graphiti

Build Real-Time Knowledge Graphs for AI Agents

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Graphiti is a framework for building and querying temporally-aware knowledge graphs, tailored for AI agents in dynamic environments. It continuously integrates user interactions, structured and unstructured data, and external information into a coherent, queryable graph. The framework supports incremental data updates, efficient retrieval, and precise historical queries without complete graph recomputation, making it suitable for developing interactive, context-aware AI applications.

README:

Graphiti

Build Real-Time Knowledge Graphs for AI Agents

⭐ Help us reach more developers and grow the Graphiti community. Star this repo!

[!TIP] Check out the new MCP server for Graphiti! Give Claude, Cursor, and other MCP clients powerful Knowledge Graph-based memory.

Graphiti is a framework for building and querying temporally-aware knowledge graphs, specifically tailored for AI agents operating in dynamic environments. Unlike traditional retrieval-augmented generation (RAG) methods, Graphiti continuously integrates user interactions, structured and unstructured enterprise data, and external information into a coherent, queryable graph. The framework supports incremental data updates, efficient retrieval, and precise historical queries without requiring complete graph recomputation, making it suitable for developing interactive, context-aware AI applications.

Use Graphiti to:

Integrate and maintain dynamic user interactions and business data.
Facilitate state-based reasoning and task automation for agents.
Query complex, evolving data with semantic, keyword, and graph-based search methods.

A knowledge graph is a network of interconnected facts, such as "Kendra loves Adidas shoes." Each fact is a "triplet" represented by two entities, or nodes ("Kendra", "Adidas shoes"), and their relationship, or edge ("loves"). Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to autonomously build a knowledge graph while handling changing relationships and maintaining historical context.

Graphiti and Zep's Context Engineering Platform.

Graphiti powers the core of Zep's context engineering platform for AI Agents. Zep offers agent memory, Graph RAG for dynamic data, and context retrieval and assembly.

Using Graphiti, we've demonstrated Zep is the State of the Art in Agent Memory.

Read our paper: Zep: A Temporal Knowledge Graph Architecture for Agent Memory.

We're excited to open-source Graphiti, believing its potential reaches far beyond AI memory applications.

Zep vs Graphiti

Aspect	Zep	Graphiti
What they are	Fully managed platform for context engineering and AI memory	Open-source graph framework
User & conversation management	Built-in users, threads, and message storage	Build your own
Retrieval & performance	Pre-configured, production-ready retrieval with sub-200ms performance at scale	Custom implementation required; performance depends on your setup
Developer tools	Dashboard with graph visualization, debug logs, API logs; SDKs for Python, TypeScript, and Go	Build your own tools
Enterprise features	SLAs, support, security guarantees	Self-managed
Deployment	Fully managed or in your cloud	Self-hosted only

When to choose which

Choose Zep if you want a turnkey, enterprise-grade platform with security, performance, and support baked in.

Choose Graphiti if you want a flexible OSS core and you're comfortable building/operating the surrounding system.

Why Graphiti?

Traditional RAG approaches often rely on batch processing and static data summarization, making them inefficient for frequently changing data. Graphiti addresses these challenges by providing:

Real-Time Incremental Updates: Immediate integration of new data episodes without batch recomputation.
Bi-Temporal Data Model: Explicit tracking of event occurrence and ingestion times, allowing accurate point-in-time queries.
Efficient Hybrid Retrieval: Combines semantic embeddings, keyword (BM25), and graph traversal to achieve low-latency queries without reliance on LLM summarization.
Custom Entity Definitions: Flexible ontology creation and support for developer-defined entities through straightforward Pydantic models.
Scalability: Efficiently manages large datasets with parallel processing, suitable for enterprise environments.

Graphiti vs. GraphRAG

Aspect	GraphRAG	Graphiti
Primary Use	Static document summarization	Dynamic data management
Data Handling	Batch-oriented processing	Continuous, incremental updates
Knowledge Structure	Entity clusters & community summaries	Episodic data, semantic entities, communities
Retrieval Method	Sequential LLM summarization	Hybrid semantic, keyword, and graph-based search
Adaptability	Low	High
Temporal Handling	Basic timestamp tracking	Explicit bi-temporal tracking
Contradiction Handling	LLM-driven summarization judgments	Temporal edge invalidation
Query Latency	Seconds to tens of seconds	Typically sub-second latency
Custom Entity Types	No	Yes, customizable
Scalability	Moderate	High, optimized for large datasets

Graphiti is specifically designed to address the challenges of dynamic and frequently updated datasets, making it particularly suitable for applications requiring real-time interaction and precise historical queries.

