framework

Pydantic DeepAgents vs AutoGen (AG2)

Different approaches: DeepAgents builds autonomous deep agents (Claude Code-style) with planning, filesystem, and context management on Pydantic AI. AutoGen (AG2) focuses on multi-agent conversations with group chats, code execution, and Microsoft-backed research. DeepAgents is simpler; AutoGen is more powerful for agent-to-agent dialogues.

Key Differences

Advantage: Pydantic DeepAgents

Simplicity & API

DeepAgents: one function call (create_deep_agent) gives you a working agent. AutoGen v0.4 requires wiring agents, teams (RoundRobinGroupChat or SelectorGroupChat), model clients, and termination conditions separately.

Advantage: AutoGen (AG2)

Multi-Agent Conversations

AutoGen excels at agent-to-agent dialogues: RoundRobinGroupChat, SelectorGroupChat with LLM-based speaker selection, Swarm orchestration, and MagenticOne for complex multi-agent workflows. DeepAgents uses subagent delegation (one primary agent spawns helpers).

Advantage: Pydantic DeepAgents

Deep Agent Pattern

DeepAgents implements the full Claude Code pattern: planning, filesystem, context compression, checkpointing, cost tracking, hooks, and persistent memory — all in one call. AutoGen focuses on conversation orchestration and requires custom tooling for these capabilities.

Feature Comparison

Feature	Pydantic DeepAgents	AutoGen (AG2)
Foundation	Pydantic AI	Custom runtime
Agent Pattern	Deep agent	Conversational
Type Safety	✓	Partial
Planning (TODOs)	✓	✗
Filesystem Tools	✓	✗
Code Execution	✓	✓
Group Chat	Via subagents	✓
Context Management	✓	✗
Checkpointing	✓	✗
Cost Tracking	✓	✗
Human-in-the-Loop	✓	✓
Persistent Memory	✓	✗
CLI	✓	✗
Multi-Provider Support	✓	✓

Code Comparison

from pydantic_deep import (
    create_deep_agent, create_default_deps,
    LocalBackend,
)

agent = create_deep_agent(
    model="openai:gpt-4.1",
    instructions="Research and summarize topics.",
    include_filesystem=True,
    include_subagents=True,
    include_memory=True,
    include_todo=True,
)

deps = create_default_deps(
    LocalBackend("./workspace")
)
result = await agent.run(
    "Research AI safety and summarize",
    deps=deps,
)

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import (
    RoundRobinGroupChat,
)
from autogen_agentchat.conditions import (
    TextMentionTermination,
)
from autogen_ext.models.openai import (
    OpenAIChatCompletionClient,
)

model = OpenAIChatCompletionClient(model="gpt-4o")
researcher = AssistantAgent(
    "researcher", model_client=model,
)
writer = AssistantAgent(
    "writer", model_client=model,
)
termination = TextMentionTermination("APPROVE")
team = RoundRobinGroupChat(
    [researcher, writer],
    termination_condition=termination,
)
result = await team.run(
    task="Research AI safety and summarize",
)

When to Use Which

Choose Pydantic DeepAgents when:

Choose Pydantic DeepAgents when you want autonomous Claude Code-style agents with planning, filesystem access, context management, and cost tracking. Best for production systems, coding assistants, and long-running autonomous tasks where one powerful agent needs to plan and execute independently.

Choose AutoGen (AG2) when:

Choose AutoGen (AG2) when you need sophisticated agent-to-agent conversations, group chat orchestration (RoundRobin, Selector, Swarm), built-in code execution sandboxing, or complex multi-agent research workflows. Best when agents need to debate, negotiate, or iterate on solutions together.

Frequently Asked Questions

What happened to the old AutoGen API?

AutoGen v0.4 (now AG2) was a complete rewrite. The old AssistantAgent/UserProxyAgent API is deprecated. The new API uses autogen_agentchat with team-based orchestration (RoundRobinGroupChat, SelectorGroupChat, Swarm) and autogen_ext for model clients and tools.

Can DeepAgents do multi-agent conversations like AutoGen?

DeepAgents supports subagent delegation and teams — one primary agent can spawn and coordinate helpers. But it's not designed for the round-robin debate-style conversations AutoGen specializes in. If you need agents talking to each other, AutoGen is the better fit.

Which handles code execution better?

Both support code execution but differently. DeepAgents has built-in execute tool as part of its filesystem toolset, with Docker sandbox support. AutoGen has dedicated code executor extensions (DockerCommandLineCodeExecutor, LocalCommandLineCodeExecutor) designed for agents that generate and run code iteratively.

Which has better enterprise backing?

AutoGen originated at Microsoft Research and is now maintained by the AG2 community with Microsoft's continued involvement. DeepAgents is built by Vstorm with 30+ production AI agent deployments. AutoGen has the larger community; DeepAgents has more focused production experience.

Related Comparisons

Pydantic DeepAgents vs LangChain Deep Agents

Both implement the same deep agent pattern (planning, filesystem, subagents, context management). Pydantic DeepAgents is built on Pydantic AI with full type safety. LangChain Deep Agents is built on LangGraph with access to the LangChain ecosystem.

Pydantic DeepAgents vs CrewAI

Different philosophies: DeepAgents implements the deep agent pattern (Claude Code-style autonomous agents with planning, filesystem, and context management). CrewAI focuses on role-based multi-agent crews with predefined coordination patterns. DeepAgents gives more control; CrewAI is faster for team-of-agents prototypes.

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