vigil-agent 2.3.0

Cognitive infrastructure for AI agents — awareness daemon, frame-based tool filtering, signal protocol, session handoff, and MCP server

Vigil

Cognitive infrastructure for AI agents.

Vigil gives your AI agents a nervous system — awareness of what's happening, memory of what happened, and the ability to coordinate without talking directly to each other.

Most agent memory tools are filing cabinets. Vigil is a nervous system.

The Problem

AI agents forget everything between sessions. They load all tools regardless of context (wasting 50K+ tokens). They can't coordinate across sessions or hand off work to each other. Every conversation starts cold.

What Vigil Does

Awareness Daemon — A background process compiles system state every 90 seconds. Agents boot with pre-compiled context in <1 second. No startup latency, no "remind me what we were doing."

Frame-Based Tool Filtering — Tag tools with context frames. An agent in "backend" mode sees 14 tools, not 95. Saves 50-90% of tool-definition tokens per session.

Signal Protocol — Lightweight event bus with content budgets. Agents emit signals (max 300-800 chars by type), the daemon synthesizes them into awareness. Agents coordinate without direct communication.

Session Handoff — Agents end sessions with structured summaries (files touched, decisions, next steps). The next agent boots with full context of what happened and what to do next.

Signal Compaction — Old signals get summarized, not deleted. Tiered retention (raw → daily → weekly → monthly) keeps context fresh without losing history.

MCP Server — Expose Vigil as an MCP tool server. Any Claude Code, Claude Desktop, Cursor, or Windsurf agent connects and gets persistent awareness instantly.

Articles

• Your MCP Servers Are Flying Blind (Here's How to Fix It) — MCPWatch deep dive on Dev.to

Install

# Core library (daemon, signals, handoff, compaction)
pip install vigil-agent

# With MCP server support
pip install vigil-agent[mcp]

30-Second Demo

See Vigil work in four commands:

pip install vigil-agent
vigil init
vigil signal my-agent "Hello from Vigil!"
vigil status

Expected output:

Current Awareness
─────────────────
  Agents:  my-agent (1 signal)
  Latest:  "Hello from Vigil!" (just now)
  Frame:   default
  Status:  active — 1 unacknowledged signal

That's it — your agent has awareness. Read on for the full quickstart with daemon, handoff, and MCP server.

Quickstart

# Initialize
vigil init

# Emit a signal
vigil signal my-agent "Deployed new API endpoint"

# Start the daemon (compiles awareness every 90s)
vigil daemon start

# Check awareness
vigil status

# See what agents boot with
vigil boot --json

# End a session with a structured handoff
vigil handoff my-agent "Shipped auth module" --files "auth.py, tests.py" --next-steps "Write docs"

# Resume from where the last agent left off
vigil resume next-agent

# Start as an MCP server (Claude Code / Claude Desktop)
vigil serve

# Run signal compaction manually
vigil compact --dry-run

MCP Server

Vigil runs as an MCP server so any AI agent can connect and get persistent awareness.

# stdio (Claude Code, Claude Desktop)
vigil serve

# SSE (remote clients)
vigil serve --transport sse --port 8300

Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "vigil": {
      "command": "vigil",
      "args": ["serve"]
    }
  }
}

12 MCP tools available:

Tool	Description
vigil_boot	Boot with pre-compiled hot context
vigil_compile	Force a fresh awareness compilation
vigil_signal	Emit a signal from an agent
vigil_status	Get current awareness state
vigil_signals	Read recent signals
vigil_handoff	End session with structured handoff
vigil_resume	Resume from last handoff
vigil_chain	Get briefing of last N handoffs
vigil_stale	Find agents that have gone silent
vigil_focus	Manage priority work queue
vigil_frames	Manage context frames
vigil_agents	List known agents and activity

Python API

from vigil import VigilDB, SignalBus, AwarenessCompiler, HandoffProtocol

# Initialize
db = VigilDB("vigil.db")
bus = SignalBus(db)
compiler = AwarenessCompiler(db)
proto = HandoffProtocol(db)

# Emit signals from agents
bus.emit("backend-agent", "Deployed auth service v2")
bus.emit("frontend-agent", "Updated dashboard layout")

# Compile awareness
compiler.synthesize()
context = compiler.compile()
# {'frame': 'backend', 'awareness': '...', 'focus': [...], 'compiled_at': '...'}

# Boot an agent with pre-compiled context (<1 second)
hot_context = compiler.boot()

# Structured session handoff
proto.end_session(
    agent_id="backend-agent",
    summary="Shipped auth v2 with JWT tokens",
    files_touched=["auth.py", "middleware.py"],
    decisions=["Switched from session cookies to JWT"],
    next_steps=["Add rate limiting", "Write integration tests"],
)

# Next agent resumes with full context
context = proto.resume("next-agent")
# {'awareness': ..., 'last_handoff': {...}, 'signals_since_handoff': [...], 'pending_next_steps': [...]}

Frame-Based Tool Filtering

from vigil.registry import tool, get_tools, tool_count

# Tag tools with frames
@tool(name="deploy", description="Deploy to production", frames=["backend", "devops"])
async def deploy(args):
    return {"content": [{"type": "text", "text": f"Deployed {args['service']}"}]}

@tool(name="render", description="Render component", frames=["frontend"])
async def render(args):
    ...

@tool(name="health", description="Health check", frames=["core"])  # Always visible
async def health(args):
    ...

