Alternatives to Graphify
Compare Graphify alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Graphify in 2026. Compare features, ratings, user reviews, pricing, and more from Graphify competitors and alternatives in order to make an informed decision for your business.
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1
CMEM Cloud
cmem.ai
CMEM Cloud is the cloud sync layer for claude-mem, built to link AI agent memory everywhere through one private MCP link. claude-mem is the open source engine that takes notes while an agent works, and CMEM Cloud mirrors that local memory so agents can recall it across every session, machine, editor, and MCP-compatible client. Instead of making users re-explain context, paste old notes, or restart from zero, the system captures decisions, bug fixes, dead ends, environment notes, architecture choices, and other structured observations as the agent works. Those observations are stored in a temporal database, searched by meaning through vector recall, and made available through a private MCP endpoint that any compatible agent can read and write through. It starts with installing the local engine, letting a second model write structured notes out of band, syncing the local database to CMEM Cloud, and then recalling that memory anywhere.Starting Price: Free -
2
MemPalace
MemPalace
MemPalace is a local-first storage and retrieval system for AI workflows, built to give AI a memory while keeping the user’s words under their own control. It stores conversations verbatim instead of reducing them to summaries, then organizes that memory into a navigable “palace” structure inspired by the ancient memory palace technique. Conversations can be arranged into wings for people, projects, or topics, with rooms and drawers used to make information easier to locate, narrow, and retrieve later. It is designed for people who believe their words are theirs, with local-first storage, zero telemetry, and a privacy-focused approach that keeps memory on the user’s machine. MemPalace supports AI workflows through MCP tooling, including tools for palace reads and writes, knowledge-graph operations, cross-wing navigation, drawer management, and agent diaries.Starting Price: Free -
3
MythOS
MythOS
MythOS is a shared memory system between you and every AI you use, built to help people stop re-explaining themselves across models, agents, and channels. It is designed for people who write to think, giving them a modular thinking system for structured notes, memos, contextual maps, and AI-powered workflows. Users can capture what they read, connect what they think, and publish what matters while keeping their library one click away from every AI. MythOS works as a personal knowledge operating system where memory, notes, ideas, resources, and context can be organized into structured documents that stay useful over time. Its approach treats knowledge as a process, not a one-time activity, so living documents can remain in progress, evolve, and connect with related people, projects, topics, and ideas. It supports contextual maps, public memos, private knowledge, AI-ready memory, exportable data, and workflows that help users build a durable layer of context.Starting Price: $10 per month -
4
OpenViking
OpenViking
OpenViking is an open source context database designed specifically for AI agents, built around a file-system paradigm that unifies the management of memories, resources, and skills. Instead of treating context as scattered chunks in a fragmented vector store, OpenViking organizes agent context into a virtual file system under the viking protocol, giving agents a structured way to store, navigate, retrieve, and observe the information they need. It is designed to help developers move beyond the hassle of manual context management by giving agents a minimalist interaction model for context, similar to reading and writing files. OpenViking supports hierarchical context loading, semantic retrieval, recursive retrieval, sessions, metrics, and observability, making it possible for AI agents to access the right level of information without stuffing everything into the prompt.Starting Price: Free -
5
Hindsight
Vectorize
Hindsight is an agent memory system built to create smarter AI agents that learn over time instead of starting every conversation from zero. Most agent memory systems focus on recalling conversation history, but Hindsight is focused on making agents learn, not just remember. It gives AI agents persistent long-term memory using biomimetic data structures, helping them retain facts, recall relevant context, and reflect on experience as part of reasoning. Hindsight is designed for agents that need to understand who a user is, what has been discussed, what preferences have emerged, what decisions were made, and how behavior should adapt across sessions. It provides three core operations: retain, recall, and reflect. Retain stores new information, recall retrieves the right memories when needed, and reflect helps agents synthesize observations, form mental models, and learn from prior interactions.Starting Price: Free -
6
claude-mem
cmem.ai
claude-mem is an offline-first cloud memory for AI agents, built around an open source engine and a cloud sync layer that links agent memory everywhere through one private MCP link. It is designed so coding agents and AI assistants do not start from zero every session, every machine, or every editor. claude-mem takes notes while an agent works, capturing decisions, fixes, dead ends, environment notes, architecture choices, and other structured observations in a temporal database. CMEM Cloud then mirrors that local memory behind a private Model Context Protocol endpoint, allowing any compatible agent or IDE to read and write the same memory across tools such as Claude Code, Cursor, Windsurf, OpenCode, Codex CLI, Gemini CLI, and VS Code. It works locally first, with or without a network, while keeping memory synchronized when cloud access is available.Starting Price: Free -
