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Comparison

How AgentSpec compares to similar projects.

vs. gitagent (Lyzr AI)

Feature AgentSpec gitagent
Manifest format YAML, multi-format YAML
Resolver ✓ auto-negotiates env
Inheritance ✓ enforced merge strategies partial (URL-based extends)
Trust enforcement ✓ hardcoded restrict
Signing/profiles ✓ Ed25519 portfolios
Registry ✓ via Noether proprietary
Owner community (EUPL-1.2) Lyzr AI (single company)

gitagent has a clean format but no resolver, no profile system, and is tied to one vendor.

vs. Agent Format (Snap Inc.)

Feature AgentSpec Agent Format
Spec quality ✓ comprehensive ✓ clean
Resolver
Inheritance
Tooling ✓ CLI + SDK spec-only
Profiles
Active abandoned-ish

Agent Format had a great spec but no working tooling. AgentSpec borrows the cleanest ideas and adds the missing pieces.

vs. OSSA

Feature AgentSpec OSSA
Lightweight ✓ pip install ✗ Drupal-based
Cryptographic verification ✓ Ed25519 ✓ chains
Discovery semantic search DNS-native
Complexity medium high
Audience developers enterprises

OSSA is over-engineered for most use cases. AgentSpec aims for the 80% with less ceremony.

vs. LangGraph / CrewAI / AutoGen

These are orchestrators, not standards:

Feature AgentSpec LangGraph/CrewAI/AutoGen
Manifest format ✓ universal each has its own
Cross-orchestrator portability
Resolver partial
Profiles varies

AgentSpec is complementary. You can use LangGraph/CrewAI/AutoGen as the orchestrator and AgentSpec as the agent definition format. The .agent file becomes portable across orchestrators.

vs. MCP (Model Context Protocol)

MCP is about tool/resource exposure to agents. AgentSpec is about agent definition.

Layer Standard
Tools/resources for agents MCP
Agent definition AgentSpec
Orchestration LangGraph, CrewAI, caloron-noether, ...
Composition Noether

They stack:

caloron-noether  (orchestrator)
agentspec  (defines the agents)
mcp servers  (provide tools to the agents)
noether  (composes verified stages)

Use them together.

vs. Docker / OCI

The analogy is direct:

Docker / OCI AgentSpec
Dockerfile .agent file
docker build agentspec resolve
docker run agentspec run
docker pull/push agentspec pull/push
Docker Hub registry.agentspec.dev (via noether-cloud)
Image hash content hash (ag1:xxx)
Image layers inheritance chain

But agents are not containers — they're declarations resolved at runtime. The closest analogy is Helm charts for AI agents: declarative, parameterized, registry-distributable.