AgentSight is a observability tool designed specifically for monitoring LLM agent behavior through SSL/TLS traffic interception and process monitoring. Unlike traditional application-level instrumentation, AgentSight observes at the system boundary using eBPF technology, providing tamper-resistant insights into AI agent interactions with minimal performance overhead.
✨ Zero Instrumentation Required - No code changes, no new dependencies, no SDKs. Works with any AI framework or application out of the box.
wget https://github.com/eunomia-bpf/agentsight/releases/latest/download/agentsight && chmod +x agentsight
# Record agent behavior from claude
sudo ./agentsight record -c "claude"
# Record agent behavior from gemini-cli (comm is "node")
sudo ./agentsight record -c "node"
# For Python AI tools
sudo ./agentsight record -c "python"
# Record claude or gemini activity with NVM Node.js, if bundle OpenSSL statically
sudo ./agentsight record --binary-path /usr/bin/node -c nodeVisit http://127.0.0.1:7395 to view the recorded data.
Visit http://127.0.0.1:7395 to view the captured data in real-time.
| Challenge | Application-Level Tools | AgentSight Solution |
|---|---|---|
| Framework Adoption | ❌ New SDK/proxy for each framework | ✅ Drop-in daemon, no code changes |
| Closed-Source Tools | ❌ Limited visibility into operations | ✅ Complete visibility into prompts & behaviors |
| Dynamic Agent Behavior | ❌ Logs can be silenced or manipulated | ✅ Kernel-level hooks, tamper-resistant |
| Encrypted Traffic | ❌ Only sees wrapper outputs | ✅ Captures real unencrypted requests/responses |
| System Interactions | ❌ Misses subprocess executions | ✅ Tracks all process behaviors & file operations |
| Multi-Agent Systems | ❌ Isolated per-process tracing | ✅ Global correlation and analysis |
AgentSight captures critical interactions that application-level tools miss:
- Subprocess executions that bypass instrumentation
- Raw encrypted payloads before agent processing
- File operations and system resource access
- Cross-agent communications and coordination
┌─────────────────────────────────────────────────┐
│ AI Agent Runtime │
│ ┌─────────────────────────────────────────┐ │
│ │ Application-Level Observability │ │
│ │ (LangSmith, Helicone, Langfuse, etc.) │ │
│ │ 🔴 Tamper Vulnerable │ │
│ └─────────────────────────────────────────┘ │
│ ↕ (Can be bypassed) │
├─────────────────────────────────────────────────┤ ← System Boundary
│ 🟢 AgentSight eBPF Monitoring (Tamper-proof) │
│ ┌─────────────────┐ ┌─────────────────────┐ │
│ │ SSL Traffic │ │ Process Events │ │
│ │ Monitoring │ │ Monitoring │ │
│ └─────────────────┘ └─────────────────────┘ │
└─────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────┐
│ Rust Streaming Analysis Framework │
│ ┌─────────────┐ ┌──────────────┐ ┌────────┐ │
│ │ Runners │ │ Analyzers │ │ Output │ │
│ │ (Collectors)│ │ (Processors) │ │ │ │
│ └─────────────┘ └──────────────┘ └────────┘ │
└─────────────────────────────────────────────────┘
↓
┌─────────────────────────────────────────────────┐
│ Frontend Visualization │
│ Timeline • Process Tree • Event Logs │
└─────────────────────────────────────────────────┘
-
eBPF Data Collection (Kernel Space)
- SSL Monitor: Intercepts SSL/TLS read/write operations via uprobe hooks
- Process Monitor: Tracks process lifecycle and file operations via tracepoints
- <3% Performance Overhead: Operates below application layer with minimal impact
-
Rust Streaming Framework (User Space)
- Runners: Execute eBPF programs and stream JSON events (SSL, Process, Agent, Combined)
- Analyzers: Pluggable processors for HTTP parsing, chunk merging, filtering, logging
- Event System: Standardized event format with rich metadata and JSON payloads
-
Frontend Visualization (React/TypeScript)
- Timeline View: Interactive event timeline with zoom and filtering
- Process Tree: Hierarchical process visualization with lifecycle tracking
- Log View: Raw event inspection with syntax highlighting
- Real-time Updates: Live data streaming and analysis
eBPF Programs → JSON Events → Runners → Analyzer Chain → Frontend/Storage/Output
- Linux kernel: 4.1+ with eBPF support (5.0+ recommended)
- Root privileges: Required for eBPF program loading
- Rust toolchain: 1.88.0+ (for building collector)
- Node.js: 18+ (for frontend development)
- Build tools: clang, llvm, libelf-dev
# Pull the latest Docker image
docker pull ghcr.io/eunomia-bpf/agentsight:latest
# Or build locally
git clone https://github.com/eunomia-bpf/agentsight.git --recursive
cd agentsight
make build # Build the agentsight binary first
docker build -t agentsight:local -f dockerfile .Run with Docker:
# Basic monitoring with web UI (access at http://localhost:7395)
docker run --privileged --pid=host -p 7395:7395 -v /sys:/sys:ro \
ghcr.io/eunomia-bpf/agentsight:latest record --comm claude
# Monitor Python AI tools (requires library mounts for SSL capture)
docker run --privileged --pid=host --network=host \
-v /sys:/sys:ro -v /lib:/lib:ro -v /usr/lib:/usr/lib:ro \
ghcr.io/eunomia-bpf/agentsight:latest record --comm python3
# Save logs to host directory
docker run --privileged --pid=host -p 7395:7395 -v /sys:/sys:ro \
-v $(pwd)/logs:/logs \
ghcr.io/eunomia-bpf/agentsight:latest record --comm node --log-file /logs/record.log