7
Constellation
ShiftinBits Inc
Graph-backed code intelligence for your AI assistant. Constellation turns your codebase into a queryable knowledge graph, giving AI assistants the structural understanding they need to reason about real software — not just the plain text. Why Constellation? Text search tells you where a string appears, *everywhere* that string appears. Constellation tells you the exact location of the symbol in question, what it means, what calls it, and what breaks if you change it. Before your assistant edits a function, it can ask: - Where is this defined, and where is it used across the codebase? - What's the blast radius of this change? - Which modules have circular dependencies or dead code? - How does data flow through the call graph? Answers come from a semantic graph, not a grep loop. One Tool, Countless Capabilities A single `code_intel` tool exposes a rich JavaScript API as a "Code Mode" tool, allowing AI agents to craft complex composite queries.Starting Price: $29.99/month -
8
Superpowers
Superpowers
Superpowers is an open-source software development methodology and skills framework designed to improve how coding agents plan, build, test, and review software. The project gives AI coding tools a structured workflow that helps them clarify requirements before writing code. It supports agents such as Claude Code, Codex CLI, Codex App, Factory Droid, Gemini CLI, OpenCode, Cursor, and GitHub Copilot CLI. Superpowers guides agents through brainstorming, design approval, implementation planning, test-driven development, subagent-driven execution, code review, and branch completion. Its skills library emphasizes red-green-refactor testing, systematic debugging, isolated git worktrees, verification, and evidence-based completion. Superpowers helps developers turn AI coding agents into more disciplined engineering partners that follow repeatable processes instead of jumping straight into code.Starting Price: Free -
9
Textalytic
Textalytic
Text Analysis is a complex and specialized task. Textalytic makes it super simple to extract insight from the textual content. Use our corpus builder to preprocess your text. Copy and paste the text into the editor or upload a file from your computer or Dropbox. You can view your results in a table, graphs, or export them to csv and pdf format. Graphs can be saved as image files, embedded on websites, or emailed. Gain insights from rich colorful charts and graphs. The compare features function allows you to compare features in a dynamic scatterplot. Analyze the frequency of words that describe a noun or pronoun. Analyze the frequency of words that describe an action or state of being. Analyze the frequency of words that indicates the relationship of words. Analyze frequency of group of words that define the subject.Starting Price: $19 per month -
10
RecallGraph
RecallGraph
RecallGraph is a versioned-graph data store - it retains all changes that its data (vertices and edges) have gone through to reach their current state. It supports point-in-time graph traversals, letting the user query any past state of the graph just as easily as the present. RecallGraph is a potential fit for scenarios where data is best represented as a network of vertices and edges (i.e., a graph) having the following characteristics: 1. Both vertices and edges can hold properties in the form of attribute/value pairs (equivalent to JSON objects). 2. Documents (vertices/edges) mutate within their lifespan (both in their individual attributes/values and in their relations with each other). 3. Past states of documents are as important as their present, necessitating retention and queryability of their change history. Also see this blog post for an intro - https://blog.recallgraph.tech/never-lose-your-old-data-again. -
11
Papr
Papr.ai
Papr is an AI-native memory and context intelligence platform that provides a predictive memory layer combining vector embeddings with a knowledge graph through a single API, enabling AI systems to store, connect, and retrieve context across conversations, documents, and structured data with high precision. It lets developers add production-ready memory to AI agents and apps with minimal code, maintaining context across interactions and powering assistants that remember user history and preferences. Papr supports ingestion of diverse data including chat, documents, PDFs, and tool data, automatically extracting entities and relationships to build a dynamic memory graph that improves retrieval accuracy and anticipates needs via predictive caching, delivering low latency and state-of-the-art retrieval performance. Papr’s hybrid architecture supports natural language search and GraphQL queries, secure multi-tenant access controls, and dual memory types for user personalization.Starting Price: $20 per month -
12
Cognee
Cognee
Cognee is an open source AI memory engine that transforms raw data into structured knowledge graphs, enhancing the accuracy and contextual understanding of AI agents. It supports various data types, including unstructured text, media files, PDFs, and tables, and integrates seamlessly with several data sources. Cognee employs modular ECL pipelines to process and organize data, enabling AI agents to retrieve relevant information efficiently. It is compatible with vector and graph databases and supports LLM frameworks like OpenAI, LlamaIndex, and LangChain. Key features include customizable storage options, RDF-based ontologies for smart data structuring, and the ability to run on-premises, ensuring data privacy and compliance. Cognee's distributed system is scalable, capable of handling large volumes of data, and is designed to reduce AI hallucinations by providing AI agents with a coherent and interconnected data landscape.Starting Price: $25 per month -
