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Agency Swarm

Name: Agency Swarm
Brand: Agency Swarm
Availability: InStock

Agency Swarm is a free, open-source Python framework that lets you build teams of AI agents that work together like a real organization. You can create different agent roles (like CEO, developer, assistant) and define how they communicate and collaborate to complete complex tasks automatically.

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In Plain English

Overview

Agency Swarm is an AI Automation open-source Python framework that orchestrates teams of specialized AI agents modeled after real-world organizational structures, with pricing that is completely free under an MIT license. It targets Python developers, AI engineers, and businesses building production-grade multi-agent automation systems on top of the OpenAI Agents SDK. Originally released publicly in 2023 by Arsenii Shatokhin (VRSEN), the framework has evolved into a mature orchestration layer that provides explicit directional communication flows, type-safe Pydantic-based tool development, and multi-LLM provider support via LiteLLM covering 50+ providers. Unlike broadcast-based frameworks, Agency Swarm forces developers to declare exactly which agents can communicate, resulting in lower token consumption and more predictable behavior in production. The v1.x release added FastAPI integration, MCP Tools Server support, built-in guardrails, streaming responses, and agent memory persistence.

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Editorial Review

Agency Swarm prioritizes control over convenience, forcing explicit communication flows to prevent token-burning chaos. Best for Python developers who need production reliability and can trade setup complexity for deterministic behavior.

Key Features

Role-Based Agent Architecture+

Define specialized agents with distinct responsibilities, instructions, and tool sets — for example CEO, developer, or virtual assistant roles. Each agent operates on its own system prompt and toolbox, mirroring how real organizations divide labor. This structure scales cleanly from 2-agent prototypes to 20+ agent hierarchies without performance degradation.

Directional Communication Flows+

Communication between agents is explicitly declared using a directional syntax (A > B means A can initiate conversation with B). This prevents the chaotic broadcast behavior common in AutoGen-style frameworks and helps reduce token usage versus broadcast-based models. Developers can model hierarchies, peer-to-peer chat, star topologies, or meshes with full determinism.

Type-Safe Pydantic Tool System+

Tools are defined as Pydantic models, giving automatic input validation and schema generation compatible with the OpenAI Agents SDK FunctionTool format. This eliminates entire classes of runtime errors common in loosely-typed tool frameworks. OpenAPI schemas can also be auto-converted into tools, accelerating integration with existing services and APIs.

Multi-LLM Provider Support via LiteLLM+

Natively supports OpenAI models (GPT-4o, GPT-5) and plugs into 50+ additional providers — Anthropic, Google Gemini, Grok, Azure OpenAI, and open-source endpoints — through LiteLLM. Teams can mix cheap models (GPT-4o-mini) for routine agents with frontier models for reasoning roles, optimizing cost without rewriting code.

Production Observability & FastAPI Deployment+

Built-in observability tracks token usage, cost, and multi-agent workflow traces, with integration points for logging and monitoring backends. FastAPI integration and MCP Tools Server support turn agencies into deployable HTTP services with streaming, guardrails, and state persistence — suitable for production workloads requiring high availability.

Pricing Plans

Open Source (MIT)

✓Full framework with all features
✓MIT license — free for commercial use
✓Unlimited agents and agencies
✓Multi-LLM provider support via LiteLLM
✓Community support via Discord

See Full Pricing →Free vs Paid →Is it worth it? →

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Getting Started with Agency Swarm

1Install Agency Swarm via pip install agency-swarm and set up your OpenAI API key in the environment variables
2Clone the official examples repository from GitHub and run the 'basic_agency' example to understand agent roles and communication
3Create your first custom agency using the agency.py template, defining 2-3 specialized agents with specific roles and responsibilities
4Define agent tools using Pydantic models following the provided tool development guide and test agent interactions in the web UI
5Deploy your agency to production using the deployment guide and monitor agent performance through the built-in observability dashboard

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Best Use Cases

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Software development teams building production-ready multi-agent applications with clear organizational structure, role separation, and explicit communication flows for long-running workloads

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Businesses needing automated workflows that mirror real-world team dynamics — for example content pipelines with research, writing, and editing agents that can significantly reduce production time

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Customer service organizations deploying intake, analysis, and response agents to handle complex inquiries and improve first-contact resolution rates

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Engineering teams building automated code review, testing, and deployment pipelines where multi-agent orchestration can streamline release cycles

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Companies that require multi-LLM flexibility (OpenAI, Anthropic, Google, Azure) and want to avoid vendor lock-in while maintaining production reliability

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Agencies and consultancies productizing AI automation services where MIT licensing enables commercial resale without runtime royalties

Integration Ecosystem

5 integrations

Agency Swarm works with these platforms and services:

🧠 LLM Providers

OpenAIAnthropicGoogle

🔗 Other

GitHubopenrouter

View full Integration Matrix →

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Agency Swarm doesn't handle well:

