xanther-context-engine
OfficialXanther Context Engine (XCE)
Your coding agent stops guessing.
๐ xanther.ai ยท Benchmarks ยท Docs ยท XME โ Memory Engine
Your agent reads files one by one, forgets the architecture, and burns tokens re-deriving structure every session. XCE indexes your codebase into a multi-layer knowledge graph your agent queries over MCP โ so it gets precise architectural context on every tool call instead of guessing.
78.2% on SWE-bench Verified at $0.22/instance. Works with Claude Code, Kiro, Cursor, Codex, and any MCP-compatible tool.
Multi-layer knowledge graph. AST structure up to architecture docs, linked into one queryable graph (RAFT).
Semantic + structural search. Find code by meaning or by symbol, across all four layers.
Impact analysis. See the blast radius of a change before you make it โ callers, dependents, affected modules.
Traceability. Follow any symbol from code โ component โ architecture, or back down to the exact line.
MCP-native. Five tools any agent can call. No custom pipeline per setup.
Cross-session memory (optional). Bundle XME so decisions and attempts persist across sessions.
Open source, self-hostable. Neo4j runs locally in Docker. MIT licensed.
# Install both engines (XCE + XME) in one command
pip install "xanther-xce[all]"
xanther index /path/to/repo
xanther query "how does auth work?" --repo my-repoWhy XCE
Coding agents are smart enough. They just lack context. The usual workarounds have limits:
Reading files one by one starts from zero every session and burns tokens re-deriving structure.
Grep / keyword search finds text matches with no understanding of relationships.
RAG / vector stores return fuzzy chunks ranked by similarity and hope the model reconnects them.
LSP "go to definition" answers one hop at a time โ no impact analysis, no architecture view.
XCE takes a different path: a persistent, multi-layer knowledge graph the agent traverses instead of re-reading source.
Structural relationships (calls, imports, inherits) are real graph edges, not guesses.
LLM-generated docs (summaries, algorithms, architecture) let smaller models reason without reading raw code.
Every answer traces to a symbol at a file and line you can open.
Served over MCP, so any compatible agent gets it on every tool call with no agent changes.
Related MCP server: state-trace
Prerequisites
Before you start, make sure you have these ready:
Requirement | Required? | Purpose | How to get it |
Python 3.9+ | โ Required | Runtime |
|
Docker | โ Required | Runs Neo4j locally | |
Neo4j 5.x | โ Required | Knowledge graph + vector search | Via Docker (see Quick Start) |
OpenRouter API key | โ
Required for | Embeddings + LLM doc generation (Layers 2โ4) | |
PostgreSQL | โฌ Optional | Incremental indexing state | Via Docker ( |
OpenSearch | โฌ Optional | Episodic memory search (falls back to SQLite) | Via Docker |
โ ๏ธ Important โ OpenRouter API key
An OpenRouter API key is required for
fullmode indexing (which generates the L2โL4 documentation layers and vector embeddings) and for semantic search.
Sign up at openrouter.ai
Create a key at openrouter.ai/keys
Add it to your
.env:OPENROUTER_API_KEY=sk-or-v1-your-key-hereWithout an OpenRouter key you can still run
--mode xme(AST parse + memory sync only), which uses regex-based heuristics and needs no LLM. But you lose semantic search, doc generation, and the richer L2โL4 layers.
Quick Start
1. Install
# Run instantly with uvx โ bundles XCE + XME (no install needed)
uvx --from "xanther-xce[all]" xanther --help
# Or install with pip (includes XCE + XME memory engine)
pip install "xanther-xce[all]"
# Minimal install (XCE code intelligence only, no memory)
pip install xanther-xce
# Or from source
git clone https://github.com/Xanther-Ai/xanther-context-engine.git
cd xanther-context-engine
pip install -e ".[all]"The
[all]extra bundles the Xanther Memory Engine (XME) alongside XCE โ one command installs both engines together.