13
Grok Code Fast 1
xAI
Grok Code Fast 1 is a high-speed, economical reasoning model designed specifically for agentic coding workflows. Unlike traditional models that can feel slow in tool-based loops, it delivers near-instant responses, excelling in everyday software development tasks. Built from scratch with a programming-rich corpus and refined on real-world pull requests, it supports languages like TypeScript, Python, Java, Rust, C++, and Go. Developers can use it for everything from zero-to-one project building to precise bug fixes and codebase Q&A. With optimized inference and caching techniques, it achieves impressive responsiveness and a 90%+ cache hit rate when integrated with partners like GitHub Copilot, Cursor, and Cline. Offered at just $0.20 per million input tokens and $1.50 per million output tokens, Grok Code Fast 1 strikes a strong balance between speed, performance, and affordability.Starting Price: $0.20 per million input tokens -
14
Hyperspell
Hyperspell
Hyperspell is an end-to-end memory and context layer for AI agents that lets you build data-powered, context-aware applications without managing the underlying pipeline. It ingests data continuously from user-connected sources (e.g., drive, docs, chat, calendar), builds a bespoke memory graph, and maintains context so future queries are informed by past interactions. Hyperspell supports persistent memory, context engineering, and grounded generation, producing structured or LLM-ready summaries from the memory graph. It integrates with your choice of LLM while enforcing security standards and keeping data private and auditable. With one-line integration and pre-built components for authentication and data access, Hyperspell abstracts away the work of indexing, chunking, schema extraction, and memory updates. Over time, it “learns” from interactions; relevant answers reinforce context and improve future performance. -
15
Coral
Coral
Coral is an open-source query layer that allows AI agents and developers to access data across APIs, databases, and file systems using SQL. The platform turns connected sources such as GitHub, Slack, Linear, Datadog, Sentry, Stripe, and PagerDuty into readonly tables that can be explored and joined together. Instead of building custom integrations, ETL pipelines, or API wrappers, teams can use Coral to query multiple systems from one runtime. Coral supports CLI and MCP access, making it usable with tools such as Claude Code, Codex, and other agent frameworks. The platform handles authentication, pagination, rate limits, schema mapping, caching, and semantic hints to improve accuracy and reduce cost. Coral helps engineering teams give AI agents safer, faster, and more useful context for production workflows.Starting Price: $249/month -
16
HQ
Indigo AI
HQ is the shared AI context layer for teams, giving the whole team and every AI tool one workspace to work from, with knowledge, skills, and workflows compounding in one place, and any agent running on top. It works as an operating system for AI workers over Claude Code, Cursor, Codex, ChatGPT, and Claude chat through MCP, so every teammate and every agent can start from the same shared context instead of separate chat histories, scattered files, and siloed workflows. HQ turns one person’s best work into team infrastructure: any prompt or workflow can become a reusable /command, then /hq-sync ships it to the whole team so anyone can run it in one step. Knowledge that usually lives across decisions, docs, playbooks, policies, projects, code, and ideas accumulates in HQ as the team works, creating one source of truth that every agent can search, reuse, and build on. Agents can be deployed into email and Slack, acting on top of the team’s skills and knowledge with full context. -
17
AgentScreenshots
AgentScreenshots
AgentScreenshots gives AI coding agents a practical visual feedback loop for frontend development. The `agentshot` CLI captures screenshots of localhost, preview, staging, or production pages, saves PNGs into your project, and lets the agent inspect the rendered pixels before claiming the UI is done. It is built for Claude Code, Codex, Cursor, OpenCode, and other coding agents.Starting Price: €5/month -
18
Multilith
Multilith
Multilith gives AI coding tools a persistent memory so they understand your entire codebase, architecture decisions, and team conventions from the very first prompt. With a single configuration line, Multilith injects organizational context into every AI interaction using the Model Context Protocol. This eliminates repetitive explanations and ensures AI suggestions align with your actual stack, patterns, and constraints. Architectural decisions, historical refactors, and documented tradeoffs become permanent guardrails rather than forgotten notes. Multilith helps teams onboard faster, reduce mistakes, and maintain consistent code quality across contributors. It works seamlessly with popular AI coding tools while keeping your data secure and fully under your control. -
19
GloVe
Stanford NLP
GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm developed by the Stanford NLP Group to obtain vector representations for words. It constructs word embeddings by analyzing global word-word co-occurrence statistics from a given corpus, resulting in vector spaces where the geometric relationships reflect semantic similarities and differences among words. A notable feature of GloVe is its ability to capture linear substructures within the word vector space, enabling vector arithmetic to express relationships. The model is trained on the non-zero entries of a global word-word co-occurrence matrix, which records how frequently pairs of words appear together in a corpus. This approach efficiently leverages statistical information by focusing on significant co-occurrences, leading to meaningful word representations. Pre-trained word vectors are available for various corpora, including Wikipedia 2014.Starting Price: Free -
20
MemU
NevaMind AI
MemU is an intelligent memory layer designed specifically for large language model (LLM) applications, enabling AI companions to remember and organize information efficiently. It functions as an autonomous, evolving file system that links memories into an interconnected knowledge graph, improving accuracy, retrieval speed, and reducing costs. Developers can easily integrate MemU into their LLM apps using SDKs and APIs compatible with OpenAI, Anthropic, Gemini, and other AI platforms. MemU offers enterprise-grade solutions including commercial licenses, custom development, and real-time user behavior analytics. With 24/7 premium support and scalable infrastructure, MemU helps businesses build reliable AI memory features. The platform significantly outperforms competitors in accuracy benchmarks, making it ideal for memory-first AI applications. -
21
ArangoDB
ArangoDB
Natively store data for graph, document and search needs. Utilize feature-rich access with one query language. Map data natively to the database and access it with the best patterns for the job – traversals, joins, search, ranking, geospatial, aggregations – you name it. Polyglot persistence without the costs. Easily design, scale and adapt your architectures to changing needs and with much less effort. Combine the flexibility of JSON with semantic search and graph technology for next generation feature extraction even for large datasets. -
22
Corpus-X
Corpus-X
Dive deep into your data with custom AI chatbots and analytics. Corpus-X provides advanced AI-driven solutions, including AI chat applications and VizGPT for dynamic data visualization and analytics. Whether it's semantic searches, chatbots tailored to your docs, or extracting insights from CSV data, we've got you covered. VizGPT is designed for seamless data visualization. Simply upload your CSV data, and the tool will assist you in generating insightful graphs and answering data-related queries, all powered by AI. Absolutely! VizGPT is designed for easy integration into various platforms, ensuring you can visualize and query your data wherever you need it. From website docs, Notion pages, PDFs, CSVs to Slack data, we support a wide array of data sources, ensuring your chatbot is comprehensively trained for optimal performance. Our chatbots are crafted for instantaneous interactions, ensuring users receive prompt and accurate information, and enhancing their experience. -
23
ByteRover
ByteRover
ByteRover is a self-improving memory layer for AI coding agents that unifies the creation, retrieval, and sharing of “vibe-coding” memories across projects and teams. Designed for dynamic AI-assisted development, it integrates into any AI IDE via the Memory Compatibility Protocol (MCP) extension, enabling agents to automatically save and recall context without altering existing workflows. It provides instant IDE integration, automated memory auto-save and recall, intuitive memory management (create, edit, delete, and prioritize memories), and team-wide intelligence sharing to enforce consistent coding standards. These capabilities let developer teams of all sizes maximize AI coding efficiency, eliminate repetitive training, and maintain a centralized, searchable memory store. Install ByteRover’s extension in your IDE to start capturing and leveraging agent memory across projects in seconds.Starting Price: $19.99 per month -
24
Honggfuzz
Google
Honggfuzz is a security-oriented software fuzzer. Supports evolutionary, feedback-driven fuzzing based on code coverage (SW and HW-based). It’s multi-process and multi-threaded, there’s no need to run multiple copies of your fuzzer, as Honggfuzz can unlock the potential of all your available CPU cores with a single running instance. The file corpus is automatically shared and improved between all fuzzed processes. It’s blazingly fast when the persistent fuzzing mode is used. A simple/empty LLVMFuzzerTestOneInput function can be tested with up to 1mo iteration per second on a relatively modern CPU. Has a solid track record of uncovered security bugs, the only (to date) vulnerability in OpenSSL with the critical score mark was discovered by Honggfuzz. As opposed to other fuzzers, it will discover and report hijacked/ignored signals from crashes (intercepted and potentially hidden by a fuzzed program).Starting Price: Free -
25
MemMachine
MemVerge
An open-source memory layer for advanced AI agents. It enables AI-powered applications to learn, store, and recall data and preferences from past sessions to enrich future interactions. MemMachine’s memory layer persists across multiple sessions, agents, and large language models, building a sophisticated, evolving user profile. It transforms AI chatbots into personalized, context-aware AI assistants designed to understand and respond with better precision and depth.Starting Price: $2,500 per month -
26
Tuning Engines
CerebrixOS
