pydantic-acp 1.5.0

ACP adapter for pydantic-ai agents.

pydantic-acp

pydantic-acp adapts pydantic_ai.Agent instances to the ACP agent interface without rewriting the underlying agent.

Install the stable v1 package with optional harness integration:

uv add "pydantic-acp[harness]>=1.0.0,<2.0.0"

pip install "pydantic-acp[harness]>=1.0.0,<2.0.0"

The core contract is simple:

1. keep the existing pydantic_ai.Agent
2. expose it through ACP
3. only publish ACP-visible state the runtime can actually honor

The package also includes the inverse bridge: an ACP agent can be consumed as a Pydantic AI v2 provider/model pair via AcpProvider and AcpModel. The final v1 stability boundary is defined by the ACP Kit versioning policy.

Entry Points

• run_acp(...)
• create_acp_agent(...)
• AdapterConfig
• AcpSessionContext
• StaticAgentSource
• FactoryAgentSource
• AcpProvider
• AcpModel
• AcpHostBridge

What It Covers

pydantic-acp includes:

• ACP session lifecycle, replay, resume, and persistence
• session-local model selection
• mode and slash-command control
• native ACP plan state with structured TaskPlan
• approval bridging
• prompt resources including files, embedded resources, images, and audio
• projection maps for filesystem, hooks, web tools, and builtin tool families
• capability bridges for upstream Pydantic AI capabilities
• client-backed filesystem and terminal helpers
• ACP client/provider bridging for consuming ACP agents from the Pydantic AI ecosystem

Quick Start

Expose a Pydantic AI agent through ACP:

from pydantic_ai import Agent
from pydantic_acp import run_acp

agent = Agent("openai:gpt-5", name="demo-agent")
run_acp(agent=agent)

ACP 0.11 Controls

The adapter targets agent-client-protocol==0.11.0. Model changes use the selectable "model" session config option rather than the removed session/set_model RPC. AdapterConfig(plan_update_mode="content") emits incremental plan updates only for clients that advertise plan support and otherwise falls back to complete plan updates.

additional_directories are durable session roots for client-backed file and terminal bridges, subject to any explicit workspace_root policy. Typed elicitation is available through AcpSessionContext.create_elicitation(...).

AcpMcpServer session descriptors are retained for host-owned ACP delegation; they are not connected or advertised as an ACP MCP transport because the SDK does not currently expose a public router for it. Use HTTP, SSE, or stdio MCP servers when SessionMcpBridge must execute tools.

If another runtime should own transport lifecycle:

from acp import run_agent
from pydantic_ai import Agent
from pydantic_acp import AdapterConfig, MemorySessionStore, create_acp_agent

agent = Agent("openai:gpt-5", name="composable-agent")

acp_agent = create_acp_agent(
    agent=agent,
    config=AdapterConfig(session_store=MemorySessionStore()),
)

run_agent(acp_agent)

ACP Client Provider Bridge

create_acp_model(...) is the client-side mirror of the ACP server adapter. It wraps an ACP agent, or an ACP stdio command, as a Pydantic AI v2 model. AcpProvider and AcpModel remain available when lower-level provider ownership is needed.

from pydantic_ai import Agent
from pydantic_acp import create_acp_agent, create_acp_model

inner_acp = create_acp_agent(agent=some_pydantic_agent)
model = create_acp_model(acp_agent=inner_acp, cwd="/workspace")
agent = Agent(model)

result = await agent.run("Summarize the current workspace state.")
print(result.output)

acp_command is for child processes that speak ACP JSON-RPC on stdin/stdout. It is not an arbitrary CLI wrapper.

from pydantic_ai import Agent
from pydantic_acp import create_acp_model

model = create_acp_model(
    acp_command=("npx", "@zed-industries/codex-acp"),
    cwd="/workspace",
    stderr_mode="inherit",
)
agent = Agent(model)

For lower-level ownership, construct the provider directly:

from pydantic_ai import Agent
from pydantic_acp import AcpProvider

# `remote_acp_agent` can be any object implementing the ACP Agent interface.
provider = AcpProvider(acp_agent=remote_acp_agent, cwd="/workspace")
model = provider.model()
agent = Agent(model)

result = await agent.run("Summarize the current workspace state.")
print(result.output)

This keeps ownership boundaries explicit:

• Pydantic AI owns the outer agent run, output validation, and normal model/provider lifecycle.
• ACP owns the delegated agent session, ACP-visible updates, and any editor or host capabilities requested by that agent.
• create_acp_model(...) and provider.model() leave ACP model selection to the wrapped agent's session default; pass model_name="zed-agent" or provider.model("zed-agent") only when the ACP agent exposes a selectable "model" session/set_config_option option.
• AcpHostBridge records ACP session_update messages and can delegate filesystem, terminal, approval, and extension callbacks to a real ACP host client when one is supplied.
• Pydantic AI function tools are intentionally not executed directly by AcpModel; register tools on the ACP agent or expose host capabilities through ACP.

