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Awesome GitHub RepositoriesAgent State Persistence

Mechanisms for storing and retrieving agent sessions, chat history, and internal context across execution turns.

Distinguishing note: Specifically targets the persistence of agent-specific state and conversational context rather than generic database operations.

Explore 38 awesome GitHub repositories matching data & databases · Agent State Persistence. Refine with filters or upvote what's useful.

Awesome Agent State Persistence GitHub Repositories

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  • paperclipai/paperclippaperclipai 的头像

    paperclipai/paperclip

    70,619在 GitHub 上查看↗

    Paperclip is an LLM agent orchestration platform and governance suite designed to coordinate teams of autonomous AI agents. It provides a management plane for defining organizational hierarchies, assigning roles, and aligning individual agent tasks with a structured mission tree to ensure work maps to business objectives. The project distinguishes itself through a specialized agent skill registry and workspace manager. It allows for the discovery and injection of reusable workflows into agent runtimes without retraining and provides isolated, sandboxed execution environments with persistent s

    Persists task context and session state across execution heartbeats so agents can resume work seamlessly.

    TypeScript
    在 GitHub 上查看↗70,619
  • badlogic/pi-monobadlogic 的头像

    badlogic/pi-mono

    63,163在 GitHub 上查看↗

    Pi-mono is an autonomous coding agent orchestrator designed to coordinate multiple intelligent agents for complex software development tasks. It functions as a framework that integrates directly with local file systems and terminal environments to automate development workflows. The system distinguishes itself through a stateful session manager that serializes the entire context of a coding interaction to disk, allowing agents to maintain project awareness across separate sessions. It utilizes a plugin architecture for tool registration and prompt-template injection, enabling the integration

    Maintains and restores full development context to ensure agents retain project awareness across separate coding interactions.

    TypeScript
    在 GitHub 上查看↗63,163
  • agno-agi/agnoagno-agi 的头像

    agno-agi/agno

    40,717在 GitHub 上查看↗

    Agno is an agent operating system designed to manage the lifecycle, tool execution, and persistent state of autonomous agents across distributed infrastructure. It provides a unified runtime environment that wraps diverse agent frameworks into a consistent, interoperable protocol, allowing developers to build and deploy complex multi-agent systems that coordinate tasks and delegate sub-processes. The platform distinguishes itself through a robust governance and orchestration layer that includes human-in-the-loop approval gates, role-based access control, and a centralized API gateway. It feat

    AgentOS persists agent sessions, chat history, internal state, and knowledge by connecting a database to store and retrieve context across turns.

    Pythonagentsaiai-agents
    在 GitHub 上查看↗40,717
  • reworkd/agentgptreworkd 的头像

    reworkd/AgentGPT

    36,194在 GitHub 上查看↗

    AgentGPT is a browser-based platform for deploying autonomous AI agents. It serves as a web-based orchestrator and self-hosted framework that allows users to configure agents that decompose high-level goals into smaller, actionable tasks for iterative execution. The system manages the full lifecycle of autonomous agents, from defining behaviors and parameters to overseeing goal-oriented task automation. It enables the deployment of agents that use a recursive loop of planning and analysis to reach a desired outcome. The platform includes a command line interface for bootstrapping the project

    Persists agent thoughts and action results in a database to maintain context across cycles.

    TypeScriptagentagentgptagents
    在 GitHub 上查看↗36,194
  • openai/openai-agents-pythonopenai 的头像

    openai/openai-agents-python

    27,191在 GitHub 上查看↗

    This project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime for orchestrating multi-agent workflows, managing persistent conversation state, and executing code within secure, isolated sandbox environments. The framework is designed to handle complex task delegation, allowing agents to invoke other agents as tools while maintaining context across multi-turn interactions. The framework distinguishes itself through its deep integration with the Model Context Protocol, enabling agents to connect to external data sources and remote services

    Maintains agent context across execution loops to ensure continuity.

    Pythonagentsaiframework
    在 GitHub 上查看↗27,191
  • agentscope-ai/agentscopeagentscope-ai 的头像

    agentscope-ai/agentscope

    26,895在 GitHub 上查看↗

    Agentscope is a comprehensive toolkit for developing and orchestrating autonomous multi-agent systems. It provides a unified framework for building agents that can reason, execute tools, and manage memory, enabling the creation of complex, collaborative workflows where multiple specialized agents interact to solve multi-step objectives. The platform distinguishes itself through a robust orchestration engine that supports both sequential and concurrent agent pipelines. It utilizes a centralized event bus for real-time telemetry, allowing developers to track agent reasoning, tool usage, and sys

    Captures and restores runtime data snapshots for agents and memory components to support stateful recovery.

