Overview

Name: SHIPIT Agent
Author: SHIPIT

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SHIPIT Agent's Cognitive Architectures provide autonomous, self-directing agent capabilities out of the box. From simple loops to complex multi-agent teams, these architectures are designed for reliability, observability, and scale.

mermaid

graph TD
    subgraph Loops [Agent Loops]
        R[ReflectiveAgent]
        G[GoalAgent]
    end
    subgraph Multi [Multi-Agent]
        T[AgentTeam]
        P[Pipeline]
        S[Supervisor]
    end
    subgraph Core [Core]
        A[Agent]
    end
    Core --> Loops
    Core --> Multi

All deep agents support MCP servers, built-in tools, real-time streaming, and memory.

Deep agents also support the same skills system as plain Agent, including packaged skills, custom skill catalogs, explicit skills=[...], and prompt-based auto-matching. See the Skills guide.

Agent	What it does	Key feature
GoalAgent	Decomposes goals, tracks success criteria	Autonomous multi-step execution
ReflectiveAgent	Self-evaluates and revises output	Quality threshold with scores
Supervisor	Delegates to workers, reviews quality	Hierarchical multi-agent
AdaptiveAgent	Creates new tools at runtime	Dynamic tool creation
PersistentAgent	Checkpoint and resume	Long-running tasks
Channel	Typed agent-to-agent messaging	Structured communication
AgentBenchmark	Systematic agent testing	Pass/fail reports

All deep agents support:

python

# All tools (web search, code exec, etc.)
agent = GoalAgent.with_builtins(llm=llm, goal=goal)

# Real-time streaming with output content
for event in agent.stream():
    print(event.payload.get("output", ""))

# Memory across runs
agent = GoalAgent(llm=llm, memory=memory, goal=goal)

Runnable examples

python examples/11_deep_agents.py — all deep agents
Notebooks: 14, 16, 17, 18, 19, 20, 21