2. Infrastructure (Neo4j required)
# Neo4j (knowledge graph + vector search)
docker run -d --name xce-neo4j \
-p 7474:7474 -p 7687:7687 \
-e NEO4J_AUTH=neo4j/xce_dev_password \
neo4j:5-community3. Configure
cp .env.example .envEdit .env and set:
# Required
NEO4J_PASSWORD=xce_dev_password
# Required for `full` mode (embeddings + L2-L4 doc generation + semantic search)
# Get your key at https://openrouter.ai/keys
OPENROUTER_API_KEY=sk-or-v1-your-key-hereIf you skip the OpenRouter key, only
--mode xme(AST + memory, no LLM) will work.
4. Index a repo
# Fast mode โ AST parse + memory sync only (30s)
xanther index /path/to/repo --mode xme
# Full mode โ all 4 layers + memory sync (5-20 min, resumable)
xanther index /path/to/repo --mode full5. Query
xanther query "how does the auth middleware handle JWT tokens?" --repo my-repo6. Visualize
xanther dashboard
# โ http://localhost:8001E2E Setup Guide (Production)
Prerequisites
Component | Purpose | Install |
Python 3.9+ | Runtime |
|
Docker | Neo4j container | |
Neo4j 5.x | Graph + vector storage | Via Docker (see below) |
OpenRouter API key | Embeddings + LLM docs |
Step-by-Step Setup
# 1. Install Xanther
pip install xanther-xce
# 2. Start Neo4j
docker run -d --name xce-neo4j \
-p 7474:7474 -p 7687:7687 \
-e NEO4J_AUTH=neo4j/xce_dev_password \
-v xce_neo4j_data:/data \
neo4j:5-community
# 3. Set environment variables
export NEO4J_URI=bolt://localhost:7687
export NEO4J_USER=neo4j
export NEO4J_PASSWORD=xce_dev_password
export OPENROUTER_API_KEY=sk-or-v1-your-key-here
# 4. Index your repository
xanther index ~/Projects/my-app --mode full
# 5. Verify
xanther statusWith XME (Cross-Session Memory)
For full memory capabilities, install the Xanther Memory Engine:
# Clone XME alongside XCE
git clone https://github.com/Xanther-Ai/xanther-memory-engine.git
# XCE auto-detects XME if it's a sibling directory
# Memory features are then available automaticallyPython API (Programmatic Setup)
from xce.memory.setup import XCESetup
async def main():
# One-liner setup (reads from env vars)
xce = await XCESetup.create("/path/to/repo", repo_id="my-repo")
# Query codebase
ctx = await xce.query("how does auth work?")
print(ctx["context_str"]) # LLM-ready context
# Record what you learned
await xce.record("fixed auth bug in middleware", files=["src/auth.py"])
# Record architectural decisions
await xce.decide("Use JWT for stateless auth", rationale="Scales horizontally")
# Search past actions (cross-session memory)
past = await xce.search_episodes("auth middleware fix")
await xce.close()MCP Server (for Kiro, Claude Code, Cursor)
# Start as MCP server (stdio)
xce serve
# Start as SSE server (HTTP)
xce serve --sse --port 8000Then connect your client. Pick yours:
Add to ~/.kiro/settings/mcp.json (global) or .kiro/settings/mcp.json (workspace):
{
"mcpServers": {
"xanther-xce": {
"command": "xce",
"args": ["serve"],
"env": { "NEO4J_PASSWORD": "your-password" },
"autoApprove": ["xce_search", "xce_architecture_context", "xce_trace", "xce_impact_analysis"]
}
}
}claude mcp add xanther-xce -- xce serveAdd to .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):
{
"mcpServers": {
"xanther-xce": {
"command": "xce",
"args": ["serve"],
"env": { "NEO4J_PASSWORD": "your-password" }
}
}
}Add to your User Settings (JSON):
{
"mcp": {
"servers": {
"xanther-xce": {
"command": "xce",
"args": ["serve"],
"env": { "NEO4J_PASSWORD": "your-password" }
}
}
}
}Start XCE as an SSE server and point any MCP-over-HTTP client at it:
xce serve --sse --port 8000Once connected, XCE exposes these MCP tools to your agent:
Tool | Purpose |
| Architectural context for a file or symbol |
| Search the knowledge graph ( |
| Predict the blast radius of proposed changes |
| Trace across abstraction levels (code โ component โ architecture) |
| Index / re-index a repository |
See
AGENTS.mdfor the recommended agent workflow โ when to reach for each tool (orient withxce_architecture_context, check impact before editing, keep the graph fresh).
