LightAgent

LightAgent is an ultra‑lightweight, open‑source framework that now natively supports Skills — letting you compose reusable capabilities with persistent memory, tool use, and tree‑of‑thought reasoning. It streamlines multi‑agent collaboration (build self‑learning agents in one step), connects to MCP over stdio and SSE, runs on any modern LLM (OpenAI, DeepSeek, Qwen, and more), and outputs OpenAI‑compatible streaming APIs for instant drop‑in with any chat interface. Small, modular, and skill‑ready — spin it up in five minutes.

News

• [2026-08-15] LightAgent v0.10.0 Development: Adds the unified event-sourced Agent Runtime with durable Sessions, async execution, Capability Registry and Policy, Inbox/Goals/Budgets, compaction and recovery, Jobs/subagents, standardized Skills/MCP adapters, and SQLite FTS5 retrieval.
• [2026-08-15] LightAgent v0.9.7 Released: Adds a dependency-free Connector contract with offline validation and examples, expands Python executor security checks, introduces an opt-in Mem0 Graph security matrix, and adds a public API compatibility inventory for v1.0 stabilization.
• [2026-07-30] LightAgent v0.9.6 Released: Adds production trace summaries and exporters, deterministic evaluation, durable human approval for tools, handoffs, and LightFlow, plus fail-closed shared Graph Memory admission and audit controls.
• [2026-07-10] LightAgent v0.9.3 Released: Completes runtime hook lifecycle coverage and hardens streaming tool safety with max_tool_iterations, consistent on_error / after_run closure, and expanded regression coverage.
• [2026-06-24] LightAgent v0.9.0 Development: Adds checkpointed LightFlow workflows with resume/rerun support, approval nodes, richer step status and trace metadata, reusable Guardrails templates, stronger MemoryPolicy controls, and the first SharedMemoryPool prototype.
• [2026-06-14] LightAgent v0.8.1 Development: Adds MemoryScope metadata conventions, stricter MemoryPolicy provenance filters, and guidance for separating trace, user memory, self-reflection memory, and LightSwarm delegation state.
• [2026-06-02] LightAgent v0.8.0 Development: Adds initial LightFlow workflow orchestration for deterministic multi-step agent execution with DAG dependencies, step output passing, retries, and flow trace events.
• [2026-05-29] LightAgent v0.7.0 Development: Adds opt-in trace observability with structured run/model/tool/error events, agent.export_trace(), and prompt-safe model request summaries for production debugging.
• [2026-05-28] LightAgent v0.6.5 Released: Adds opt-in structured run results, structured streaming events, catchable LightAgent errors, and tool argument validation while keeping legacy agent.run() and stream=True behavior compatible.
• [2026-05-27] LightAgent v0.6.4 Released: Improves runtime tool dispatch reliability, adds structured error codes and troubleshooting guidance, expands OpenAI-compatible provider documentation for OpenRouter and local models, and updates browser-use integration examples.

Older release notes are available on GitHub Releases.

✨ Features

• Lightweight and Efficient 🚀: Minimalist design, quick deployment, suitable for various application scenarios. (No LangChain, No LlamaIndex) The core framework stays small, modular, and fully open source while using focused dependencies for provider, MCP, memory, and tracing integrations.
• Memory Support 🧠: Supports custom long-term memory for each user, natively supporting the mem0 memory module, automatically managing user personalized memory during conversations, making agents smarter.
• Autonomous Learning 📚️: Each agent possesses autonomous learning capabilities, and admins with permissions can manage each agent.
• Tool Integration 🛠️: Support for custom tools (Tools) and MCP tool integration, flexible expansion to meet diverse needs.
• Complex Goals 🌳: Built-in Tree of Thought (ToT) module with reflection, supporting complex task decomposition and multi-step reasoning, enhancing task processing capabilities.
• Multi-Agent Collaboration 🤖: Simpler to implement multi-agent collaboration than Swarm, with built-in LightSwarm for intent recognition and task delegation, enabling smarter handling of user input and delegating tasks to other agents as needed.
• Workflow Orchestration 🔁: LightFlow chains agents into deterministic multi-step workflows with explicit dependencies, step output passing, retries, checkpointed run records, resume/rerun support, approval nodes, fallback agents, and traceable execution.
• Shared Memory Prototype 🧠: SharedMemoryPool provides append-first in-memory shared memory with provenance metadata, scoped retrieval, and MemoryPolicy-compatible results for multi-agent experiments.
• Independent Execution 🤖: Tasks and tool calls are completed autonomously without human intervention.
• Multi-Model Support 🔄: Compatible with OpenAI-style providers such as OpenAI, OpenRouter, Zhipu ChatGLM, Baichuan, StepFun, DeepSeek, Qwen, vLLM, llama.cpp, and other OpenAI-compatible endpoints.
• Streaming API 🌊: Supports OpenAI streaming format API service output, seamlessly integrates with mainstream chat frameworks, enhancing user experience.
• Trace Observability 🔎: Opt-in trace=True run traces record structured run lifecycle, model request summaries, tool calls, tool results, and errors without changing the default string return value.
• Evaluation Harness 📊: LightEvaluator runs deterministic agent or LightFlow regression cases for output, tool choice, policy events, recovery, latency, usage, and estimated cost.
• Human Review 👤: HumanApprovalHook, durable review stores, and LightFlow approval checkpoints support approve, reject, argument editing, human responses, batches, and trace feedback for high-impact actions.
• Runtime Hooks 🧩: Ordered hooks=[...] middleware can observe, replace, or block run, model, tool, memory, and LightFlow step phases while recording hook decisions in trace events.
• Event-Sourced Runtime 🧱: Optional durable Sessions, replay, checkpoints, forks, async entry points, scoped Capability Providers, unified Policy, Inbox, Goals, Budgets, Jobs, subagents, context compaction, and SQLite FTS5 retrieval.
• Guardrails Templates 🛡️: Reusable input/tool/output guardrail templates help block private data, require confirmation for sensitive tools, validate high-risk parameters, and redact sensitive output.
• Tool Generator 🚀: Just provide your API documentation to the [Tool Generator], which will automatically create exclusive tools for you, allowing you to quickly build hundreds of personalized custom tools in just 1 hour to improve efficiency and unleash your creative potential.
• Agent Self-Learning 🧠️: Each agent has its own scene memory capabilities and the ability to self-learn from user conversations.
• Adaptive Tool Mechanism 🛠️: Supports adding an unlimited number of tools, allowing the large model to first select a candidate tool set from thousands of tools, filtering irrelevant tools before submitting context to the large model, significantly reducing token consumption.

🧭 Architecture At A Glance