Use this bridge when the thing you have is already an ACP agent and you want it to participate in code that expects a Pydantic AI provider/model. It is not another ACP server adapter and it does not replace create_acp_agent(...). If you are using Codex-backed Pydantic models through codex-auth-helper, pass explicit instructions when building the model. That is the preferred seam for Codex-specific system behavior:

from codex_auth_helper import create_codex_responses_model
from pydantic_ai import Agent

model = create_codex_responses_model(
    "gpt-5.4",
    instructions="You are a careful coding assistant.",
)
agent = Agent(model, name="codex-agent")

On the Pydantic path, Agent(instructions=...) is also valid and may still be useful for agent-specific behavior layered on top of the model:

from codex_auth_helper import create_codex_responses_model
from pydantic_ai import Agent

model = create_codex_responses_model(
    "gpt-5.4",
    instructions="You are a careful coding assistant.",
)
agent = Agent(
    model,
    name="codex-agent",
    instructions="Ask for clarification when the task is underspecified.",
)

In short: Codex-backed Pydantic models should not rely on an implicit default instruction string. Set instructions explicitly at the factory level, and add Agent(instructions=...) when you want extra agent-owned behavior.

Native Plan Mode

TaskPlan is the structured native plan output surface.

Use PrepareToolsBridge to expose plan mode:

from pydantic_ai import Agent
from pydantic_ai.tools import RunContext, ToolDefinition
from pydantic_acp import (
    AdapterConfig,
    PrepareToolsBridge,
    PrepareToolsMode,
    run_acp,
)


def read_only_tools(
    ctx: RunContext[None],
    tool_defs: list[ToolDefinition],
) -> list[ToolDefinition]:
    del ctx
    return list(tool_defs)


agent = Agent("openai:gpt-5", name="plan-agent")

run_acp(
    agent=agent,
    config=AdapterConfig(
        capability_bridges=[
            PrepareToolsBridge(
                default_mode_id="plan",
                default_plan_generation_type="structured",
                modes=[
                    PrepareToolsMode(
                        id="plan",
                        name="Plan",
                        description="Return a structured ACP task plan.",
                        prepare_func=read_only_tools,
                        plan_mode=True,
                    ),
                ],
            ),
        ],
    ),
)

Important behavior:

• plan_generation_type="structured" is the default plan-mode behavior
• structured mode expects structured TaskPlan output instead of exposing acp_set_plan
• switch to plan_generation_type="tools" when you explicitly want tool-based native plan recording
• keep plan_tools=True when you also want progress tools such as acp_update_plan_entry

Projection Maps

Projection maps decide how known tool families render into ACP-visible updates.

Built-in projection helpers:

• FileSystemProjectionMap
• HookProjectionMap
• WebToolProjectionMap
• BuiltinToolProjectionMap

Example:

from pydantic_acp import (
    AdapterConfig,
    BuiltinToolProjectionMap,
    FileSystemProjectionMap,
    HookProjectionMap,
    run_acp,
)

run_acp(
    agent=agent,
    config=AdapterConfig(
        projection_maps=[
            FileSystemProjectionMap(
                default_read_tool="read_file",
                default_write_tool="write_file",
            ),
            HookProjectionMap(
                hidden_event_ids=frozenset({"after_model_request"}),
                event_labels={"before_model_request": "Preparing Request"},
            ),
            BuiltinToolProjectionMap(),
        ],
    ),
)

Capability Bridges

Current built-in bridges include:

• ThinkingBridge
• PrepareToolsBridge
• ThreadExecutorBridge
• SetToolMetadataBridge
• IncludeToolReturnSchemasBridge
• WebSearchBridge
• WebFetchBridge
• ImageGenerationBridge
• McpCapabilityBridge
• SessionMcpBridge
• ToolsetBridge
• PrefixToolsBridge
• OpenAICompactionBridge
• AnthropicCompactionBridge

Use bridges when the runtime should gain upstream Pydantic AI capabilities and ACP-visible metadata without rewriting the adapter core.

Harness-backed Capabilities

pydantic-acp also ships a maintained bridge and projection layer for pydantic-ai-harness.