Auto-Recording Hooks (XME Memory)
Install hooks to automatically record agent actions into XME memory. Every turn, tool call, and session end is captured for cross-session recall.
# Install hooks for Kiro + Claude Code
xce memory hooks install /path/to/repo
# Preview what would be installed (dry run)
xce memory hooks install /path/to/repo --dry-run
# Remove hooks
xce memory hooks uninstall /path/to/repoWhat gets installed:
Hook | Event | What it records |
|
| Flush journal, compact, save session |
|
| User turn in journal |
|
| Tool calls in journal |
Or via Python API:
from xce.memory.setup import XCESetup
xce = await XCESetup.create("/path/to/repo")
xce.install_hooks() # Installs Kiro + Claude Code hooksAfter installation, every agent session automatically builds cross-session memory โ no manual recording needed.
Indexing Modes
Mode | Time | What it does | When to use |
| 30-60s | AST parse + embeddings + XME memory sync | Quick iteration, memory-focused |
| 5-20min | All 4 layers + embeddings + memory | First-time deep index |
| 5-20min | Code graph only, no memory sync | Pure code intelligence |
Indexing Layers Explained
Layer 1: AST Parse (tree-sitter)
โ Classes, functions, methods, imports
โ All languages: Python, TS, JS, Go, Rust, Java, Kotlin, C#, Ruby, Swift, C, C++
โ ~30 seconds for most repos
Layer 2: Component Summaries (LLM)
โ One-sentence description of each function/class
โ Dependencies and responsibilities
โ ~2-5 minutes
Layer 3: Detailed Documentation (LLM)
โ Algorithm descriptions, data flow, error handling, edge cases
โ Parallelized (10 workers by default, set XCE_LAYER3_WORKERS)
โ ~5-10 minutes
Layer 4: Architecture (LLM)
โ High-level design per module
โ Design patterns, integration points, quality attributes
โ ~2-5 minutes
Embeddings: Vector Encoding (OpenRouter)
โ 512-dimensional vectors for each node
โ Enables semantic search via Neo4j vector index
โ ~1-2 minutesIncremental & Resumable
# Only re-index changed files (default)
xanther index /path/to/repo
# Force full re-index
xanther index /path/to/repo --full
# Only git-changed files
xanther index /path/to/repo --diff
# If interrupted (Ctrl+C), just re-run โ picks up where it left off
xanther index /path/to/repo --mode fullAuto-Indexing on Commit (Git Post-Commit Hook)
Keep the knowledge graph in sync automatically โ install a git post-commit hook that
incrementally re-indexes changed files after every commit. No more manual xanther index runs.
# Install the post-commit hook into a repo (defaults to fast xme mode)
xanther git-hook install /path/to/repo
# Preview what would be installed without writing anything
xanther git-hook install /path/to/repo --dry-run
# Choose the indexing mode the hook runs (xme | xce | full)
xanther git-hook install /path/to/repo --mode full
# Remove the hook
xanther git-hook uninstall /path/to/repoWhat the hook does: after each git commit, it runs the following in the background
so it never blocks your commit flow, appending output to .xanther/post-commit.log:
xanther index <repo> --diff --mode xme--difflimits parsing to files changed in the commit (fast, incremental).--mode xme(default) keeps it quick: AST parse + embeddings + memory sync, no LLM doc generation. Use--mode fullif you want the L2โL4 docs regenerated on every commit.
Notes:
The hook is idempotent โ re-installing replaces the prior Xanther block and preserves any existing
post-commithook content you already have.Works with git worktrees and submodules (resolves the real
.gitdirectory).Prefers the
xantherexecutable from your active virtualenv, so it keeps working inside venvs.
Smart Docs (Cost Optimization)
By default, Xanther skips generating LLM docs for trivial nodes (one-liners, getters/setters). This reduces LLM cost ~80% with minimal quality loss.
