All integrations/LangChain90s setupmoderate

Hooklayer for LangChain

LangChain 0.3+ ships an MCP client adapter. Pass Hooklayer's URL and Bearer header to the MCPToolkit constructor; all 12 tools are returned as standard LangChain Tool objects you can hand to AgentExecutor, LangGraph nodes, or any chain composition. Works with OpenAI, Anthropic, Vertex AI models.

Get free API key Try the playground first

What works in LangChain

All 12 Hooklayer tools as LangChain Tools
Cross-provider model support
LangGraph node integration
Streaming responses
OpenTelemetry tracing

Setup (90s)

Config file: your_agent.py

PythonCopy + paste

from langchain_mcp_adapters import MCPToolkit
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_openai import ChatOpenAI

# Connect to Hooklayer's remote MCP
toolkit = MCPToolkit(
    server_url="https://hooklayer.dev/api/mcp",
    headers={"Authorization": f"Bearer {HOOKLAYER_API_KEY}"},
    transport="http",
)
hooklayer_tools = toolkit.get_tools()  # list of 7 LangChain Tools

# Hand off to a tool-calling agent
llm = ChatOpenAI(model="gpt-5", temperature=0)
agent = create_tool_calling_agent(llm, hooklayer_tools, prompt)
executor = AgentExecutor(agent=agent, tools=hooklayer_tools, verbose=True)

result = executor.invoke({
    "input": "Analyze @humphreytalks and execute the recommended_chain."
})

1
Install the adapter
pip install langchain-mcp-adapters langchain langchain-openai. v0.3+ required.
2
Get your API key
Sign up at hooklayer.dev/auth/signup for an hl_live_ Bearer key.
3
Wire the MCPToolkit
Instantiate MCPToolkit with server_url + Authorization header. Call get_tools() to receive 7 LangChain Tool objects.
4
Compose with your chain
Pass the tools to AgentExecutor, LangGraph nodes, or any chain that accepts BaseTool[]. Standard LangChain composition rules apply.

Example prompts for LangChain

Paste any of these to see Hooklayer respond live.

LangGraph workflow: analyze → remix → score

# LangGraph nodes wired in sequence:
# Node 1: extract_handle (string parser)
# Node 2: hooklayer_analyze (calls analyze_account tool)
# Node 3: hooklayer_remix (calls viral_remix on top recommended_chain url)
# Node 4: hooklayer_score (calls score_hook on the generated hook)
# Node 5: conditional_edge (if score >= 75 → save, else → loop to node 3)

Expected: Declarative state-machine workflow using LangGraph. Each Hooklayer call is a typed node. Loop-back gives quality-gated content generation.

AgentExecutor with multi-model fallback

# Try Claude first, fall back to GPT-5 if rate-limited:
for model in [ChatAnthropic("claude-opus-4-7"), ChatOpenAI("gpt-5")]:
    try:
        agent = create_tool_calling_agent(model, hooklayer_tools, prompt)
        result = AgentExecutor(agent=agent, tools=hooklayer_tools).invoke(...)
        break
    except RateLimitError:
        continue

Expected: Cross-provider resilience. Hooklayer tools work identically across models because they're MCP-defined, not provider-specific. Failover is graceful.

Tracing with LangSmith / OpenTelemetry

# Set LANGCHAIN_TRACING_V2=true and LANGCHAIN_API_KEY for LangSmith.
# Every Hooklayer tool call is traced as a span with:
#   - tool name (analyze_account, score_hook, etc.)
#   - latency
#   - credits consumed (from response)
#   - cache_hit boolean
# Use the trace to debug long agent runs and identify which tool is the latency hotspot.

Expected: Full observability. LangSmith / OTel traces show each Hooklayer call with timing, credits, and cache behavior — essential for cost-tuning agent workflows.

Frequently asked

Does LangChain support OAuth or only Bearer?

For backend agents (no end-user in the loop), Bearer auth via header injection is the standard pattern — LangChain doesn't need OAuth here. For end-user-facing agents where each user authorizes their own Hooklayer access, implement an OAuth handshake before instantiating MCPToolkit per-user.

Can I use Hooklayer tools in a custom LangChain chain (not Agent)?

Yes. The tools returned by MCPToolkit.get_tools() are standard LangChain BaseTool instances — you can call them directly via .invoke() in any chain composition, not just AgentExecutor.

How does this work with LangGraph state machines?

Each Hooklayer tool becomes a node in your LangGraph. Wire them with conditional edges (e.g., if score_hook returns < 70, loop back to viral_remix). The recommended_chain field from analyze_account can directly populate downstream node parameters.

Will LangChain re-fetch the tool catalog on every call?

No. MCPToolkit caches the catalog for the lifetime of the toolkit instance. Re-instantiate the toolkit if you deploy a new Hooklayer tool you want to use mid-process.

Can I use Hooklayer with LangSmith evaluation?

Yes. Hooklayer's response includes deterministic fields (signals[], would_fail_because, calibration_check) that make agent outputs evaluable. LangSmith's LLM-as-judge can grade against these structured fields rather than free-text outputs.

Are there async versions of the tools?

Yes. MCPToolkit returns async-capable tools — use .ainvoke() instead of .invoke() to call Hooklayer asynchronously. Essential for parallel calls (e.g., analyzing 5 creators in parallel via asyncio.gather).

Hooklayer also works with

Hooklayer for Claude Desktop

Hooklayer for OpenAI Agents SDK

60s setup

Hooklayer for Windsurf

45s setup

Try it in LangChain.

25 free credits at signup. No card. LangChain setup in 90 seconds.