AgentPool
A unified agent orchestration hub that lets you configure and manage heterogeneous AI agents via YAML and expose them through standardized protocols.
The Problem
You want to use multiple AI agents together - Claude Code for refactoring, Codex for code editing with advanced reasoning, a custom analysis agent, maybe Goose for specific tasks. But each has different APIs, protocols, and integration patterns. Coordinating them means writing glue code for each combination.
The Solution
AgentPool acts as a protocol bridge. Define all your agents in one YAML file - whether they're native (PydanticAI-based), direct integrations (Claude Code, Codex), external ACP agents (Goose), or AG-UI agents. Then expose them all through ACP or AG-UI protocols, letting them cooperate, delegate, and communicate through a unified interface.
flowchart TB
subgraph AgentPool
subgraph config[YAML Configuration]
native[Native Agents<br/>PydanticAI]
direct[Direct Integrations<br/>Claude Code, Codex]
acp_agents[ACP Agents<br/>Goose, etc.]
agui_agents[AG-UI Agents]
workflows[Teams & Workflows]
end
subgraph interface[Unified Agent Interface]
delegation[Inter-agent delegation]
routing[Message routing]
context[Shared context]
end
config --> interface
end
interface --> acp_server[ACP Server]
interface --> opencode_server[OpenCode Server]
interface --> agui_server[AG-UI Server]
acp_server --> clients1[Zed, Toad, ACP Clients]
opencode_server --> clients2[OpenCode TUI/Desktop]
agui_server --> clients3[AG-UI Clients]
Quick Start
uv tool install agentpool
Minimal Configuration
# agents.yml
agents:
assistant:
type: native
model: openai:gpt-4o
system_prompt: "You are a helpful assistant."
# Run via CLI
agentpool run assistant "Hello!"
# Or start as ACP server (for Zed, Toad, etc.)
agentpool serve-acp agents.yml
Integrating External Agents
The real power comes from mixing agent types:
agents:
# Native PydanticAI-based agent
coordinator:
type: native
model: openai:gpt-4o
tools:
- type: subagent # Can delegate to all other agents
system_prompt: "Coordinate tasks between available agents."
# Claude Code agent (direct integration)
claude:
type: claude_code
description: "Claude Code for complex refactoring"
# Codex agent (direct integration)
codex:
type: codex
model: gpt-5.1-codex-max
reasoning_effort: medium
description: "Codex for code editing with advanced reasoning"
# ACP protocol agents
goose:
type: acp
provider: goose
description: "Goose for file operations"
# AG-UI protocol agent
agui_agent:
type: agui
url: "http://localhost:8000"
description: "Custom AG-UI agent"
Now coordinator can delegate work to any of these agents, and all are accessible through the same interface.
Key Features
Multi-Agent Coordination
Agents can form teams (parallel) or chains (sequential):
teams:
review_pipeline:
mode: sequential
members: [analyzer, reviewer, formatter]
parallel_coders:
mode: parallel
members: [claude, goose]
async with AgentPool("agents.yml") as pool:
# Parallel execution
team = pool.get_agent("analyzer") & pool.get_agent("reviewer")
results = await team.run("Review this code")
# Sequential pipeline
chain = analyzer | reviewer | formatter
result = await chain.run("Process this")
Rich YAML Configuration
Everything is configurable - models, tools, connections, triggers, storage:
agents:
analyzer:
type: native
model:
type: fallback
models: [openai:gpt-4o, anthropic:claude-sonnet-4-0]
tools:
- type: subagent
- type: resource_access
mcp_servers:
- "uvx mcp-server-filesystem"
knowledge:
paths: ["docs/**/*.md"]
connections:
- type: node
name: reporter
filter_condition:
type: word_match
words: [error, warning]
Server Protocols
AgentPool can expose your agents through multiple server protocols:
| Server | Command | Use Case |
|---|---|---|
| ACP | agentpool serve-acp |
IDE integration (Zed, Toad) - bidirectional communication with tool confirmations |
| OpenCode | agentpool serve-opencode |
OpenCode TUI/Desktop - supports remote filesystems via fsspec |
| MCP | agentpool serve-mcp |
Expose tools to other agents |
| AG-UI | agentpool serve-agui |
AG-UI compatible frontends |
| OpenAI API | agentpool serve-api |
Drop-in OpenAI API replacement |
The ACP server is ideal for IDE integration - it provides real-time tool confirmations and session management. The OpenCode server enables the OpenCode TUI to control AgentPool agents, including agents operating on remote environments (Docker, SSH, cloud sandboxes).
Additional Capabilities
- Structured Output: Define response schemas inline or import Python types
- Storage & Analytics: Track all interactions with configurable providers
- File Abstraction: UPath-backed operations work on local and remote sources
- Triggers: React to file changes, webhooks, or custom events
- Streaming TTS: Voice output support for all agents
Usage Patterns
CLI
agentpool run agent_name "prompt" # Single run
agentpool serve-acp config.yml # ACP server for IDEs
agentpool serve-opencode config.yml # OpenCode TUI server
agentpool serve-mcp config.yml # MCP server
agentpool watch --config agents.yml # React to triggers
agentpool history stats --group-by model # View analytics
Programmatic
from agentpool import AgentPool
async with AgentPool("agents.yml") as pool:
agent = pool.get_agent("assistant")
# Simple run
result = await agent.run("Hello")
# Streaming
async for event in agent.run_stream("Tell me a story"):
print(event)
# Multi-modal
result = await agent.run("Describe this", Path("image.jpg"))
Documentation
For complete documentation including advanced configuration, connection patterns, and API reference, visit phil65.github.io/agentpool.
1 maintainer
Metadata
1 maintainer
Release files for agentpool 2.9.18
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