hembramagenticcore 1.1.2

Production-grade Python library for building scalable agentic AI systems with MCP and A2A integration

AgentiCore — Python Agentic Library

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What Is AgentiCore?

AgentiCore is a production-grade, open-source Python library for building scalable agentic AI systems. It provides:

• A composable core framework — build any agent type without being forced into a specific pattern
• Built-in reasoning strategies — ReAct, Plan-Execute, Reflection, Tree-of-Thought, and custom
• Tiered memory system — working, episodic, semantic (RAG), and procedural memory
• Type-safe tool system — define, validate, and compose tools with full schema enforcement
• Multi-agent orchestration — supervisor, pipeline, fan-out, debate, swarm patterns
• Built-in Web UI — visual agent builder, tool creator, real-time run viewer, trace inspector
• Plugin ecosystem — extend anything without forking the library
• First-class observability — structured tracing, cost tracking, step logging out of the box

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Design Philosophy

1. Explicit Over Magic

No hidden auto-wiring. Every component is explicitly configured. If an agent calls a tool, you see exactly where and why.

2. Composable, Not Opinionated

The core module is pure Python with zero mandatory dependencies. LangGraph, Qdrant, OpenAI — all are optional integrations, not requirements.

3. Schema-First

Every interface (tool inputs/outputs, agent state, memory schema, API payloads) is defined with strict types before implementation. Prevents entire classes of bugs.

4. Production-Ready From Day One

Retries, timeouts, cost budgets, circuit breakers, human-in-the-loop, and audit logging are built-in — not afterthoughts.

5. Observable By Default

Every agent step, tool call, LLM invocation, and memory operation emits structured events. You never wonder "what is the agent doing right now?"

6. Developer Experience First

The Web UI exists so that non-engineers can configure, run, and monitor agents without writing code. But engineers get full programmatic control of everything the UI does.

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Library Name & Namespace

Package name:  hembramagenticcore
Import as:     import hembramagenticcore as ag
PyPI slug:     hembramagenticcore
CLI command:   hembramagenticcore

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Core Value Propositions

Other Libraries	AgentiCore
Lock you into one reasoning pattern	Support all patterns, switchable at runtime
Memory is bolted on	Memory is a first-class design concern
No Web UI — code only	Full Web UI with visual builder
Framework-heavy (forces LangChain/LangGraph)	Framework-agnostic core, optional integrations
Hard to extend tools	Plugin system + decorator-based tool registration
Poor observability	Structured tracing, cost tracking, step logging built-in
No multi-language vision	Python first, then Rust/C++/JS with shared protocols

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Quick Concept Demo (Target API)

import hembramagenticcore as ag

# 1. Define a tool with a decorator
@ag.tool(description="Search the web for current information")
def search_web(query: str, max_results: int = 5) -> list[ag.SearchResult]:
    ...

# 2. Build an agent
agent = (
    ag.AgentBuilder()
    .name("ResearchAgent")
    .llm(ag.LLM.GPT4O)
    .reasoning(ag.ReAct)
    .tools([search_web, ag.tools.CodeExecutor()])
    .memory(ag.memory.Qdrant(url="localhost:6333"))
    .max_steps(20)
    .build()
)

# 3. Run it
result = agent.run("Summarize key AI releases from Q1 2026")
print(result.output)
print(result.trace)   # Full step-by-step trace
print(result.cost)    # Token cost in USD

# 4. Or launch the Web UI
ag.serve(agents=[agent], port=8080)

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License & Contributing

• License: Apache 2.0 (permissive, commercial-friendly)
• Contributing: See CONTRIBUTING.md
• Code of Conduct: See CODE_OF_CONDUCT.md