Atomic Agents

Features

Schema-Aligned Frameworks - Provides the core framework for chaining single-purpose agents and tools via Pydantic schema alignment.
Agent Context Providers - Provides runtime context injection into agent system prompts via custom providers.
Instructor-Compatible Execution - Runs agents with any Instructor-compatible AI provider, switching between local and cloud APIs seamlessly.
Schema-Driven Compositions - Provides a schema-driven orchestrator that composes agents, tools, and context providers into reusable pipelines without manual code changes.
Schema-Driven Orchestration - Connects agents and tools through shared Pydantic schemas for swappable, chainable pipelines.
Runtime Context Injections - Injects runtime-generated strings into agent system prompts via custom context providers.
Schema-Aligned Chaining - Enables plug-and-play composition of agents and tools by matching their Pydantic schemas.
Instructor-Compatible Switching - Switches between any Instructor-compatible AI model provider at runtime without code changes.
Schema-Aligned Assemblers - Assembles single-purpose agents and tools into reusable pipelines using Pydantic schema alignment.
Schema-Aligned Assembly - Assembles single-purpose AI agents and tools into reusable pipelines via schema alignment.
Schema-Aligned Composition - Enables plug-and-play agent and tool composition by matching Pydantic input/output schemas.
Provider-Agnostic Model Interfaces - Switches AI model providers at runtime through the Instructor library, supporting any compatible service.
Instructor-Compatible Runners - Runs agents with any Instructor-compatible AI provider, switching between local and cloud models seamlessly.
Pydantic-Driven Pipelines - Assembles AI agents and tools into type-safe pipelines using Pydantic models for data flow.
Prebuilt Tool Collections - Ships a collection of prebuilt tools for agents including web search, PDF reading, and calculator functions.
Agent Tool Registries - Provides a registry of prebuilt tools conforming to a shared schema interface for agent composition.
Prebuilt Tool Integrations - Extends agents with prebuilt tools for web search, PDF reading, weather, and calculations.
Schema-Enforced Swappability - Guarantees agents and tools with matching schemas can be swapped in pipelines without breaking execution.
Prebuilt Agent Tools - Ships a collection of ready-made tools for web search, PDF reading, weather, and calculations.
Agent Orchestration - Framework for building agents in a modular, atomic fashion.

Istoric stele

BrainBlend-AIatomic-agents

Name: brainblend-ai/atomic-agents
Author: BrainBlend-AI

View on GitHub

5,688 stele466 fork-uriPythonmit6 vizualizări

Atomic Agents

Features

Schema-Aligned Frameworks - Provides the core framework for chaining single-purpose agents and tools via Pydantic schema alignment.
Agent Context Providers - Provides runtime context injection into agent system prompts via custom providers.
Instructor-Compatible Execution - Runs agents with any Instructor-compatible AI provider, switching between local and cloud APIs seamlessly.
Schema-Driven Compositions - Provides a schema-driven orchestrator that composes agents, tools, and context providers into reusable pipelines without manual code changes.
Schema-Driven Orchestration - Connects agents and tools through shared Pydantic schemas for swappable, chainable pipelines.
Runtime Context Injections - Injects runtime-generated strings into agent system prompts via custom context providers.
Schema-Aligned Chaining - Enables plug-and-play composition of agents and tools by matching their Pydantic schemas.
Instructor-Compatible Switching - Switches between any Instructor-compatible AI model provider at runtime without code changes.
Schema-Aligned Assemblers - Assembles single-purpose agents and tools into reusable pipelines using Pydantic schema alignment.
Schema-Aligned Assembly - Assembles single-purpose AI agents and tools into reusable pipelines via schema alignment.
Schema-Aligned Composition - Enables plug-and-play agent and tool composition by matching Pydantic input/output schemas.
Provider-Agnostic Model Interfaces - Switches AI model providers at runtime through the Instructor library, supporting any compatible service.
Instructor-Compatible Runners - Runs agents with any Instructor-compatible AI provider, switching between local and cloud models seamlessly.
Pydantic-Driven Pipelines - Assembles AI agents and tools into type-safe pipelines using Pydantic models for data flow.
Prebuilt Tool Collections - Ships a collection of prebuilt tools for agents including web search, PDF reading, and calculator functions.
Agent Tool Registries - Provides a registry of prebuilt tools conforming to a shared schema interface for agent composition.
Prebuilt Tool Integrations - Extends agents with prebuilt tools for web search, PDF reading, weather, and calculations.
Schema-Enforced Swappability - Guarantees agents and tools with matching schemas can be swapped in pipelines without breaking execution.
Prebuilt Agent Tools - Ships a collection of ready-made tools for web search, PDF reading, weather, and calculations.
Agent Orchestration - Framework for building agents in a modular, atomic fashion.

