CogniRepo MCP Server

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 35 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 (~3,500 tokens for 35 tools) takes more than you save. Break-even is roughly 4 tool calls on a medium-sized repo.

CogniRepo vs. claude-context / similar tools:

Feature	CogniRepo	claude-context / similar
Pure code retrieval	✓ (FAISS + graph + AST)	✓ Often faster on first use
Episodic memory (what happened last sprint)	✓ Persistent BM25 + vector	✗
Cross-agent handoff (Claude → Gemini → Cursor)	✓ last_context.json shared	✗
User behaviour profile (adapts depth/style)	✓ get_user_profile()	✗
Error pattern avoidance (learns from past fails)	✓ record_error()	✗
Architectural decision records	✓ record_decision()	✗
Multi-repo org graph (microservices)	✓ CHILD_OF / CALLS_API edges	✗

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.

Metric	Value	Notes
Token reduction — Python repos	50–84%	FastAPI FA-2: 12 000 → 2 500 · FA-4: 2 000 → 450 · FL-4: 8 000 → 1 250
Token reduction — average (all tested)	~60%	Across FA/FL/CE/AN where both baselines were captured
Token reduction — complex dynamic codebases	20–35%	Celery CE-4/CE-5; deep async/dynamic-dispatch patterns reduce gains
Symbol lookup latency	< 1 ms	vs. grep at 2–8 s on large repos
Accuracy vs. baseline	equal or better in 100% of tests	No regression observed; FA-2 accuracy improved Moderate → High
Cross-agent context handoff	✅ validated	CE-4: Claude primed index, Gemini CLI consumed it — 35% token saving, same accuracy
Dynamic dispatch coverage	honest gap	CE-3 (APScheduler beat dispatch) returned NA for both; CogniRepo does not fabricate call chains
Go/multi-language coverage	grammar now ships	Moby MO-2 showed 67% savings; MO-3-5 / K8-* not yet re-run since Go tree-sitter grammar landed (COGNIREPO-500, 2026-09)

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.

Repo	Files	Lookup latency	Token reduction (naive)	Token reduction (targeted)	context_relevance
flask	92	0.002 ms	97.3%	29.5%	80.5%
fastapi	1,178	0.003 ms	97.6%	63.5%	97.1%
celery	444	0.003 ms	99.7%	77.7%	100.0%
ansible	4,196	0.008 ms	96.1%	28.6%	56.6%

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

# Check everything is working:
cognirepo status                        # shows symbol count, graph nodes, signal warmth
cognirepo doctor                        # full health check

# Query through multi-model orchestrator:
cognirepo ask "why is auth slow?"

# Manage background watchers:
cognirepo list                          # show all running watcher daemons
cognirepo list -n <PID> --view          # tail the log of a specific watcher
cognirepo list -n <PID> --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-<project>:

{
  "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 35 tools are available to Claude, Cursor, and any MCP-compatible client.

Core retrieval

Tool	Description	When to use
context_pack(query, max_tokens=2000)	Token-budget code + memory context	Every session — FIRST call before any file read
lookup_symbol(name)	O(1) symbol lookup → file + line	Before grepping for a function
who_calls(function_name)	Trace callers + dynamic dispatch fallback	Impact analysis, refactoring
search_token(word)	Word-level reverse index across names, docs, comments	Finding where a concept lives
retrieve_memory(query, top_k=5)	Semantic similarity search over stored memories	Before answering — pull past context
search_docs(query)	Full-text search in all .md files	Documentation lookups
semantic_search_code(query, language=None)	Vector search over code symbols only	Code-specific semantic queries
subgraph(entity, depth=2)	Local knowledge graph neighbourhood	Understand symbol relationships
graph_stats()	Node/edge count and graph health	Check if graph has data
episodic_search(query, limit=10)	BM25 keyword search in event history	Find past decisions or incidents
dependency_graph(module, direction="both")	Import/dependency relationships	Module coupling analysis
explain_change(target, since="7d")	What changed in a file/function + git cross-ref	Understanding recent changes
architecture_overview(scope="root")	Pre-computed LLM architectural summaries	Big-picture questions

User & session intelligence

Tool	Description	When to use
get_user_profile()	User's interaction style: depth pref, question types, vocabulary	Call at session start — calibrates Claude's response style
get_session_history(limit=10)	Recent conversation exchanges across sessions	Resuming context from prior sessions
record_user_preference(key, value, context="")	Store a style or format preference	When user corrects interpretation or states a preference

