Agent Action Logging Platform — log all AI/LLM agent actions to any platform
Project description
ReskPoints
The AI Agent Logger — track every action your agents take, with probability, parameters, and results. Ship to any platform.
You have AI agents making decisions — calling tools, executing code, sending messages, searching databases.
ReskPoints captures every action: what the agent did, how confident it was, what parameters it used, and what happened.
Pipe it to Datadog, Prometheus, OpenTelemetry, webhooks, JSON files, or your console — all from a single logger.
One line, full trace
from reskpoints import AgentLogger
logger = AgentLogger()
logger.log("agent-1", "tool_call", 0.95, {"tool": "search", "query": "RAG papers"}, "3 results")
Async? Same API:
await logger.alog("agent-1", "tool_call", 0.95, {"tool": "search"}, "3 results")
Decorator? Wrap any function:
@log_action(agent_id="coder")
def execute_python(code: str) -> str:
...
Why ReskPoints?
| Problem | ReskPoints |
|---|---|
| "I don't know what my agent is doing" | Every tool call, API request, and decision is logged with full context |
| "It's too slow to add logging" | One decorator, one line of code. Done. |
| "My logs go everywhere and nowhere" | Ship to Console, File, Webhook, Datadog, Prometheus, OpenTelemetry — or all at once |
| "Sensitive data leaks in params" | Auto-mask api_key, token, password, and custom fields before they leave your app |
| "I log too much / too little" | Probabilistic sampling per action pattern (heartbeat: 1%, tool_*: 100%) |
| "One platform going down kills my logs" | Retry + backoff + circuit breaker + in-memory buffering |
Install
pip install reskpoints
pip install reskpoints[datadog,prometheus,opentelemetry] # with extras
Features
- AgentLogger — sync
log()and asyncalog()with auto-enrichment (timestamp, host, env, UUID) - Decorator —
@log_actionlogs every call automatically with params + result + duration - Sampling — per-action probabilistic rate (
tool_call: 100%,heartbeat: 1%) - Masking — automatic redaction of sensitive fields (
api_key,token,password, regex patterns) - 7 platforms — Console, File (JSONL), Webhook (HMAC-signed), Datadog, Prometheus, OpenTelemetry, Mock
- Reliability — exponential backoff retry, circuit breaker, buffering, batching
- CLI —
reskpoints log,test,status,tail,replay - Config — YAML-driven with
${ENV_VAR:default}interpolation
Platforms
| Platform | Extra | Use case |
|---|---|---|
| Console | built-in | Dev/debug |
| File (JSONL) | built-in | Local storage, replay |
| Webhook | built-in | Custom endpoints, Zapier, N8n |
| Datadog | [datadog] |
Datadog Logs + Metrics |
| Prometheus | [prometheus] |
Pushgateway metrics |
| OpenTelemetry | [opentelemetry] |
OTLP spans → any backend |
| Mock | built-in | Testing |
Architecture
Agent code ReskPoints Your observability stack
─────────────────────────────────────────────────────────────────────────────────────
@log_action ┌─────────────┐ ┌──────────┐
def search(q): ───────────▶ │ Sampler │ │ Console │
... │ (rate per │ ├──────────┤
│ action) │ │ File │
logger.log( └──────┬──────┘ ├──────────┤
agent_id="agent-1", │ │ Webhook │
action="tool_call", ┌────────▼───────┐ ├──────────┤
probability=0.95, │ FieldMasker │ │ Datadog │
params={...}, │ (auto-redact │ ├──────────┤
result="ok", │ secrets) │ │Prometheus│
) └────────┬───────┘ ├──────────┤
│ │ OTel │
┌───────▼────────┐ └──────────┘
│ MultiPlatform │
│ ┌──────────┐ │
│ │ retry │ │
│ │ circuit │ │
│ │ buffer │ │
│ └──────────┘ │
└────────────────┘
Each platform is wrapped with retry (exp backoff), circuit breaker (5 fails → 30s recovery), and buffering (1000 entries).
Quick tour
# Minimal
logger.log("agent-1", "search", 0.95, {"q": "papers"}, "3 results")
# Full
logger.log(
agent_id="agent-1",
action="tool_call",
probability=0.95,
params={"tool": "search", "query": "RAG papers 2025"},
result=["paper1", "paper2"],
success=True,
duration_ms=1240.5,
session_id="sess_abc123",
correlation_id="req_xyz789",
)
# Async
results = await logger.alog("agent-1", "search", 0.95, {"q": "papers"}, "3 results")
# Decorator
@log_action(agent_id="coder")
def execute_python(code: str) -> str: ...
# Check health
logger.health()
# → {"console": {"status": "ok"}, "datadog": {"status": "degraded", "error": ...}}
CLI
reskpoints log --agent-id agent-1 --action tool_call --params '{"tool":"search"}'
reskpoints test # Test all platforms
reskpoints status # Platform health
reskpoints tail # Live log stream
reskpoints replay logs.jsonl # Replay from file
Config (reskpoints.yaml)
agent_logger:
sampling:
default_rate: 1.0
rules:
- action: "heartbeat" rate: 0.01
- action: "tool_*" rate: 1.0
masking:
enabled: true
sensitive_fields: [api_key, token, secret, password]
platforms:
console:
enabled: true
format: "human"
webhook:
enabled: false
url: "${WEBHOOK_URL}"
signing_secret: "${WEBHOOK_SECRET}"
datadog:
enabled: false
api_key: "${DD_API_KEY}"
site: "datadoghq.eu"
Development
git clone https://github.com/Resk-Security/ReskPoints.git
cd ReskPoints
pip install -e ".[all,dev]"
pytest tests/ -v # 26 tests
ruff check src/ # clean
mypy src/ # passes
License
MIT. Built with resklogits.
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