Tuning Engines is a unified AI control and governance layer for teams building production intelligence across models, agents, tools, and fine-tuned systems. It brings together the full AI lifecycle in one governed platform: inference, model routing, fallback policies, fine-tuning jobs, datasets, evaluations, model imports and exports, custom models, agents, MCP servers, reusable skills, guardrails, AGT YAML policies, data capture, runtime traces, usage analytics, API keys, billing, team roles, and integrations. Developers get OpenAI-compatible APIs, Anthropic-compatible routes, CLI workflows, MCP access, coding-agent integrations, and resource catalogs for models, agents, tools, and skills. Teams can connect Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and other AI workflows through a single governed platform. -
27
Maximem
Maximem
Maximem is an AI context management and memory platform designed to give generative AI systems a persistent, secure memory layer that retains and organizes information across conversations, applications, and models. Large language models typically operate with limited session memory, meaning they lose context between interactions and require users to repeatedly provide the same background information. Maximem addresses this limitation by creating a private memory vault that stores relevant context, preferences, historical data, and workflow information so AI systems can reference it in future interactions. It operates between AI models and applications, ensuring that conversations, knowledge, and user data are consistently available across different tools and sessions. This persistent memory allows AI assistants to deliver responses that are more personalized, accurate, and context-aware because the system can retrieve previously stored information. -
28
LlamaIndex
LlamaIndex
LlamaIndex is a “data framework” to help you build LLM apps. Connect semi-structured data from API's like Slack, Salesforce, Notion, etc. LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. LlamaIndex provides the key tools to augment your LLM applications with data. Connect your existing data sources and data formats (API's, PDF's, documents, SQL, etc.) to use with a large language model application. Store and index your data for different use cases. Integrate with downstream vector store and database providers. LlamaIndex provides a query interface that accepts any input prompt over your data and returns a knowledge-augmented response. Connect unstructured sources such as documents, raw text files, PDF's, videos, images, etc. Easily integrate structured data sources from Excel, SQL, etc. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs. -
29
MemClaw
Caura AI
MemClaw is a persistent-memory service for LLM-based agents and a governed shared memory layer for agent fleets. It is designed to help AI agents learn from each other by turning isolated agent context into a Company Brain with memory, governance, provenance, contradiction detection, and visibility scopes built in from day one. MemClaw separates an organization’s agent force, including tenants, fleets, nodes, and agents, from the governed memory plane through MCP Server, REST API, OpenClaw plugin, MemClaw Core, and persistent storage. Agents can write to and recall from the Company Brain through MCP-compatible tools, direct HTTPS calls, or OpenClaw integration, while MemClaw Core runs enrichment such as entity extraction, contradiction detection, PII scanning, and lifecycle transitions before anything is stored. Every memory can be stamped with a visibility scope, auto-classified into types such as fact, episode, decision, preference, rule, plan, commitment, action, and outcome.Starting Price: $49 per month -
30
TopBraid
TopQuadrant
Graphs are the most flexible formal data structures (making it simple to map other data formats to graphs) that capture explicit relationships between items so that you can easily connect new data items as they are added and traverse the links to understand the connections. The semantics of data are explicit and include formalisms for supporting inferencing and data validation. As a self-descriptive data model, knowledge graphs enable data validation and can offer recommendations for how data may need to be adjusted to meet data model requirements. The meaning of the data is stored alongside the data in the graph, in the form of the ontologies or semantic models. This makes knowledge graphs self-descriptive. Knowledge graphs are able to accommodate diverse data and metadata that adjusts and grows over time, much like living things do. -
31
LangMem
LangChain
LangMem is a lightweight, flexible Python SDK from LangChain that equips AI agents with long-term memory capabilities, enabling them to extract, store, update, and retrieve meaningful information from past interactions to become smarter and more personalized over time. It supports three memory types and offers both hot-path tools for real-time memory management and background consolidation for efficient updates beyond active sessions. Through a storage-agnostic core API, LangMem integrates seamlessly with any backend and offers native compatibility with LangGraph’s long-term memory store, while also allowing type-safe memory consolidation using schemas defined in Pydantic. Developers can incorporate memory tools into agents using simple primitives to enable seamless memory creation, retrieval, and prompt optimization within conversational flows. -
32
KgBase
KgBase