Error tracking & prevention

Tool	Description	When to use
get_error_patterns(min_count=1)	Recurring errors with prevention hints	Before proposing a fix — check if it has failed before
record_error(error_type, message, file_path, query_context)	Log an error for future avoidance	After any error Claude or user encounters

Session start

Tool	Description	When to use
get_agent_bootstrap()	Single-call session start: brief + last context + profile + errors (~300 tokens vs ~900)	Preferred first call — replaces the 4-call sequence
get_session_brief()	Architecture + hot symbols + index health	First call when you need granular parts separately
get_last_context()	Most recent context_pack snapshot from prior session	Resume where previous agent left off

Memory & storage

Tool	Description	When to use
store_memory(text, source="")	Persist a memory to the FAISS index	After solving bugs, recording decisions
log_episode(event, metadata={})	Append event to episodic journal	Track milestones, incidents, deployments
record_decision(summary, rationale="")	Record architectural decision to episodic memory	When making non-obvious design choices
supersede_learning(old_memory_id, new_text)	Deprecate and replace an outdated memory in one call	When a past decision or fact has changed

Reporting

Tool	What it returns	When to use
generate_insights(since="90d", repo_path=None)	Self-contained HTML repo-history report (timeline, decisions, challenges, activity, index health), sourced only from real stored records	"What happened in this repo" / repo-history requests — see Repo insights below

Cross-repo (organization)

Tool	Description	When to use
org_search(query)	Search memories across all org repos	Multi-repo context queries
org_wide_search(query)	Search across every project in the org	Broadest cross-repo sweep
org_dependencies(depth=2)	Bidirectional inter-repo dependency graph	"What does this service depend on?"
cross_repo_search(query, scope="project")	Project-scoped or org-scoped search	Finding shared components
cross_repo_traverse(symbol, direction="both")	Traverse org graph from a repo or symbol	Tracing bugs across service boundaries
find_symbol_path(from_symbol, to_symbol)	Shortest call-graph path between two symbols, across services	Tracing a request flow end-to-end
get_service_endpoints(repo_path)	HTTP endpoint registry for a service	Listing a microservice's API surface
list_org_context()	Org metadata + sibling repos	Understanding repo relationships
link_repos(src_repo, dst_repo, relationship)	Record cross-repo dependency	When you discover one repo imports another

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Knowledge graph — what gets indexed

The knowledge graph is significantly richer than a simple call graph.

Node types

Type	Description
FILE	Every indexed source file
FUNCTION	Function and method definitions with docstrings
CLASS	Class definitions with base classes
CONCEPT	Semantic concepts extracted from docstrings and identifiers
QUERY	Recorded query nodes (for retrieval scoring)
SESSION	Conversation session nodes
ERROR	Recurring error pattern nodes
MEMORY	Cross-agent memory nodes (synced from Claude/Gemini)

Edge types

Type	Direction	Description
DEFINED_IN	symbol → file	Symbol lives in this file
CALLS / CALLED_BY	bidirectional	Function call relationships with purpose labels
IMPORTS	file → file	Python import dependencies
INHERITS	class → parent	Inheritance hierarchy
CO_OCCURS	file ↔ file	Files edited together (behavioural co-edit signal)
RELATES_TO	concept → symbol	Semantic concept linkage
QUERIED_WITH	query → symbol	Retrieval tracking for scoring

IMPORTS and INHERITS edges are built automatically during index-repo from Python AST. Use subgraph("MyClass", depth=2) or dependency_graph("mymodule") to query them.

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User behavior profiling

CogniRepo tracks how you interact across sessions and builds a profile that Claude uses to calibrate its responses — without you having to repeat preferences every session.

What gets tracked

• Depth preference — inferred from average query length: concise / medium / detailed
• Question types — distribution across: why, what, how, fix, explain, where, refactor, add
• Domain vocabulary — top terms that appear frequently in your queries
• Code focus — percentage of queries referencing code identifiers (symbols, functions)
• Sample queries — last 3 queries for Claude to infer framing style

Accessing your profile

# MCP tool (Claude calls automatically at session start):
get_user_profile()

# CLI:
cognirepo user-prefs

Example profile output

{
  "depth_preference": "detailed",
  "top_question_type": "how",
  "question_type_distribution": {"how": 12, "why": 8, "fix": 5},
  "top_terminology": ["auth", "token", "session", "middleware", "validate"],
  "code_focus_percent": 73,
  "framing_hints": "prefers detailed responses; often asks 'how' questions; domain vocabulary: auth, token, session",
  "total_queries_tracked": 47
}

Claude receives framing_hints at session start and adjusts response length, code density, and terminology accordingly. The profile accumulates over time — more accurate the more you use it.