⚠Requires substantial Python development expertise and multi-agent system architecture knowledge for effective implementation
⚠Complex debugging process when agent communication flows break down, requiring understanding of distributed system troubleshooting
⚠High computational overhead with multiple concurrent agents can lead to significant infrastructure costs for large-scale deployments
⚠Limited built-in integration with popular business tools (CRM, ERP, project management) requiring extensive custom development work
⚠Agent conversation context can become unwieldy in long-running workflows, potentially leading to degraded performance or lost context
⚠Steep learning curve for designing effective agent hierarchies and communication patterns without creating bottlenecks or conflicts

Pros & Cons

✓ Pros

✓Free and open-source under MIT license — zero cost for commercial deployments, unlike many competing frameworks
✓Production-oriented architecture with explicit communication flows that reduce unpredictable agent behavior in deployed systems
✓Lower token consumption compared to broadcast-based communication models like CrewAI, translating directly to API cost savings
✓Type-safe Pydantic-based tool validation prevents runtime errors and reduces production incidents compared to loosely-typed alternatives
✓Intuitive organizational model (CEO, developer, assistant roles) that mirrors real-world team structures, shortening onboarding time
✓Multi-LLM flexibility with 50+ providers via LiteLLM, avoiding single-vendor lock-in
✓Scales from 2-agent setups to 20+ agent hierarchies without performance degradation

✗ Cons

✗Requires Python 3.12+ and solid development experience — not accessible to no-code users
✗Steep learning curve for developers new to multi-agent architecture and async patterns
✗Community-only support via Discord — no enterprise SLA or guaranteed response times
✗Self-hosted only, meaning teams bear full responsibility for infrastructure, scaling, and monitoring
✗API costs scale multiplicatively with agent count and conversation length — a five-agent workflow can use 5-10x the tokens of single-agent work, making cost management critical for production deployments
✗Limited pre-built integrations with business tools (CRM, ERP, project management) requiring custom tool development

Frequently Asked Questions

Does Agency Swarm require hierarchical agent organization?+

No. Agency Swarm uses directional flows (A > B means A can message B) but does not require a strict hierarchy. You can create bidirectional flows, peer-to-peer patterns, star topologies, or mesh networks depending on your use case. The only constraint is that communication paths must be explicitly declared, which prevents chaotic broadcast patterns. This explicit control is a core design principle of the framework.

How does Agency Swarm compare to CrewAI for production use?+

CrewAI typically gets you to a working prototype faster thanks to its simpler role-based abstractions and flexible communication defaults. Agency Swarm takes longer to set up but provides deterministic behavior through explicit communication control and type-safe tools. Teams running continuous workloads generally see lower token costs with Agency Swarm due to its directional communication model, which avoids the broadcast overhead common in other frameworks.

Can I use models other than OpenAI?+

Yes, via the built-in LiteLLM router, which supports 50+ providers including Anthropic Claude, Google Gemini, Grok, Azure OpenAI, and open-source models. OpenAI models (GPT-4o, GPT-5) work natively with full feature support including streaming and function calling. Other providers work but may have partial compatibility issues — particularly around advanced function calling and streaming features.

How much do multi-agent workflows cost in API fees?+

Each agent makes independent API calls, so a five-agent conversation typically uses 5-10x the tokens of single-agent work. To control costs, use GPT-4o-mini for routine agents (intake, routing, simple tool calls) and reserve GPT-5 or Claude Opus for complex reasoning roles. Budget carefully, since costs scale multiplicatively with agent count and conversation length. Agency Swarm's explicit communication flows help limit unnecessary token usage compared to broadcast models.

What Python version is required?+

Python 3.12 or higher is required for v1.x. The current architecture uses async-first patterns and OpenAI Agents SDK features that depend on Python 3.12+ runtime capabilities such as improved asyncio and type system enhancements. If you are still on Python 3.10 or 3.11, you can use the legacy v0.x branch, but new features like FastAPI integration, MCP Tools Server, and guardrails are only available in v1.x.

🔒 Security & Compliance

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SOC2

Unknown

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GDPR

Unknown

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HIPAA

Unknown

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SSO

Unknown

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Self-Hosted

Yes

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On-Prem

Yes

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RBAC

Unknown

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Audit Log

Unknown

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API Key Auth

Unknown

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Open Source

Yes

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Encryption at Rest

Unknown

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Encryption in Transit

Unknown

Data Retention: configurable

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What's New in 2026

Agency Swarm v1.x (current release) introduces FastAPI integration, MCP Tools Server support, built-in guardrails, streaming responses, agent memory persistence across sessions, and expanded LiteLLM coverage to 50+ providers. The framework also added Azure OpenAI support, third-party agent compatibility, and improved observability. A documented migration guide from v0.x to v1.x is available in the official repository.

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