GitHub - openlegion-ai/openlegion: Secure autonomous AI agent fleet platform — Docker-isolated, multi-provider, with built-in cost controls. OpenClaw alternative for production use.

The AI agent framework built for builders who can't afford a security incident.

Autonomous AI agent fleets — isolated, auditable, and production-ready. Every agent runs in its own Docker container. API keys never leave the vault. Chat via Telegram, Discord, Slack, or WhatsApp. Built-in cost controls. 100+ LLM providers.

Quick Start · Full Setup Guide · Why Not OpenClaw? · Docs

Demo

openlegion-demo.mp4

openlegion start → inline setup → multiple agents running. Live cost tracking. No configuration files edited by hand. Connect Telegram, WhatsApp, Slack, and Discord.

Quick Start

Requirements: Python 3.10+, Docker (running), an LLM API key (Anthropic / Moonshot / OpenAI)

macOS / Linux:

git clone https://github.com/openlegion-ai/openlegion.git && cd openlegion
./install.sh                     # checks deps, creates venv, makes CLI global
openlegion start                 # inline setup on first run, then launch agents

Windows (PowerShell):

git clone https://github.com/openlegion-ai/openlegion.git
cd openlegion
powershell -ExecutionPolicy Bypass -File install.ps1
openlegion start

First install downloads ~70 packages and takes 2-3 minutes. Subsequent installs are fast.

Need help? See the full setup guide for platform-specific instructions and troubleshooting.

# Add more agents later
openlegion agent add researcher

# Check status
openlegion status

# Run in background
openlegion start -d
openlegion chat researcher   # connect from another terminal
openlegion stop              # clean shutdown

Why Not OpenClaw?

OpenClaw is the most popular personal AI assistant framework — 200K+ GitHub stars, brilliant for single-user use. For production workloads and team deployments, it has documented problems:

42,000+ exposed instances with no authentication (Bitsight, Feb 2026)
341 malicious skills found stealing user data (Koi Security / The Hacker News)
CVE-2026-25253: one-click remote code execution
No per-agent cost controls — runaway spend is a real risk
No deterministic routing — a CEO agent (LLM) decides what runs next
API keys stored directly in agent config

OpenLegion was designed from day one assuming agents will be compromised.

	OpenClaw	OpenLegion
API key storage	Agent config files	Vault proxy — agents never see keys
Agent isolation	Process-level	Docker container per agent + microVM option
Cost controls	None	Per-agent daily + monthly budget caps
Multi-agent routing	LLM CEO agent	Deterministic YAML DAG workflows
LLM providers	Broad	100+ via LiteLLM with health-tracked failover
Test coverage	Minimal	1276 tests including full Docker E2E
Codebase size	430,000+ lines	~21,000 lines — auditable in a day

What It Does

OpenLegion is an open-source autonomous AI agent framework for running multi-agent fleets in isolated Docker containers. Each agent gets its own memory, tools, schedule, and budget — coordinated through deterministic YAML workflows with no LLM routing layer.

Chat with your agent fleet via Telegram, Discord, Slack, WhatsApp, or CLI. Agents act autonomously via cron schedules, webhooks, heartbeat monitoring, and file watchers — without being prompted.

1276 tests passing across ~21,000 lines of application code. Fully auditable in a day. No LangChain. No Redis. No Kubernetes. No CEO agent. BSL License.

Security by architecture — every agent runs in an isolated Docker container (microVM when available). API keys live in the credential vault — agents call through a proxy and never handle credentials directly. Defense-in-depth with 6 security layers.
Production-grade cost control — per-agent LLM token tracking with enforced daily and monthly budget caps at the vault layer. Agents physically cannot spend what you haven't authorized. View live spend with /costs in the REPL.
Deterministic multi-agent orchestration — YAML-defined DAG workflows with step dependencies, conditions, retries, and failure handlers. No LLM decides what runs next. Predictable, debuggable, auditable.
Acts autonomously — cron schedules, heartbeat probes, webhook triggers, and file watchers let agents work without being prompted.
Self-aware and self-improving — agents understand their own permissions, budget, fleet topology, and system architecture via auto-generated SYSTEM.md and live runtime context. They learn from tool failures and user corrections, injecting past learnings into future sessions.
Self-extends — agents write their own Python skills at runtime and hot-reload them. Agents can also spawn sub-agents for specialized work.
Multi-channel — connect agents to Telegram, Discord, Slack, and WhatsApp. Also accessible via CLI and API.
Real-time dashboard — web-based fleet observability at /dashboard with consolidated navigation, slide-over chat panels, keyboard command palette, grouped request traces, live event streaming, streaming broadcast with real-time per-agent responses, LLM prompt/response previews, agent management, agent identity editor (personality, instructions, preferences, heartbeat rules, memory, activity logs, learnings), cost charts, cron management, and embedded KasmVNC viewer for persistent browser agents.
Tracks and caps spend — per-agent LLM cost tracking with daily and monthly budget enforcement.
Runs deterministic workflows — YAML-defined DAG workflows chain agents in sequence with conditions, retries, and failure handlers.
Fails over across providers — configurable model failover chains cascade across LLM providers with per-model health tracking and exponential cooldown.
Token-level streaming — real-time token-by-token LLM responses across CLI, dashboard, Telegram, Discord, and Slack with progressive message editing and graceful non-streaming fallback.