# Default (smart filtering ON)
xanther index /path/to/repo --mode full
# Generate docs for ALL nodes (slower, more expensive)
xanther index /path/to/repo --mode full --no-smart-docsCLI Commands
xanther index <path> # Index a repository
xanther index <path> --mode xme # Fast: AST + memory only (no LLM)
xanther index <path> --mode full # Full: all layers + memory
xanther index <path> --mode xce # XCE only (no memory sync)
xanther index <path> --diff # Only index git-changed files
xanther index <path> --full # Force re-index (no incremental)
xanther status # Show all indexed repositories
xanther dashboard # Launch graph visualization UI
xanther dashboard --port 8080 # Custom port
xanther query "question" --repo flask # Query code memory
xanther git-hook install <path> # Auto-index changed files after each commit
xanther git-hook uninstall <path> # Remove the post-commit hook
xanther memory hooks install <path> # Auto-record agent sessions into XME memory
xanther memory hooks uninstall <path> # Remove the XME recording hooksBenchmarks (SWE-bench Verified)
Model | Configuration | Resolve Rate | Cost/Instance |
Sonnet 4.0 (baseline) | mini-swe-agent | 66% | $1.50 |
Sonnet 4.0 + XCE | Resolve@1 | 73.4% | $1.20 |
MiniMax M2.5 + XCE | SWE-bench Verified | 78.2% | $0.22 |
Claude 4.5 Opus | Leaderboard | 76.8% | $8.50 |
8,427 XCE tool calls across 499 instances. Full results: xanther.ai/benchmarks
How it works
You ask your agent to fix a bug in the auth flow. Instead of opening files at random, it queries XCE over MCP:
1. Orient. The agent calls xce_architecture_context on the auth module and gets the design back โ role, patterns, integration points โ without reading a single file.
Auth subsystem ยท Strategy + Decorator patterns
Integrates: user_service, token_manager, audit_logger
Entry: authenticate() โ _validate() โ token_gen()2. Check impact before editing. Before changing token_gen(), it calls xce_impact_analysis:
token_gen() is called by 7 functions across 3 modules.
Affected tests: test_auth.py, test_session.py
โ session.refresh() depends on the current return shape.3. Make the change knowing the blast radius โ updating session.refresh() in the same pass instead of breaking it.
4. Remember (with XME). The decision and the fix are captured automatically. Next session, the agent recalls "we moved token_gen to HMAC-SHA256, and session.refresh depends on it" instead of relearning it.
Every answer traces to a symbol at a file and line you can open โ the agent reasons over a graph, not a pile of guessed chunks.
How it compares
Most tools that give agents "context" pick one lane โ raw structure, or fuzzy retrieval, or one-hop navigation. XCE combines a real graph, LLM-generated docs, semantic search, and impact analysis, then serves them over MCP.
Grep / keyword | RAG / vector store | LSP (go to def) | Graph-only tools | XCE | |
Structural graph (calls/imports) | โ | โ | partial | โ | โ |
Semantic search | โ | โ | โ | โ | โ |
LLM-generated docs (L2โL4) | โ | โ | โ | โ | โ |
Impact analysis (blast radius) | โ | โ | โ | partial | โ |
Cross-abstraction traceability | โ | โ | โ | partial | โ |
Cross-session memory | โ | โ | โ | โ | โ via XME |
MCP-native tools | โ | varies | โ | varies | โ (5) |
Answer traces to file:line | โ | โ | โ | โ | โ |
Open source / self-hostable | โ | varies | โ | varies | โ (MIT) |
Xanther Memory & Context Architecture
XCE (Context Engine) โ Code Intelligence
XCE indexes your codebase across 4 layers:
Layer | Description | Output |
L1: AST | Tree-sitter parsing of all source files | Classes, functions, methods, imports, dependencies |
L2: Summaries | LLM-generated descriptions | One-sentence summaries of each symbol |
L3: Docs | Detailed documentation | Algorithm, data flow, error handling, edge cases |
L4: Architecture | Module-level design docs | High-level design, patterns, integration points |
Key Features:
4096+ relationships tracked per large codebase (calls, imports, inherits, decorates)
512-dim vector embeddings for semantic search
Impact analysis to trace dependencies and predict change effects
Traceability linking code to requirements and tests
XME (Memory Engine) โ Agent Memory
XME provides persistent, cross-session memory for agents:
Layer | Description | Storage |
Episodic Store | Session transcripts, tool calls, decisions | SQLite + OpenSearch |
Fact Graph | Extracted facts (decisions, attempts, preferences) | Neo4j temporal |
Context Layer | Live, updated facts during agent sessions | Redis-style |
Key Features:
Cross-session recall โ remember past agent actions across sessions
Hybrid search โ semantic + full-text over memories
Automatic hooking โ record agent actions automatically
Fact deduplication โ merge similar memories with configurable thresholds
XCE โ XME Bridge
The bridge syncs code facts from XCE into XME memory:
Indexed Code Facts โ XME Episodic Store
โ Code symbols become queryable memories
โ Search "how does auth work?" returns both code facts + past sessionsBenefits:
Memory contains code knowledge from indexing
Search returns unified results (code + conversation)
No need to re-index for memory updates
Metrics & Statistics
Real-World Indexing Stats
Repository | Nodes | Edges | Index Time | Memory Used |
httpx | 2,392 | 4,213 | 142s | 1.2GB |
Flask | 2,895 | 5,095 | 168s | 1.5GB |
FastAPI | 1,523 | 3,102 | 118s | 0.9GB |
Express | 253 | 150 | 42s | 0.3GB |
Celery | 3,102 | 6,234 | 203s | 2.1GB |
Sympy | 114,240 | 604,776 | 2,845s | 12.5GB |
Performance Benchmarks
Operation | Time (httpx) | Time (Flask) | Time (Sympy) |
L1 AST Parse | 32s | 38s | 210s |
L2 Summaries | 48s | 56s | 320s |
L3 Detailed Docs | 62s | 72s | 415s |
L4 Architecture | 38s | 44s | 280s |
Embeddings | 28s | 34s | 195s |
Total | 208s | 244s | 1,420s |
Memory Efficiency
Feature | Memory | CPU | Storage |
Indexed graph (httpx) | 1.2GB | 1.5 cores | 450MB |
Cross-session memory (100 sessions) | +0.8GB | +0.2 cores | +200MB |
Concurrent queries (5) | +0.5GB | +0.8 cores | - |
Examples
Example 1: Understanding a New Codebase
# Install and index a new project
xanther index ~/Projects/my-new-project --mode full
# Query to understand the architecture
xanther query "How does the authentication flow work?" --repo my-new-project
# Get specific function details
xanther query "What does the PaymentProcessor.process() method do?" --repo my-new-project
# Find related components
xanther query "What files depend on the database module?" --repo my-new-projectExample 2: Agent Integration (Python)
import asyncio
from xce.memory.setup import XCESetup
async def main():
# Setup with cross-session memory
xce = await XCESetup.create(
path="/path/to/repo",
repo_id="my-app",
mode="full" # Enables XME bridge
)
# First session - learn the codebase
ctx = await xce.query("What is the entry point?")
print(f"Context: {ctx['context_str'][:200]}...")
# Record what we learned
await xce.record(
"Entry point is main.py, uses FastAPI app instance",
files=["src/main.py"]
)
# Second session - same memory persists!
ctx2 = await xce.query("What framework is used?")
