agentskills-langchain

LangChain integration for the Agent Skills SDK - turn a skill registry into LangChain tools.

Generates a set of LangChain StructuredTool instances from a SkillRegistry, ready to be passed to any LangChain agent.

Installation

pip install agentskills-langchain

Requires Python 3.12 or newer. Installs agentskills-core and langchain-core as dependencies.

Usage

from pathlib import Path

from agentskills_core import SkillRegistry
from agentskills_fs import LocalFileSystemSkillProvider
from agentskills_langchain import get_tools, get_tools_usage_instructions

# Set up registry
provider = LocalFileSystemSkillProvider(Path("./skills"))
registry = SkillRegistry()
await registry.register("incident-response", provider)

# Build tools + system prompt
tools = get_tools(registry)
catalog = await registry.get_skills_catalog(format="xml")
instructions = get_tools_usage_instructions()
system_prompt = f"{catalog}\n\n{instructions}"

Pass tools to your LangChain agent and inject system_prompt into the system message. The catalog tells the agent what skills exist; the usage instructions tell it how to use the tools.

Generated Tools

All tools are async-compatible (StructuredTool with coroutine).

list_skill_resources returns a JSON object keyed by resource kind. Not every backend can enumerate resources — a plain static HTTP host cannot. Rather than surfacing an exception, the tool returns {"supported": false, "note": "..."} in that case: "this cannot be listed" is something the model can act on by falling back to the names in the skill body, not an error worth retrying.

API

get_tools(registry: SkillRegistry, *, max_inline_binary_bytes: int = 65536) -> list[StructuredTool]

Returns a list of LangChain structured tools bound to the given registry.

get_tools_usage_instructions() -> str

Returns a markdown string explaining the progressive-disclosure workflow - read metadata, then body, then fetch resources on demand. Designed for system-prompt injection alongside the skill catalog.

Example

See examples/langchain/ for a full working demo.

Error Handling

All exceptions inherit from AgentSkillsError (from agentskills-core).

Binary Resources

Skill resources may be arbitrary files. Valid UTF-8 is returned as-is; anything else is returned as a JSON envelope, so a binary payload is never silently mangled into replacement characters:

{
  "name": "architecture.png",
  "media_type": "image/png",
  "size_bytes": 20481,
  "encoding": "base64",
  "content": "iVBORw0KGgo..."
}

Base64 costs roughly 1.37 characters per byte, so binaries above 64 KiB are described rather than inlined - "encoding": "none" plus a note explaining the omission. Adjust the ceiling with:

tools = get_tools(registry, max_inline_binary_bytes=256 * 1024)

License

MIT