    Pythonagentchatbotlarge-language-models
    在 GitHub 上查看↗26,895
  • langchain-ai/deepagentslangchain-ai 的头像

    langchain-ai/deepagents

    25,006在 GitHub 上查看↗

    Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing AI agents built with computational graphs. It provides a containerized runtime environment that handles agent execution, state persistence, and the versioning of AI assistants. The platform distinguishes itself through deep integration with the Model Context Protocol, allowing agents to function as servers that expose tools and capabilities to external clients. It features a sophisticated observability suite for capturing execution traces, performing LLM-based evaluations agai

    Stores application checkpoints and thread metadata to a local disk or database to ensure execution continuity.

    Pythonagentsdeepagentslangchain
    在 GitHub 上查看↗25,006
  • facebook/lexicalfacebook 的头像

    facebook/lexical

    23,562在 GitHub 上查看↗

    Lexical is a modular rich text editor framework used to build extensible web-based editors. It functions as a state-driven content editor that maintains a serializable, immutable snapshot of document content to ensure predictable updates and accessibility compliance. The framework is distinguished by its plugin-based architecture and customizable node framework, which allow developers to extend editor behavior through specialized content nodes and encapsulated runtime logic. It also includes a collaborative editing engine capable of synchronizing document state across multiple clients in real

    Converts internal editor state into string formats for network transmission or persistent storage.

    TypeScript
    在 GitHub 上查看↗23,562
  • redis/go-redisredis 的头像

    redis/go-redis

    22,159在 GitHub 上查看↗

    This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha

    Automatically extracts and stores facts from conversation history as searchable vector data to build long-term user profiles.

    Gogogolangredis
    在 GitHub 上查看↗22,159
  • vercel/aivercel 的头像

    vercel/ai

    21,885在 GitHub 上查看↗

    This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I

    Stores and retrieves agent session history and context to maintain continuity across interactions.

    TypeScriptanthropicartificial-intelligencegemini
    在 GitHub 上查看↗21,885
  • openai/swarmopenai 的头像

    openai/swarm

    21,640在 GitHub 上查看↗

    Swarm is a framework for building conversational systems that coordinate multi-agent workflows. It functions as an orchestration engine that manages persistent, multi-turn dialogues by routing tasks between specialized agents and executing local functions. The system is designed to handle complex, multi-step processes by maintaining shared state and context across agent interactions. The framework distinguishes itself through its approach to dynamic task delegation and execution control. It enables agents to hand off tasks to one another by returning agent objects, allowing for modular, domai

    Maintains a persistent dictionary of variables passed between agents to preserve session state.

    Python
    在 GitHub 上查看↗21,640
  • letta-ai/lettaletta-ai 的头像

    letta-ai/letta

    21,168在 GitHub 上查看↗

    Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

    Maintains persistent AI agents that store and recall information about users and environments to improve performance over time.

    Pythonaiai-agentsllm
    在 GitHub 上查看↗21,168
  • langchain-ai/langchainjslangchain-ai 的头像

    langchain-ai/langchainjs

    17,818在 GitHub 上查看↗

    LangChain.js is a framework for building, executing, and monitoring stateful agentic applications. It provides an orchestration engine that models workflows as directed graphs, allowing developers to connect language models, data sources, and external tools into modular, multi-step processes. The platform distinguishes itself through its focus on stateful execution and human-in-the-loop control. It manages agent lifecycles by persisting execution state across threads, enabling fault tolerance and the ability to pause workflows at designated breakpoints for manual review or modification. This

    Maintains durable checkpoints and long-term memory across execution threads to ensure task continuity and state retention.

    TypeScript
    在 GitHub 上查看↗17,818
  • camel-ai/camelcamel-ai 的头像

    camel-ai/camel

    17,253在 GitHub 上查看↗

    This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

    Stores and retrieves agent sessions, chat history, and internal context across execution turns.