# Memory includes: FastAPI app instance, main.py entry point
# Search past sessions
past = await xce.search_episodes("FastAPI", top_k=3)
print(f"Found {len(past)} relevant past sessions")
await xce.close()
asyncio.run(main())Example 3: Impact Analysis
# Find all callers of a function
xanther query "Who calls auth.middleware()?" --repo my-app
# Get impact before making changes
xanther query "What would break if I change the User model?" --repo my-app
# Find test coverage
xanther query "Which tests cover the payment processor?" --repo my-appExample 4: Dashboard Visualization
# Launch the dashboard
xanther dashboard
# Open http://localhost:8001 in browser
# - Click nodes to see details
# - Toggle layers L1-L4
# - Search for symbols
# - Export graph visualizationExample 5: Automatic Hooking
# Install hooks for automatic memory recording
xanther memory hooks install ~/Projects/my-app
# Now any agent session automatically records:
# - User prompts
# - Tool calls
# - Decisions made
# - Files modified
# View recorded sessions
xanther status # Shows indexed repos AND recorded sessions
# Search across sessions and code
xanther query "How did we fix the auth bug last week?" --repo my-app
# Returns: Code facts about auth + Session where fix was discussedโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ xanther CLI โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
โ โ
โ XCE (Code Intelligence) XME (Agent Memory) โ
โ โโ Layer 1: AST Parse โโ Episodic Store โ
โ โ (tree-sitter, all langs) โ (sessions, actions) โ
โ โโ Layer 2: Summaries โโ Fact Graph โ
โ โ (LLM descriptions) โ (Neo4j temporal) โ
โ โโ Layer 3: Detailed Docs โโ Context Layer โ
โ โ (algorithm, data flow) (live UPSERT) โ
โ โโ Layer 4: Architecture โ
โ โ (HLD per module) โ
โ โโ Embeddings (vector search) โ
โ โ
โ XME Bridge: syncs code facts โ memory โ
โ CodeMemory: unified query interface โ
โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
โ Storage: Neo4j (graph) + SQLite (episodes) + OpenSearchโ
โ Dashboard: localhost:8001/graph.html (vis-network) โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโGraph Visualization
Launch the dashboard with xanther dashboard and open http://localhost:8001 to explore your codebase as an interactive knowledge graph:

The graph explorer provides:
Interactive force-directed graph of your codebase
Layer toggles: L1 (AST) โ L2 (Descriptions) โ L3 (Docs) โ L4 (Architecture)
Code Facts โ structural knowledge from indexing
Agent Memory โ decisions and actions from agent sessions
Color by Module โ clusters files by directory
Hierarchy view โ top-down L4โL3โL2โL1 layout
Search โ find and focus on any symbol
Click any node for detailed info panel
Supported Languages
Python, TypeScript, JavaScript, Go, Rust, Java, Kotlin, C#, Ruby, Swift, C, C++
Environment Variables
See .env.example for full documentation. Key ones:
# Required
NEO4J_PASSWORD=xce_dev_password
OPENROUTER_API_KEY=sk-or-... # for doc generation + embeddings
# Optional
XCE_DEEP_DOCS=true # Layer 3 (default: on)
XCE_ARCH_DOCS=true # Layer 4 (default: on)
XME_BRIDGE_ENABLED=true # XME memory sync (default via --mode)
XCE_LLM_PROVIDER=openrouter # force OpenRouter over AWS BedrockAPI (for integrations)
When the dashboard is running:
GET /api/graph/repos # list indexed repos
GET /api/graph/nodes?repo_id=flask&limit=500 # AST nodes
GET /api/graph/edges?repo_id=flask&limit=1000 # edges (CALLS, IMPORTS, INHERITS)
GET /api/graph/layers?repo_id=flask&limit=300 # all layers (L1-L4 + memory)Project Structure
xce/
โโโ cli/interactive.py # xanther CLI (index, status, dashboard, query)
โโโ indexing/
โ โโโ indexer.py # multi-layer indexing pipeline
โ โโโ checkpoint.py # resumable progress tracking
โ โโโ doc_generator.py # LLM doc generation (Layers 2-4)
โ โโโ embedding.py # vector encoding
โโโ parsers/ # tree-sitter language parsers
โโโ git_hooks.py # post-commit auto-index hook installer
โโโ graph/store.py # Neo4j graph operations
โโโ memory/
โ โโโ xme_bridge.py # XCE โ XME fact sync
โ โโโ code_memory.py # unified query interface
โโโ dashboard/
โ โโโ server.py # FastAPI backend (30 routes)
โ โโโ static/graph.html # standalone graph visualization
โ โโโ ui/ # React frontend (legacy)
โโโ models.py # ASTNode, ComponentDesc, ArchitectureDocLicense
MIT
Links
Website: xanther.ai
Benchmarks: xanther.ai/benchmarks
XCE (this repo): github.com/Xanther-Ai/xanther-context-engine
XME (memory engine): github.com/Xanther-Ai/xanther-memory-engine
PyPI (XCE): pypi.org/project/xanther-xce
PyPI (XME): pypi.org/project/xanther-xme
Community & Support
Discord: Join our community for help and discussions
GitHub Issues: Report bugs and suggest features
Documentation: See
docs/folder for detailed guides
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