Installation

Requirements:

Python 3.10 or higher
Neo4j 5.26 / FalkorDB 1.1.2 / Kuzu 0.11.2 / Amazon Neptune Database Cluster or Neptune Analytics Graph + Amazon OpenSearch Serverless collection (serves as the full text search backend)
OpenAI API key (Graphiti defaults to OpenAI for LLM inference and embedding)

[!IMPORTANT] Graphiti works best with LLM services that support Structured Output (such as OpenAI and Gemini). Using other services may result in incorrect output schemas and ingestion failures. This is particularly problematic when using smaller models.

Optional:

Google Gemini, Anthropic, or Groq API key (for alternative LLM providers)

[!TIP] The simplest way to install Neo4j is via Neo4j Desktop. It provides a user-friendly interface to manage Neo4j instances and databases. Alternatively, you can use FalkorDB on-premises via Docker and instantly start with the quickstart example:

docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:latest

pip install graphiti-core

uv add graphiti-core

Installing with FalkorDB Support

If you plan to use FalkorDB as your graph database backend, install with the FalkorDB extra:

pip install graphiti-core[falkordb]

# or with uv
uv add graphiti-core[falkordb]

Installing with Kuzu Support

If you plan to use Kuzu as your graph database backend, install with the Kuzu extra:

pip install graphiti-core[kuzu]

# or with uv
uv add graphiti-core[kuzu]

Installing with Amazon Neptune Support

If you plan to use Amazon Neptune as your graph database backend, install with the Amazon Neptune extra:

pip install graphiti-core[neptune]

# or with uv
uv add graphiti-core[neptune]

You can also install optional LLM providers as extras:

# Install with Anthropic support
pip install graphiti-core[anthropic]

# Install with Groq support
pip install graphiti-core[groq]

# Install with Google Gemini support
pip install graphiti-core[google-genai]

# Install with multiple providers
pip install graphiti-core[anthropic,groq,google-genai]

# Install with FalkorDB and LLM providers
pip install graphiti-core[falkordb,anthropic,google-genai]

# Install with Amazon Neptune
pip install graphiti-core[neptune]

Default to Low Concurrency; LLM Provider 429 Rate Limit Errors

Graphiti's ingestion pipelines are designed for high concurrency. By default, concurrency is set low to avoid LLM Provider 429 Rate Limit Errors. If you find Graphiti slow, please increase concurrency as described below.

Concurrency controlled by the SEMAPHORE_LIMIT environment variable. By default, SEMAPHORE_LIMIT is set to 10 concurrent operations to help prevent 429 rate limit errors from your LLM provider. If you encounter such errors, try lowering this value.

If your LLM provider allows higher throughput, you can increase SEMAPHORE_LIMIT to boost episode ingestion performance.

Quick Start

[!IMPORTANT] Graphiti defaults to using OpenAI for LLM inference and embedding. Ensure that an OPENAI_API_KEY is set in your environment. Support for Anthropic and Groq LLM inferences is available, too. Other LLM providers may be supported via OpenAI compatible APIs.

For a complete working example, see the Quickstart Example in the examples directory. The quickstart demonstrates:

Connecting to a Neo4j, Amazon Neptune, FalkorDB, or Kuzu database
Initializing Graphiti indices and constraints
Adding episodes to the graph (both text and structured JSON)
Searching for relationships (edges) using hybrid search
Reranking search results using graph distance
Searching for nodes using predefined search recipes

The example is fully documented with clear explanations of each functionality and includes a comprehensive README with setup instructions and next steps.

Running with Docker Compose

You can use Docker Compose to quickly start the required services:

Neo4j Docker:
```
docker compose up
```
This will start the Neo4j Docker service and related components.
FalkorDB Docker:
```
docker compose --profile falkordb up
```
This will start the FalkorDB Docker service and related components.

MCP Server

The mcp_server directory contains a Model Context Protocol (MCP) server implementation for Graphiti. This server allows AI assistants to interact with Graphiti's knowledge graph capabilities through the MCP protocol.

Key features of the MCP server include:

Episode management (add, retrieve, delete)
Entity management and relationship handling
Semantic and hybrid search capabilities
Group management for organizing related data
Graph maintenance operations

The MCP server can be deployed using Docker with Neo4j, making it easy to integrate Graphiti into your AI assistant workflows.

