LightAgent
A lightweight Python runtime for OpenAI-compatible agents with tools, memory, routing, and recoverable workflows.
Per-dimension scores and reasoning
Evidence shows security mechanisms like memory policies, guardrails, and hooks, but least privilege is not explicitly addressed; user confirmation exists (e.g., sensitive tool confirmation guardrail) but not comprehensive; data flow transparency via tracing and hooks but not explicit; sensitive data handling via guardrails and memory policies but no encryption; dependency security lacks vulnerability scanning or locked versions (some pinned); external effects have guardrails and approval but not all external calls; rollback via checkpoints and resume but not data rollback; source attribution not verified for publisher identity.
Self-consistency is good, docs and code examples align; dependency availability not explicitly tested or locked; failure messages have clear error prefixes and trace events.
Audience clearly developers, scenarios diverse; capability boundaries via tools and guardrails; trigger precision via guardrails and hooks; environment fit supports multiple models and local deployment.
Information architecture clear, docs comprehensive; install notes clear; naming stable; examples and FAQ rich; known limitations not explicitly listed; license Apache-2.0; versioning and changelog present; maintenance responsibility not explicit.
Output usability high, supports multiple formats; marginal value high, unique features; cost-benefit reasonable, lightweight design.
Claims traceable, docs and code correspond; cross-source corroboration insufficient, only single repo; fact-inference separation good.
- Publisher identity unverified, assess supply chain risk carefully.
- Dependency security lacks vulnerability scanning or locked versions, check for known vulnerabilities.
- Known limitations not explicitly listed, potential issues may be hidden.
What does this agent do, and when should you use it?
LightAgent is a Python framework for building agents that call OpenAI-compatible chat-completion endpoints. Its core LightAgent runtime combines model calls, Python tools, memory, streaming, guardrails, runtime hooks, and optional tracing. LightSwarm routes requests among role-specific agents, while LightFlow executes deterministic DAG-based workflows with retries, checkpoints, and human-approval steps. A normal agent.run(query) returns a string, with options for OpenAI-compatible streaming chunks or structured results containing trace metadata. It fits Python teams that want to own their application logic while adding safety, evaluation, and observability controls incrementally.
A developer instantiates LightAgent with model, api_key, and base_url, then calls agent.run(query) to send a request and receive a response, or uses stream=True to iterate streaming chunks. Tools are Python functions supplied through tools with tool_info metadata; the framework also documents ToolRegistry, ToolLoader, AsyncToolDispatcher, generated tools, and MCP tools. A memory backend can store data with store(data, user_id) and retrieve it with retrieve(query, user_id), while MemoryPolicy constrains namespaces, sources, scopes, and writes. LightSwarm registers specialized agents and delegates with swarm.run(...); LightFlow defines dependent steps with step(...), then can persist run records, resume incomplete runs, or rerun a selected step. With trace=True, the runtime records run, model, tool, and error events, and LightEvaluator runs fixed cases against outputs, tool choices, policy events, recovery, latency, usage, and estimated cost.
- A customer-support team wraps order-status or internal-service functions with tool_info so a chat assistant can invoke controlled Python tools when needed.
- A business operations team builds a research, drafting, and review pipeline in LightFlow with dependencies, retries, checkpoints, and approval gates.
- A security-conscious team places HumanApprovalHook, tool guardrails, and PolicyHook checks before high-impact actions such as payments or file deletion.
- A multi-role service application uses LightSwarm to route front-desk requests to focused finance, support, research, or data-analysis agents.
- A Python team uses LightEvaluator to regression-test tool selection, policy events, recovery behavior, latency, and estimated cost against fixed cases.
What are this agent's strengths and limitations?
- The default
agent.run()path stays small, while structured results, streaming, tracing, guardrails, and hooks can be added only when needed. - LightFlow supplies DAG validation, retries, checkpoints, resume, targeted step reruns, fallback agents, and approval steps for deterministic business processes.
- MemoryPolicy and MemoryScope provide controls over tenant namespace, provenance, scope, agent name, expiration, and memory-write admission.
- Runtime hooks can observe, replace, or block payloads across model, tool, memory, handoff, and workflow phases, and record decisions in traces.
- It supports multiple OpenAI-style endpoints as well as Python tools and MCP connections over stdio and SSE.
- Model connectivity depends on OpenAI-compatible chat-completion endpoints; the supplied material does not document native adapters for non-compatible providers.
- Tools require
tool_infometadata, and generated or custom Python tools still need application-level review and testing. - SharedMemoryPool is explicitly an in-memory prototype for experiments rather than a durable shared-memory backend.
- The checkpoint example uses JsonLightFlowStore; the supplied material does not specify a production concurrency, storage, or operations design.
- Human approvals, guardrails, and hooks must be configured by the application; they are not default safety policies.
How do you install or deploy this agent?
Install with pip install lightagent. If you want the mem0 memory module, also run pip install mem0ai. Obtain an api_key and base_url for an OpenAI-compatible model service, then create LightAgent(model="gpt-4.1", api_key="your_api_key", base_url="your_base_url") in Python and call agent.run("Hello, who are you?").
How do you use this agent?
For the minimal path, import LightAgent, create an instance, and run response = agent.run("Hello, who are you?"). For chat delivery, iterate agent.run(query, stream=True) for OpenAI-compatible streaming chunks; for inspection, use agent.run(query, result_format="object", trace=True) to access content, a trace ID, and trace events. For deterministic orchestration, define steps with LightFlow().step(...).run(query); pass JsonLightFlowStore to LightFlow when checkpoint persistence and resume are needed.
How does this agent compare with similar options?
The README positions LightAgent as a lightweight framework without LangChain or LlamaIndex dependencies. Its LightSwarm component handles multi-agent delegation, and the README describes that collaboration approach as simpler to implement than Swarm.