trelix 2.8.0

Fast, reliable code indexing and retrieval — contextual hybrid search, adaptive planning, call-graph expansion, LLM synthesis

trelix

Code intelligence for your entire codebase — search, ask, review, and watch, locally with zero infra.

trelix indexes any repository with Tree-sitter, embeds every symbol, and answers natural-language questions using hybrid BM25 + vector + call-graph search. Works offline with no API key. Integrates with Claude Code, Cursor, LangChain, and LlamaIndex in one command.

Why trelix over grep, plain embeddings, or your editor's built-in search? See docs/WHY_TRELIX.md.

Documentation

Goal	Doc
Full documentation index	docs/README.md
First time here	docs/GETTING_STARTED.md
Deep dive on how retrieval/indexing works	docs/architecture.md / docs/USER_GUIDE.md
All env vars + .env reference	docs/CONFIGURATION.md
Something broken	docs/TROUBLESHOOTING.md / docs/FAQ.md
Upgrading / breaking changes	docs/BACKWARDS_COMPATIBILITY.md / docs/ROADMAP.md
Contributing, security, support	CONTRIBUTING.md · SECURITY.md · SUPPORT.md

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Contents

Install · MCP Setup · Quickstart · Features · Configuration · Troubleshooting · Knowledge Graph · How it works · Integrations · Development

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Install

pip install "trelix[local]"        # offline — no API key needed

pip install trelix                 # + OpenAI planner & synthesis
export OPENAI_API_KEY=sk-...

---

Use in Claude Code / Cursor / Windsurf (MCP)

pip install trelix-mcp
claude mcp add trelix -- trelix-mcp   # Claude Code

Cursor — add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "trelix": { "command": "trelix-mcp", "args": [] }
  }
}

Continue.dev — add to ~/.continue/config.json:

{ "mcpServers": [{ "name": "trelix", "command": "trelix-mcp" }] }

Then in Claude Code / Cursor ask: "index my repo at /path/to/repo, then find how authentication works"

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Use in Python (LangChain / LlamaIndex)

pip install trelix-langchain          # LangChain
pip install trelix-llama-index        # LlamaIndex

# LangChain
from trelix_langchain import TrelixRetriever
retriever = TrelixRetriever(repo_path="/path/to/repo")
docs = retriever.invoke("how does authentication work?")

# LlamaIndex
from trelix_llama_index import TrelixIndexRetriever
retriever = TrelixIndexRetriever(repo_path="/path/to/repo")
nodes = retriever.retrieve("how does authentication work?")

---

30-Second Quickstart (CLI)

pip install "trelix[local]"

# 1. Index your repo (one-time, ~30s for a medium repo)
trelix index ./my-repo

# 2. Search for code
trelix search ./my-repo "JWT validation"

# 3. Ask a question (no API key needed for search)
trelix query ./my-repo "how does the authentication middleware work?"

# 4. Ask with LLM synthesis (needs OPENAI_API_KEY or AZURE_API_KEY)
trelix ask ./my-repo "explain the request lifecycle end-to-end"

# 5. Watch for changes (auto-reindex on save)
trelix watch ./my-repo

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What trelix does

Need	Command
Find where a function is defined	trelix search ./repo "login function"
Understand a feature before editing	trelix ask ./repo "how does auth work?"
Review a GitHub PR	trelix review --pr owner/repo#42
Watch all repos simultaneously	trelix watch-all
Search across multiple repos	trelix federation add myapp ./myapp → trelix search-all "query"
Index stats	trelix stats ./repo
Call graph for a symbol	trelix call-graph ./repo AuthService.login
Build a knowledge graph	trelix graph ./repo

Every query is answered offline by default — no data leaves your machine. Enable LLM synthesis for natural-language answers.

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What's New

v2.7.2 — Scale & Concurrency Hardening: Qdrant Cloud readiness (gRPC + configurable timeout), incremental per-symbol embedding on partial re-index, an opt-in parallel BM25 read pool, Linux ARM64 binaries, and 5 concurrency/correctness fixes.

Full version history: CHANGELOG.md.

