SpiderFoot is a Python OSINT automation framework that has been solving multi-source orchestration problems since 2012. With 21,985 GitHub stars and 200+ modules, it represents a mature approach to the same challenges modern agentic systems face: integrating heterogeneous APIs, managing rate limits, correlating findings across tools, and exposing real-time progress through a web UI without blocking collection threads.
The framework runs as either a CLI tool or an embedded web server. Each scan targets a domain, IP, email, or other entity and fans out across selected modules. Modules query external data sources (DNS, WHOIS, Shodan, VirusTotal, Censys, and 190+ others), emit typed events, and feed a correlation engine that links findings across sources. Results land in SQLite and can be exported as CSV, JSON, or GEXF graph formats.
Module Registration and Invocation
SpiderFoot modules are Python classes that inherit from SpiderFootPlugin. Each module declares:
-
Meta: name, description, category, flags (e.g.,
errorprone,slow,apikey) -
Produced event types: what data this module emits (e.g.,
IP_ADDRESS,EMAILADDR,DOMAIN_NAME) - Consumed event types: what triggers this module to run
At startup, the framework scans the modules/ directory, imports all sfp_*.py files, and builds a registry. When a scan starts, the orchestrator:
- Seeds the event queue with the target entity
- Checks which modules consume that event type
- Spawns a thread for each matching module
- Passes the event to the module's
handleEvent()method
Modules call self.notifyListeners(event) to emit new findings. The orchestrator receives these, appends them to the queue, and triggers any modules that consume the new event type. This creates a cascading pipeline: a DNS lookup emits an IP address, which triggers a Shodan lookup, which emits open ports, which triggers a banner-grabbing module.
class sfp_example(SpiderFootPlugin):
meta = {
"name": "Example Module",
"summary": "Demonstrates module structure",
"flags": ["apikey"],
"useCases": ["Footprint", "Investigate"],
"categories": ["Search Engines"],
}
opts = {
"api_key": "",
"timeout": 30,
}
optdescs = {
"api_key": "API key for service",
"timeout": "Query timeout in seconds",
}
results = None
def setup(self, sfc, userOpts=dict()):
self.sf = sfc
self.results = self.tempStorage()
for opt in list(userOpts.keys()):
self.opts[opt] = userOpts[opt]
def watchedEvents(self):
return ["DOMAIN_NAME"]
def producedEvents(self):
return ["IP_ADDRESS", "AFFILIATE_DOMAIN"]
def handleEvent(self, event):
eventData = event.data
if eventData in self.results:
return
self.results[eventData] = True
# Perform lookup, emit findings
ip = self.sf.resolveHost(eventData)
if ip:
evt = SpiderFootEvent("IP_ADDRESS", ip, self.__name__, event)
self.notifyListeners(evt)
Modules are stateless between events. The results dictionary prevents duplicate processing of the same data. The framework handles threading, queuing, and event routing.
Queuing and Threading Model
SpiderFoot uses a producer-consumer pattern with a shared event queue. The orchestrator runs in the main thread and manages:
- Event queue: thread-safe FIFO of pending events
- Module threads: one thread per module instance per event
- Status tracking: scan state, module completion, error counts
When a module emits an event, it's added to the queue. The orchestrator polls the queue, checks which modules haven't yet processed that event type, and spawns threads. Modules can run long-running API calls without blocking the UI because the web server runs in a separate thread and queries the SQLite database for current results.
The web UI uses AJAX polling to fetch scan status and new findings. The database acts as the shared state boundary: modules write, the UI reads. This avoids locks on the event queue and allows the UI to remain responsive even when hundreds of modules are running.
Failure Modes
With 200+ modules querying external APIs, failures are common:
-
Rate limits: Modules sleep and retry, or skip if
errorproneflag is set - Timeouts: Configurable per module, default 30 seconds
- API key exhaustion: Modules check quotas and halt gracefully
- Partial failures: One module crash doesn't stop the scan; errors are logged and the scan continues
The framework tracks module status (running, completed, failed) and surfaces errors in the UI. Operators can disable unreliable modules or adjust timeouts per scan.