KgBase, or Knowledge Graph Base, is a collaborative, robust database with versioning, analytics & visualizations. With KgBase, any community or individual can create knowledge graphs to build insights about their data. Import your CSVs and spreadsheets, or use our API to work on data together. Build no-code knowledge graphs with KgBase, our easy-to-use UI lets you traverse the graph, show the results as tables and charts, and much more. Play with your graph data. Build your query and see results update in real time. It's like writing query code in Cypher or Gremlin, except easier. And fast. Your graph can be viewed as a table, allowing you to browse all results - no matter the size. KgBase works great with large graphs (millions of nodes), as well as simple projects. In the cloud, or self-hosted, with wide database support. Introduce graphs into your organization by seeding graph from a template. Results of any query can be easily turned into a chart visualization.Starting Price: $19 per month -
33
Preloop
Preloop
Preloop is the open source AI agent control plane for agents that take real actions. It combines an MCP firewall for tool access, an AI model gateway for cost, safety, and attribution, policy-as-code with human approvals, runtime session observability, and audit trails in a single self-hostable platform. AI agents can deploy code, change infrastructure, move money, touch production data, and burn model spend in seconds, so Preloop helps teams control what agents can do, how much they spend, and which actions require human approval. It works with OpenClaw, Hermes, Claude Code, Codex CLI, Cursor, Gemini CLI, Windsurf, Cline, OpenCode, and any MCP-compatible agent or managed runtime. Access rules can inspect arguments and context, not just tool names, with CEL expressions for fine-grained conditions. Teams can start with observability, then layer in approvals and deny rules without SDKs or invasive app changes.Starting Price: $290 per month -
34
Big Pickle
OpenCode Zen
Big Pickle is an AI model available through OpenCode Zen, a curated model provider focused on coding-agent workflows. The model is designed for text-based input, reasoning tasks, function calling, and developer workflows that require long-context understanding. Big Pickle supports a large context window, making it useful for working across bigger codebases, project files, technical prompts, and multi-step coding tasks. It can be accessed through OpenCode Zen using an OpenAI-compatible API format, allowing developers to integrate it into agentic coding tools and automation workflows. The model is positioned as a free or low-cost option within OpenCode’s coding-agent ecosystem. Big Pickle helps developers experiment with AI-assisted coding, reasoning, tool use, and long-context automation without relying only on premium frontier models.Starting Price: Free -
35
Supermodel
Supermodel
Supermodel is a developer-focused platform that provides graph-powered tools and APIs to help AI agents and engineers better understand complex codebases, improving the quality and accuracy of AI-generated outputs. At its core is the CodeGraph API, which builds structured representations of software systems, such as dependency graphs, call graphs, and architectural maps, allowing both humans and AI models to navigate and reason about large codebases more effectively. It enables deep codebase analysis by extracting relationships between files, functions, and modules, giving instant visibility into how systems are structured and how components interact. It supports use cases like generating architecture documentation, browsing repository structure, and visualizing dependencies, helping developers quickly understand unfamiliar projects or large-scale systems.Starting Price: $19 per month -
36
Cosyra
Cosyra
Cosyra is a mobile-first cloud development environment that enables users to run AI-powered coding tools directly from their phone through a full Linux terminal. It allows developers to use tools such as Claude Code, Codex CLI, OpenCode, and Gemini CLI, all pre-installed and ready to run by simply adding an API key and opening the terminal. It provides an isolated Ubuntu container with essential development tools, including Node.js, Python, Git, tmux, and vim, along with 30 GB of persistent storage that contains data between sessions. Cosyra is designed to replicate the experience of working on a local machine, allowing users to build, test, and manage projects entirely from a mobile device. It supports workflows such as cloning repositories, reviewing pull requests, running tests, and deploying code, all within a persistent session that can hibernate and resume seamlessly.Starting Price: $29.99 per month -
37
Multiplayer
Multiplayer
Multiplayer runs locally alongside tools like Claude Code, Codex, and Copilot. From there, it feeds your agent the full-stack, pre-correlated, and unsampled data and context observability tools miss. Operating with a secure, local-first approach, we intelligently deduplicate issues to eliminate review fatigue. Multiplayer replaces log grepping and "PR slop" with a handful of high-quality, automated pull requests.Starting Price: $0 -
38
Kimi Code
Kimi
Kimi Code is a developer-centric AI coding agent included as part of the Kimi Membership, designed to boost productivity by automating software development tasks and seamlessly integrating into popular workflows. It offers high-performance CLI tools and supports integration with terminal environments and IDEs like VS Code, allowing developers to read and edit code, answer questions about codebases, generate features, fix bugs, refactor, and verify changes through a natural-language interface. With a dedicated console showing real-time logs, request quotas, and pace controls, the platform lets users configure API keys for use in tools such as Kimi CLI, Claude Code, and Roo Code, enabling faster coding with AI assistance within commits and existing workflows. In VS Code, Kimi Code features a native chat panel with slash commands, file and folder references, diff views, and integration with external tools for context-aware coding support.Starting Price: $15 per month -
39
Memgraph