Architecture

OpenLegion's architecture separates concerns across three trust zones: untrusted external input, sandboxed agent containers, and a trusted mesh host that holds credentials and coordinates the fleet. All inter-agent communication flows through the mesh — no agent has direct network access or peer-to-peer connections.

┌──────────────────────────────────────────────────────────────────────────┐
│                           User Interface                                │
│                                                                         │
│   CLI (click)          Webhooks            Cron Scheduler               │
│   - setup              - POST /webhook/    - "0 9 * * 1-5"             │
│   - start (REPL)         hook/{id}         - "every 30m"               │
│   - stop / status      - Trigger agents    - Heartbeat pattern          │
│   - agent add/list                                                      │
└──────────────┬──────────────────┬──────────────────┬────────────────────┘
               │                  │                  │
               ▼                  ▼                  ▼
┌──────────────────────────────────────────────────────────────────────────┐
│                         Mesh Host (FastAPI)                              │
│                         Port 8420 (default)                              │
│                                                                         │
│  ┌────────────┐ ┌─────────┐ ┌───────────┐ ┌────────────────────────┐   │
│  │ Blackboard │ │ PubSub  │ │  Message   │ │   Credential Vault     │   │
│  │ (SQLite)   │ │         │ │  Router    │ │   (API Proxy)          │   │
│  │            │ │ Topics, │ │            │ │                        │   │
│  │ Key-value, │ │ subs,   │ │ Permission │ │ LLM, Anthropic,       │   │
│  │ versioned, │ │ notify  │ │ enforced   │ │ OpenAI, Apollo,        │   │
│  │ TTL, GC    │ │         │ │ routing    │ │ Hunter, Brave Search   │   │
│  └────────────┘ └─────────┘ └───────────┘ └────────────────────────┘   │
│                                                                         │
│  ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐   │
│  │ Orchestrator │ │  Permission  │ │  Container   │ │    Cost      │   │
│  │              │ │  Matrix      │ │  Manager     │ │   Tracker    │   │
│  │ DAG executor,│ │              │ │              │ │              │   │
│  │ step deps,   │ │ Per-agent    │ │ Docker life- │ │ Per-agent    │   │
│  │ conditions,  │ │ ACLs, globs, │ │ cycle, nets, │ │ token/cost,  │   │
│  │ retry/fail   │ │ default deny │ │ volumes      │ │ budgets      │   │
│  └──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘   │
└──────────────────────────────────────────────────────────────────────────┘
               │
               │  Docker Network (bridge / host)
               │
     ┌─────────┼──────────┬──────────────────────┐
     ▼         ▼          ▼                      ▼
┌─────────┐ ┌─────────┐ ┌─────────┐       ┌─────────┐
│ Agent A │ │ Agent B │ │ Agent C │  ...  │ Agent N │
│ :8401   │ │ :8402   │ │ :8403   │       │ :840N   │
└─────────┘ └─────────┘ └─────────┘       └─────────┘
  Each agent: isolated Docker container, own /data volume,
  own memory DB, own workspace, 512MB RAM, 0.5 CPU cap

Trust Zones

Level	Zone	Description
0	Untrusted	External input (webhooks, user prompts). Sanitized before reaching agents.
1	Sandboxed	Agent containers. Isolated filesystem, no external network, no credentials.
2	Trusted	Mesh host. Holds credentials, manages containers, routes messages.

Mesh Host

The mesh host is the central coordination layer. It runs on the host machine as a single FastAPI process.

Blackboard (Shared State Store)

SQLite-backed key-value store with versioning, TTL, and garbage collection.

Namespace	Purpose	Example
`tasks/*`	Task assignments	`tasks/research_abc123`
`context/*`	Shared agent context	`context/prospect_acme`
`signals/*`	Inter-agent signals	`signals/research_complete`
`history/*`	Append-only audit log	`history/action_xyz`

Credential Vault (API Proxy)

Agents never hold API keys. All external API calls route through the mesh. The vault uses a two-tier prefix system: OPENLEGION_SYSTEM_* for LLM provider keys (never agent-accessible) and OPENLEGION_CRED_* for agent-tier tool/service keys. Budget limits are enforced before dispatching LLM calls and token usage is recorded after each response.

Model Failover

Configurable failover chains cascade across LLM providers transparently. ModelHealthTracker applies exponential cooldown per model (transient errors: 60s → 300s → 1500s, billing/auth errors: 1h). Permanent errors (400, 404) don't cascade. Streaming failover is supported — if a connection fails mid-stream, the next model in the chain picks up.

Permission Matrix

Every inter-agent operation is checked against per-agent ACLs:

{
  "researcher": {
    "can_message": ["orchestrator"],
    "can_publish": ["research_complete"],
    "can_subscribe": ["new_lead"],
    "blackboard_read": ["tasks/*", "context/*"],
    "blackboard_write": ["context/prospect_*"],
    "allowed_apis": ["llm", "brave_search"],
    "allowed_credentials": ["brightdata_*"]
  }
}

Orchestrator (Workflow DAG Executor)

Workflows are defined in YAML and executed as directed acyclic graphs:

name: prospect_pipeline
trigger: new_prospect
timeout: 600
steps:
  - id: research
    agent: research
    task_type: research_prospect
    input_from: trigger.payload
  - id: qualify
    agent: qualify
    task_type: qualify_lead
    depends_on: [research]
    condition: "research.result.score >= 5"

Container Manager

Each agent runs in an isolated Docker container with:

Image: openlegion-agent:latest (Python 3.12, system tools, Playwright, Chromium, Camoufox, KasmVNC)
Network: Bridge with port mapping (macOS/Windows) or host network (Linux)
Volume: openlegion_data_{agent_id} mounted at /data (agent names with spaces/special chars are sanitized)
Resources: 512MB RAM limit, 50% CPU quota
Security: no-new-privileges, runs as non-root agent user (UID 1000)
Ports: 8400 (FastAPI), 6080 (KasmVNC for persistent browser)

Agent Architecture

Each agent container runs a FastAPI server with endpoints for task assignment, chat, status, capabilities, and results.

