RemembrallMCP – persistent memory and code graph for AI agents

Persistent knowledge memory and code intelligence for AI agents. Rust core, Postgres + pgvector, MCP protocol. The problem: AI coding agents are stateless. Every session starts from zero - no memory of past decisions, no understanding of how the codebase fits together, no way to know what breaks when you change something. The solution: RemembrallMCP gives agents two things most memory tools don't: 1. Persistent Memory - Decisions, patterns, and organizational knowledge that survive between sessions. Hybrid semantic + full-text search finds relevant context instantly. 2. Code Dependency Graph - A live map of your codebase built with tree-sitter. Functions, classes, imports, and call relationships across 8 languages. Ask "what breaks if I change this?" and get an answer in milliseconds - before the agent touches anything. remembrall_recall("authentication middleware patterns") -> 3 relevant memories from past sessions remembrall_index("/path/to/project", "myapp") -> Builds dependency graph: 847 symbols, 1,203 relationships remembrall_impact("AuthMiddleware", direction="upstream") -> 12 files depend on AuthMiddleware (with confidence scores) remembrall_store("Switched from JWT to session tokens because...") -> Decision stored for future sessions Without RemembrallMCP, agents explore your codebase from scratch every session. Claude Code spawns Explore agents, Codex reads dozens of files, Cursor greps through directories - all burning tokens and time just to understand what calls what. A single "find all callers of this function" task can cost thousands of tokens across multiple tool calls. With RemembrallMCP, that same query is a single remembrall_impact call that returns in "Store a memory: We chose Postgres over MongoDB because our query patterns are relational. Type: decision, tags: database, architecture" > "Recall what we know about database decisions" > "Index this project and show me the impact of changing UserService" | Tool | Description | |---|---| remembrall_recall | Search memories - hybrid semantic + full-text with RRF fusion | remembrall_store | Store decisions, patterns, knowledge with vector embeddings | remembrall_update | Update an existing memory (content, summary, tags, or importance) | remembrall_delete | Remove a memory by UUID | remembrall_ingest_github | Bulk-import merged PR descriptions from a GitHub repo | remembrall_ingest_docs | Scan a directory for markdown files and ingest them as memories | | Tool | Description | |---|---| remembrall_index | Parse a project directory into a dependency graph (8 languages) | remembrall_impact | Blast radius analysis - "what breaks if I change this?" | remembrall_lookup_symbol | Find where a function or class is defined across the project | | Language | Extensions | Quality Score | |---|---|---| | Python | .py | A (94.1) | | Java | .java | A (92.6) | | JavaScript | .js, .jsx | A (92.0) | | Rust | .rs | A (91.0) | | Go | .go | A (90.7) | | Ruby | .rb | B (87.9) | | TypeScript | .ts, .tsx | B (84.3) | | Kotlin | .kt, .kts | B (82.9) | Scores measured against real open-source projects (Click, Gson, Axios, bat, Cobra, Sidekiq, Hono, Exposed) using automated ground truth tests. A new RemembrallMCP instance has no knowledge. Use the ingestion tools to bootstrap from existing project history. From GitHub PR history: > remembrall_ingest_github repo="myorg/myrepo" limit=100 Fetches merged PRs via gh , digests titles and bodies into memories, and tags them by project. PRs with less than 50 characters of body are skipped. Deduplication by content fingerprint prevents re-ingestion on repeat runs. From markdown docs: > remembrall_ingest_docs path="/path/to/project" Walks the directory tree, finds all .md files, splits them by H2 section headers, and stores each section as a searchable memory. Skips node_modules , .git , target , and similar directories. Good for README, ARCHITECTURE, ADRs, and any written docs. Run both once per project. After ingestion, remembrall_recall has immediate context. Source Code Organizational Knowledge | | v v Tree-sitter Parsers Ingestion Pipeline (8 languages) (GitHub PRs, Markdown docs) | | v v +--------------------------------------------------+ | Postgres + pgvector | | | | memories (text + embeddings + metadata) | | symbols (functions, classes, methods) | | relationships (calls, imports, inherits) | +--------------------------------------------------+ | MCP Server (stdio) | Any MCP-compatible AI agent - Parsing: tree-sitter (Rust bindings, no Python in the pipeline) - Embeddings: fastembed (all-MiniLM-L6-v2, 384-dim, in-process ONNX Runtime) - Search: Hybrid RRF (semantic cosine similarity + full-text tsvector) - Graph queries: Recursive CTEs with cycle detection and confidence decay - Transport: stdio via rmcp | Command | Description | |---|---| remembrall init | Set up database, schema, and embedding model | remembrall serve | Run the MCP server (default when no subcommand given) | remembrall start | Start the Docker database container | remembrall stop | Stop the Docker database container | remembrall status | Show memory count, symbol count, connection status | remembrall doctor | Check for common problems (Docker, pgvector, schema, model) | remembrall reset --force | Drop and recreate the schema (deletes all data) | remembrall version | Print version and config path | Config file: ~/.remembrall/config.toml (created by remembrall init ) Environment variables override config file values: | Variable | Description | |---|---| REMEMBRALL_DATABASE_URL or DATABASE_URL | PostgreSQL connection string | REMEMBRALL_SCHEMA | Database schema name (default: remembrall ) | crates/ remembrall-core/ # Library - parsers, memory store, graph store, embedder remembrall-server/ # MCP server + CLI binary remembrall-test-harness/ # Parser quality testing against ground truth remembrall-recall-test/ # Search quality testing docs/ # Architecture and test plan docs test-fixtures/ # Ground truth TOML files for 8 languages tests/ # Recall test fixtures | Operation | Time | |---|---| | Memory store | 7ms | | Semantic search (HNSW) | <1ms | | Full-text search | <1ms | | Hybrid recall (end-to-end) | ~25ms | | Impact analysis | 4-9ms | | Symbol lookup | <1ms | | Index 89 Python files | 2.3s | MIT