For detailed setup instructions and usage examples, see the MCP server README.

REST Service

The server directory contains an API service for interacting with the Graphiti API. It is built using FastAPI.

Please see the server README for more information.

Optional Environment Variables

In addition to the Neo4j and OpenAi-compatible credentials, Graphiti also has a few optional environment variables. If you are using one of our supported models, such as Anthropic or Voyage models, the necessary environment variables must be set.

Database Configuration

Database names are configured directly in the driver constructors:

Neo4j: Database name defaults to neo4j (hardcoded in Neo4jDriver)
FalkorDB: Database name defaults to default_db (hardcoded in FalkorDriver)

As of v0.17.0, if you need to customize your database configuration, you can instantiate a database driver and pass it to the Graphiti constructor using the graph_driver parameter.

Neo4j with Custom Database Name

from graphiti_core import Graphiti
from graphiti_core.driver.neo4j_driver import Neo4jDriver

# Create a Neo4j driver with custom database name
driver = Neo4jDriver(
    uri="bolt://localhost:7687",
    user="neo4j",
    password="password",
    database="my_custom_database"  # Custom database name
)

# Pass the driver to Graphiti
graphiti = Graphiti(graph_driver=driver)

FalkorDB with Custom Database Name

from graphiti_core import Graphiti
from graphiti_core.driver.falkordb_driver import FalkorDriver

# Create a FalkorDB driver with custom database name
driver = FalkorDriver(
    host="localhost",
    port=6379,
    username="falkor_user",  # Optional
    password="falkor_password",  # Optional
    database="my_custom_graph"  # Custom database name
)

# Pass the driver to Graphiti
graphiti = Graphiti(graph_driver=driver)

Kuzu

from graphiti_core import Graphiti
from graphiti_core.driver.kuzu_driver import KuzuDriver

# Create a Kuzu driver
driver = KuzuDriver(db="/tmp/graphiti.kuzu")

# Pass the driver to Graphiti
graphiti = Graphiti(graph_driver=driver)

Amazon Neptune

from graphiti_core import Graphiti
from graphiti_core.driver.neptune_driver import NeptuneDriver

# Create a FalkorDB driver with custom database name
driver = NeptuneDriver(
    host= < NEPTUNE
ENDPOINT >,
aoss_host = < Amazon
OpenSearch
Serverless
Host >,
port = < PORT >  # Optional, defaults to 8182,
         aoss_port = < PORT >  # Optional, defaults to 443
)

driver = NeptuneDriver(host=neptune_uri, aoss_host=aoss_host, port=neptune_port)

# Pass the driver to Graphiti
graphiti = Graphiti(graph_driver=driver)

Graph Driver Architecture

Graphiti uses a pluggable driver architecture so the core framework is backend-agnostic. All database-specific logic is encapsulated in driver implementations, allowing you to swap backends or add new ones without modifying the rest of the framework.

How Drivers are Integrated

The driver layer is organized into three tiers:

GraphDriver ABC (graphiti_core/driver/driver.py) — the core interface every backend must implement. It defines query execution, session management, index lifecycle, and exposes 11 operations interfaces as @property accessors.
GraphProvider enum — identifies the backend (NEO4J, FALKORDB, KUZU, NEPTUNE). Query builders use this enum in match/case statements to return dialect-specific query strings.
11 Operations ABCs (graphiti_core/driver/operations/) — abstract interfaces covering all CRUD and search operations for every graph element type:
- Node ops: EntityNodeOperations, EpisodeNodeOperations, CommunityNodeOperations, SagaNodeOperations
- Edge ops: EntityEdgeOperations, EpisodicEdgeOperations, CommunityEdgeOperations, HasEpisodeEdgeOperations, NextEpisodeEdgeOperations
- Search & maintenance: SearchOperations, GraphMaintenanceOperations