┌─────────────────────────────────────────────────────────────┐
│                    Agent Container                           │
│                                                              │
│  FastAPI Server (:8400)                                      │
│    POST /task    POST /chat    POST /chat/reset               │
│    GET /status   GET /result   GET /capabilities              │
│    GET /workspace  GET|PUT /workspace/{file}                  │
│    GET /heartbeat-context                                     │
│                                                              │
│  ┌──────────────────────────────────────────────────────┐    │
│  │                     AgentLoop                         │    │
│  │                                                       │    │
│  │  Task Mode: bounded 20-iteration loop                 │    │
│  │  Chat Mode: conversational with tool use              │    │
│  │                                                       │    │
│  │  Both: LLM call → tool execution → context mgmt      │    │
│  └──┬──────────┬──────────┬──────────┬──────────┬───────┘    │
│     │          │          │          │          │             │
│  ┌──▼───┐  ┌──▼───┐  ┌──▼──────┐ ┌─▼──────┐ ┌─▼─────────┐ │
│  │ LLM  │  │ Mesh │  │ Skill   │ │Work-   │ │ Context   │ │
│  │Client│  │Client│  │Registry │ │space   │ │ Manager   │ │
│  │(mesh │  │(HTTP)│  │(builtins│ │Manager │ │(token     │ │
│  │proxy)│  │      │  │+custom) │ │(/data/ │ │tracking,  │ │
│  └──────┘  └──────┘  └─────────┘ │workspace│ │compact)   │ │
│                                   └─────────┘ └───────────┘ │
└─────────────────────────────────────────────────────────────┘

Task Mode (`execute_task`)

Accepts a TaskAssignment from the orchestrator. Runs a bounded loop (max 20 iterations) of decide → act → learn. Returns a TaskResult with structured output and optional blackboard promotions.

Chat Mode (`chat`)

Accepts a user message. On the first message, loads workspace context (AGENTS.md, SOUL.md, USER.md, MEMORY.md, SYSTEM.md) into the system prompt, injects a live Runtime Context block (permissions, budget, fleet, cron), and searches memory for relevant facts. Executes tool calls in a bounded loop (max 30 rounds) and runs context compaction when needed.

Tool Loop Detection

Both modes include automatic detection of stuck tool-call loops. A sliding window tracks recent (tool_name, params_hash, result_hash) tuples and escalates through three levels:

Level	Trigger	Action
Warn	3rd identical call	System message: "Try a different approach"
Block	5th identical call	Tool skipped, error returned to agent
Terminate	10th call with same params	Loop terminated with failure status

Memory retrieval tools (memory_search, memory_recall) are exempt since repeated searches are legitimate. Detection uses SHA-256 hashes of canonicalized parameters and results over a 15-call sliding window.

Built-in Tools

Tool	Purpose
`exec`	Shell command execution with timeout
`read_file`	Read file contents from `/data`
`write_file`	Write/append file in `/data`
`list_files`	List/glob files in `/data`
`http_request`	HTTP GET/POST/PUT/DELETE/PATCH
`browser_navigate`	Open URL, extract page text (auto-recovers from dead CDP sessions)
`browser_snapshot`	Accessibility tree snapshot with element refs (e1, e2, ...)
`browser_screenshot`	Capture page screenshot
`browser_click`	Click element by ref or CSS selector
`browser_type`	Fill input by ref or CSS selector
`browser_evaluate`	Run JavaScript in page
`browser_reset`	Force-close browser and reconnect fresh (new IP for Bright Data)
`memory_search`	Hybrid search across workspace files and structured DB
`memory_save`	Save fact to workspace and structured memory DB
`memory_recall`	Semantic search over structured facts with category filtering
`web_search`	Search the web via DuckDuckGo (no API key)
`list_agents`	Discover other agents in the fleet
`read_shared_state`	Read from the shared blackboard
`write_shared_state`	Write to the shared blackboard
`list_shared_state`	Browse blackboard entries by prefix
`publish_event`	Publish event to mesh pub/sub
`save_artifact`	Save deliverable file and register on blackboard
`update_workspace`	Update identity files (HEARTBEAT.md, USER.md)
`notify_user`	Send notification to user across all connected channels
`set_cron`	Schedule a recurring job
`set_heartbeat`	Enable autonomous monitoring with probes
`list_cron` / `remove_cron`	Manage scheduled jobs
`create_skill`	Write a new Python skill at runtime
`list_custom_skills`	List all custom skills the agent has created
`reload_skills`	Hot-reload all skills
`spawn_agent`	Spawn an ephemeral sub-agent in a new container
`spawn_subagent`	Spawn a lightweight in-container subagent for parallel subtasks
`list_subagents`	List active subagents and their status
`vault_generate_secret`	Generate and store a random secret (returns opaque handle)
`vault_capture_from_page`	Capture text from browser element and store as credential
`vault_list` / `vault_status`	List credential names or check if a credential exists
`introspect`	Query own runtime state: permissions, budget, fleet, cron, health
`read_agent_history`	Read another agent's conversation logs

Custom skills are Python functions decorated with @skill, auto-discovered from the agent's skills_dir at startup. Agents can also create new skills at runtime and hot-reload them.

