slimchain 0.1.1

Local tracing hooks for LangChain and LangGraph

slimchain

Local tracing hooks for LangChain and LangGraph. Stores traces locally or posts them to a webhook.

Install

pip install -e .

Enable tracing

Tracing is controlled by SLIMCHAIN_TRACING and an output directory:

• SLIMCHAIN_TRACING=true to enable recording.
• SLIMCHAIN_DIR=/path/to/output to control where JSON is written (default: ~/.slimchain).

Traces are written to:

• traces.jsonl (append-only)
• traces.json (pretty JSON)

Webhook ingestion

To post traces to slimchain servers, set the webhook endpoint and project id:

• SLIMCHAIN_ENDPOINT=https://your-server
• SLIMCHAIN_PROJECT_ID=123
• SLIMCHAIN_API_KEY=your_project_api_key

When SLIMCHAIN_ENDPOINT and SLIMCHAIN_PROJECT_ID are set, traces are POSTed to:

POST {SLIMCHAIN_ENDPOINT}/traces/{SLIMCHAIN_PROJECT_ID}

.env support

If a .env file exists in the working directory, slimchain will load it on startup. You can override the path with SLIMCHAIN_DOTENV=/path/to/.env.

LangChain integration

Attach the callback to any LangChain model or runnable:

from slimchain.client import Client
from slimchain.watcher import SlimchainCallback

client = Client(root=".slimchain")
client.config.tracing = True
callback = SlimchainCallback(client=client)

# Example with any LangChain chat model
model = SomeChatModel(
    model="...",
    callbacks=[callback],
)
response = model.invoke("Say hello in one sentence.")

LangGraph integration

Pass the callback via the LangGraph node or runnable config:

from slimchain.client import Client
from slimchain.watcher import SlimchainCallback
from langgraph.graph import END, StateGraph

client = Client(root=".slimchain")
client.config.tracing = True
callback = SlimchainCallback(client=client)

# inside a node function
message = model.invoke(prompt, config={"callbacks": [callback]})

Examples

Run the smoke examples after installing their dependencies:

pip install langchain-openai langchain-google-genai langgraph

Gemini:

SLIMCHAIN_TRACING=true \
GOOGLE_API_KEY=... \
.venv/bin/python examples/gemini_smoke.py

OpenRouter (OpenAI-compatible client):

SLIMCHAIN_TRACING=true \
OPENROUTER_API_KEY=... \
.venv/bin/python examples/openrouter_smoke.py

LangGraph (OpenRouter):

SLIMCHAIN_TRACING=true \
OPENROUTER_API_KEY=... \
.venv/bin/python examples/langgraph_smoke.py

Multi-agent (Gemini + OpenRouter):

SLIMCHAIN_TRACING=true \
GOOGLE_API_KEY=... \
OPENROUTER_API_KEY=... \
.venv/bin/python examples/multi_agent_smoke.py

Optional model overrides for the multi-agent example:

GEMINI_MODEL=gemini-3.1-flash-lite
OPENROUTER_MODEL=openai/gpt-4o-mini

Output schema

Each trace record includes:

• trace_id, node_name, framework
• input, output
• tokens_in, tokens_out
• duration_ms, model_used
• timestamp, project, status, error
• metadata

TEST

.venv/bin/python -m pytest -q