# Vcoderun Acpkit Langchain Acp > langchain-acp Skill - Skill: `tomevault-io/vcoderun-acpkit-langchain-acp` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/vcoderun-acpkit-langchain-acp` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/vcoderun-acpkit-langchain-acp/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/vcoderun-acpkit-langchain-acp --- # langchain-acp Skill Use this skill when the task is centered on the `langchain-acp` adapter package. This package is the LangChain-side ACP adapter boundary in the repo. Treat it as a first-class adapter, not as a secondary package behind Pydantic. It owns: - LangGraph/LangChain graph adaptation - session-aware graph rebuilding - provider-backed model/mode/config state - tool and event projection for stable LangChain tool families - DeepAgents compatibility - ACP-native plan extraction from graph state and tool activity ## Start Here If you only need the shortest high-signal path: 1. read `Quick Routing` 2. open the [adapter config module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/config.py) and the [package entrypoint](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/__init__.py) for public-surface questions 3. open the [runtime adapter](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/runtime/adapter.py) for lifecycle and dispatch questions 4. then branch into graph build, projections, or plan runtime ## Quick Routing | If the task is about... | Use this skill? | Open first | | --- | --- | --- | | `run_acp(graph=...)` or `create_acp_agent(...)` | Yes | [package entrypoint](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/__init__.py), [adapter config module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/config.py), [runtime adapter](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/runtime/adapter.py) | | session-aware graph rebuilding | Yes | [graph source module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/graph_source.py), [graph builder](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/builders/graph.py), [providers module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/providers.py) | | DeepAgents compatibility | Yes | [built-in bridge module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/bridges/builtin.py), [projection module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/projection.py), public examples | | search/browser/http/file/finance projection presets | Yes | [projection module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/projection.py), [event projection module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/event_projection.py) | | plan extraction or plan persistence | Yes | [plan module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/plan.py), [native plan runtime](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/runtime/_native_plan_runtime.py), [session store](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/session/store.py) | | Codex-backed LangChain model construction | Pair with `codex-auth-helper` | [Codex helper skill](https://raw.githubusercontent.com/vcoderun/acpkit/main/.agents/skills/codex-auth-helper/SKILL.md) | | root CLI import/dispatch behavior | No, pair with `acpkit-sdk` | [root runtime package](https://github.com/vcoderun/acpkit/tree/main/src/acpkit) | | WebSocket transport or remote mirroring | No, pair with `acpremote` | [remote transport package](https://github.com/vcoderun/acpkit/tree/main/packages/transports/acpremote) | ## Package Boundary `langchain-acp` adapts LangChain-family graph runtimes into ACP. It owns: - how ACP session state becomes graph build input - provider-backed model/mode/config state - how graph outputs and tool activity become ACP updates - how stable tool families get first-class projections - how DeepAgents surfaces are normalized - how native plan state is extracted and persisted It does not own: - root CLI target resolution - Codex auth handling - WebSocket transport ## Do Not Confuse With - `langchain-acp` vs `pydantic-acp` this package adapts graph runtimes, not `pydantic_ai.Agent` - `langchain-acp` vs `acpremote` this package adapts LangChain-family graphs; `acpremote` only transports ACP - `langchain-acp` vs `acpkit-sdk` this package owns adapter semantics; `acpkit` owns CLI target loading and dispatch ## Primary References Package references: - [Raw skill](https://raw.githubusercontent.com/vcoderun/acpkit/main/.agents/skills/langchain-acp/SKILL.md) - [Raw overview docs](https://raw.githubusercontent.com/vcoderun/acpkit/main/docs/langchain-acp.md) - [Raw projections docs](https://raw.githubusercontent.com/vcoderun/acpkit/main/docs/langchain-acp/projections.md) - [Raw providers docs](https://raw.githubusercontent.com/vcoderun/acpkit/main/docs/langchain-acp/providers.md) - [Rendered overview](https://vcoderun.github.io/acpkit/langchain-acp/) - [Source tree](https://github.com/vcoderun/acpkit/tree/main/packages/adapters/langchain-acp) Cross-skill references: - [Root package skill](https://raw.githubusercontent.com/vcoderun/acpkit/main/.agents/skills/acpkit-sdk/SKILL.md) - [Codex helper skill](https://raw.githubusercontent.com/vcoderun/acpkit/main/.agents/skills/codex-auth-helper/SKILL.md) - [Remote transport skill](https://raw.githubusercontent.com/vcoderun/acpkit/main/.agents/skills/acpremote/SKILL.md) ## Public Surface High-value public seams: - `run_acp(graph=...)` - `create_acp_agent(...)` - `AdapterConfig(...)` - `GraphSource` - `StaticGraphSource` - `FactoryGraphSource` - session stores - projection maps - event projection maps - bridge manager and built-in bridges Package entrypoint: - [Package entrypoint](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/__init__.py) ## Module Guide | Subsystem | Key files | Use them for | | --- | --- | --- | | public config and graph source | [package entrypoint](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/__init__.py), [adapter config module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/config.py), [graph source module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/graph_source.py), [providers module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/providers.py), [shared types module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/types.py) | public API shape, graph source selection, provider contracts | | graph building and bridge management | [graph builder](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/builders/graph.py), [bridge manager](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/bridge_manager.py), [base bridge module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/bridges/base.py), [built-in