Agents also support MCP (Model Context Protocol) — any MCP-compatible tool server can be plugged in via config, giving agents access to databases, filesystems, APIs, and more without writing custom skills.

Memory System

Five layers give agents persistent, self-improving memory:

Layer 5: Context Manager          ← Manages the LLM's context window
  │  Monitors token usage
  │  Proactive flush at 60% capacity
  │  Auto-compacts at 70% capacity
  │  Extracts facts before discarding messages
  │
Layer 4: Learnings                ← Self-improvement through failure tracking
  │  learnings/errors.md         (tool failures with context)
  │  learnings/corrections.md   (user corrections and preferences)
  │  Auto-injected into system prompt each session
  │
Layer 3: Workspace Files          ← Durable, human-readable storage
  │  AGENTS.md, SOUL.md, USER.md  (loaded into system prompt)
  │  SYSTEM.md                    (auto-generated architecture guide + runtime snapshot)
  │  MEMORY.md                    (curated long-term facts)
  │  HEARTBEAT.md                 (autonomous monitoring rules)
  │  memory/YYYY-MM-DD.md         (daily session logs)
  │  BM25 search across all files
  │
Layer 2: Structured Memory DB     ← Hierarchical vector database
  │  SQLite + sqlite-vec + FTS5
  │  Facts with embeddings (KNN similarity search)
  │  Auto-categorization with category-scoped search
  │  3-tier retrieval: categories → scoped facts → flat fallback
  │  Reinforcement scoring with access-count boost + recency decay
  │
Layer 1: Salience Tracking        ← Prioritizes important facts
     Access count, decay score, last accessed timestamp
     High-salience facts auto-surface in initial context

Write-Then-Compact Pattern

Before the context manager discards messages, it:

Asks the LLM to extract important facts from the conversation
Stores facts in both MEMORY.md and the structured memory DB
Summarizes the conversation
Replaces message history with: summary + last 4 messages

Nothing is permanently lost during compaction.

Cross-Session Memory

Facts saved with memory_save are stored in both the workspace (daily log) and the structured SQLite database. After a reset or restart, memory_recall retrieves them via semantic search:

Session 1: User says "My cat's name is Whiskerino"
           Agent saves to daily log + structured DB

  ═══ Chat Reset ═══

Session 2: User asks "What is my cat's name?"
           Agent recalls "Whiskerino" via memory_recall

Triggering & Automation

Agents act autonomously through trigger mechanisms running in the mesh host (not inside containers, so they survive container restarts).

Cron Scheduler

Persistent cron jobs that dispatch messages to agents on a schedule. Agents can schedule their own jobs using the set_cron tool.

Supports 5-field cron expressions (minute hour dom month dow), interval shorthand (every 30m, every 2h), and state persisted to config/cron.json.

Heartbeat System

Cost-efficient autonomous monitoring. Heartbeat jobs run cheap deterministic probes first — disk usage, pending signals, pending tasks — and only dispatch to the agent (costing LLM tokens) when probes detect something actionable.

When a heartbeat fires, the agent receives enriched context: its HEARTBEAT.md rules, recent daily logs, probe alerts, and actual pending signal/task content — all in a single message. If HEARTBEAT.md is the default scaffold, no recent activity exists, and no probes triggered, the dispatch is skipped entirely (zero LLM cost).

This 5-stage architecture (scheduler → probes → context → policy → action) makes always-on agents economically viable.

Webhook Endpoints

Named webhook URLs that dispatch payloads to agents:

curl -X POST http://localhost:8420/webhook/hook/hook_a1b2c3d4 \
  -H "Content-Type: application/json" \
  -d '{"event": "push", "repo": "myproject"}'

File Watchers

Poll directories for new/modified files matching glob patterns. Uses polling (not inotify) for Docker volume compatibility.

# config/watchers.yaml
watchers:
  - path: "/data/inbox"
    pattern: "*.csv"
    agent: "researcher"
    message: "New prospect list uploaded: {filename}. Begin research."

Cost Tracking & Budgets

Every LLM call is tracked at the Credential Vault layer. Per-agent budgets prevent runaway spend. View costs from the interactive REPL (/costs) or configure budgets in config/agents.yaml:

agents:
  researcher:
    budget:
      daily_usd: 5.00
      monthly_usd: 100.00

When an agent exceeds its budget, the vault rejects LLM calls with an error instead of forwarding them to the provider.