Each backend provides a concrete driver class and a matching operations/ directory with implementations of all 11 ABCs. The key directories and files are shown below (simplified; see source for complete structure):

graphiti_core/driver/
├── driver.py                        # GraphDriver ABC, GraphProvider enum
├── query_executor.py                # QueryExecutor protocol
├── record_parsers.py                # Shared record → model conversion
├── operations/                      # 11 operation ABCs
│   ├── entity_node_ops.py
│   ├── episode_node_ops.py
│   ├── community_node_ops.py
│   ├── saga_node_ops.py
│   ├── entity_edge_ops.py
│   ├── episodic_edge_ops.py
│   ├── community_edge_ops.py
│   ├── has_episode_edge_ops.py
│   ├── next_episode_edge_ops.py
│   ├── search_ops.py
│   ├── graph_ops.py
│   └── graph_utils.py              # Shared algorithms (e.g., label propagation)
├── graph_operations/                # Legacy graph operations interface
├── search_interface/                # Legacy search interface
├── neo4j_driver.py                  # Neo4jDriver
├── neo4j/operations/                # 11 Neo4j implementations
├── falkordb_driver.py               # FalkorDriver
├── falkordb/operations/             # 11 FalkorDB implementations
├── kuzu_driver.py                   # KuzuDriver
├── kuzu/operations/                 # 11 Kuzu implementations + record_parsers.py
├── neptune_driver.py                # NeptuneDriver
└── neptune/operations/              # 11 Neptune implementations

Operations are decoupled from the driver itself — each operation method receives an executor: QueryExecutor parameter (a protocol for running queries) rather than a concrete GraphDriver, which makes operations testable and driver-agnostic. The driver class instantiates all 11 operation classes in its __init__ and exposes them as properties. The base GraphDriver ABC defines each property with an optional return type (| None, defaulting to None); concrete drivers override these to return their implementations:

# In your concrete driver (e.g., Neo4jDriver):
@property
def entity_node_ops(self) -> EntityNodeOperations:
    return self._entity_node_ops

Provider-specific query strings are generated by shared query builders in graphiti_core/models/nodes/node_db_queries.py and graphiti_core/models/edges/edge_db_queries.py, which use match/case on the GraphProvider enum to return the correct dialect for each backend.

Adding a New Graph Driver

To integrate a new graph database backend, follow these steps:

Add to GraphProvider — add your enum value in graphiti_core/driver/driver.py:

class GraphProvider(Enum):
    NEO4J = 'neo4j'
    FALKORDB = 'falkordb'
    KUZU = 'kuzu'
    NEPTUNE = 'neptune'
    MY_BACKEND = 'my_backend'  # New backend

Create directory structure — create graphiti_core/driver/<backend>/operations/ with an __init__.py exporting all 11 operation classes.
Implement GraphDriver subclass — create graphiti_core/driver/<backend>_driver.py:
- Set provider = GraphProvider.<BACKEND>
- Implement the abstract methods: execute_query(), session(), close(), build_indices_and_constraints(), delete_all_indexes()
- Instantiate all 11 operation classes in __init__ and return them via @property overrides
Implement all 11 operation ABCs — one file per ABC in <backend>/operations/, each inheriting from the corresponding ABC in graphiti_core/driver/operations/.
Add query variants — add case GraphProvider.<BACKEND>: branches to graphiti_core/models/nodes/node_db_queries.py and graphiti_core/models/edges/edge_db_queries.py for your database's query dialect.
Implement GraphDriverSession — if your backend needs session or connection management, subclass GraphDriverSession from driver.py and implement run(), close(), and execute_write().

Register as optional dependency — add an extras group in pyproject.toml:

[project.optional-dependencies]
my_backend = ["my-backend-client>=1.0.0"]

For reference implementations, look at:

Neo4j — the most straightforward, full-featured reference
FalkorDB — a lightweight client-server alternative
Kuzu — example of an embedded/in-process database with dialect differences
Neptune — example of a cloud backend with an external search index (OpenSearch)

Using Graphiti with Azure OpenAI

Graphiti supports Azure OpenAI for both LLM inference and embeddings using Azure's OpenAI v1 API compatibility layer.

Quick Start

from openai import AsyncOpenAI
from graphiti_core import Graphiti
from graphiti_core.llm_client.azure_openai_client import AzureOpenAILLMClient
from graphiti_core.llm_client.config import LLMConfig
from graphiti_core.embedder.azure_openai import AzureOpenAIEmbedderClient

# Initialize Azure OpenAI client using the standard OpenAI client
# with Azure's v1 API endpoint
azure_client = AsyncOpenAI(
    base_url="https://your-resource-name.openai.azure.com/openai/v1/",
    api_key="your-api-key",
)

# Create LLM and Embedder clients
llm_client = AzureOpenAILLMClient(
    azure_client=azure_client,
    config=LLMConfig(model="gpt-5-mini", small_model="gpt-5-mini")  # Your Azure deployment name
)
embedder_client = AzureOpenAIEmbedderClient(
    azure_client=azure_client,
    model="text-embedding-3-small"  # Your Azure embedding deployment name
)