# Use custom port
docker run --privileged --pid=host -p 8080:8080 -v /sys:/sys:ro \
ghcr.io/eunomia-bpf/agentsight:latest record --comm claude --server-port 8080Docker Requirements:
--privileged: Required for eBPF (loads kernel programs)--pid=host: Access host processes for monitoring-v /sys:/sys:ro: Read-only access to kernel interfaces-v /lib:/lib:ro -v /usr/lib:/usr/lib:ro: Required for Python SSL monitoring in docker (eBPF needs to read shared libraries to attach uprobes)-p 7395:7395or--network=host: Expose web UI port (default: 7395)
# Clone repository with submodules
git clone https://github.com/eunomia-bpf/agentsight.git --recursive
cd agentsight
# Install system dependencies (Ubuntu/Debian)
make install
# Build all components (frontend, eBPF, and Rust)
make build
# Or build individually:
# make build-frontend # Build frontend assets
# make build-bpf # Build eBPF programs
# make build-rust # Build Rust collector
# Monitor all SSL traffic from system applications
sudo ./agentsight record -c "python" # For Python AI tools
sudo ./agentsight record -c "claude" # For Claude Code
sudo ./agentsight record -c "node" # For Node.js AI tools like gemini-cli
# Combined SSL and process monitoring with web interface
sudo ./agentsight trace --ssl --process --serverFor Node.js installed via NVM, use the --binary-path option:
# Monitor Node.js applications with statically-linked SSL
sudo ./agentsight ssl --binary-path ~/.nvm/versions/node/v20.0.0/bin/node --comm node
# Record with custom binary path
sudo ./agentsight record -c node -- --binary-path ~/.nvm/versions/node/v20.0.0/bin/node# Run eBPF programs directly for development/testing
sudo ./bpf/sslsniff --binary-path ~/.nvm/versions/node/v20.0.0/bin/node --verbose
sudo ./bpf/process -c pythonAll monitoring commands with --server flag provide web visualization at:
- Timeline View: http://127.0.0.1:7395/timeline
- Process Tree: http://127.0.0.1:7395/tree
- Raw Logs: http://127.0.0.1:7395/logs
To test AgentSight with Python scripts inside containers (like script/test-python/test_openai.py):
# 1. Start AgentSight in Docker (captures SSL traffic from host Python processes)
docker run --privileged --pid=host --network=host \
-v /sys:/sys:ro -v /lib:/lib:ro -v /usr/lib:/usr/lib:ro \
-v $(pwd)/logs:/logs \
ghcr.io/eunomia-bpf/agentsight:latest \
record --comm python3 --log-file /logs/capture.log
# 2. In another terminal, run your Python script on the host
python3 your_script.py
# 3. View captured traffic at http://localhost:7395 or check logs/capture.logWhy library mounts are needed:
- Python uses dynamically-linked SSL libraries (
libssl.so.3,libcrypto.so.3) - eBPF uprobes need to read these libraries from
/liband/usr/libto attach to SSL functions - With
--pid=host, the container monitors host processes but needs access to host libraries
Alternative: Run agentsight directly on host (simpler for testing):
sudo ./agentsight record --comm python3 --server-port 7395Q: How does AgentSight differ from traditional APM tools?
A: AgentSight operates at the kernel level using eBPF, providing tamper-resistant monitoring that agents cannot bypass. Traditional APM requires instrumentation that can be compromised.
Q: What's the performance impact?
A: Minimal impact (<3% CPU overhead). eBPF runs in kernel space with optimized data collection.
Q: Can agents detect they're being monitored?
A: Detection is extremely difficult since monitoring occurs at the kernel level without code modification.
Q: Which Linux distributions are supported?
A: Any distribution with kernel 4.1+ and eBPF support. Tested on Ubuntu 20.04+, CentOS 8+, RHEL 8+.
Q: Can I monitor multiple agents simultaneously?
A: Yes, use combined monitoring modes for concurrent multi-agent observation with correlation.
Q: How do I filter sensitive data?
A: Built-in analyzers can remove authentication headers and filter specific content patterns.
Q: Why doesn't AgentSight capture traffic from my NVM Node.js application?
A: NVM Node.js binaries statically link OpenSSL instead of using system libraries. Use the --binary-path option to attach directly to your Node.js binary: --binary-path ~/.nvm/versions/node/v20.0.0/bin/node
Q: "Permission denied" errors
A: Ensure you're running with sudo or have CAP_BPF and CAP_SYS_ADMIN capabilities.
Q: "Failed to load eBPF program" errors
A: Check kernel version and eBPF support. Update vmlinux.h for your architecture if needed.
We welcome contributions! See our development setup:
# Clone with submodules
git clone --recursive https://github.com/eunomia-bpf/agentsight.git
# Install development dependencies
make install
# Run tests
make test
# Frontend development
cd frontend && npm run dev
# Build debug versions with AddressSanitizer
make -C bpf debug- CLAUDE.md - Project guidelines and architecture
- collector/DESIGN.md - Framework design details
- docs/why.md - Problem analysis and motivation
MIT License - see LICENSE for details.
💡 The Future of AI Observability: As AI agents become more autonomous and capable of self-modification, traditional observability approaches become insufficient. AgentSight provides the independent, tamper-resistant monitoring foundation needed for safe AI deployment at scale.