Security Model

Defense-in-depth with six layers:

Layer	Mechanism	What It Prevents
Runtime isolation	Docker Sandbox microVMs when available; falls back to Docker containers	Agent escape, kernel exploits
Container hardening	Non-root user, no-new-privileges, memory/CPU limits	Privilege escalation, resource abuse
Credential separation	Vault holds keys, agents call via proxy	Key leakage, unauthorized API use
Permission enforcement	Per-agent ACLs for messaging, blackboard, pub/sub, APIs	Unauthorized data access
Input validation	Path traversal prevention, safe condition eval (no `eval()`), token budgets, iteration limits	Injection, runaway loops
Unicode sanitization	Invisible character stripping at three choke points	Prompt injection via hidden Unicode

Dual Runtime Backend

OpenLegion supports two isolation levels:

	Docker Containers (default)	Docker Sandbox microVMs
Isolation	Shared kernel, namespace separation	Own kernel per agent (hypervisor)
Escape risk	Kernel exploit could escape	Hypervisor boundary — much harder
Performance	Native speed	Near-native (Rosetta 2 on Apple Silicon)
Requirements	Any Docker install	Docker Desktop 4.58+
Enable	`openlegion start`	`openlegion start --sandbox`

Docker containers (default) run agents as non-root with no-new-privileges, 512MB memory limit, 50% CPU cap, and no host filesystem access. This is secure for most use cases.

Docker Sandbox microVMs give each agent its own Linux kernel via Apple Virtualization.framework (macOS) or Hyper-V (Windows). Even if an agent achieves code execution, it's trapped inside a lightweight VM with no visibility into other agents or the host. Use this when running untrusted code or when compliance requires hypervisor isolation.

# Default: container isolation (works everywhere)
openlegion start

# Maximum security: microVM isolation (Docker Desktop 4.58+ required)
openlegion start --sandbox

Check compatibility: Run docker sandbox version — if it returns a version number, your Docker Desktop supports sandboxes. If not, update Docker Desktop to 4.58+.

CLI Reference

openlegion [--version]
├── start [--config PATH] [-d] [--sandbox]  # Start runtime + interactive REPL (inline setup on first run)
├── stop                                 # Stop all containers
├── chat <name> [--port PORT]            # Connect to a running agent
├── status [--port PORT]                 # Show agent status
│
├── agent
│   ├── add [name]                       # Add a new agent
│   ├── edit <name>                      # Edit agent settings (model, browser, budget)
│   ├── list                             # List configured agents
│   └── remove <name> [--yes]            # Remove an agent
│
├── skill
│   ├── install <repo_url> [--ref TAG]         # Install skill from git repo
│   ├── list                                    # List installed marketplace skills
│   └── remove <name> [--yes]                   # Remove an installed skill
│
└── channels
    ├── add [telegram|discord|slack|whatsapp]  # Connect a messaging channel
    ├── list                                    # Show configured channels
    └── remove <name>                           # Disconnect a channel

Interactive REPL Commands

@agent <message>                     Send message to a specific agent
/use <agent>                         Switch active agent
/agents                              List all running agents
/add                                 Add a new agent (hot-adds to running system)
/edit [name]                         Edit agent settings (model, browser, budget)
/remove [name]                       Remove an agent
/status                              Show agent health
/broadcast <msg>                     Send message to all agents
/steer <msg>                         Inject message into busy agent's context
/costs                               Show today's LLM spend + context usage + model health
/blackboard [list|get|set|del]       View/edit shared blackboard entries
/queue                               Show agent task queue status
/workflow [list|run]                 List or trigger workflows
/cron [list|del|pause|resume|run]    Manage cron jobs
/debug [trace]                       Show recent request traces
/addkey <svc> [key]                  Add an API credential to the vault
/removekey [name]                    Remove a credential from the vault
/reset                               Clear conversation with active agent
/quit                                Exit and stop runtime

Aliases: /exit = /quit, /agents = /status

Team Templates

Templates are offered during first-run setup (via openlegion start):

Template	Agents	Description
`starter`	assistant	Single general-purpose agent
`sales`	researcher, qualifier, outreach	Sales pipeline team
`devteam`	pm, engineer, reviewer	Software development team
`content`	researcher, writer, editor	Content creation team

Configuration

`PROJECT.md` — Fleet-Wide Context

Shared across all agents. Loaded into every agent's system prompt.

# PROJECT.md

## What We're Building
SaaS platform for automated lead qualification

## Current Priority
Ship the email personalization pipeline this week

## Hard Constraints
- Budget: $50/day total across all agents
- No cold outreach to .edu or .gov domains

`config/mesh.yaml` — Framework Settings

mesh:
  host: "0.0.0.0"
  port: 8420

llm:
  default_model: "openai/gpt-4.1-mini"
  embedding_model: "text-embedding-3-small"
  max_tokens: 4096
  temperature: 0.7

`config/agents.yaml` — Agent Definitions

Created automatically by openlegion start (inline setup) or openlegion agent add.

agents:
  researcher:
    role: "research"
    model: "openai/gpt-4.1-mini"
    skills_dir: "./skills/research"
    system_prompt: "You are a research specialist..."
    browser_backend: "stealth"          # basic (default), stealth, advanced, or persistent
    thinking: "medium"                   # off (default), low, medium, or high
    resources:
      memory_limit: "512m"
      cpu_limit: 0.5
    budget:
      daily_usd: 5.00
      monthly_usd: 100.00

`config/permissions.json` — Agent Permissions

Per-agent access control with glob patterns for blackboard paths and explicit allowlists for messaging, pub/sub, and API access.

`config/workflows/*.yaml` — Workflow Definitions

DAG-based workflows with step dependencies, conditions, timeouts, retry policies, and failure handlers.

`.env` — API Keys

Managed automatically by openlegion start and openlegion channels add. You can also edit directly. Uses a two-tier prefix system:

# System tier — LLM provider keys (never accessible by agents)
OPENLEGION_SYSTEM_ANTHROPIC_API_KEY=sk-ant-...
OPENLEGION_SYSTEM_OPENAI_API_KEY=sk-...
OPENLEGION_SYSTEM_MOONSHOT_API_KEY=sk-...