# Initialize Graphiti with Azure OpenAI clients
graphiti = Graphiti(
    "bolt://localhost:7687",
    "neo4j",
    "password",
    llm_client=llm_client,
    embedder=embedder_client,
)

# Now you can use Graphiti with Azure OpenAI

Key Points:

Use the standard AsyncOpenAI client with Azure's v1 API endpoint format: https://your-resource-name.openai.azure.com/openai/v1/
The deployment names (e.g., gpt-5-mini, text-embedding-3-small) should match your Azure OpenAI deployment names
See examples/azure-openai/ for a complete working example

Make sure to replace the placeholder values with your actual Azure OpenAI credentials and deployment names.

Using Graphiti with Google Gemini

Graphiti supports Google's Gemini models for LLM inference, embeddings, and cross-encoding/reranking. To use Gemini, you'll need to configure the LLM client, embedder, and the cross-encoder with your Google API key.

Install Graphiti:

uv add "graphiti-core[google-genai]"

# or

pip install "graphiti-core[google-genai]"

from graphiti_core import Graphiti
from graphiti_core.llm_client.gemini_client import GeminiClient, LLMConfig
from graphiti_core.embedder.gemini import GeminiEmbedder, GeminiEmbedderConfig
from graphiti_core.cross_encoder.gemini_reranker_client import GeminiRerankerClient

# Google API key configuration
api_key = "<your-google-api-key>"

# Initialize Graphiti with Gemini clients
graphiti = Graphiti(
    "bolt://localhost:7687",
    "neo4j",
    "password",
    llm_client=GeminiClient(
        config=LLMConfig(
            api_key=api_key,
            model="gemini-2.0-flash"
        )
    ),
    embedder=GeminiEmbedder(
        config=GeminiEmbedderConfig(
            api_key=api_key,
            embedding_model="embedding-001"
        )
    ),
    cross_encoder=GeminiRerankerClient(
        config=LLMConfig(
            api_key=api_key,
            model="gemini-2.5-flash-lite"
        )
    )
)

# Now you can use Graphiti with Google Gemini for all components

The Gemini reranker uses the gemini-2.5-flash-lite model by default, which is optimized for cost-effective and low-latency classification tasks. It uses the same boolean classification approach as the OpenAI reranker, leveraging Gemini's log probabilities feature to rank passage relevance.

Using Graphiti with Ollama (Local LLM)

Graphiti supports Ollama for running local LLMs and embedding models via Ollama's OpenAI-compatible API. This is ideal for privacy-focused applications or when you want to avoid API costs.

Note: Use OpenAIGenericClient (not OpenAIClient) for Ollama and other OpenAI-compatible providers like LM Studio. The OpenAIGenericClient is optimized for local models with a higher default max token limit (16K vs 8K) and full support for structured outputs.

Install the models:

ollama pull deepseek-r1:7b # LLM
ollama pull nomic-embed-text # embeddings

from graphiti_core import Graphiti
from graphiti_core.llm_client.config import LLMConfig
from graphiti_core.llm_client.openai_generic_client import OpenAIGenericClient
from graphiti_core.embedder.openai import OpenAIEmbedder, OpenAIEmbedderConfig
from graphiti_core.cross_encoder.openai_reranker_client import OpenAIRerankerClient

# Configure Ollama LLM client
llm_config = LLMConfig(
    api_key="ollama",  # Ollama doesn't require a real API key, but some placeholder is needed
    model="deepseek-r1:7b",
    small_model="deepseek-r1:7b",
    base_url="http://localhost:11434/v1",  # Ollama's OpenAI-compatible endpoint
)

llm_client = OpenAIGenericClient(config=llm_config)

# Initialize Graphiti with Ollama clients
graphiti = Graphiti(
    "bolt://localhost:7687",
    "neo4j",
    "password",
    llm_client=llm_client,
    embedder=OpenAIEmbedder(
        config=OpenAIEmbedderConfig(
            api_key="ollama",  # Placeholder API key
            embedding_model="nomic-embed-text",
            embedding_dim=768,
            base_url="http://localhost:11434/v1",
        )
    ),
    cross_encoder=OpenAIRerankerClient(client=llm_client, config=llm_config),
)

# Now you can use Graphiti with local Ollama models

Ensure Ollama is running (ollama serve) and that you have pulled the models you want to use.