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Features

• Tree-sitter parsing for 20+ languages — functions, classes, methods, call edges, imports
• Contextual hybrid search — contextual embeddings + contextual BM25 + grep via Reciprocal Rank Fusion
• 3-tier adaptive query planner — direct (skip retrieval) → single-step (8-intent) → multi-step decomposition
• Call-graph + import expansion — PageRank-weighted graph traversal with qualified-name precision
• Reranking — Cohere, cross-encoder, or PLAID late-interaction reranker for final precision
• LLM synthesis — trelix ask streams tokens live; GraphRAG map-reduce for large corpora
• Universal LLM client — OpenAI, Azure, Anthropic, Bedrock, Vertex AI, LiteLLM (100+ providers)
• Zero-infra default — single SQLite file (.trelix/index.db) with sqlite-vec HNSW + FTS5 BM25
• Real-time watching — trelix watch auto-indexes on every file save
• Works offline — --provider local uses sentence-transformers, no API key needed
• BGE-Code-v1 / Nomic CodeRankEmbed — CoIR SOTA embedding models (bge-code, nomic-code providers)
• Matryoshka voyage embeddings — compact 256/512-dim voyage-code-3 via TRELIX_EMBEDDER_VOYAGE_OUTPUT_DIMENSIONS
• PLAID late-interaction reranker — 7–45× faster ColBERT via RAGatouille (rerank_provider=plaid)
• Multi-granularity indexing — LLM file-level summaries alongside symbol chunks (TRELIX_FILE_SUMMARIES_ENABLED=true)
• Streaming synthesis — trelix ask streams tokens live; GET /ask SSE endpoint
• REST API — trelix serve ./repo --port 8765 exposes /search, /ask, /index, /health
• LanceDB backend — 3–5× faster vector insert at 100k+ chunks (TRELIX_STORE_BACKEND=lance)
• Knowledge Graph — trelix graph ./repo builds a Code Property Graph (calls + imports + type hierarchy) as a NetworkX MultiDiGraph; Louvain community detection clusters the codebase into architectural modules; Pyvis interactive HTML visualization; graph-aware BFS as 4th retrieval leg (TRELIX_RETRIEVAL_GRAPH_SEARCH_ENABLED=true); pip install 'trelix[knowledge-graph]'
• File-summary 5th retrieval leg — semantic search over LLM file summaries surfaces high-level architecture answers (TRELIX_RETRIEVAL_FILE_SUMMARY_LEG=true)
• HyDE query expansion — synthesizes a hypothetical code answer as the ANN query vector, improving recall on abstract questions (TRELIX_RETRIEVAL_HYDE_FALLBACK=true)
• FLARE confidence-gated re-retrieval — detects low-confidence synthesis spans and re-queries before finalising the answer (TRELIX_RETRIEVAL_FLARE=true)
• PageRank symbol boost — weights retrieval candidates by graph centrality so hub symbols surface first (TRELIX_RETRIEVAL_PAGERANK_BOOST=true)
• Incremental graph updater — trelix watch automatically patches the Code Property Graph on every file save (no manual trelix graph re-run needed)
• Query telemetry — per-query latency breakdown, retrieval leg hit rates, and token usage via trelix telemetry CLI or TRELIX_TELEMETRY_ENABLED=true
• CoIR eval harness — trelix eval ./repo --golden <path> measures Recall@1/5/10, MRR, and NDCG against a JSONL golden set

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More CLI Commands

Beyond the 30-Second Quickstart above:

trelix stats ./my-repo                                              # index statistics
trelix update-index ./my-repo src/auth/middleware.py                # re-index one file after editing
trelix migrate-vectors ./my-repo --to qdrant --url http://localhost:6333  # move to Qdrant at scale
trelix serve ./my-repo --port 8765                                  # start the REST API server
trelix graph ./my-repo --visualize                                  # build knowledge graph + HTML viz
trelix watch-all                                                    # watch all federated repos
trelix review --pr owner/repo#42 --post-comments                   # review + post a GitHub PR

GitHub Actions — index in CI

Add the trelix-index-action to any workflow to build and cache the index on every push:

- uses: actions/checkout@v4
- uses: sairam0424/trelix-index-action@v1

The action handles Python setup, caching (keyed to the commit SHA), and exposes the index path as an output so downstream steps can query it directly.

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Beast-Mode Activation (v2.1.0)

Enable every retrieval enhancement at once. Copy this block into your .env and run the three commands in order.