Alternative open-source pentru Atomic Agents

Proiecte open-source similare, clasificate după numărul de funcționalități comune cu Atomic Agents.

crmne/ruby_llm
crmne/ruby_llm
3,566Vezi pe GitHub
ruby_llm is an LLM integration framework and AI agent orchestrator designed to connect applications to multiple large language model providers through a unified interface. It serves as a toolkit for building autonomous assistants with custom personas, managing structured output via JSON schemas, and implementing vector embedding engines for semantic search. The project distinguishes itself as an observability suite and multimodal toolkit. It provides specialized capabilities for tracking token usage, calculating model costs, and tracing workflows via OpenTelemetry, while supporting the proces
Rubyaianthropicchatgpt
Vezi pe GitHub3,566
katanemo/archgw
katanemo/archgw
6,603Vezi pe GitHub
Archgw is a gateway proxy and data plane designed for agentic applications, providing a centralized layer for routing, safety, and orchestration between application logic and multiple large language model providers. It functions as an AI agent orchestrator that automates the execution of agent workflows to remove repetitive plumbing from the core codebase. The system features a provider-agnostic interface layer that standardizes disparate model APIs into a single format and a transparent proxy data plane to intercept traffic. It employs rule-based routing to decouple application logic from sp
Rust
Vezi pe GitHub6,603
langchain4j/langchain4j
langchain4j/langchain4j
12,346Vezi pe GitHub
LangChain4j is a framework and library for building applications powered by large language models on the JVM. It provides a unified API for developing AI agents, implementing retrieval augmented generation, and integrating generative AI capabilities into professional software built with frameworks like Spring Boot or Quarkus. The project enables the creation of autonomous agents that can reason through tasks, manage memory, and execute external tools to achieve specific goals. It differentiates itself through a unified model interface that allows developers to switch between multiple model pr
Javaanthropicchatgptchroma
Vezi pe GitHub12,346
mediar-ai/screenpipe
mediar-ai/screenpipe
19,337Vezi pe GitHub
Screenpipe is a local screen and audio recorder that captures and indexes digital activity to create a searchable archive of computer usage. It functions as an AI context engine, providing a local database of visual and auditory history to ground large language models. The system serves as a Model Context Protocol server, delivering screen history and meeting transcriptions to external AI assistants. It utilizes an OCR screen search tool to extract text from visual data and a speech-to-text transcription tool for identifying speakers in system and microphone audio. The software includes capa
Rust
Vezi pe GitHub19,337

Vezi toate cele 30 alternative pentru Atomic Agents

Întrebări frecvente

Care sunt principalele funcționalități ale brainblend-ai/atomic-agents?

Principalele funcționalități ale brainblend-ai/atomic-agents sunt: Schema-Aligned Frameworks, Agent Context Providers, Instructor-Compatible Execution, Schema-Driven Compositions, Schema-Driven Orchestration, Runtime Context Injections, Schema-Aligned Chaining, Instructor-Compatible Switching.

Care sunt câteva alternative open-source pentru brainblend-ai/atomic-agents?

Alternativele open-source pentru brainblend-ai/atomic-agents includ: crmne/ruby_llm — ruby_llm is an LLM integration framework and AI agent orchestrator designed to connect applications to multiple large… katanemo/archgw — Archgw is a gateway proxy and data plane designed for agentic applications, providing a centralized layer for routing,… yaoapp/yao — Yao is an LLM agent framework and low-code web app builder designed for orchestrating autonomous AI agents. It… langchain4j/langchain4j — LangChain4j is a framework and library for building applications powered by large language models on the JVM. It… mediar-ai/screenpipe — Screenpipe is a local screen and audio recorder that captures and indexes digital activity to create a searchable… alexzhang13/rlm — rlm is an LLM code execution engine and orchestration framework designed to coordinate multiple language model calls…