Documentation

Telemetry

Graphiti collects anonymous usage statistics to help us understand how the framework is being used and improve it for everyone. We believe transparency is important, so here's exactly what we collect and why.

What We Collect

When you initialize a Graphiti instance, we collect:

Anonymous identifier: A randomly generated UUID stored locally in ~/.cache/graphiti/telemetry_anon_id
System information: Operating system, Python version, and system architecture
Graphiti version: The version you're using
Configuration choices:
- LLM provider type (OpenAI, Azure, Anthropic, etc.)
- Database backend (Neo4j, FalkorDB, Kuzu, Amazon Neptune Database or Neptune Analytics)
- Embedder provider (OpenAI, Azure, Voyage, etc.)

What We Don't Collect

We are committed to protecting your privacy. We never collect:

Personal information or identifiers
API keys or credentials
Your actual data, queries, or graph content
IP addresses or hostnames
File paths or system-specific information
Any content from your episodes, nodes, or edges

Why We Collect This Data

This information helps us:

Understand which configurations are most popular to prioritize support and testing
Identify which LLM and database providers to focus development efforts on
Track adoption patterns to guide our roadmap
Ensure compatibility across different Python versions and operating systems

By sharing this anonymous information, you help us make Graphiti better for everyone in the community.

View the Telemetry Code

The Telemetry code may be found here.

How to Disable Telemetry

Telemetry is opt-out and can be disabled at any time. To disable telemetry collection:

Option 1: Environment Variable

export GRAPHITI_TELEMETRY_ENABLED=false

Option 2: Set in your shell profile

# For bash users (~/.bashrc or ~/.bash_profile)
echo 'export GRAPHITI_TELEMETRY_ENABLED=false' >> ~/.bashrc

# For zsh users (~/.zshrc)
echo 'export GRAPHITI_TELEMETRY_ENABLED=false' >> ~/.zshrc

Option 3: Set for a specific Python session

import os

os.environ['GRAPHITI_TELEMETRY_ENABLED'] = 'false'

# Then initialize Graphiti as usual
from graphiti_core import Graphiti

graphiti = Graphiti(...)

Telemetry is automatically disabled during test runs (when pytest is detected).

Technical Details

Telemetry uses PostHog for anonymous analytics collection
All telemetry operations are designed to fail silently - they will never interrupt your application or affect Graphiti functionality
The anonymous ID is stored locally and is not tied to any personal information

Status and Roadmap

Graphiti is under active development. We aim to maintain API stability while working on:

[x] Supporting custom graph schemas:
- Allow developers to provide their own defined node and edge classes when ingesting episodes
- Enable more flexible knowledge representation tailored to specific use cases
[x] Enhancing retrieval capabilities with more robust and configurable options
[x] Graphiti MCP Server
[ ] Expanding test coverage to ensure reliability and catch edge cases

Contributing

We encourage and appreciate all forms of contributions, whether it's code, documentation, addressing GitHub Issues, or answering questions in the Graphiti Discord channel. For detailed guidelines on code contributions, please refer to CONTRIBUTING.

Support

Join the Zep Discord server and make your way to the #Graphiti channel!

For Tasks:

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PyLLMCore is a light-weighted interface with Large Language Models with native support for llama.cpp, OpenAI API, and Azure deployments. It offers a Pythonic API that is simple to use, with structures provided by the standard library dataclasses module. The high-level API includes the assistants module for easy swapping between models. PyLLMCore supports various models including those compatible with llama.cpp, OpenAI, and Azure APIs. It covers use cases such as parsing, summarizing, question answering, hallucinations reduction, context size management, and tokenizing. The tool allows users to interact with language models for tasks like parsing text, summarizing content, answering questions, reducing hallucinations, managing context size, and tokenizing text.

github

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langgraph4j

Langgraph4j is a Java library for language processing tasks such as text classification, sentiment analysis, and named entity recognition. It provides a set of tools and algorithms for analyzing text data and extracting useful information. The library is designed to be efficient and easy to use, making it suitable for both research and production applications.

github

: 916

UCAgent

UCAgent is an AI-powered automated UT verification agent for chip design. It automates chip verification workflow, supports functional and code coverage analysis, ensures consistency among documentation, code, and reports, and collaborates with mainstream Code Agents via MCP protocol. It offers three intelligent interaction modes and requires Python 3.11+, Linux/macOS OS, 4GB+ memory, and access to an AI model API. Users can clone the repository, install dependencies, configure qwen, and start verification. UCAgent supports various verification quality improvement options and basic operations through TUI shortcuts and stage color indicators. It also provides documentation build and preview using MkDocs, PDF manual build using Pandoc + XeLaTeX, and resources for further help and contribution.

