splyntra 1.3.1

Splyntra SDK - Agent observability & security, built on OpenTelemetry

Splyntra Python SDK

Unified observability and security for AI agents in Python. Built on OpenTelemetry, the Splyntra SDK captures every agent step, LLM call, and tool invocation as a structured trace — enriched with real-time risk scoring for leaked secrets, PII exposure, prompt injection, content moderation, and unsafe tool calls. It also ships trace-correlated structured logging, an inline block/redact guardrail, evaluation, and governance helpers.

Installation

pip install splyntra

With framework auto-instrumentation:

pip install "splyntra[langgraph,openai]"

Available extras: langgraph, openai, openai-agents, crewai

Getting Started

Initialize once at application startup. The instrument parameter enables automatic tracing for supported frameworks — no per-call changes required.

from splyntra import Splyntra

Splyntra(
    api_key="splyntra_dev_key",
    project="my-app",
    endpoint="http://localhost:4318",
    framework="langgraph",
    instrument=("langgraph", "openai"),
)

# Run your LangGraph / OpenAI agent as usual — spans are captured automatically.

To instrument separately (e.g., after configuring the client elsewhere):

from splyntra import instrument

instrument()                 # auto-detect all installed frameworks
instrument("langgraph")      # or target a specific one

Manual Instrumentation

For custom agent, tool, and LLM functions, use decorators. Both sync and async functions are supported.

from splyntra import trace_agent, trace_tool, trace_llm

@trace_agent(name="support_agent", workflow="refund")
def run(query: str):
    customer = read_customer("42")
    return call_llm(query)

@trace_tool(name="crm.read")
def read_customer(id: str):
    ...

@trace_llm(model="gpt-4o", provider="openai")
def call_llm(prompt: str) -> dict:
    # Return a dict with a "usage" key for token/cost analytics
    ...

Configuration

Parameter	Default	Description
api_key	required	Splyntra API key (sent as Bearer token)
project	required	Project slug
endpoint	http://localhost:4318	Collector base URL
environment	development	Deployment environment label
service_name	value of project	OpenTelemetry service.name resource
framework	None	Framework label shown on the Agents page
redact_by_default	True	Strip secrets from spans before export
instrument	None	Tuple of frameworks to auto-instrument
guard	"off"	Inline guardrail mode: "off", "monitor", or "block"
guard_fail_open	True	On a guard-service error, allow (fail open) vs raise

Client-Side Redaction

High-confidence secrets (AWS keys, JWTs, bearer tokens, API keys) are stripped from span attributes before they leave your process. The collector applies a second pass on ingest as defence-in-depth.

Disable with redact_by_default=False (not recommended for production).

Structured Logs

Emit trace-correlated logs to the same collector. Each entry auto-attaches the active trace_id/span_id and is redacted with the same rules as spans, so logs line up with the trace timeline on the dashboard's Logs page.

from splyntra import log

log.info("charged card", {"amount": 42})
log.warn("rate limited", {"server": "stripe"})
log.error("payment failed", {"code": "card_declined"})
# also: log.debug(...), log.fatal(...)

The attributes mapping is optional and redacted before export.

Inline Guard

The guardrail runs a fast, high-confidence check before a model/tool call completes, so you can block or redact rather than only detect after the fact. Enable it at init with guard="monitor" (log only) or guard="block" (raise on a high-confidence prompt-injection match):

from splyntra import Splyntra, SplyntraBlocked

Splyntra(api_key="...", project="my-app", guard="block", instrument=("openai",))

try:
    run_agent(user_input)
except SplyntraBlocked as e:
    # A high-precision injection signature was detected pre-flight.
    handle_blocked(e)

Secrets are redacted in place; only high-precision injection signatures block, so benign role-play prompts pass through (deep analysis stays on the async detector path). guard_fail_open=True (default) allows the call if the guard service is unreachable — set it to False to fail closed.

Supported Frameworks

Framework	instrument name	Span mapping
OpenAI SDK	openai	Chat completions → llm_call spans
Anthropic SDK	anthropic	Messages → llm_call spans
Ollama	ollama	Generate/chat → llm_call spans
LangGraph	langgraph	Graph run → agent span, nodes → step spans
OpenAI Agents	openai_agents	Runner.run → agent span
CrewAI	crewai	Crew kickoff → agent, tasks → step, tools → tool_call
Google ADK	google_adk	Agent runs → agent/tool_call spans
Pydantic AI	pydantic_ai	Agent runs → agent span
LlamaIndex	llamaindex	Query engine → agent; retriever → retrieval
Chroma	chroma	Collection query/get → vector_search
MCP	mcp	tools/call → tool_call (server, tool, args)

Each instrumentor is a safe no-op when its target package is not installed, so instrument() with no arguments auto-detects everything present. Only the four frameworks with a published PyPI dependency ship a pip extra (openai, langgraph, openai-agents, crewai); the rest instrument whatever is already in your environment.

For out-of-process platforms (Dify, n8n), see Integrations.

Evaluation

Run scored evaluations against the Splyntra evaluation service. The service scores caller-produced results against a dataset's ground truth (joined by input) — it never executes your agent. Pick scorers explicitly; run(..., gate=True) exits non-zero on a regression versus the dataset baseline, making it a CI gate.

from splyntra import eval as ev

ev.push_dataset("support-qa", [
    {"input": "capital of France?", "expected_output": "Paris",
     "context": "Paris is the capital of France."},  # context powers groundedness
])

result = ev.run(
    dataset_id,
    results=[{"input": "capital of France?", "actual": "Paris"}],
    scorers=["exact_match", "groundedness"],
    gate=True,          # exit non-zero on regression
    set_baseline=False, # promote this run to the dataset baseline
)

Built-in scorers: exact_match, rule_based, tool_call_success, tool_call_precision, precision_token_overlap, recall_token_overlap, groundedness, latency, cost (LLM-as-judge faithfulness is available in the commercial edition). groundedness/faithfulness require a context on the item. GET /v1/scorers returns the live catalog.

# In CI (SPLYNTRA_API_KEY + SPLYNTRA_EVAL_ENDPOINT set):
splyntra eval push --name support-qa --file dataset.jsonl
splyntra eval run  --dataset <id> --file results.jsonl --scorers exact_match,groundedness --gate

Governance

Request delegation decisions and record consequential actions to the immutable ledger:

from splyntra import authorize, log_action

decision = authorize(
    "payments.refund",
    agent_id="support_agent",
    context={"amount": 80},
)

if decision["decision"] == "allow":
    # proceed with action
    ...
elif decision["decision"] == "needs_approval":
    # routed to human approval in the dashboard
    ...

log_action("refund", actor="support_agent", resource="order_42", metadata={"amount": 80})

Examples

python examples/quickstart.py             # Decorator-based, framework-free
python examples/langgraph_quickstart.py   # LangGraph end-to-end
python examples/crewai_quickstart.py      # CrewAI crew
python examples/security_demo.py          # Deliberately triggers security detections

License

Apache-2.0 — see LICENSE.