tracesage 0.1.1

Production observability adapter for LangChain/LangGraph multi-agent systems

tracesage

Production observability for LangChain & LangGraph multi-agent systems. Drop in two lines, see live execution traces in your browser.

from tracesage import TraceSage

# Illustrative only — `await` runs inside an async function. See Quick start
# below for a complete, runnable `async def main()`.
tracer = await TraceSage.create()                          # one-time setup

result = await graph.ainvoke(
    {"input": payload},
    config={"callbacks": [tracer.handler]},                # only line you add
)

# Open http://localhost:7842/ui to see the trace live

---

Contents

• Why tracesage
• Install
• Quick start
• Concepts: topology node kinds
• Features
• Examples
• Documentation
• Comparison
• Performance
• Status
• Contributing
• License

---

Why tracesage

LangChain agents emit a rich callback stream — chain start/end, tool start/end, LLM start/end, retrieval, errors. tracesage captures all of it without changing your workflow logic, persists it locally (SQLite + gzipped blobs), and renders it in an interactive graph + timeline UI in real time.

• Zero infrastructure. No Docker. No Postgres. No external services. pip install.
• Two-line integration. One callback added to your existing ainvoke.
• Production-grade safety. The handler never raises. The tracer never crashes your pipeline.
• Interactive graph view. Custom SVG graph (no framework), auto-laid-out. Hover, click, replay any run.
• MCP-aware. Tools loaded from MCP servers are attributed by source, so you can see which tools came from which server vs. which are hardcoded. See docs/mcp.md.
• Pluggable storage. SQLite today; Postgres / remote-collector / object-store backends planned (see production_roadmap.md).
• MIT licensed. Free forever.

Install

pip install tracesage[langchain]

Requires Python 3.11+. The [langchain] extra pulls langchain-core; that's the only mandatory third-party dep beyond the standard FastAPI / aiosqlite / pydantic stack. If your app uses LangGraph, also pip install langgraph (tracesage doesn't pull it).

tracesage is provider-agnostic — it traces LangChain's callback stream, so OpenAI / Anthropic / local models are all captured automatically; there's no provider setting in tracesage. Install whichever provider you use:

pip install langchain-openai      # OPENAI_API_KEY=...
pip install langchain-anthropic   # ANTHROPIC_API_KEY=...

For MCP tool-source attribution (loads tools from MCP servers and tags them by source), install the mcp extra:

pip install 'tracesage[mcp]'

Quick start

See it in 5 seconds — seed a sample trace and open the UI:

tracesage demo

Sync scripts / notebooks — wrap your code; every LangChain call is captured automatically (no callbacks= wiring) and a clickable trace link is printed:

import tracesage

with tracesage.trace() as tl:          # starts the UI + global capture
    result = agent.invoke("your input")     # 🔍 tracesage: http://127.0.0.1:7842/ui/#run=...
    input("Trace ready — open the printed link, then Enter to exit.")  # keep the UI up

(The embedded UI stops when the with block / process exits; traces persist to ~/.tracesage, so you can also reopen them later with tracesage serve.)

Async apps — use the context manager (or await TraceSage.create() for full control):

import asyncio
from tracesage import TraceSage

async def main():
    async with TraceSage.session(install=True) as tl:   # install=True → global capture
        await graph.ainvoke({"input": "your payload"}, config={"tags": ["my-system"]})
        await tl.flush()                                 # ensure events are persisted
        print(tl.run_url("<run_id>"))                    # deep link to a run

asyncio.run(main())

Prefer explicit wiring? Pass config={"callbacks": [tl.handler]} instead of install=True.

That's it. Open http://localhost:7842/ui and explore.

Developer workflow

Once you have traces, debug them without leaving your terminal:

tracesage show <run_id>          # render a run as a tree in the terminal
tracesage watch <run_id>         # live-tail events as they stream
tracesage diff <run_a> <run_b>   # compare two runs (tokens, tools, errors)
tracesage view trace.jsonl       # open an exported trace in the UI directly

Test your agents — the tracesage_capture pytest fixture is auto-registered:

def test_agent_uses_search(tracesage_capture):
    agent.invoke("find me a hotel")
    tracesage_capture.assert_tool_called("search")
    tracesage_capture.assert_no_errors()
    assert tracesage_capture.total_tokens()[0] < 5000

See docs/development.md for the full developer guide, and examples/showcase/ for 30 before/after apps across popular use cases.

Concepts: topology node kinds

When you open the UI, every node in the topology graph is one of five event-based kinds — plus a synthesized mcp node when you attribute tools to an MCP server. Knowing what each means is the prerequisite to reading a trace:

Kind	What it is	Examples you'll see
agent	A function you registered as a LangGraph node, that calls other components	agent:billing_agent, agent:fact_extractor, agent:supervisor
tool	A side-effect function (DB query, API call, calculation) decorated with @tool	tool:lookup_account, tool:run_sql, tool:cite_sources
llm	A language-model call (chat or completion)	llm:FakeListChatModel, llm:ChatOpenAI, llm:ChatAnthropic
retriever	A BaseRetriever subclass — the "R" in RAG	retriever:Chroma, retriever:FAISS, retriever:_FixedCorpusRetriever
chain	Plumbing — LCEL primitives, the LangGraph orchestrator, routing functions	chain:LangGraph, chain:RunnableSequence, chain:ChatPromptTemplate, chain:route_after_quality
mcp	An MCP server (synthesized) — groups the tools loaded from it	mcp:weather, mcp:math, mcp:github

Quick mental model:

• agent is your code that calls something. It does reasoning.
• tool does the actual side-effect work and returns a result.
• llm is what you count, cost, and cache.
• retriever is its own dimension — "did we find the right docs?" is a different question from "did the LLM use them well?".
• chain is the wrapping machinery (the prompt | llm | parser pipe operator, the LangGraph state machine, routing functions). It's infrastructure, not business logic.
• mcp groups tools by the MCP server they came from — provenance, so you can tell server-provided tools from your hardcoded ones (see docs/mcp.md).