Public seams:

• HarnessFileSystemBridge
• HarnessShellBridge
• HarnessCodeModeBridge
• HarnessFileSystemProjectionMap
• HarnessShellProjectionMap
• HarnessCodeModeProjectionMap

Minimal example:

from pathlib import Path

from pydantic_ai import Agent
from pydantic_acp import (
    AdapterConfig,
    HarnessFileSystemBridge,
    HarnessFileSystemProjectionMap,
    HarnessShellBridge,
    HarnessShellProjectionMap,
    MemorySessionStore,
    run_acp,
)

workspace_root = Path("agent_demos/harness-agent")

agent = Agent(
    "openai:gpt-5",
    name="harness-agent",
    instructions="Use the harness filesystem and shell tools inside the workspace only.",
)

run_acp(
    agent=agent,
    config=AdapterConfig(
        session_store=MemorySessionStore(),
        capability_bridges=[
            HarnessFileSystemBridge(root_dir=workspace_root),
            HarnessShellBridge(cwd=workspace_root),
        ],
        projection_maps=[
            HarnessFileSystemProjectionMap(),
            HarnessShellProjectionMap(),
        ],
    ),
)

Use HarnessCodeModeBridge only when the run should expose CodeMode. The maintained example keeps that bridge opt-in so the native ACP target stays limited to filesystem and shell by default:

• example source
• detailed guide

The harness filesystem projection now renders read_file as a read-specific preview instead of a fake diff, which makes ACP transcript output much more truthful for inspection-only tool calls.

Factories, Sources, And Host-owned State

Use agent_factory= when the ACP session should influence which agent gets built:

from pydantic_ai import Agent
from pydantic_acp import AcpSessionContext, AdapterConfig, MemorySessionStore, run_acp


def build_agent(session: AcpSessionContext) -> Agent[None, str]:
    workspace_name = session.cwd.name
    model_name = "openai:gpt-5.4-mini"
    if workspace_name.endswith("-deep"):
        model_name = "openai:gpt-5.4"
    return Agent(model_name, name=f"workspace-{workspace_name}")


run_acp(
    agent_factory=build_agent,
    config=AdapterConfig(session_store=MemorySessionStore()),
)

Use AgentSource when the agent and its dependencies should be built separately. Use providers when models, modes, config values, plans, or approvals belong to the host layer instead of the adapter.

Session Store Notes

Use MemorySessionStore for ephemeral local runs and FileSessionStore when ACP sessions should survive process restarts. FileSessionStore is a local durable store, not a distributed coordination layer.

File-backed session ids are constrained before they become filenames:

• allowed characters are ASCII letters, digits, _, and -
• maximum length is 128 characters
• path separators, dot-prefixed ids, whitespace, and shell metacharacters are rejected

The file store writes through a temp file, fsync, and atomic replace. Malformed or partially written session files are skipped by public load/list flows.

Maintained Examples

Maintained runnable examples:

• finance_agent.py
• travel_agent.py

Focused docs recipes:

• Dynamic Factory Agents

Documentation

• Pydantic ACP Overview
• AdapterConfig
• Plans, Thinking, and Approvals
• Models, Modes, and Slash Commands
• Prompt Resources and Context
• Session State and Lifecycle
• Bridges
• Providers
• Security Guidance
• Host Backends and Projections
• API Reference

Compatibility Policy

pydantic-acp supports pydantic-ai-slim>=2.9.0,<=2.16.0. Pydantic AI V1 and Pydantic AI 2.x releases before 2.9.0 are outside the supported range.

The ACP client provider bridge depends on the Pydantic AI v2 Provider and Model contracts. Upgrades across major Pydantic AI versions should be deliberate because the adapter exposes both server-side ACP translation and client-side ACP provider integration.

Every supported minor is exercised by the repository's runtime and type-check compatibility matrix. The adapter keeps upstream compatibility behind ACP Kit's bridge and runtime seams instead of scattering version checks through callers.

Pydantic AI V2 defaults agent dependency and output generics to object. When your tools or hooks explicitly use RunContext[None] or Hooks[None], also declare the dependency type on the agent:

from pydantic_ai import Agent

agent: Agent[None, str] = Agent(
    "openai:gpt-5",
    deps_type=type(None),
    name="typed-agent",
)

The supported surface includes tool and output-tool preparation, output validation and processing hooks, deferred tool-call hooks, run metadata, conversation IDs, and the run_stream_events() lifecycle used through 2.16.0.

Harness-backed filesystem, shell, and CodeMode bridges are validated against pydantic-ai-harness[code-mode]==0.10.0 using its public capability imports. Harness 0.10.0 requires pydantic-ai-slim>=2.14.1; the core adapter itself remains compatible with Pydantic AI 2.9.0 through 2.16.0.