langchain-router 0.1.0

Cost-aware model routing for LangChain agents based on task phase

langchain-router

Your agent doesn't need the expensive model for every call.

Most calls are just the model picking which file to read next or which pattern to search for. A smaller model does that fine. This middleware detects when the agent is doing that kind of work and routes to a fast model automatically.

Quick Install

pip install langchain-router

🤔 What is this?

Agent sessions have a pattern. The user says something, the agent thinks about it (planning). Then it reads files, searches code, runs commands (execution). Sometimes something breaks (recovery). Then the user says something again.

Planning and recovery need the primary model. Execution doesn't. RouterMiddleware detects which phase the agent is in and routes accordingly.

What just happened	Phase	Model
User spoke	planning	primary
Tool call succeeded	execution	fast
Tool call failed	recovery	primary

On a simulated 18-call session, 83% of calls route to the fast model.

from langchain.agents import create_agent
from langchain_router import RouterMiddleware

agent = create_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[...],
    middleware=[RouterMiddleware(fast="anthropic:claude-haiku-4-5-20251001")],
)

With CollapseMiddleware

from langchain_collapse import CollapseMiddleware

middleware = [
    CollapseMiddleware(),
    RouterMiddleware(fast="anthropic:claude-haiku-4-5-20251001"),
]

flowchart TB
    A["📥 37 messages"] --> B["CollapseMiddleware"]
    B --> C["📥 9 messages"]
    C --> D["RouterMiddleware"]
    D --> E{"phase?"}
    E --> |"execution  ·  83%"| F["⚡ Haiku"]
    E --> |"planning"| G["🧠 Sonnet"]
    E --> |"recovery"| G

    style A fill:#ff6b6b,stroke:#e03131,color:#fff
    style B fill:#339af0,stroke:#1c7ed6,color:#fff
    style C fill:#339af0,stroke:#1c7ed6,color:#fff
    style D fill:#51cf66,stroke:#2f9e44,color:#fff
    style E fill:#fff3bf,stroke:#f59f00,color:#333
    style F fill:#20c997,stroke:#099268,color:#fff
    style G fill:#845ef7,stroke:#7048e8,color:#fff

On false positives

The error heuristic checks for error, traceback, exception, failed in tool output. Code containing those words (like def handle_error) routes to the primary model. That's the safe direction: more capability than needed, never less.

📖 Documentation

• Source (single file, ~170 lines)
• Benchmark (simulated session with cost breakdown)
• Tests (unit tests + property based invariant tests)

💁 Contributing

git clone https://github.com/johanity/langchain-router.git
cd langchain-router
pip install -e ".[test]"
pytest

📕 License

MIT