Read the full reference at docs/concepts.md — it covers how tracesage classifies events into kinds, why agents with no descendants get demoted to chain, and walks through a research-pipeline topology piece by piece.

Features

Live interactive UI

• Run list with status badges (running / completed / failed), search, status filter
• SVG graph showing agents, tools, and execution paths — pulses as events arrive
• MCP attribution — mcp: server nodes with per-server colors, agent→server and server→tool edges, and a draggable "Tools by source" panel grouping tools by origin
• Timeline with click-to-expand step cards, lazy-loaded full payloads
• Replay mode that re-animates a run at 1x / 2x / 5x speed
• Dark / light themes, persisted in localStorage
• Keyboard shortcuts: j/k next/prev run, / focus search, t toggle theme, Esc, ?

Production safety

• Refuses to bind non-loopback addresses without auth_token (hard fail-stop)
• Bearer-token HTTP auth + WebSocket auth via ?token= or subprotocol
• Constant-time token comparison
• Path-traversal blocked at runtime
• Per-run event cap (circuit breaker) and root-level sampling for high volumes
• Bounded internal state (no unbounded memory growth in long-running processes)
• Kill switch: TRACESAGE_ENABLED=false makes it a complete no-op (no server, no DB/worker, no-op handler) — integrate once, disable per-environment. See docs/production.md.

CLI

tracesage serve  --data-dir ~/.tracesage          # read-only viewer
tracesage export --run-id RUN_ID -o trace.jsonl   # export to JSONL
tracesage import -i trace.jsonl                   # import a JSONL export
tracesage stats  --data-dir ~/.tracesage          # summary stats
tracesage runs   --status failed --limit 20       # list runs
tracesage gc     --max-runs 10000                 # retention cleanup
tracesage doctor --data-dir ~/.tracesage          # data-dir diagnostics
tracesage version

See docs/cli.md for full reference.

Examples

The examples/ directory has three tiers:

• getting_started/ — 3 standalone demos driven by FakeListChatModel (no API key): smart-search agent, research supervisor, RAG.
• mcp/ — tools from 2 local MCP servers + 2 hardcoded tools, attributed by source in the topology (needs tracesage[mcp]).
• showcase/ — 30 real before/after apps across popular use cases (customer support, RAG, multi-agent, MCP, reasoning loops, finance/legal/insurance). Each ships a plain before.py and an after.py with tracesage added, so diff shows the exact integration.

# instant, no key:
python examples/getting_started/01_smart_search_agent.py       # then open http://localhost:7842/ui

# the real-world gallery (needs an LLM key):
pip install -r examples/showcase/requirements.txt
export OPENAI_API_KEY=...
python examples/showcase/01_support_faq_router/after.py

The showcase gallery has 30 before/after apps spanning LangChain + LangGraph: routing, parallel fan-out, the supervisor pattern, RAG variants, writer-critic loops, map-reduce, MCP, self-correction, and finance/legal/insurance verticals.

Documentation

Doc	What's in it
Quickstart	First trace in five minutes
Developer guide	Trace links, sync/notebook setup, CLI debugging, pytest fixture
Configuration	Every TRACESAGE_* env var explained
CLI reference	All tracesage subcommands
Production guide	Sampling, auth, retention, deployment
Comparison	tracesage vs LangSmith / LangFuse / Phoenix
Extending tracesage	Adding framework adapters and storage backends
Examples	30 before/after apps with tracesage added

Comparison

When to use tracesage vs alternatives:

	tracesage	LangSmith	LangFuse	Phoenix
Zero infra	✓	cloud / enterprise self-host	Docker + Postgres	✓
Pure pip install	✓	✓ (cloud)	✗	✓
Live UI	✓	✓	✓	✓
MIT licensed	✓	proprietary	MIT	Elastic v2
Eval framework	non-goal	✓	✓	✓
OpenTelemetry export	v0.3+	partial	✓	✓

See docs/comparison.md for the full breakdown.

Performance

Bench results (5,000 events, 100 distinct run_ids, 20% blob-eligible):

Platform	Sustained	p99 write	Drops
Linux x86 + NVMe	800–1200 ev/s	80–150 ms	0
Windows NTFS	60–100 ev/s	1–2 s	0

Windows NTFS is the bottleneck (gzip + fsync amplification). For very high throughput on Windows, raise TRACESAGE_WORKER_BATCH_SIZE to 200 and TRACESAGE_WORKER_BATCH_TIMEOUT to 0.5.

Status

v0.1 — alpha. API may still shift before v1.0. Production-monitoring-ready for single-Python-process deployments; centralized multi-process / remote-collector mode is on the roadmap (see production_roadmap.md).

See the changelog for release notes.

Contributing

Issues and pull requests are welcome.

• Read docs/contributing.md before sending a PR
• For non-trivial changes, open a discussion first
• Bugs and feature requests use the issue templates
• Security reports: see SECURITY.md — please don't open public issues for vulnerabilities
• All participation is governed by the Code of Conduct

License

MIT © tracesage contributors