agent-squad
Agent Squad is an open-source framework for orchestrating multiple AI agents in conversations, routing queries intelligently to specialized agents while maintaining context. It runs on Python, TypeScript, and Swift—the latter bringing on-device orchestration to iOS and macOS.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | 2FastLabs/agent-squad |
| Owner | 2FastLabs |
| Primary language | Swift |
| License | Apache-2.0 — OSI-approved |
| Stars | 7.7k |
| Forks | 726 |
| Open issues | 60 |
| Latest release | typescript_1.1.0 (2026-07-01) |
| Last updated | 2026-07-07 |
| Source | https://github.com/2FastLabs/agent-squad |
What agent-squad is
A polyglot agent orchestration framework using LLM-based or rule-based classifiers to route user input to specialized agents, with support for streaming responses, tool invocation (including MCP servers in Swift), supervisor coordination, and grounded two-LLM patterns to reduce hallucination. Integrates with Bedrock, Anthropic, OpenAI, and local models.
Get the agent-squad source
Clone the repository and explore it locally.
git clone https://github.com/2FastLabs/agent-squad.gitcd agent-squad# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- All three runtimes (Python, TypeScript, Swift) maintain feature parity; choose based on deployment target—Swift for on-device mobile, Python/TypeScript for cloud/Lambda.
- Classifier performance (intent routing accuracy) directly impacts user experience; test with representative queries before production.
- Context window and cost scale with conversation length; plan for session truncation or archival in long-running chatbots.
- Tool definitions and MCP server availability differ by runtime; validate tool coverage for your use case before migration.
- Grounded agent pattern requires two LLM calls per turn (gatherer + presenter); cost and latency implications are non-trivial.
When to avoid it — and what to weigh
- Standardized single-agent chatbot — If you need only one LLM endpoint with no routing logic, the framework's multi-agent overhead adds unnecessary complexity.
- Real-time low-latency requirements (<100ms) — Classifier routing and inter-agent context synchronization add latency; not suitable for sub-100ms SLA systems.
- Closed, proprietary LLM integrations only — Framework focuses on open APIs and Bedrock; if locked to a single vendor's closed SDK, integration effort increases.
- Requirement for HIPAA/FedRAMP-certified agents — No explicit certification data provided; requires security review before health/federal use.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive open-source license permitting commercial use, modification, and distribution under Apache 2.0 terms.
Apache 2.0 is a permissive OSI license that explicitly allows commercial use, derivative works, and closed-source applications provided the license text and notice are retained. No royalties or restrictions on profit-bearing products. Recommended for commercial buyers to review the LICENSE file and ensure internal compliance teams audit dependencies.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Framework itself does not claim specific security certifications. LLM responses are not inherently safe; GroundedAgent reduces hallucination but does not guarantee correctness. Credentials (API keys, AWS IAM) must be managed by the deployment environment, not the framework. Tool invocation introduces remote execution risk—validate MCP servers and HTTP endpoints. On-device Swift runtime avoids cloud transmission but does not prevent local data exfiltration. Recommend security review before handling PII, health data, or financial information.
Alternatives to consider
LangGraph (LangChain)
State-machine-based agent orchestration with strong Python ecosystem; better for complex DAGs, worse for simple classifier-driven routing.
Crew AI
Role-based multi-agent framework with built-in hierarchies; more opinionated, less flexible for custom classifier logic.
AWS Lambda with raw boto3 + step functions
Lower abstraction, more control; requires manual orchestration and context management; no multi-language support.
Build on agent-squad with DEV.co software developers
Explore Agent Squad's documentation, try a quick-start example, and assess fit for your multi-agent use case. Contact us to discuss cloud deployment or mobile integrations.
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agent-squad FAQ
Does Agent Squad run on-device without cloud APIs?
How does the classifier choose which agent to use?
What is the cost overhead of the GroundedAgent pattern?
Is Agent Squad suitable for healthcare or financial applications?
Work with a software development agency
Adopting agent-squad is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai frameworks software in production.
Ready to orchestrate intelligent agent workflows?
Explore Agent Squad's documentation, try a quick-start example, and assess fit for your multi-agent use case. Contact us to discuss cloud deployment or mobile integrations.