# Agent tier — tool/service keys (access controlled per-agent)
OPENLEGION_CRED_BRAVE_SEARCH_API_KEY=BSA...

# Channel tokens
OPENLEGION_CRED_TELEGRAM_BOT_TOKEN=123456:ABC...
OPENLEGION_CRED_DISCORD_BOT_TOKEN=MTIz...
OPENLEGION_CRED_SLACK_BOT_TOKEN=xoxb-...
OPENLEGION_CRED_SLACK_APP_TOKEN=xapp-...
OPENLEGION_CRED_WHATSAPP_ACCESS_TOKEN=EAAx...
OPENLEGION_CRED_WHATSAPP_PHONE_NUMBER_ID=1234...

# Log format: "json" (default) or "text" (human-readable)
OPENLEGION_LOG_FORMAT=text

Connecting Channels

openlegion channels add telegram    # prompts for bot token from @BotFather
openlegion channels add discord     # prompts for bot token
openlegion channels add slack       # prompts for bot + app tokens
openlegion channels add whatsapp    # prompts for access token + phone ID
openlegion channels list            # check what's connected
openlegion channels remove telegram # disconnect

On next openlegion start, a pairing code appears — send it to your bot to link.

MCP Tool Support

OpenLegion supports the Model Context Protocol (MCP) — the emerging standard for LLM tool interoperability. Any MCP-compatible tool server can be plugged into an agent via config, with tools automatically discovered and exposed to the LLM alongside built-in skills.

Configuration

Add mcp_servers to any agent in config/agents.yaml:

agents:
  researcher:
    role: "research"
    model: "openai/gpt-4.1-mini"
    mcp_servers:
      - name: filesystem
        command: mcp-server-filesystem
        args: ["/data"]
      - name: database
        command: mcp-server-sqlite
        args: ["--db", "/data/research.db"]

Each server is launched as a subprocess inside the agent container using stdio transport. Tools are discovered automatically via the MCP protocol and appear in the LLM's tool list alongside built-in skills.

How It Works

Agent container reads MCP_SERVERS from environment (set by the runtime)
MCPClient launches each server subprocess via stdio transport
MCP protocol handshake discovers available tools and their schemas
Tools are registered in SkillRegistry with OpenAI function-calling format
LLM tool calls route through MCPClient.call_tool() to the correct server
Name conflicts with built-in skills are resolved by prefixing (mcp_{server}_{tool})

Server Config Options

Field	Type	Description
`name`	string	Server identifier (used for logging and conflict prefixes)
`command`	string	Command to launch the server
`args`	list	Command-line arguments (optional)
`env`	dict	Environment variables for the server process (optional)

See the full MCP Integration Guide for advanced usage, custom server setup, and troubleshooting.

Testing

# Unit and integration tests (fast, no Docker needed)
pytest tests/ --ignore=tests/test_e2e.py --ignore=tests/test_e2e_chat.py \
  --ignore=tests/test_e2e_memory.py --ignore=tests/test_e2e_triggering.py

# E2E tests (requires Docker + API key)
pytest tests/test_e2e.py tests/test_e2e_chat.py \
  tests/test_e2e_memory.py tests/test_e2e_triggering.py

# Everything
pytest tests/

Test Coverage

Category	Tests	What's Tested
Built-in Tools	133	exec, file, browser (all 4 backends, screenshots, reset/recovery, KasmVNC), memory, mesh, vault, introspect, path traversal, discovery
Dashboard	93	Fleet management, blackboard, costs, traces, queues, cron, settings, config, streaming broadcast, workspace proxy
Workspace	68	File scaffold, loading, BM25 search, daily logs, learnings, heartbeat, identity files, SYSTEM.md
Agent Loop	67	Task execution, tool calling, cancellation, tool memory, chat helpers, daily log enrichment, task logging
Credentials	67	Vault, API proxy, provider detection, two-tier system, credential lifecycle
CLI	65	Agent add/list/edit/remove, chat, REPL commands, cron management, version
Channels (base)	47	Abstract channel, commands, per-user routing, chunking, steer, debug, addkey normalization, parallel broadcast
Cron	44	Cron expressions, intervals, dispatch, persistence, enriched heartbeat, skip-LLM, concurrent mutations
Mesh	42	Blackboard, PubSub, MessageRouter, permissions
Integration	42	Multi-component mesh operations, notifications
Sanitization	38	Invisible Unicode stripping, bidi overrides, tag chars, zero-width
Discord Channel	36	Slash commands, message routing, pairing, chunking, embed formatting
Memory Store	34	SQLite ops, vector search, categories, hierarchical search, tool outcomes
Context Manager	34	Token estimation (tiktoken + model-aware), compaction, flushing, flush reset
Runtime Backend	33	DockerBackend, SandboxBackend, browser_backend, extra_env, name sanitization, detection, selection
Events	31	Event streaming, filtering, WebSocket, notification events
Traces	30	Trace recording, grouping, summaries, prompt preview extraction
Orchestrator	26	Workflows, conditions, retries, failures
Transcript	24	Transcript formatting, safety, round-trip fidelity
WhatsApp Channel	22	Cloud API, webhook verification, message chunking, non-text reply
Agent Server	21	Workspace API, heartbeat-context endpoint, content sanitization, file allowlist
Slack Channel	21	Socket Mode, thread routing, pairing, command translation
Skills	20	Discovery, execution, injection, MCP integration
Setup Wizard	20	Quickstart, full setup, API key validation, templates, inline setup, two-tier credentials
Marketplace	20	Install, manifest parsing, validation, path traversal, remove
Transport	18	HttpTransport, SandboxTransport, resolve_url
Dashboard Workspace	17	Workspace proxy endpoints, filename validation, transport forwarding, sanitization
Vault	16	Credential storage, budget enforcement, failover
Failover	15	Health tracking, chain cascade, cooldown
Loop Detector	14	Agent loop detection and intervention
Lanes	13	Per-agent FIFO task queues
Costs	13	Usage recording, budgets, vault integration, budget overrun warnings
Chat	12	Chat mode, streaming, workspace integration
Subagent	11	Spawn, depth/concurrent limits, TTL timeout, skill cloning, memory isolation
MCP Client	10	Tool discovery, routing, conflicts, lifecycle
Chat Workspace	10	Cross-session memory, corrections, learnings
Health Monitor	8	Ephemeral cleanup, TTL expiry, event emission, restart with missing config, parallel health checks
Types	8	Pydantic validation, serialization
MCP E2E	7	Real MCP protocol with live server subprocess
Webhooks	7	Add/remove, persistence, dispatch
File Watchers	7	Polling, dispatch, pattern matching
Memory Tools	6	memory_search, memory_save, memory_recall
Memory Integration	6	Vector search, cross-task recall, salience
E2E	17	Container health, workflow, chat, memory, triggering
Total	1276