# .env — beast-mode flags
TRELIX_RETRIEVAL_GRAPH_SEARCH_ENABLED=true          # 4th leg: graph BFS
TRELIX_RETRIEVAL_FILE_SUMMARY_LEG=true    # 5th leg: file-summary semantic search
TRELIX_RETRIEVAL_HYDE_FALLBACK=true       # HyDE query expansion
TRELIX_RETRIEVAL_FLARE=true               # FLARE confidence-gated re-retrieval
TRELIX_RETRIEVAL_PAGERANK_BOOST=true      # PageRank symbol boost
TRELIX_TELEMETRY_ENABLED=true             # Per-query telemetry
TRELIX_FILE_SUMMARIES_ENABLED=true        # Generate LLM file summaries at index time

Activation order

# 1. Index — builds chunks, embeddings, and file summaries
trelix index ./my-repo

# 2. Graph — builds Code Property Graph + community detection
#    trelix watch will keep the graph in sync automatically from here
trelix graph ./my-repo
pip install 'trelix[knowledge-graph]'   # if not already installed

# 3. Query — all five retrieval legs active
trelix ask ./my-repo "explain the full request lifecycle"

# 4. Inspect telemetry
trelix telemetry ./my-repo --limit 20

# 5. Measure quality
trelix eval ./my-repo --golden eval/golden.jsonl

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Troubleshooting

Common issues: sqlite-vec load failures on macOS, Bedrock ValidationExceptions, tree-sitter warning spam, dependency conflicts. Full guide with a diagnostic checklist: docs/TROUBLESHOOTING.md.

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Installation

pip install "trelix[local]"   # minimal, offline, no API key
pip install trelix            # + OpenAI planner & synthesis
pip install "trelix[all]"     # every optional extra (voyage, qdrant, lance, rerank, LLM providers, ...)

For every other install path — Voyage/BGE/Bedrock/Vertex/LiteLLM extras, Qdrant/LanceDB backends, standalone binaries, Docker, uv, or upgrading from an older version — see docs/INSTALLATION_GUIDE.md.

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Configuration

All settings via environment variables or a .env file in the working directory.

LLM Provider (v0.7.0)

Switch chat provider with a single env var — no code changes required.

# Switch chat provider (one env var)
TRELIX_LLM_PROVIDER=bedrock     # Claude sonnet-4-6 default, haiku fallback
TRELIX_LLM_PROVIDER=azure       # Azure OpenAI (existing .env unchanged)
TRELIX_LLM_PROVIDER=anthropic   # Direct Anthropic API

# Switch embedding provider
TRELIX_EMBEDDER_PROVIDER=bedrock-cohere  # Cohere 1024-dim (best retrieval)
TRELIX_EMBEDDER_PROVIDER=bedrock-titan   # Titan v2 (256/512/1024 dims)
TRELIX_EMBEDDER_PROVIDER=azure           # Azure text-embedding-3-large (default)

Variable	Default	Description
TRELIX_LLM_PROVIDER	openai	openai | azure | anthropic | bedrock | vertex | litellm
TRELIX_LLM_MODEL	gpt-4o	Chat model override
TRELIX_LLM_BEDROCK_PRIMARY_MODEL	us.anthropic.claude-sonnet-4-6	Bedrock primary model
TRELIX_LLM_BEDROCK_FALLBACK_MODEL	us.anthropic.claude-haiku-4-5-20251001-v1:0	Bedrock fallback on ValidationException
ANTHROPIC_API_KEY	—	Anthropic API key (trelix[anthropic])
GOOGLE_CLOUD_PROJECT	—	Google Cloud project (trelix[vertex])
GOOGLE_API_KEY	—	Google AI Studio API key (trelix[vertex])
AWS_ACCESS_KEY_ID	—	AWS credentials (trelix[bedrock])
AWS_SECRET_ACCESS_KEY	—	AWS credentials (trelix[bedrock])
AWS_REGION	us-east-1	AWS region (trelix[bedrock])

Embedding Providers

Variable	Default	Description
TRELIX_EMBEDDER_PROVIDER	local	local | openai | azure | voyage | local-code | bge-code | nomic-code | bedrock-titan | bedrock-cohere
OPENAI_API_KEY	—	OpenAI API key
OPENAI_MODEL	gpt-4o	Chat model for planner + synthesis
AZURE_API_KEY	—	Azure OpenAI API key
AZURE_ENDPOINT	—	Azure OpenAI endpoint URL
VOYAGE_API_KEY	—	Voyage AI API key (trelix[voyage])
TRELIX_EMBEDDER_VOYAGE_MODEL	voyage-code-3	Voyage model name
COHERE_API_KEY	—	Cohere reranker API key

Contextual Chunking (v0.4.0)