# Filter by context
tool_count()              # 3 (all tools)
tool_count("backend")     # 2 (deploy + health)
tool_count("frontend")    # 2 (render + health)

Signal Compaction

from vigil import SignalCompactor

compactor = SignalCompactor(db)

# Run compaction (tiered: raw → daily → weekly → monthly)
stats = compactor.compact()
# {'daily_summaries': 5, 'weekly_digests': 2, 'monthly_snapshots': 1, 'signals_compacted': 47}

# Browse compacted history
history = compactor.get_history(days=30, agent="backend-agent")

Signal Types & Budgets

Type	Budget	Use
observation	400 chars	Regular activity updates
handoff	600 chars	Session conclusions
summary	800 chars	Comprehensive summaries
alert	300 chars	Urgent notifications

Architecture

Agents emit signals → SQLite → Daemon compiles → Hot context → Agents boot instantly
                                    ↓
                            Frame detection
                            Awareness synthesis
                            Signal compaction
                            Focus queue

• Zero infrastructure — SQLite storage, no Redis/Postgres/Docker required
• Framework-agnostic — Works with any MCP-compatible client, or standalone
• Lightweight — Pure Python, no heavy dependencies (mcp is optional)

Integrations

Ready-to-use configs for popular AI tools. See the examples/ directory for full setup guides.

Tool	Setup
Claude Code	claude mcp add vigil -- vigil serve (guide)
Claude Desktop	Add to claude_desktop_config.json (guide)
Cursor	Add to .cursor/mcp.json (guide)
GitHub Actions	Emit signals from CI/CD (workflow)
Slack	Route alerts to Slack via triggers (guide)
Discord	Route alerts to Discord via triggers (guide)

Shell Completion

# Bash
source completions/vigil.bash

# Zsh
cp completions/vigil.zsh ~/.zsh/completions/_vigil

CLI Reference

Command	Description
vigil init	Initialize a new project
vigil quickstart	Interactive setup wizard
vigil daemon start	Start the awareness daemon
vigil daemon status	Check daemon compilation status
vigil serve	Start as MCP server (stdio or SSE)
vigil signal <agent> <msg>	Emit a signal
vigil status	Show current awareness
vigil boot	Show compiled hot context
vigil frames	List registered frames
vigil tools [--frame X]	List tools (optionally filtered)
vigil handoff <agent> <summary>	Write a structured session handoff
vigil resume <agent>	Resume from last handoff
vigil history	Browse compacted signal history
vigil agents	List known agents
vigil compact	Run signal compaction manually
vigil know <key> <value>	Store a knowledge entry
vigil recall <query>	Fuzzy-search knowledge
vigil knowledge	List all knowledge entries
vigil forget <key>	Delete a knowledge entry
vigil extract	Auto-extract knowledge from signal patterns
vigil export	Export state to markdown
vigil mcp-health	MCP server health (calls, errors, latency)
vigil mcp-health-check <cmd>	Probe MCP server in CI (exit 0/1)
vigil doctor	Diagnose common issues
vigil version	Show version

MCP Production Observability

Monitor any MCP server with one line of code. Tracks tool calls, latency, errors, and emits alerts automatically.

from mcp.server.fastmcp import FastMCP
from vigil.mcpwatch import instrument

mcp = FastMCP("my-server")

@mcp.tool()
async def search(query: str) -> str:
    return "results"

# One line — all tools are now monitored
watch = instrument(mcp)

What it monitors:

• Every tool call: name, duration, success/error
• Latency spikes (configurable threshold, default 5s)
• Error patterns with full tracebacks
• Silent failures (no calls for N minutes)

Three ways to use it:

# 1. Local Vigil — store in same DB as your signals
watch = instrument(mcp, db_path="vigil.db")

# 2. Vigil Cloud — send to your hosted instance
watch = instrument(mcp, api_key="vgl_...")

# 3. Memory-only — just in-process stats
watch = instrument(mcp)

Check health anytime:

health = watch.health()
# {'server': 'my-server', 'status': 'healthy', 'total_calls': 1247,
#  'error_rate': 0.02, 'tools': {'search': {'avg_ms': 42, 'p95_ms': 180}}}

CLI:

vigil mcp-health              # All monitored servers
vigil mcp-health -s my-server # Specific server

REST API (5 endpoints):

Endpoint	Description
GET /mcp/health	Server health summary
GET /mcp/tools	Per-tool analytics
GET /mcp/errors	Recent errors
GET /mcp/latency	p50/p95/p99 percentiles
GET /mcp/volume	Call volume over time

Why Not Just Use Mem0/Letta/LangGraph?

	Vigil	Mem0	Letta	LangGraph
Approach	Awareness daemon	Memory retrieval	Stateful runtime	State machine
Context	Pre-compiled, instant boot	Query on demand	LLM-managed	Checkpoint-based
Tool filtering	Frame-based (50-90% savings)	None	None	None
Multi-agent	Signal protocol + handoff	Shared memory	Single agent	Graph edges
Compaction	Tiered (daily/weekly/monthly)	None	LLM-managed	None
MCP native	Built-in server	No	No	No
Infrastructure	SQLite (zero setup)	API + LLM costs	Full runtime	LangChain ecosystem
Lock-in	None (framework-agnostic)	Mem0 API	Letta platform	LangChain

Vigil is the nervous system. Others are the filing cabinet. Use them together — Vigil handles awareness and coordination, Mem0/Letta handles deep memory.

License

MIT