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Error tracking & prevention

CogniRepo logs every error that occurs during sessions — whether it's a Python exception, a failed build step, or a tool call that went wrong. Errors are stored with:

• Dedup signature — prevents the same error from inflating the count
• Prevention hint — a targeted suggestion to avoid the same error class
• Occurrence context — last 5 occurrences with file path and error message
• Query context — the query or action that triggered the error

Logging errors

# MCP tool (Claude calls after errors):
record_error("TypeError", "expected str got int", "config/parser.py", "fix config loading")

Viewing error patterns

# MCP tool:
get_error_patterns()

Returns:

[
  {
    "error_type": "TypeError",
    "count": 7,
    "files": ["config/parser.py", "api/handlers.py"],
    "last_seen": "2026-04-22T10:30:00Z",
    "prevention_hint": "Wrong type — validate inputs at function boundary.",
    "recent_context": "expected str got int in parse_config"
  }
]

Built-in prevention hints

Error class	Prevention hint
NameError	Undefined variable — check imports and scope before use
ImportError	Import failed — verify package is installed and module path is correct
AttributeError	Object missing attribute — check type, None-guard, or spelling
TypeError	Wrong type — validate inputs at function boundary
KeyError	Missing dict key — use .get() with default or check existence first
IndexError	List out of range — guard with len() check before access
OSError	File/IO error — always guard file ops with try/except OSError
SyntaxError	Syntax error — run a linter before committing
Timeout	Timeout — add explicit timeout parameter and retry logic
AssertionError	Assertion failed — review invariants; do not use assert in prod

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Session history

Every cognirepo ask exchange is persisted to .cognirepo/sessions/. Sessions are indexed by UUID and retrievable via:

# List recent sessions:
cognirepo sessions

# MCP tool — Claude calls at session start to resume context:
get_session_history(limit=5)

Each entry returns: session ID, created timestamp, message count, model used, and the last user/assistant exchange for quick context scan.

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Architectural summaries

cognirepo init automatically prompts to run cognirepo summarize after the first index. This produces a 3-level LLM summary of the entire codebase:

• Level 1 — repo-wide summary (what the project does, key modules, entry points)
• Level 2 — per-directory summaries (what each package is responsible for)
• Level 3 — per-file summaries (what each file contains, key functions/classes)

Summaries are stored in .cognirepo/index/summaries.json and served via the architecture_overview MCP tool — zero token cost for Claude to understand the big picture.

# Auto-prompted on first init. Run manually anytime:
cognirepo summarize

# Fully local — no API key required. Reads from ast_index.json, runs in < 1 second.
# File summaries are also embedded into FAISS for semantic architecture queries.

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Repo insights

What CogniRepo can tell you about your repo: generate_insights() (or cognirepo insights) turns everything CogniRepo has recorded about a project — episodic events, architectural decisions, open challenges, branch/commit activity, index health — into one self-contained HTML report. Sourced only from real stored records; nothing fabricated. Screenshots below are from a real report generated on this repo (cognirepo insights --since 365d), not mockups:

<p align="center"> <img src="docs/assets/insights-report-light.jpg" alt="CogniRepo insights report, light mode" width="49%"> <img src="docs/assets/insights-report-dark.jpg" alt="CogniRepo insights report, dark mode" width="49%"> </p>

cognirepo insights --since 90d   # writes .claude/insights/<repoName>-insights.html

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Multi-model orchestration

cognirepo ask automatically picks the right model for each query:

Tier	Score	Default model	Use case
QUICK	≤2	local resolver	Single-token / trivial — zero API, fastest path
STANDARD	≤4	Haiku	Quick lookup, factual, single symbol
COMPLEX	≤9	Sonnet	Moderate reasoning
EXPERT	>9	Opus	Cross-file, architectural, ambiguous — full context, best model

cognirepo ask "where is verify_token defined?"       # → QUICK, answered locally
cognirepo ask "why is auth slow?"                    # → EXPERT, Claude with full context
cognirepo ask --verbose "explain the circuit breaker"  # show tier/score/signals

Provider fallback chain: Grok → Gemini → Anthropic → OpenAI. All errors are logged to .cognirepo/errors/<date>.log — no raw tracebacks shown to users.