Dependencies

Package	Purpose
fastapi	HTTP servers (mesh + agent)
uvicorn	ASGI server
httpx	Async HTTP client
pydantic	Data validation
litellm	Multi-provider LLM interface (100+ providers)
sqlite-vec	Vector search in SQLite
pyyaml	YAML config parsing
click	CLI framework
docker	Docker API client
python-dotenv	`.env` file loading
playwright	Browser automation — basic tier (in container only)
camoufox	Anti-detect browser — stealth tier (in container only)
mcp	MCP tool server client (in container only, optional)
slack-bolt	Slack channel adapter (optional)

Dev: pytest, pytest-asyncio, ruff.

No LangChain. No Redis. No Kubernetes. Real-time web dashboard at /dashboard.

Project Structure

src/
├── cli/
│   ├── main.py                         # Click commands and entry point
│   ├── config.py                       # Config loading, Docker helpers, agent management
│   ├── runtime.py                      # RuntimeContext — full lifecycle management
│   ├── repl.py                         # REPLSession — interactive command dispatch
│   ├── channels.py                     # ChannelManager — messaging channel lifecycle
│   └── formatting.py                   # Tool display, styled output, response rendering
├── agent/
│   ├── __main__.py                     # Container entry
│   ├── loop.py                         # Execution loop (task + chat)
│   ├── loop_detector.py                # Tool loop detection (warn/block/terminate)
│   ├── skills.py                       # Skill registry + discovery
│   ├── mcp_client.py                   # MCP server lifecycle + tool routing
│   ├── memory.py                       # Hierarchical memory (SQLite + sqlite-vec + FTS5)
│   ├── workspace.py                    # Persistent workspace + BM25 search
│   ├── context.py                      # Context manager (token tracking, compaction)
│   ├── llm.py                          # LLM client (routes through mesh proxy)
│   ├── mesh_client.py                  # Mesh HTTP client
│   ├── server.py                       # Agent FastAPI server
│   └── builtins/
│       ├── exec_tool.py                # Shell execution
│       ├── file_tool.py                # File I/O (read, write, list)
│       ├── http_tool.py                # HTTP requests
│       ├── browser_tool.py             # Playwright automation (basic/stealth/advanced/persistent)
│       ├── web_search_tool.py          # Web search via DuckDuckGo
│       ├── memory_tool.py              # Memory search, save, recall
│       ├── mesh_tool.py                # Shared state, fleet awareness, artifacts
│       ├── vault_tool.py               # Credential vault operations
│       ├── skill_tool.py               # Runtime skill creation + hot-reload
│       ├── introspect_tool.py          # Live runtime state queries
│       └── subagent_tool.py            # Spawn in-process subagents
├── host/
│   ├── server.py                       # Mesh FastAPI server
│   ├── mesh.py                         # Blackboard, PubSub, MessageRouter
│   ├── orchestrator.py                 # Workflow DAG executor
│   ├── permissions.py                  # Permission matrix
│   ├── credentials.py                  # Credential vault + API proxy
│   ├── failover.py                     # Model health tracking + failover chains
│   ├── runtime.py                      # RuntimeBackend ABC + Docker/Sandbox backends
│   ├── transport.py                    # Transport ABC + Http/Sandbox transports
│   ├── containers.py                   # Docker image build + management
│   ├── cron.py                         # Cron scheduler + heartbeats
│   ├── webhooks.py                     # Named webhook endpoints
│   ├── watchers.py                     # File watchers (polling)
│   ├── costs.py                        # Cost tracking + budgets (SQLite)
│   ├── health.py                       # Health monitor + auto-restart
│   ├── lanes.py                        # Per-agent FIFO task queues
│   ├── traces.py                       # Request tracing + grouped summaries
│   └── transcript.py                   # Conversation transcript formatting
├── shared/
│   ├── types.py                        # All Pydantic models (the contract)
│   ├── utils.py                        # ID generation, logging, sanitization
│   └── trace.py                        # Trace ID generation + correlation
├── channels/
│   ├── base.py                         # Abstract channel with unified UX
│   ├── telegram.py                     # Telegram adapter
│   ├── discord.py                      # Discord adapter
│   ├── slack.py                        # Slack adapter (Socket Mode)
│   ├── whatsapp.py                     # WhatsApp Cloud API adapter
│   └── webhook.py                      # Workflow trigger webhook adapter
├── dashboard/
│   ├── server.py                       # Dashboard FastAPI router + API
│   ├── events.py                       # EventBus for real-time streaming
│   ├── templates/index.html            # Dashboard UI (Alpine.js + Tailwind)
│   └── static/                         # CSS + JS assets
└── templates/
    ├── starter.yaml                    # Single-agent template
    ├── sales.yaml                      # Sales pipeline team
    ├── devteam.yaml                    # Dev team template
    └── content.yaml                    # Content creation team