Variable	Default	Description
TRELIX_CHUNKER_CONTEXTUAL	false	Enable LLM context summary per chunk
TRELIX_CHUNKER_CONTEXTUAL_MODEL	gpt-4o-mini	Model for generating summaries
TRELIX_CHUNKER_CONTEXTUAL_MAX_TOKENS	100	Max tokens per context summary

Vector Store (v0.4.0 / v2.0.0)

Variable	Default	Description
TRELIX_STORE_BACKEND	sqlite	sqlite | qdrant | lance
TRELIX_STORE_HNSW	true	Enable HNSW index (sqlite backend)
TRELIX_STORE_HNSW_M	16	HNSW M parameter
TRELIX_STORE_HNSW_EF_SEARCH	50	HNSW ef_search at query time
QDRANT_URL	http://localhost:6333	Qdrant server URL
QDRANT_API_KEY	—	Qdrant API key (cloud)
QDRANT_COLLECTION	trelix	Qdrant collection name

Multi-Granularity Indexing (v2.0.0)

Variable	Default	Description
TRELIX_FILE_SUMMARIES_ENABLED	false	Generate LLM file-level summaries alongside symbol chunks (RAPTOR-inspired). Uses the shared TRELIX_LLM_MODEL chat client — no separate model override exists.

Reranking

Variable	Default	Description
TRELIX_RETRIEVAL_RERANK_PROVIDER	cohere	cohere | cross_encoder | plaid | xtr
TRELIX_RETRIEVAL_PLAID_MODEL	colbert-ir/colbertv2.0	RAGatouille PLAID model (trelix[plaid])

Retrieval Tuning

Variable	Default	Description
TRELIX_RETRIEVAL_CONTEXT_TOKEN_BUDGET	12000	Max context tokens sent to LLM
TRELIX_RETRIEVAL_GRAPH_RAG	true	Enable GraphRAG map-reduce synthesis
TRELIX_RETRIEVAL_GRAPH_RAG_THRESHOLD_TOKENS	8000	Token threshold to activate GraphRAG
TRELIX_RETRIEVAL_GRAPH_RAG_THRESHOLD_RESULTS	20	Result count threshold to activate GraphRAG
TRELIX_PARSE_WORKERS	4	Parallel threads for parsing phase

Beast-Mode Retrieval (v2.1.0)

Variable	Default	Description
TRELIX_RETRIEVAL_FILE_SUMMARY_LEG	false	Enable 5th retrieval leg: ANN search over LLM file summaries
TRELIX_RETRIEVAL_HYDE_FALLBACK	false	Enable HyDE — generate a hypothetical code answer as the ANN query vector
TRELIX_RETRIEVAL_FLARE	false	Enable FLARE — re-retrieve when synthesis confidence falls below threshold
TRELIX_RETRIEVAL_PAGERANK_BOOST	false	Boost retrieval candidates by PageRank graph centrality score

Query Telemetry (v2.1.0)

Variable	Default	Description
TRELIX_TELEMETRY_ENABLED	false	Record per-query latency, leg hit rates, and token usage to .trelix/telemetry.db

# CLI — inspect stored telemetry
trelix telemetry ./my-repo              # last 20 queries
trelix telemetry ./my-repo --limit 100  # last 100 queries

See .env.example for the full reference.

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Supported Languages

Code (Tree-sitter AST)

Python, TypeScript/TSX, JavaScript/JSX, Go, Java, Rust, C, C++, C#, Kotlin, Ruby

.NET / Razor

Razor Components (.razor), Razor MVC Views (.cshtml), MSBuild projects (.csproj)

Config (key-path extraction)

JSON/JSONC, TOML, YAML (multi-document)

Markup

Markdown (heading sections), HTML (custom elements), CSS/SCSS

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Embedding Providers

9 providers, from fully offline (local, default) to SOTA-quality (bge-code, CoIR 2025) to API-based (voyage, openai, Bedrock). Full comparison with CoIR benchmark scores, model IDs, and per-provider setup: docs/PROVIDERS.md.

voyage-code-3 Matryoshka: Set TRELIX_EMBEDDER_VOYAGE_OUTPUT_DIMENSIONS=512 for 2× faster HNSW search with minimal quality loss.

