LM Studio) easier but requires manual server setup.
Config validation is enforced (`openswarm validate`); repos without valid `config.yaml` or `openswarm.json` will not execute tasks — plan for CI/CD integration points.
Long-term memory via LanceDB persists per-repository embeddings; no documented encryption, retention policy, or data cleanup strategy for sensitive codebases.
Cost tracking shown in TUI status bar; hybrid mode (frontier diagnostic + lightweight implementation) reduces API spend but requires model availability across multiple providers.

When to avoid it — and what to weigh

Mission-critical production deployments without human review — Agent-generated code requires manual review before merge. No evidence of formal verification, formal methods, or audit trails for compliance-sensitive systems.
Disconnected or offline environments — Requires real-time LLM API access (OpenAI, OpenRouter) or local inference setup (Ollama/LM Studio). No graceful degradation or offline-first design documented.
Organizations requiring commercial SLA or support contracts — Project is open-source MIT with no documented commercial support, SLA, or liability model. Community-driven issue resolution only.
Highly domain-specific or proprietary code domains — Agent quality depends on training data and in-context examples. No documented approach for fine-tuning or domain adaptation beyond per-repo memory.

License & commercial use

MIT License — permissive OSI-approved license allowing commercial use, modification, and distribution with attribution.

MIT license permits commercial use, but project offers no formal support, SLA, indemnification, or liability disclaimers. No evidence of commercial backing or warranty. Organizations using this in production should establish internal support processes and assume liability for agent-generated code quality.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Project accepts LLM API keys and Linear/Discord tokens; `.env` written to disk with `chmod 600` suggested but not enforced. No documented encryption for stored credentials, audit logging, or secret rotation policy. Agent-generated code is not formally verified; human code review is essential before merge. Vector DB (LanceDB) stores task outcomes locally — no encryption at rest or in transit documented. No security audit or responsible disclosure policy mentioned.

Alternatives to consider

Anthropic Compute

Official multi-turn agent framework from Anthropic (Claude) with stronger guarantees around model behavior and safety. May offer better integration if Claude Code CLI is your primary backend.

AutoGPT / AgentGPT variants

Established open-source agent frameworks with larger communities and broader LLM support. May have better documentation and battle-tested patterns, but less tight integration with dev tools (Linear, Discord).

GitHub Copilot Workspace / Devin (Cognition AI)

Commercial, closed-source alternatives with stronger integration into GitHub workflows and industry-scale UX. Trade off open-source flexibility for vendor support and battle-tested reliability.

Software development agency

Build on OpenSwarm with DEV.co software developers

Start with `npm install -g @intrect/openswarm && openswarm init` to set up your AI agent team. Docs and discussions at github.com/unohee/OpenSwarm.

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OpenSwarm FAQ

Can I run OpenSwarm without a local `claude` CLI or external LLM API?

Yes, via `lmstudio` or `local` (Ollama) backends — both run inference on your machine. You must have LM Studio or Ollama already running on localhost. Otherwise, you need at least one of: OpenAI auth, OpenRouter API key, or Claude CLI on PATH.

Does OpenSwarm store my code or conversation history externally?

No — code and task outcomes are stored locally in `.openswarm/` directory using SQLite + LanceDB. Discord/Slack notifications are sent to those services if configured. LLM prompts are sent to your chosen provider (OpenAI, OpenRouter, etc.). Per-repo embeddings are not synced to external storage by default.

What happens if an agent fails or times out?

Task timeout is configurable (default 600s). Exit codes are: 0 (success), 1 (failure), 2 (timeout). No automatic retry logic documented; reviewer agents may detect failures and report to Linear, but recovery strategy is not specified.

Is OpenSwarm production-ready?

The project claims SWE-bench Lite verification on real GitHub issues (3/3 hybrid instances), but it is ~5 months old and offers no commercial SLA. Suitable for augmenting developer workflows (code review, CI gates, issue triage) — not as a standalone ship-to-production system without human oversight.

Work with a software development agency

DEV.co helps companies turn open-source tools like OpenSwarm into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your ai coding agents stack.

Ready to Automate Code Tasks?

Start with `npm install -g @intrect/openswarm && openswarm init` to set up your AI agent team. Docs and discussions at github.com/unohee/OpenSwarm.

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