config/
├── mesh.yaml                           # Framework settings
├── agents.yaml                         # Agent definitions (per-project)
├── permissions.json                    # Per-agent ACLs
└── workflows/                          # Workflow YAML definitions

Design Principles

Principle	Rationale
Messages, not method calls	Agents communicate through HTTP/JSON. Never shared memory or direct invocation.
The mesh is the only door	No agent has network access except through the mesh. No agent holds credentials.
Private by default, shared by promotion	Agents keep knowledge private. Facts are explicitly promoted to the blackboard.
Explicit failure handling	Every workflow step declares what happens on failure. No silent error swallowing.
Small enough to audit	~21,000 total lines. The entire codebase is auditable in a day.
Skills over features	New capabilities are agent skills, not mesh or orchestrator code.
SQLite for all state	Single-file databases. No external services. WAL mode for concurrent reads.
Zero vendor lock-in	LiteLLM supports 100+ providers. Markdown workspace files. No proprietary formats.

License

OpenLegion.ai is source-available under the Business Source License 1.1 (BSL).

You may view, modify, and self-host the software.

You may NOT offer it as a competing hosted or SaaS product.

See LICENSE. for details.

Related Projects & Comparisons

Looking for alternatives? OpenLegion is often compared to:

OpenClaw — personal AI assistant, 200K+ stars, not designed for production security
nanobot — ultra-lightweight Python agent (~4K lines), limited multi-agent support
ZeroClaw — Rust-based AI agent runtime, extreme resource efficiency, early-stage
NanoClaw — container-isolated, Claude-only, no cost tracking
LangChain Agents — feature-rich but complex, heavy framework overhead
CrewAI — multi-agent framework, no built-in container isolation or cost controls
AutoGen — Microsoft's multi-agent framework, requires Azure/OpenAI, no self-hosting

OpenLegion differs from all of these in combining fleet orchestration, Docker isolation, credential vaulting, and cost enforcement in a single ~21,000 line auditable codebase.

Keywords: autonomous AI agents, multi-agent framework, LLM agent orchestration, self-hosted AI agents, Docker AI agents, OpenClaw alternative, AI agent security, agent cost tracking, Telegram AI bot, Python AI agent framework

Name		Name	Last commit message	Last commit date
Latest commit History 358 Commits
.github/workflows		.github/workflows
docs		docs
skills		skills
src		src
tests		tests
.env.example		.env.example
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CLA.md		CLA.md
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile.agent		Dockerfile.agent
LICENSE		LICENSE
Makefile		Makefile
QUICKSTART.md		QUICKSTART.md
README.md		README.md
ROADMAP.md		ROADMAP.md
benchmark.py		benchmark.py
install.ps1		install.ps1
install.sh		install.sh
pyproject.toml		pyproject.toml

License

openlegion-ai/openlegion

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The AI agent framework built for builders who can't afford a security incident.

Demo

Table of Contents

Quick Start

Why Not OpenClaw?

What It Does

Architecture

Trust Zones

Mesh Host

Blackboard (Shared State Store)

Credential Vault (API Proxy)

Model Failover

Permission Matrix

Orchestrator (Workflow DAG Executor)

Container Manager

Agent Architecture

Task Mode (execute_task)

Chat Mode (chat)

Tool Loop Detection

Built-in Tools

Memory System

Write-Then-Compact Pattern

Cross-Session Memory

Triggering & Automation

Cron Scheduler

Heartbeat System

Webhook Endpoints

File Watchers

Cost Tracking & Budgets

Security Model

Dual Runtime Backend

CLI Reference

Interactive REPL Commands

Team Templates

Configuration

PROJECT.md — Fleet-Wide Context

config/mesh.yaml — Framework Settings

config/agents.yaml — Agent Definitions

config/permissions.json — Agent Permissions

config/workflows/*.yaml — Workflow Definitions

.env — API Keys

Connecting Channels

MCP Tool Support

Configuration

How It Works

Server Config Options

Testing

Test Coverage

Dependencies

Project Structure

Design Principles

License

Related Projects & Comparisons

About

Topics

Resources

License

Contributing

Security policy

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Contributors 4

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Languages

Task Mode (`execute_task`)

Chat Mode (`chat`)

`PROJECT.md` — Fleet-Wide Context

`config/mesh.yaml` — Framework Settings

`config/agents.yaml` — Agent Definitions

`config/permissions.json` — Agent Permissions

`config/workflows/*.yaml` — Workflow Definitions

`.env` — API Keys

Packages