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Vector Store Backends

Backend	Best for	Install
SQLite (default)	Repos up to ~100k chunks	included
Qdrant	500k+ chunks, multi-repo	trelix[qdrant]
LanceDB	100k+ chunks, ARM/Apple Silicon	trelix[lance]

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REST API

pip install "trelix[serve]"
trelix serve ./my-repo --port 8765

Endpoint	Method	Description
/health	GET	Health check
/search	GET	Hybrid code search
/ask	GET	Streaming synthesis (SSE)
/index	POST	Index or re-index the repository
/stats	GET	Index statistics
/graph	GET	Knowledge graph stats (node_count, edge_count, community_count) — requires trelix graph to have run first
/graph/communities	GET	Louvain community summary list
/graph/visualize	GET	Export Pyvis HTML visualization, returns file path
/graph/search	GET	BFS from a symbol (symbol_id, depth params)

Full endpoint reference with curl/JSON examples: docs/USER_GUIDE.md.

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Knowledge Graph

trelix graph ./repo turns your indexed codebase into a traversable Code Property Graph (calls + imports + type hierarchy as a NetworkX MultiDiGraph), with Louvain community detection, Pyvis visualization, and BFS as an optional 4th retrieval leg.

pip install 'trelix[knowledge-graph]'
trelix graph ./repo --visualize                          # build + export interactive HTML
TRELIX_RETRIEVAL_GRAPH_SEARCH_ENABLED=true trelix ask ./repo "how does auth relate to the data layer?"

Full guide (REST endpoints, MCP tools, config vars, community detection internals): docs/USER_GUIDE.md and docs/architecture.md §11.

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How it works

This is a simplified map of the default path — trelix actually runs up to 7 retrieval legs plus two alternate synthesis modes (agentic ReAct, FLARE). Full pipeline detail: docs/architecture.md.

flowchart TD
    subgraph INDEXING["INDEXING — trelix index"]
        A[Repository] --> B[FileWalker]
        B --> C[Tree-sitter Parser: 21 languages]
        C --> D[Chunker: context header + optional LLM summary]
        D --> E[Embedder: voyage / local-code / openai / azure / bedrock / local]
        E --> F[(sqlite-vec HNSW / Qdrant / LanceDB)]
        C --> G[(SQLite: symbols, calls, FTS5 BM25, sparse/file-summary tables)]
    end

    subgraph RETRIEVAL["RETRIEVAL — trelix search / ask"]
        H[User Query] --> I[AdaptiveRouter: direct / 8-intent / multi-step]
        I --> J[Vector Search: HyDE + ANN]
        I --> K[Contextual BM25: FTS5 + summaries]
        I --> L[Grep Search: exact / regex]
        I -.->|optional legs| L2[Sparse SPLADE / File-Summary RAPTOR / Sub-chunk MGS3]
        J --> M[RRF Fusion k=60]
        K --> M
        L --> M
        L2 -.-> M
        M --> N[Graph Expansion: calls + imports + types]
        N -.->|optional 4th leg| N2[Graph-BFS CodeGraph seed expansion]
        N --> O[Reranker: Cohere / cross-encoder / PLAID]
        N2 -.-> O
        O --> P2[PageRank Boost — optional]
        P2 --> P[Context Assembler: greedy / breadth_first]
        P --> Q{Context size or result count?}
        Q -->|below threshold| R[Direct LLM Synthesis]
        Q -->|above threshold| S[GraphRAG Map-Reduce]
    end

    F --> J
    G --> K
    G --> L
    G --> L2
    G --> N

    R -.->|agentic mode| T[Agent ReAct loop: think / act / observe]
    R -.->|low confidence| U[FLARE: re-retrieve with enriched query]

Indexing phases

Phase	What	Parallelism
1 — Parse	Tree-sitter AST traversal per file	ThreadPoolExecutor (parse_workers=4)
2 — Write	Symbol + chunk insertion, parent_id remapping. Content-hash diff skips unchanged symbols (v2.7.2). Optional file-summary + sub-chunk generation.	Sequential (DB consistency)
3 — Embed	Async batch embedding (+ optional sparse SPLADE pass), up to 4 concurrent API calls	asyncio.gather + Semaphore(4)
4 — Resolve	Cross-file call edges (qualified-name priority), imports, type edges	Sequential

An alternate streaming pipeline (TRELIX_INDEXER_STREAMING=true) replaces phases 1-3 with a bounded producer/consumer queue for very large repos.