    Pythonagentai-societiesartificial-intelligence
    在 GitHub 上查看↗17,253
  • itwanger/tobebetterjavaeritwanger 的头像

    itwanger/toBeBetterJavaer

    16,678在 GitHub 上查看↗

    This project serves as a dual-purpose platform that functions both as a comprehensive software engineering learning resource and an autonomous agent orchestration framework. It provides a structured curriculum focused on the Java ecosystem, offering technical roadmaps, interview preparation materials, and career mentorship. Simultaneously, it acts as a technical foundation for building intelligent systems, enabling developers to construct complex, multi-step agent pipelines. The framework distinguishes itself by integrating advanced automation capabilities directly into its educational missio

    Configures long-term storage for agents to maintain context, history, and state across multiple user interactions and sessions.

    javajvmmysql
    在 GitHub 上查看↗16,678
  • weaviate/weaviateweaviate 的头像

    weaviate/weaviate

    15,620在 GitHub 上查看↗

    Weaviate is an AI-native vector database designed to store and index high-dimensional vector embeddings alongside traditional data objects. It serves as a backend infrastructure for retrieval-augmented generation, enabling applications to ground language model responses in private, context-aware data. The platform distinguishes itself by combining vector similarity search with traditional keyword filtering through a hybrid storage architecture. It integrates directly with external machine learning models to automate the generation of embeddings and perform complex inference tasks during inges

    Maintains long-term context by storing interaction history and semantic state alongside primary data objects for retrieval by agents.

    Goapproximate-nearest-neighbor-searchgenerative-searchgrpc
    在 GitHub 上查看↗15,620
  • memorilabs/memoriMemoriLabs 的头像

    MemoriLabs/Memori

    15,358在 GitHub 上查看↗

    Memori is an AI agent memory middleware platform designed to provide persistent, context-aware recall for language models. It functions as a non-intrusive layer that intercepts outbound model requests to automatically capture interaction history and execution traces, ensuring that agents maintain continuity across sessions without requiring modifications to existing application logic. The platform distinguishes itself through a dual-model storage architecture that maintains information as both structured relational primitives for precise fact retrieval and rolling narrative summaries for situ

    Captures conversation history, agent traces, and coding decisions across sessions to maintain long-term state.

    Pythonagentaiaiagent
    在 GitHub 上查看↗15,358
  • coleam00/archoncoleam00 的头像

    coleam00/Archon

    13,728在 GitHub 上查看↗

    Archon is an artificial intelligence agent automation engine designed to orchestrate complex development workflows. It functions as a platform for chaining multi-step tasks into directed graphs, allowing developers to standardize and execute repeatable coding patterns through declarative configuration files. The system distinguishes itself by maintaining stateful context across long-running sessions and executing operations within isolated, containerized worktrees to prevent file interference. It integrates with external language models and provides a centralized registry for sharing and inst

    Maintains persistent session memory and task history to support continuous development and observable agent activity.

    Python
    在 GitHub 上查看↗13,728
  • aden-hive/hiveaden-hive 的头像

    aden-hive/hive

    10,578在 GitHub 上查看↗

    Hive is an artificial intelligence workflow automation engine and development platform designed for building and deploying autonomous agents. It provides a framework for orchestrating complex, multi-step business processes by coordinating tasks across multiple specialized agents using directed graph structures. The platform distinguishes itself through a focus on production-grade reliability and state management. It maintains persistent execution context and conversation history on disk, enabling crash recovery and continuity for long-running automated sessions. Furthermore, it incorporates a

    Persists agent execution context to enable crash recovery and session continuity.

    Pythonagentagent-frameworkagent-skills
    在 GitHub 上查看↗10,578
  • tporadowski/redistporadowski 的头像

    tporadowski/redis

    9,987在 GitHub 上查看↗

    Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

    Stores intermediate workflow states as checkpointer data to maintain agent context and ensure recovery.

    Credisredis-for-windowsredis-msi-installer
    在 GitHub 上查看↗9,987
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  3. Agent State Persistence

探索子标签

  • Redis-Backed State Stores3 个子标签Using Redis as a persistent store for agent task state and session data. **Distinct from Agent State Persistence:** Distinct from Agent State Persistence: focuses specifically on Redis as the storage backend.
  • Semantic Indexing for Agent StateIndexing agent state snapshots by semantic meaning so past states can be retrieved by content rather than by key. **Distinct from Agent State Persistence:** Distinct from Agent State Persistence: adds semantic indexing on top of basic state storage.
  • State Serialization1 个子标签Converting agent-specific complex objects into storable formats for persistence or transmission. **Distinct from Agent State Persistence:** Focuses on the serialization process of agent objects rather than the persistence storage mechanism.
  • State TransferProcesses for summarizing and transferring the context and discoveries of one agent session to another. **Distinct from Agent State Persistence:** Focuses on the handover of state between agents, whereas Agent State Persistence covers long-term storage.