github

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basic-memory

Basic Memory is a tool that enables users to build persistent knowledge through natural conversations with Large Language Models (LLMs) like Claude. It uses the Model Context Protocol (MCP) to allow compatible LLMs to read and write to a local knowledge base stored in simple Markdown files on the user's computer. The tool facilitates creating structured notes during conversations, maintaining a semantic knowledge graph, and keeping all data local and under user control. Basic Memory aims to address the limitations of ephemeral LLM interactions by providing a structured, bi-directional, and locally stored knowledge management solution.

github

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MCPSharp

MCPSharp is a .NET library that helps build Model Context Protocol (MCP) servers and clients for AI assistants and models. It allows creating MCP-compliant tools, connecting to existing MCP servers, exposing .NET methods as MCP endpoints, and handling MCP protocol details seamlessly. With features like attribute-based API, JSON-RPC support, parameter validation, and type conversion, MCPSharp simplifies the development of AI capabilities in applications through standardized interfaces.

github

: 142

fast-mcp

Fast MCP is a Ruby gem that simplifies the integration of AI models with your Ruby applications. It provides a clean implementation of the Model Context Protocol, eliminating complex communication protocols, integration challenges, and compatibility issues. With Fast MCP, you can easily connect AI models to your servers, share data resources, choose from multiple transports, integrate with frameworks like Rails and Sinatra, and secure your AI-powered endpoints. The gem also offers real-time updates and authentication support, making AI integration a seamless experience for developers.

github

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raglite

RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite. It offers configurable options for choosing LLM providers, database types, and rerankers. The toolkit is fast and permissive, utilizing lightweight dependencies and hardware acceleration. RAGLite provides features like PDF to Markdown conversion, multi-vector chunk embedding, optimal semantic chunking, hybrid search capabilities, adaptive retrieval, and improved output quality. It is extensible with a built-in Model Context Protocol server, customizable ChatGPT-like frontend, document conversion to Markdown, and evaluation tools. Users can configure RAGLite for various tasks like configuring, inserting documents, running RAG pipelines, computing query adapters, evaluating performance, running MCP servers, and serving frontends.

github

: 1.1k

orra

Orra is a tool for building production-ready multi-agent applications that handle complex real-world interactions. It coordinates tasks across existing stack, agents, and tools run as services using intelligent reasoning. With features like smart pre-evaluated execution plans, domain grounding, durable execution, and automatic service health monitoring, Orra enables users to go fast with tools as services and revert state to handle failures. It provides real-time status tracking and webhook result delivery, making it ideal for developers looking to move beyond simple crews and agents.

github

: 155

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Magick

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

github

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danswer

Danswer is an open-source Gen-AI Chat and Unified Search tool that connects to your company's docs, apps, and people. It provides a Chat interface and plugs into any LLM of your choice. Danswer can be deployed anywhere and for any scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your own control. Danswer is MIT licensed and designed to be modular and easily extensible. The system also comes fully ready for production usage with user authentication, role management (admin/basic users), chat persistence, and a UI for configuring Personas (AI Assistants) and their Prompts. Danswer also serves as a Unified Search across all common workplace tools such as Slack, Google Drive, Confluence, etc. By combining LLMs and team specific knowledge, Danswer becomes a subject matter expert for the team. Imagine ChatGPT if it had access to your team's unique knowledge! It enables questions such as "A customer wants feature X, is this already supported?" or "Where's the pull request for feature Y?"

github

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semantic-kernel

Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel _special_ , however, is its ability to _automatically_ orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

github

: 27.3k

floneum

Floneum is a graph editor that makes it easy to develop your own AI workflows. It uses large language models (LLMs) to run AI models locally, without any external dependencies or even a GPU. This makes it easy to use LLMs with your own data, without worrying about privacy. Floneum also has a plugin system that allows you to improve the performance of LLMs and make them work better for your specific use case. Plugins can be used in any language that supports web assembly, and they can control the output of LLMs with a process similar to JSONformer or guidance.

github

: 1.8k

mindsdb

MindsDB is a platform for customizing AI from enterprise data. You can create, serve, and fine-tune models in real-time from your database, vector store, and application data. MindsDB "enhances" SQL syntax with AI capabilities to make it accessible for developers worldwide. With MindsDB’s nearly 200 integrations, any developer can create AI customized for their purpose, faster and more securely. Their AI systems will constantly improve themselves — using companies’ own data, in real-time.