Memgraph
Memgraph is a high-performance, in-memory graph database that powers real-time AI context. It serves as the graph engine for GraphRAG pipelines, AI memory systems, and agentic workflows - delivering sub-millisecond multi-hop traversals with full provenance for any system that needs structured, connected context alongside semantic search. The same architecture that makes Memgraph the context layer for AI also drives real-time graph analytics across fraud detection, network analysis, infrastructure monitoring, and other operational use cases where speed and connectivity matter. -
40
Constellation Gate AI
Constellation Gate AI
Constellation Gate AI is a drop-in defense layer for AI agents, built to sit between the agent and the model while screening every request for attacks and leaks. Gate acts as an inline gateway for coding agents and model APIs, protecting workflows without requiring major code changes. Users can point existing tools such as Claude Code, Cursor, OpenClaw, Codex, or OpenCode at Gate and inherit prompt-injection defense, secret scanning, PII redaction, token optimization, and a verifiable audit trail. The platform is designed around three real risks: prompt injection, credential and PII leakage, and hijacked tool calls. Instead of relying on the model to defend itself, Gate blocks attacks before they reach the model, redacts secrets before responses return, and stops attacker-controlled tool outputs before an agent acts on them. Gate accepts the same calls an agent already makes, forwards them to the model, scans every call and response in both directions. -
41
RA.Aid
RA.Aid
RA.Aid is an open source AI assistant that autonomously handles research, planning, and implementation to expedite software development processes. Built on LangGraph's agent-based task execution framework, RA.Aid operates through a three-stage architecture. RA.Aid supports multiple AI providers, including Anthropic's Claude, OpenAI, OpenRouter, and Gemini, allowing users to select models that best fit their requirements. It also features web research capabilities, enabling the agent to pull real-time information from the internet to enhance its understanding and execution of tasks. It offers an interactive chat mode, allowing users to guide the agent directly, ask questions, or redirect tasks as needed. Additionally, RA.Aid integrates with 'aider' via the '--use-aider' flag to leverage specialized code editing capabilities. It is designed with a human-in-the-loop interaction mode, enabling the agent to seek user input during task execution to ensure higher accuracy.Starting Price: Free -
42
Kimi K2.7 Code
Moonshot AI
Kimi K2.7 Code is an open-source, coding-focused agentic AI model developed by Moonshot AI for long-horizon software engineering tasks. It is designed to improve coding performance, agent workflows, and real-world development assistance compared with earlier Kimi K2 versions. The model supports a 256K context window, making it useful for working with large codebases, long technical documents, and complex multi-step programming tasks. Kimi K2.7 Code is available through Kimi Code and API access, with OpenAI- and Anthropic-compatible options for easier integration into developer workflows. It is also listed on Hugging Face and supports deployment through inference engines such as vLLM, SGLang, and KTransformers. With improved agentic capabilities, long-context support, and reduced thinking-token usage compared with K2.6, Kimi K2.7 Code gives developers a flexible open-source option for AI-assisted coding.Starting Price: Free -
43
Understand
SciTools
Understand is a comprehensive static-analysis and code-comprehension platform that helps software engineers “see” and understand large, complex code bases, whether legacy, safety-critical, or modern multi-language projects. It parses your source code and builds a complete “code dictionary” of every entity (files, classes, functions, variables), populating cross-references, call trees, dependency graphs, control-flow diagrams, and more. Through interactive, customizable graphs and visualizations, call graphs, control flow graphs, dependency trees, and UML-style class diagrams, you can explore exactly how parts of the code connect, which modules depend on which, and where changes may ripple across the project. Understand also computes detailed metrics at various levels (file, class, function) such as cyclomatic complexity, lines of code, comment-to-code ratio, coupling/cohesion, and other maintainability indicators; these metrics can be viewed in treemaps, exported to HTML or CSV.Starting Price: $100 per month -
44
Baichuan-13B
Baichuan Intelligent Technology
Baichuan-13B is an open source and commercially available large-scale language model containing 13 billion parameters developed by Baichuan Intelligent following Baichuan -7B . It has achieved the best results of the same size on authoritative Chinese and English benchmarks. This release contains two versions of pre-training ( Baichuan-13B-Base ) and alignment ( Baichuan-13B-Chat ). Larger size, more data : Baichuan-13B further expands the number of parameters to 13 billion on the basis of Baichuan -7B , and trains 1.4 trillion tokens on high-quality corpus, which is 40% more than LLaMA-13B. It is currently open source The model with the largest amount of training data in the 13B size. Support Chinese and English bilingual, use ALiBi position code, context window length is 4096.Starting Price: Free -
45
Membase
Membase
