Features¶
Lango provides a comprehensive set of features for building intelligent AI agents. This section covers each subsystem in detail.
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Multi-provider support for OpenAI, Anthropic, Gemini, and Ollama with a unified interface and automatic fallback.
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Connect your agent to Telegram, Discord, and Slack. Manage conversations across channels from a single instance.
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Self-learning knowledge store with 8-layer context retrieval, pattern recognition, and agent learning tools.
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Automatic conversation compression through observations and reflections for long-running sessions.
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Vector embeddings with OpenAI, Google, or local providers. Retrieval-augmented generation for semantic context injection.
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BoltDB-backed triple store with hybrid vector + graph retrieval for deep contextual understanding.
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Hierarchical sub-agents (Operator, Navigator, Vault, Librarian, Automator, Planner, Chronicler) working together on complex tasks.
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Agent-to-Agent protocol for remote agent discovery and inter-agent communication.
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Decentralized agent-to-agent connectivity via libp2p with DID identity, knowledge firewall, and ZK-enhanced handshake.
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Budget management, risk assessment, dynamic pricing, P2P negotiation, and milestone-based escrow for agent commerce.
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EVM smart contract interaction with ABI caching, view/pure reads, and state-changing calls.
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ERC-7579 modular smart accounts with session keys, ERC-4337 paymaster support, and on-chain policy enforcement.
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Token usage tracking, health monitoring, audit logging, and metrics endpoints for operational visibility.
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Per-agent persistent memory for cross-session context retention and experience accumulation.
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File-based skills with import from URLs and GitHub repositories. Extend agent capabilities without code changes.
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Autonomous knowledge agent that observes conversations and proactively curates the knowledge base.
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Customizable prompt sections for agent personality, safety rules, and behavior tuning.
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Collaborative environments where multiple agents share code, messages, and context with git bundle exchange and contribution tracking.
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Task-scoped multi-agent collaboration with role assignment, conflict resolution, budget tracking, and payment coordination.
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Pre-built configuration templates for common deployment scenarios. Quick-start your agent with sensible defaults.
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Connect to external MCP servers for stdio, HTTP, and SSE transports. Extend agent tooling with the Model Context Protocol.
Feature Status¶
| Feature | Status | Config Key |
|---|---|---|
| AI Providers | Stable | agent.provider |
| Channels | Stable | channels.* |
| Knowledge System | Stable | knowledge.enabled |
| Observational Memory | Stable | observationalMemory.enabled |
| Embedding & RAG | Stable | embedding.* |
| Knowledge Graph | Experimental | graph.enabled |
| Multi-Agent Orchestration | Experimental | agent.multiAgent |
| A2A Protocol | Experimental | a2a.enabled |
| P2P Network | Experimental | p2p.enabled |
| P2P Economy | Experimental | economy.enabled |
| Smart Contracts | Experimental | payment.enabled |
| Smart Accounts | Experimental | smartAccount.enabled |
| Observability | Experimental | observability.enabled |
| Skill System | Stable | skill.enabled |
| Proactive Librarian | Experimental | librarian.enabled |
| System Prompts | Stable | agent.promptsDir |
| Agent Memory | Experimental | agentMemory.enabled |
| P2P Workspaces | Experimental | p2p.workspace.enabled |
| P2P Teams | Experimental | p2p.enabled + team coordination |
| Config Presets | Stable | lango onboard --preset |
| MCP Integration | Stable | mcp.enabled |
| Tool Hooks | Experimental | hooks.enabled |
| Tool Catalog | Internal | — |
| Event Bus | Internal | — |
Experimental Features
Features marked as Experimental are under active development. Their APIs, configuration keys, and behavior may change between releases. Enable them explicitly via their config flags.