bridge module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/bridges/builtin.py) | graph augmentation, built-in compatibility contributions, bridge wiring | | projection | [projection module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/projection.py), [event projection module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/event_projection.py), [serialization module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/serialization.py) | search/http/browser/command/file/finance rendering and event rendering | | plans and session state | [plan module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/plan.py), [native plan runtime](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/runtime/_native_plan_runtime.py), [session-state module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/session/state.py), [session-store module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/session/store.py) | plan extraction, persistence, replay, stored updates | | runtime core | [runtime adapter](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/runtime/adapter.py), [runtime server](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/runtime/server.py), [prompt-conversion runtime](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/runtime/_prompt_conversion.py), [approvals module](https://github.com/vcoderun/acpkit/blob/main/packages/adapters/langchain-acp/src/langchain_acp/approvals.py) | prompt conversion, approval routing, ACP session operations, runtime updates | ## Construction Seams ### `run_acp(graph=...)` Use this when one graph instance is already enough and the narrowest path to a running ACP server is desired. ### `create_acp_agent(...)` Use this when the ACP-compatible agent object is needed before it is run. Typical reasons: - combine with `acpremote` - embed into another runner - test the ACP boundary directly ### `graph_factory=` Use this when ACP session state should influence graph construction. Typical reasons: - session-root workspace binding - provider-selected model changes - mode-specific graph structure ### `graph_source=` Use this when a fully explicit graph source abstraction is more appropriate than a factory callback. ## Projection Strategy This package has its own projection story. Do not explain it with Pydantic-only terms. High-value projection families include: - `DeepAgentsProjectionMap` - `WebSearchProjectionMap` - `HttpRequestProjectionMap` - `WebFetchProjectionMap` - `BrowserProjectionMap` - `CommandProjectionMap` - `CommunityFileManagementProjectionMap` - `FinanceProjectionMap` - `StructuredEventProjectionMap` Important rule: - stable `langchain-community` tool families are good preset candidates - provider built-in tools are often more heterogeneous and should be handled more conservatively ## Bridges and Graph Contributions Important extension seams: - `CapabilityBridge` - built-in bridge manager contributions - `DeepAgentsCompatibilityBridge` - provider-backed model/mode/config contributions Use this package when the question is: - how ACP-visible capabilities get attached to a graph build - how compatibility layers affect the runtime - how session state influences graph construction ## Plans and Session Lifecycle This package supports: - session-aware graph rebuilding - session stores and replay - provider-backed models/modes/configs - native ACP plan runtime - tool-based or structured plan extraction The governing rule remains: - only expose ACP state the graph/runtime can actually honor ## Common Workflows ### Minimal LangChain ACP server ```python from langchain.agents import create_agent from langchain_acp import run_acp graph = create_agent(model='openai:gpt-5', tools=[]) run_acp(graph=graph) ``` ### ACP object first, run later Use `create_acp_agent(...)` when another runner or transport layer should own startup. ### Session-aware graph factory Use `graph_factory=` when session state should change the graph build. ### Remote-hosted LangChain ACP Adapt with `langchain-acp`, then expose with `acpremote`. ## Public Examples Maintained public examples: - [LangChain public examples](https://raw.githubusercontent.com/vcoderun/acpkit/main/examples/langchain/README.md) - [Codex-backed LangChain graph example](https://github.com/vcoderun/acpkit/blob/main/examples/langchain/codex_graph.py) - [Workspace graph example](https://github.com/vcoderun/acpkit/blob/main/examples/langchain/workspace_graph.py) - [DeepAgents graph example](https://github.com/vcoderun/acpkit/blob/main/examples/langchain/deepagents_graph.py) Use the [Codex-backed LangChain graph example](https://github.com/vcoderun/acpkit/blob/main/examples/langchain/codex_graph.py) for: - `codex-auth-helper` + LangChain integration - `create_codex_chat_openai(...)` - Responses-backed `ChatOpenAI` construction for ACP use Use the [workspace graph example](https://github.com/vcoderun/acpkit/blob/main/examples/langchain/workspace_graph.py) for: - module-level `graph` - session-aware `graph_from_session(...)` - filesystem projection - `acpkit run ...` and `acpkit serve ...` integration Use the [DeepAgents graph example](https://github.com/vcoderun/acpkit/blob/main/examples/langchain/deepagents_graph.py) for: - DeepAgents compatibility - `DeepAgentsCompatibilityBridge` - `DeepAgentsProjectionMap` Skill-local example index: - [Skill-local example index](https://github.com/vcoderun/acpkit/blob/main/.agents/skills/langchain-acp/examples/README.md) Remote-host recipe references: - [Remote command exposure recipe](https://github.com/vcoderun/acpkit/blob/main/.agents/skills/acpremote/examples/serve_command.py) - [Remote mirror recipe](https://github.com/vcoderun/acpkit/blob/main/.agents/skills/acpremote/examples/mirror_remote.py) ## Handoff Rules Pair or switch to: - `acpkit-sdk` when the graph is reached through `acpkit run ...` or `acpkit serve ...` - `acpremote` when the adapted graph is then exposed remotely over WebSocket transport Stay in this skill when the main issue is: - graph construction - provider-backed runtime state - LangChain-side tool or event projection - DeepAgents compatibility - plan extraction or session replay ## Guardrails - Do not describe LangChain support as secondary to Pydantic. - Do not reuse Pydantic-only host-policy language when the bug is really about graph or tool seams. - Do not claim a projection preset exists for an unstable tool family unless it is implemented. - If the task is really transport-only, move to `acpremote`. --- > Source: [vcoderun/acpkit](https://github.com/vcoderun/acpkit) — distributed by [TomeVault](https://tomevault.io).