Membase is a unified AI memory layer platform designed to help AI agents and tools share and persist context so they “understand you” across sessions without forced repetition or isolated memory silos, enabling consistent conversational experiences and shared knowledge across AI assistants. It provides a secure, centralized memory layer that captures, stores, and syncs context, conversation history, and relevant knowledge across multiple AI agents and integrations with tools such as ChatGPT, Claude, Cursor, and others, so all connected agents can access a common context and avoid repeating user intents. Designed as a foundational memory service, it aims to maintain consistent context across your AI ecosystem, reducing friction and improving continuity in multi-tool workflows by keeping long-term context available and shared rather than locked within individual models or sessions, and letting users focus on outcomes instead of re-entering context for each agent request. -
46
Apache TinkerPop
Apache Software Foundation
Apache TinkerPop™ is a graph computing framework for both graph databases (OLTP) and graph analytic systems (OLAP). Gremlin is the graph traversal language of Apache TinkerPop. Gremlin is a functional, data-flow language that enables users to succinctly express complex traversals on (or queries of) their application's property graph. Every Gremlin traversal is composed of a sequence of (potentially nested) steps. A graph is a structure composed of vertices and edges. Both vertices and edges can have an arbitrary number of key/value pairs called properties. Vertices denote discrete objects such as a person, a place, or an event. Edges denote relationships between vertices. For instance, a person may know another person, have been involved in an event, and/or have recently been at a particular place. If a user's domain is composed of a heterogeneous set of objects (vertices) that can be related to one another in a multitude of ways (edges).Starting Price: Free -
47
ent
ent
An entity framework for Go. Simple, yet powerful ORM for modeling and querying data. Simple API for modeling any database schema as Go objects. Run queries, and aggregations and traverse any graph structure easily. 100% statically typed and explicit API using code generation. The latest version of Ent now includes a type-safe API enabling ordering by fields and edges. This API will soon be available in our GraphQL integration too. You can now visualize your Ent schema as an ERD with one command. The API enables you to easily integrate features such as logging, tracing, caching, and even implementing soft deletion with 20 lines of code! The Ent framework supports GraphQL using the 99designs/gqlgen library and provides various integrations. Generating a GraphQL schema for nodes and edges defined in an Ent schema. Efficient field collection to overcome the N+1 problem without requiring data loaders.Starting Price: Free -
48
LibFuzzer
LLVM Project
LibFuzzer is an in-process, coverage-guided, evolutionary fuzzing engine. LibFuzzer is linked with the library under test, and feeds fuzzed inputs to the library via a specific fuzzing entry point (or target function); the fuzzer then tracks which areas of the code are reached, and generates mutations on the corpus of input data in order to maximize the code coverage. The code coverage information for libFuzzer is provided by LLVM’s SanitizerCoverage instrumentation. LibFuzzer is still fully supported in that important bugs will get fixed. The first step in using libFuzzer on a library is to implement a fuzz target, a function that accepts an array of bytes and does something interesting with these bytes using the API under test. Note that this fuzz target does not depend on libFuzzer in any way so it is possible and even desirable to use it with other fuzzing engines like AFL and/or Radamsa.Starting Price: Free -
49
CodeGeeX
AMiner
We introduce CodeGeeX, a large-scale multilingual code generation model with 13 billion parameters, pre-trained on a large code corpus of more than 20 programming languages. Based on CodeGeeX, we develop a VS Code extension (search 'CodeGeeX' in the Extension Marketplace) that assists the programming of different programming languages. Besides the multilingual code generation/translation abilities, we turn CodeGeeX into a custom programming assistant using its few-shot ability. It means that when a few examples are provided as extra prompts in the input, CodeGeeX will imitate what are done by these examples and generate codes accordingly. Some cool features can be implemented using this ability, like code explanation, summarization, generation with specific coding style, and more. For example, one can add code snippets with his/her own coding style, and CodeGeeX will generate codes in a similar way. You can also try prompts with specific formats to inspire CodeGeeX for new skills.Starting Price: Free -
50
Brokk
Brokk
Brokk is an AI-native code assistant built to handle large, complex codebases by giving language models compiler-grade understanding of code structure, semantics, and dependencies. It enables context management by selectively loading summaries, diffs, or full files into a workspace so that the AI sees just the relevant portions of a million-line codebase rather than everything. Brokk supports actions such as Quick Context, which suggests files to include based on embeddings and structural relevance; Deep Scan, which uses more powerful models to recommend which files to edit or summarize further; and Agentic Search, allowing multi-step exploration of symbols, call graphs, or usages across the project. The architecture is grounded in static analysis via Joern (offering type inference beyond simple ASTs) and uses JLama for fast embedding inference to guide context changes. Brokk is offered as a standalone Java application (not an IDE plugin) to let users supervise AI workflows clearly.Starting Price: $20 per month