Correlation Engine
The YAML correlation engine links findings across modules. A correlation rule specifies:
- Risk level: info, low, medium, high, critical
- Title and description: human-readable summary
- Conditions: SQL-like queries against event types
Example rule (simplified):
- title: "Subdomain Takeover Risk"
risk: HIGH
description: "DNS points to unclaimed cloud resource"
conditions:
- event_type: INTERNET_NAME
data_contains: ".s3.amazonaws.com"
- event_type: RAW_RIR_DATA
data_not_contains: "registered"
The engine runs after each module completes. It queries the SQLite database for events matching the conditions and emits a CORRELATION_ALERT event if all conditions are met. These alerts appear in the UI as high-priority findings.
SpiderFoot ships with 37 pre-defined rules covering common OSINT patterns: exposed credentials, subdomain takeovers, leaked API keys, and misconfigured cloud storage. Operators can add custom rules by editing YAML files in the correlations/ directory.
Observability and State Management
All findings are stored in SQLite with the schema:
| Table | Purpose |
|---|---|
tbl_scan |
Scan metadata (target, start time, status) |
tbl_event |
Individual findings (type, data, source) |
tbl_config |
Module settings and API keys |
tbl_storage |
Temporary data for module state |
The web UI queries these tables to render:
- Scan progress: percentage complete, active modules, event counts
- Graph view: entity relationships (domain → IP → open port → service)
- Data browser: filterable table of all events
- Export: CSV, JSON, or GEXF for external analysis
The CLI mode bypasses the web server and writes results directly to stdout or files. This is useful for CI/CD pipelines or headless scans.
Deployment Shape
SpiderFoot runs as a single Python process. Deployment options:
-
Local CLI:
python3 sf.py -s example.com -o json -
Web server:
python3 sf.py -l 127.0.0.1:5001 - Docker: Official Dockerfile mounts a volume for the database and exposes port 5001
The embedded web server uses CherryPy. It's single-threaded but non-blocking for static assets and database queries. Long-running scans don't block the UI because modules run in separate threads.
For production deployments, operators typically:
- Run SpiderFoot in Docker with a persistent volume for SQLite
- Proxy through Nginx with TLS termination
- Configure API keys via environment variables or the web UI
- Schedule scans via cron or a job queue (e.g., Celery)
The framework doesn't include authentication. Operators must secure the web UI with a reverse proxy or firewall rules.
Rate Limiting and API Key Management
Modules that require API keys declare the apikey flag. The framework prompts for keys during setup or reads them from the config database. Keys are stored in plaintext SQLite, so the database must be protected.
Rate limiting is per-module. Each module implements its own backoff logic:
- Fixed delay: sleep N seconds between requests
- Exponential backoff: double the delay after each 429 response
- Quota tracking: count requests and halt when limit is reached
The framework doesn't provide a global rate limiter. This allows modules to optimize for their specific API constraints but requires careful configuration when running many modules in parallel.
Trade-offs and Risks
| Aspect | Benefit | Risk |
|---|---|---|
| 200+ modules | Broad coverage of OSINT sources | High API key cost, rate limit exhaustion |
| SQLite storage | Simple deployment, easy querying | Single-file bottleneck, no horizontal scaling |
| Threaded execution | Parallel module execution | GIL contention, no async I/O |
| Embedded web server | No external dependencies | Single-threaded UI, no built-in auth |
| YAML correlation rules | Declarative, version-controlled | Limited expressiveness vs. Python code |
| Module autonomy | Easy to add new sources | Inconsistent error handling, no global policy |
Technical Verdict
Use SpiderFoot when:
- You need to orchestrate dozens of OSINT data sources in a single scan
- You want a web UI for non-technical users to run reconnaissance
- You need to correlate findings across heterogeneous APIs
- You're building a security automation pipeline and need a proven module system to extend
Avoid SpiderFoot when:
- You need real-time streaming of findings (SQLite polling has latency)
- You require horizontal scaling across multiple workers (single-process architecture)
- You need built-in authentication or multi-tenancy (no user management)
- You want async I/O for high-concurrency API calls (threaded model hits GIL limits)
SpiderFoot's architecture predates modern async frameworks but solves the same orchestration problems: plugin boundaries, event-driven execution, state management, and observability. The module system is a clean example of how to build extensible agents without tight coupling. The correlation engine shows how to add intelligence on top of raw data collection. The threading model and SQLite storage are pragmatic choices that work well for single-operator scans but don't scale to cloud-native deployments.
For teams building agentic systems, SpiderFoot is a reference implementation of multi-source orchestration. The lessons: decouple modules via event types, use a database as the state boundary, expose progress through a separate UI thread, and handle failures gracefully because external APIs will always be unreliable.
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