CogniRepo

CogniRepo

mcp-name: io.github.ashlesh-t/cognirepo

Persistent memory and context for any AI tool. Not a chatbot — infrastructure.

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`lookup_symbol` returns file:line very quickly — grep takes 2–8 seconds. On Python repos ≥ 15K LOC, CogniRepo cuts AI coding agent token usage by ~30–78% vs. a targeted grep+read baseline (up to 96–99% vs. reading every matching file naively) — benchmarked on Flask, FastAPI, Celery, and Ansible (5,900+ files). Works with Claude Code, Cursor, and Gemini CLI. Fully offline. No API keys required for indexing or any of the 36 MCP tools.

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What it does

Every AI conversation starts from zero. Claude, Cursor, Gemini — none of them remember what you fixed yesterday, which files relate to which features, or what decisions were made last sprint. CogniRepo fixes that.

It sits between your codebase and any AI tool, providing:

• Semantic memory — FAISS vector store with sentence-transformer embeddings. Store decisions, docs, architecture notes. Retrieve them with natural language.
• Episodic log — append-only event journal. Know what happened before that error.
• Knowledge graph — NetworkX DiGraph linking functions, classes, files, imports, inheritance chains, call relationships, and concepts. All queryable.
• AST reverse index — O(1) symbol lookup across your entire codebase in any supported language.
• User behavior profiling — tracks how you prompt so Claude adapts its response style without you having to re-explain preferences every session.
• Error tracking — records errors with prevention hints so Claude avoids repeating the same mistake across sessions.
• Session history — persists conversation exchanges so any session can resume where the last one ended.
• Architectural summaries — auto-generated on first init; built entirely from the local AST index (no API key needed). File → directory → repo summary tree, embedded into FAISS for semantic search.
• Multi-model orchestration — classify query complexity → build context → route to the right model. Claude for deep reasoning, Gemini Flash for quick lookups. All automatic.

Every AI tool that connects gets the same accumulated project knowledge. Memory persists across sessions, across tools, across time.

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When to use CogniRepo

Most effective on codebases ≥ 15K LOC. On small repos (< 10K LOC), native file reads are fast enough that the MCP tool schema overhead (~3,700 tokens for 36 tools) takes more than you save. Break-even is roughly 4 tool calls on a medium-sized repo.

CogniRepo vs. claude-context / similar tools:

Conclusion: prefer CogniRepo when you value institutional memory across sessions. Use simpler tools when you just need one-shot code retrieval on a small codebase.

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Why it helps — measured numbers

Benchmarked across 6 real open-source repos (FastAPI, Flask, Celery, Ansible, Moby/Docker, Kubernetes) using 30 structured prompts tested against Claude, Gemini, and Cursor/Codex.

Honest limits: CogniRepo adds the most value on Python repos with clear static structure.

Dynamic dispatch patterns (Celery beat, plugin registries), deep Go codebases, and Ansible's

22-level variable precedence chains reduce retrieval confidence. The tool reports uncertainty

rather than hallucinating call chains.

Measured: lookup latency and token reduction (4 external repos)

Indexed 4 real repos, measured with cognirepo index-repo + cognirepo benchmark --json on v2.4.0+ (2026-09-17). CPU-only, no GPU.

Measured 2026-09-17. Lookup latency < 0.1 ms on all repos. "Targeted" is the realistic baseline (grep + read top-2 matching files, approximating what an agent would actually do); "naive" reads every matching file in full and is an upper bound, not a realistic comparison. moby and kubernetes are indexable (Go support confirmed, COGNIREPO-500) but not yet in this table — hours of indexing, scheduled separately. Full numbers and methodology, including precision@k, symbol hit rate, and memory recall: docs/METRICS.md.

Run cognirepo benchmark on your own codebase to reproduce. See docs/METRICS.md.

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How it works

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Quick start

Requirements

• Python 3.11+
• API key (optional — only needed for cognirepo ask): ANTHROPIC_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY, or GROK_API_KEY. Indexing, memory, summarization, and all MCP tools work fully offline.

Install

Recommended — pipx (global, one command, works on all distros)

pipx install cognirepo

That's it. cognirepo setup handles the rest — it installs optional extras (languages, security, providers) via pipx inject automatically when you enable them in the wizard.

Why pipx? It creates an isolated venv for cognirepo automatically so fastembed

and all deps install cleanly. The cognirepo command is then globally available in

every directory — no per-repo venv needed.

Arch Linux / Debian 12+ / Ubuntu 24.04+: Do NOT pip install into system Python.

These distros enforce PEP 668 and block system-wide pip installs. Use pipx.

Install pipx first (if needed)

# Arch Linux

sudo pacman -S python-pipx

Debian / Ubuntu

sudo apt install pipx

macOS

brew install pipx

Any platform (fallback)

pip install pipx --user

Inside a virtual environment (alternative)

python -m venv .venv && source .venv/bin/activate

pip install cognirepo

extras are installed by the setup wizard automatically

Development install (from source)

pipx install -e '.[dev,security,languages]'

or inside a venv: pip install -e '.[dev,security,languages]'

Note: CPU-only embeddings are the default (fastembed/ONNX, no PyTorch/CUDA required).

For GPU: pipx inject cognirepo 'cognirepo[gpu]' then install torch separately.

Run

# One-command onboarding (init + index + auto-configure MCP for Claude/Cursor/VS Code):

cognirepo setup

Or step by step:

cognirepo init --no-index # scaffold .cognirepo/ cognirepo index-repo . # index your codebase (required before MCP tools work) cognirepo index-repo . --daemon # index and run watcher in background

Keep the index fresh after every commit (indexes just the changed files, in the background):

cognirepo install-hooks

Check everything is working:

cognirepo status # shows symbol count, graph nodes, signal warmth cognirepo doctor # full health check (exit 0 healthy / 1 warnings / 2 errors) cognirepo doctor --resources # where memory and disk go: processes, store sizes, leftovers

Query through multi-model orchestrator:

cognirepo ask "why is auth slow?"

Manage background watchers (one per repo, however many sessions run):

cognirepo watch --ensure-running # start it if it is not running cognirepo list # show all running watcher daemons cognirepo list -n --view # tail the log of a specific watcher cognirepo list -n --stop # stop a watcher

First-time setup: cognirepo init + cognirepo index-repo . must complete before

MCP tools (context_pack, lookup_symbol, who_calls, etc.) return data.

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Connect your AI tools

Claude Code / Claude Desktop (recommended — project-scoped)

Run cognirepo init inside your project — it asks if you want to configure Claude and automatically writes .claude/CLAUDE.md and .claude/settings.json with the correct project-locked connector.

Each project gets its own isolated connector named cognirepo- :

{

"mcpServers": { "cognirepo-myproject": { "command": "cognirepo", "args": ["serve", "--project-dir", "/abs/path/to/myproject"], "env": {} } } }

The --project-dir flag locks the MCP server to that project's .cognirepo/ directory. When Claude has multiple projects open simultaneously, each connector reads only its own memories — never mixing data across projects or teams.

Cursor / Copilot

cognirepo export-spec

cp adapters/cursor_mcp_config.json .cursor/mcp.json

Restart Cursor — CogniRepo tools appear in the tool selector

Docker

cp .env.example .env          # add your API keys

docker compose up mcp # MCP stdio server

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MCP Tools — complete reference

All 36 tools are available to Claude, Cursor, and any MCP-compatible client.

Core retrieval

| `who_calls(f