Mimir — Context Server

In Norse mythology, Mimir was the wisest being in all the Nine Realms — guardian of the Well of Wisdom beneath Yggdrasil, the World Tree. Odin sacrificed his eye for a single drink from that well. Mimir brings that same depth of knowledge to your codebase.

Mimir is an intelligent context engine that helps LLMs understand large, multi-repo codebases. Instead of dumping raw files into a prompt, Mimir builds a semantic code graph with real cross-file dependency edges, ranks nodes by relevance and recency, and assembles a minimal, connected, token-budget-aware context bundle — exactly what the model needs, nothing it doesn't.

The Problem

When you ask Claude or GPT to help with a large codebase, you face a brutal choice:

• Too little context — the model hallucinates or misses related code
• Too much context — you burn tokens on irrelevant files and hit limits
• Copy-paste — fragile, manual, doesn't scale across repos

The Solution

Mimir indexes your code into a hierarchical graph of repositories, files, classes, and functions. Cross-file dependencies — function calls, type references, inheritance hierarchies — are resolved into typed edges. At query time, a hybrid search finds seed nodes and a beam search assembles the tightest connected subgraph that answers your question — within your token budget.

Key Features

• Hierarchical beam search — finds connected code paths, not isolated snippets
• Cross-file symbol resolution — automatically discovers CALLS, USES_TYPE, and INHERITS edges across files using tree-sitter AST analysis
• Hybrid search — combines semantic embeddings, BM25 keyword matching, and name/path scoring for precise retrieval
• Live file watching — monitors your repos for changes and re-indexes on every save
• Query intent classification — automatically detects query type (locate, trace, write, debug) and tunes retrieval parameters
• Subgraph expansion — automatically surfaces callers, callees, type definitions, and config references
• Connectivity quality scoring — nodes scored by edge density, embedding presence, and content completeness; gap detection identifies under-indexed areas
• Temporal reranking — recently and frequently changed code scores higher
• Session deduplication — exponential decay model tracks what the LLM remembers
• Write-path context — shows interfaces, sibling implementations, test files, and DI registrations before you edit
• Impact analysis — reverse-traces callers and transitive dependencies to show blast radius
• Architectural guardrails — validates AI-generated changes against structural rules (layer violations, cycles, coupling, blast radius, scope bans) before commit, with agent policy for bounded autonomy and audit logging
• Backstage catalog integration — auto-populates service catalogs from the code graph with dependency drift detection
• Multi-repo — single server spans multiple repositories with cross-repo edge detection
• MCP server — plug-and-play with Claude Desktop, Cursor, and any MCP-compatible IDE
• HTTP API — shared team server for enterprise environments
• Docker-ready — zero Python setup, embedding model pre-baked
• 100% offline — local embeddings, no API keys required for indexing

Quick Start

pip install mimir-context-server
cd /your/project
mimir init          # creates mimir.toml
mimir indexer run   # builds and publishes the semantic code graph
mimir query search "how does authentication work?"
mimir query serve   # start MCP server for your IDE

# Architectural guardrails
mimir guardrail init                          # generate example rules + agent policy
git diff | mimir guardrail check --diff -     # validate changes before committing

Installation

# Server (full install)
pipx install mimir-context-server

# Client only (lightweight)
pipx install mimir-server-client

# From source
git clone https://github.com/repfly/mimir && cd mimir
pip install -e .

Documentation

License

MIT