Adaptive Query Router (v0.4.0)

Tier	Trigger	Behavior
1 — Direct	Simple factual patterns (what is X, define X)	Skip vector/BM25/grep/sparse legs — answer from a cheap DB-direct project-overview lookup, no fusion/rerank
2 — Single-step	Default for most code queries	8-intent classification → retrieval strategy
3 — Multi-step	Complex multi-part queries (walk me through..., end-to-end flow)	LLM decomposes into 2-3 sub-queries (optionally with multi-query expansion — TRELIX_RETRIEVAL_MULTI_QUERY=true), merged results

8 retrieval intents (Tier 2)

Intent	Legs	Graph expansion	Assembly
symbol_lookup	grep + BM25 + vector	call (depth 1)	greedy
file_overview	file-direct	none	greedy
feature_flow	vector + BM25	call+import (depth 2)	greedy
project_overview	file-direct	none	greedy
comparison	all 3	call+import (depth 1)	greedy
config_lookup	file-direct + grep	none	greedy
dependency_map	vector + BM25	import forward (depth 2)	breadth_first
blast_radius	grep + vector + BM25	import reverse (depth 1)	breadth_first

Type-edge expansion (max 15) runs unconditionally for every intent above and isn't intent-tuned.

Store layout

Single SQLite file (.trelix/index.db) — zero external infrastructure by default.

Table	Purpose
files	Indexed files with SHA-256 hash for incremental updates
symbols	Extracted symbols with line spans, context_summary, and content_hash (v2.7.2 incremental-embed diff)
calls	Directed call edges with callee_type_hint for precision
imports	File-level import edges
type_edges	Inheritance / implements / trait edges
chunks	Embeddable text (context header + summary + symbol body)
symbols_fts	FTS5 virtual table for BM25
chunk_embeddings	sqlite-vec HNSW vector table (or Qdrant/LanceDB)
sub_chunks, file_summaries, sparse_embeddings	Back the optional sub-chunk, file-summary, and sparse retrieval legs above

4 more tables (index_metadata dimension guard, query_telemetry, def_use_edges, taint_flows) are covered in docs/architecture.md §4. diff_chunks and the knowledge-graph metadata/concepts tables (written by trelix graph) live in the same file but aren't documented there yet.

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Eval Results

Recall@5 on mini_repo (10 queries, local provider)

Provider: local (sentence-transformers all-MiniLM-L6-v2, no API key)

Query	Expected file	Result
how does authentication work	auth.py	PASS
user repository get by id	user.py	PASS
hash password function	utils.py	PASS
login method	auth.py	PASS
validate token	auth.py	PASS
User dataclass	user.py	PASS
main entry point	main.py	PASS
delete user	user.py	PASS
verify password	utils.py	PASS
create user	user.py	PASS

Recall@5: 10/10 = 100%

Run the full eval harness (v0.4.0 / v2.1.0)

# Quick eval (mini_repo, 10 queries)
make eval

# Full eval (trelix-self, 50 queries, MRR + Recall@1/5/10 + NDCG@10)
make eval-full

# CoIR eval harness (v2.1.0) — run against your own golden set
# golden.jsonl format: {"query": "...", "expected_file": "path/to/file.py"}
trelix eval ./my-repo --golden eval/golden.jsonl

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Integrations

trelix works across the AI developer ecosystem:

Integration	Install	Usage
MCP (Claude Code, Cursor, Windsurf, Continue.dev)	pip install trelix-mcp	claude mcp add trelix -- trelix-mcp
LangChain	pip install trelix-langchain	TrelixRetriever(repo_path=".")
LlamaIndex	pip install trelix-llama-index	TrelixIndexRetriever(repo_path=".")
GitHub Action	uses: sairam0424/trelix-index-action@v1	Auto-index on push
VS Code Extension	cd workspace-vscode && npm install && npm run build	trelix.search and trelix.ask commands via MCP

MCP Quick Setup

pip install trelix-mcp
claude mcp add trelix -- trelix-mcp

LangChain Quick Setup

from trelix_langchain import TrelixRetriever
retriever = TrelixRetriever(repo_path="/path/to/repo")
docs = retriever.invoke("how does authentication work?")

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Development

git clone https://github.com/sairam0424/trelix
cd trelix
make install-dev
make test        # full unit + MCP suite
make lint
make eval        # recall eval on mini_repo
make eval-full   # full 50-query MRR/NDCG eval (requires Azure/OpenAI)
make binary      # build dist/trelix standalone binary via PyInstaller

See CONTRIBUTING.md for the full guide including how to add a new language parser.

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License

MIT — see LICENSE.

Contributing: CONTRIBUTING.md · Security: SECURITY.md · Support: SUPPORT.md · Roadmap: docs/ROADMAP.md