| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 15 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Trellis acts as a bridge between a codebase and multiple AI coding platforms (Claude Code, Cursor, OpenCode, Gemini CLI) by maintaining a .trellis/ directory as a Single Source of Truth. The framework auto-injects project-specific specs, task context, and coding guidelines into each AI session via platform-specific integration layers (.claude/, .cursor/, etc.), ensuring every agent operates within consistent project conventions and historical context without manual context setup per session.
Unique: Uses a declarative .trellis/ directory structure as a Single Source of Truth that bridges multiple AI platforms via platform-specific adapters (CLIAdapter pattern), rather than requiring manual context setup per platform or relying on a single vendor's ecosystem. The framework projects unified task-centered structure across heterogeneous AI tools.
vs alternatives: Unlike Cursor's workspace-only approach or Claude Code's session-based context, Trellis provides platform-agnostic, version-controlled project structure that persists across tools and team members, enabling true multi-platform AI workflows with consistent conventions.
Trellis provides a task management system (.trellis/tasks/) that structures AI-assisted work around discrete tasks, each with a PRD (product requirements document), context files, and a task.json state file. Tasks follow a defined lifecycle tracked in task.json, enabling AI agents to understand task scope, dependencies, and completion criteria. The system supports task archival (tasks/archive/) and integrates with the multi-agent pipeline to decompose high-level developer intent into concrete coding work.
Unique: Implements task lifecycle as a first-class concept with task.json state files and task.py scripts, enabling AI agents to understand and update task progress programmatically. Tasks are version-controlled and archived, creating an audit trail of AI-assisted work with explicit scope and dependencies.
vs alternatives: Unlike GitHub Issues or Jira, Trellis tasks are embedded in the codebase (.trellis/tasks/) and designed for AI agent consumption, with structured PRDs and state files that agents can read and update directly. Unlike linear task runners, Trellis integrates task context into AI sessions automatically via context injection.
Trellis provides developer workflow commands (e.g., via CLI or platform-specific slash commands) that enable developers to create tasks, update task state, and manage project context without leaving their AI platform. Commands like 'create task', 'update task status', and 'add to journal' interact with the task management system and workspace, enabling seamless integration of developer actions into the Trellis workflow. These commands are routed through the CLIAdapter and executed as backend scripts.
Unique: Implements developer workflow commands as platform-native slash commands that interact with Trellis task and workspace systems, enabling task management without leaving the AI platform. Commands are routed through CLIAdapter and executed as backend scripts.
vs alternatives: Unlike external task management tools, Trellis workflow commands are integrated into the AI platform, enabling seamless task creation and state management during coding sessions. Unlike manual task file editing, commands provide a structured interface for task operations.
Trellis includes a marketplace and template registry that enables teams to discover, share, and reuse project configurations, specs, and task templates contributed by the community. The registry is indexed and searchable, allowing developers to find templates for common project types (microservices, libraries, web apps, etc.) and integrate them into their projects. Registry entries include metadata (name, version, description, tags) and are version-controlled, enabling reproducible template usage.
Unique: Provides a community-driven marketplace for Trellis templates and configurations, enabling teams to discover and share proven project setups. Registry entries are versioned and include metadata for searchability and discoverability.
vs alternatives: Unlike generic template repositories, the Trellis marketplace is specifically designed for AI-assisted development configurations and includes specs, task structures, and platform integration. Unlike centralized template systems, the registry is community-driven and decentralized.
Trellis supports backend script execution via Python and shell scripts (.trellis/scripts/) that implement task logic, command handlers, and platform integrations. Scripts can access project context (specs, tasks, workspace) via environment variables and file system APIs, and can update task state by modifying task.json files. The script execution layer abstracts platform differences and provides a unified interface for implementing Trellis workflows in Python or shell.
Unique: Provides a unified script execution layer supporting Python and shell scripts that can access Trellis context via environment variables and file system APIs. Scripts can update task state and integrate with platform-specific workflows.
vs alternatives: Unlike generic script runners, Trellis script execution is integrated with task and context systems, enabling scripts to access and modify Trellis state. Unlike platform-specific scripting, the execution layer abstracts platform differences and provides a unified interface.
Trellis defines unit test conventions and thinking guides in the spec system that establish standards for test coverage, test structure, and code quality expectations. These conventions are auto-injected into AI sessions, guiding agents to generate code with appropriate test coverage and following project-specific testing patterns. The system includes golden tests (reference implementations) that agents can learn from, and integrates with CI/CD to validate generated code against test conventions.
Unique: Defines test conventions as specs that are auto-injected into AI sessions, guiding agents to generate code with appropriate test coverage. Golden tests provide reference implementations that agents can learn from, and conventions are validated via CI/CD.
vs alternatives: Unlike generic testing frameworks, Trellis test conventions are specifically designed for AI-generated code and include guidance on test structure and coverage. Unlike post-hoc linting, conventions guide generation in real-time and are validated via CI/CD.
Trellis supports monorepo structures with a build pipeline and release management system that coordinates builds, tests, and releases across multiple packages. The system uses a TypeScript-based build pipeline (scripts in packages/cli/src/) that orchestrates package builds, test execution, and versioning. Release versioning is managed via .trellis/.version and migration manifests, enabling coordinated releases across the Trellis framework and community templates.
Unique: Implements monorepo support with a TypeScript-based build pipeline and coordinated release management via migration manifests and version tracking. The system enables coordinated builds and releases across multiple packages.
vs alternatives: Unlike generic monorepo tools (Lerna, Nx), Trellis monorepo support is integrated with the Trellis framework and enables coordinated AI-assisted development across packages. Unlike manual release processes, the build pipeline and versioning system automate coordination.
Trellis maintains a .trellis/spec/ directory containing project standards, patterns, coding guidelines, and architectural decisions in markdown format. These specs are automatically injected into AI agent sessions via the context injection layer, ensuring every coding task adheres to project conventions without manual specification per session. The spec system supports hierarchical organization (e.g., spec/cli/backend/) and integrates with the platform integration layer to customize injections per platform.
Unique: Implements specs as version-controlled markdown files in .trellis/spec/ that are automatically injected into AI sessions via the context injection layer, rather than relying on external documentation or manual copy-paste. Specs are hierarchically organized and platform-aware, enabling selective injection per AI tool.
vs alternatives: Unlike README-based guidelines or external documentation, Trellis specs are automatically injected into every AI session, eliminating the need for agents to search for or manually load project standards. Unlike linters or formatters that catch violations post-hoc, specs guide generation in real-time.
+7 more capabilities
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
Trellis scores higher at 43/100 vs LlamaIndex at 40/100. Trellis also has a free tier, making it more accessible.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities