Agency Swarm vs AgentEval

Detailed side-by-side comparison to help you choose the right tool

Agency Swarm

🔴Developer

Voice AI Tools

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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Starting Price

Free

AgentEval

🔴Developer

Voice AI Tools

Comprehensive .NET toolkit for AI agent evaluation featuring fluent assertions, stochastic testing, model comparison, and security evaluation built specifically for Microsoft Agent Framework

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Starting Price

Free

Feature Comparison

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FeatureAgency SwarmAgentEval
CategoryVoice AI ToolsVoice AI Tools
Pricing Plans4 tiers4 tiers
Starting PriceFreeFree
Key Features
  • Multi-agent orchestration with role-based architecture
  • Type-safe tool development with Pydantic validation
  • Directional communication flows between agents
  • Fluent Should() assertion syntax for tool chains and responses
  • Stochastic evaluation with configurable run counts and success thresholds
  • Model comparison with cost/quality leaderboard output

Agency Swarm - 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

AgentEval - Pros & Cons

Pros

  • Native .NET integration with full type safety and compile-time error checking, unlike Python alternatives that rely on runtime exceptions
  • Red Team module ships with 192 attack probes across 9 attack types covering 60% of OWASP LLM Top 10 2025 with MITRE ATLAS technique mapping
  • Stochastic evaluation asserts on pass rates across N runs (e.g., 10 runs at 85% threshold) for statistically meaningful results
  • Trace record/replay eliminates API costs in CI — record once with real API, replay infinitely for free with identical outputs
  • Model comparison generates markdown leaderboards with cost/1K-request rankings across GPT-4o, GPT-4o Mini, Claude, and other providers
  • MIT licensed with explicit public commitment to remain open source forever — no bait-and-switch license changes
  • 27 detailed samples included from Hello World through Multi-Agent Workflows and Cross-Framework evaluation
  • First-class Microsoft Agent Framework (MAF) integration with automatic tool call tracking and token/cost telemetry

Cons

  • .NET-only — Python, JavaScript, and Go teams cannot use it and must rely on DeepEval, PromptFoo, or LangSmith instead
  • Red Team coverage is 60% of OWASP LLM Top 10, leaving 40% of categories uncovered compared to specialized security scanners
  • Commercial/Enterprise add-ons are still in planning phase, so enterprises requiring vendor SLAs and paid support have no tier to purchase
  • Small community relative to Python-era evaluation tools means fewer third-party integrations, tutorials, and Stack Overflow answers
  • Stochastic evaluation can become expensive — 100 tests × 50 repetitions equals 5,000 LLM calls per run if trace replay is not used
  • Tight coupling to Microsoft Agent Framework concepts means evolving with Microsoft's roadmap rather than remaining provider-neutral

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🔒 Security & Compliance Comparison

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Security FeatureAgency SwarmAgentEval
SOC2
GDPR
HIPAA
SSO
Self-Hosted✅ Yes
On-Prem✅ Yes
RBAC
Audit Log
Open Source✅ Yes
API Key Auth
Encryption at Rest
Encryption in Transit
Data Residency
Data Retentionconfigurable
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