github

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aiscript

AiScript is a lightweight scripting language that runs on JavaScript. It supports arrays, objects, and functions as first-class citizens, and is easy to write without the need for semicolons or commas. AiScript runs in a secure sandbox environment, preventing infinite loops from freezing the host. It also allows for easy provision of variables and functions from the host.

github

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activepieces

Activepieces is an open source replacement for Zapier, designed to be extensible through a type-safe pieces framework written in Typescript. It features a user-friendly Workflow Builder with support for Branches, Loops, and Drag and Drop. Activepieces integrates with Google Sheets, OpenAI, Discord, and RSS, along with 80+ other integrations. The list of supported integrations continues to grow rapidly, thanks to valuable contributions from the community. Activepieces is an open ecosystem; all piece source code is available in the repository, and they are versioned and published directly to npmjs.com upon contributions. If you cannot find a specific piece on the pieces roadmap, please submit a request by visiting the following link: Request Piece Alternatively, if you are a developer, you can quickly build your own piece using our TypeScript framework. For guidance, please refer to the following guide: Contributor's Guide

github

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superagent-js

Superagent is an open source framework that enables any developer to integrate production ready AI Assistants into any application in a matter of minutes.

github

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Awesome-LLM-RAG-Application is a repository that provides resources and information about applications based on Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) pattern. It includes a survey paper, GitHub repo, and guides on advanced RAG techniques. The repository covers various aspects of RAG, including academic papers, evaluation benchmarks, downstream tasks, tools, and technologies. It also explores different frameworks, preprocessing tools, routing mechanisms, evaluation frameworks, embeddings, security guardrails, prompting tools, SQL enhancements, LLM deployment, observability tools, and more. The repository aims to offer comprehensive knowledge on RAG for readers interested in exploring and implementing LLM-based systems and products.

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ChatGPT-On-CS

ChatGPT-On-CS is an intelligent chatbot tool based on large models, supporting various platforms like WeChat, Taobao, Bilibili, Douyin, Weibo, and more. It can handle text, voice, and image inputs, access external resources through plugins, and customize enterprise AI applications based on proprietary knowledge bases. Users can set custom replies, utilize ChatGPT interface for intelligent responses, send images and binary files, and create personalized chatbots using knowledge base files. The tool also features platform-specific plugin systems for accessing external resources and supports enterprise AI applications customization.

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call-gpt

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github

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awesome-LLM-resourses

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

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tappas

Hailo TAPPAS is a set of full application examples that implement pipeline elements and pre-trained AI tasks. It demonstrates Hailo's system integration scenarios on predefined systems, aiming to accelerate time to market, simplify integration with Hailo's runtime SW stack, and provide a starting point for customers to fine-tune their applications. The tool supports both Hailo-15 and Hailo-8, offering various example applications optimized for different common hosts. TAPPAS includes pipelines for single network, two network, and multi-stream processing, as well as high-resolution processing via tiling. It also provides example use case pipelines like License Plate Recognition and Multi-Person Multi-Camera Tracking. The tool is regularly updated with new features, bug fixes, and platform support.

github

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cloudflare-rag

This repository provides a fullstack example of building a Retrieval Augmented Generation (RAG) app with Cloudflare. It utilizes Cloudflare Workers, Pages, D1, KV, R2, AI Gateway, and Workers AI. The app features streaming interactions to the UI, hybrid RAG with Full-Text Search and Vector Search, switchable providers using AI Gateway, per-IP rate limiting with Cloudflare's KV, OCR within Cloudflare Worker, and Smart Placement for workload optimization. The development setup requires Node, pnpm, and wrangler CLI, along with setting up necessary primitives and API keys. Deployment involves setting up secrets and deploying the app to Cloudflare Pages. The project implements a Hybrid Search RAG approach combining Full Text Search against D1 and Hybrid Search with embeddings against Vectorize to enhance context for the LLM.

github

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pixeltable

Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.

github

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wave-apps

Wave Apps is a directory of sample applications built on H2O Wave, allowing users to build AI apps faster. The apps cover various use cases such as explainable hotel ratings, human-in-the-loop credit risk assessment, mitigating churn risk, online shopping recommendations, and sales forecasting EDA. Users can download, modify, and integrate these sample apps into their own projects to learn about app development and AI model deployment.

github

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