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Language support

Language	Extensions	Install
Python	.py	built-in
JavaScript / TypeScript	.js .ts .jsx .tsx	cognirepo[languages]
Java	.java	cognirepo[languages]
Go	.go	cognirepo[languages]
Rust	.rs	cognirepo[languages]
C / C++	.c .cpp .h	cognirepo[languages]

Full details and roadmap: docs/LANGUAGES.md

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Storage layout

.cognirepo/
  config.json              ← project settings (project_id, model, retrieval weights)
  vector_db/
    semantic.index         ← FAISS flat index for semantic memory
    ast.index              ← FAISS IndexIDMap2 for code symbols
    ast_metadata.json      ← parallel metadata for ast.index rows
  graph/
    graph.pkl              ← NetworkX DiGraph (optionally Fernet-encrypted)
    behaviour.json         ← per-symbol hit counts, user profile, error patterns
  index/
    ast_index.json         ← reverse symbol index + file records
    manifest.json          ← git SHA + platform info for integrity checks
    summaries.json         ← LLM architectural summaries (Level 1–3)
  memory/
    episodic.json          ← append-only event journal
  sessions/
    <uuid>.json            ← conversation session files
    current.json           ← pointer to most-recent session
  errors/
    <date>.log             ← daily error logs (full tracebacks, never shown to users)
  learnings/
    learnings.json         ← structured learnings: decisions, bugs, prod issues

Everything under .cognirepo/ is .gitignored by default — never committed. Fernet encryption is opt-in at storage.encrypt: true in config.json.

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CLI reference

# Setup
cognirepo init                  # scaffold + configure; auto-indexes + auto-summarizes
cognirepo setup-env             # interactive API key wizard
cognirepo test-connection       # test API key connectivity
cognirepo migrate-config        # migrate deprecated config keys

# Indexing
cognirepo index-repo [path]     # AST-index a codebase
cognirepo summarize             # generate LLM architectural summaries (auto-prompted on init)
cognirepo seed --from-git       # seed behaviour weights from git history
cognirepo verify-index          # verify AST index integrity
cognirepo coverage              # per-directory symbol counts

# Querying
cognirepo ask <query>           # route through multi-model orchestrator
cognirepo retrieve-memory <q>   # similarity search
cognirepo search-docs <q>       # full-text search in .md files
cognirepo log-episode <event>   # append episodic event
cognirepo history               # print recent episodic events
cognirepo sessions              # list recent conversation sessions

# Memory management
cognirepo store-memory <text>   # save a semantic memory
cognirepo user-prefs            # view/set global user preferences
cognirepo prune [--dry-run]     # prune low-score memories

# Health & monitoring
cognirepo prime                 # generate session bootstrap brief
cognirepo status                # live retrieval signal weights + index health
cognirepo doctor [--fix]        # full health check; --fix auto-repairs common issues
cognirepo benchmark             # run quantitative value benchmarks

# Organization
cognirepo org create <name>     # create local organization
cognirepo org link <org> [path] # link repo to organization
cognirepo org list              # list organizations

# Daemon management
cognirepo list                  # list MCP servers, running daemons
cognirepo watch                 # manage background file-watcher daemon

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Future Plans

Priorities drawn from the v0.3.0 benchmark findings and community feedback. Now at v2.0.0 — some items below have since landed; each is annotated where that's the case.

Near-term

• Go call-graph indexing — done (COGNIREPO-203): Go receiver-qualified method calls (recv.Method()) now resolve through who_calls/CALLS edges — tree-sitter's Go selector_expression names its method field field, not property (the JS convention the extractor previously assumed), so method calls were silently dropped; fixed in _ts_collect_calls (intelligence/indexer/ast_indexer.py). A second bug in the same function — a call-collection recursion depth cap of 12, too shallow for a Go method wrapping an if-statement around a composite-literal call argument — was also raised (to 60). Go IMPORTS edges from go.mod-resolved local package imports are implemented (_extract_imports_go/_resolve_go_import_to_file). Live-verified against cognirepo_test_repo/advanced/moby: 4/4 hand-verified callers resolved (100%, vs. the 90% target) after both fixes.
• cognirepo ask — done: multi-model orchestrator (QUICK/STANDARD/COMPLEX/EXPERT tiers) is implemented and wired as a real CLI command (_cmd_ask_local, interface/cli/main.py). Streaming REPL mode (see Longer-term) is not yet built.
• Incremental re-index on save — done: the file-watcher daemon (cognirepo watch) debounces writes (config.json → indexing.debounce_ms, default 500ms) and batches indexer/graph saves; tests/test_watcher_debounce.py covers this including flush-on-shutdown.
• CLAUDE.md mandatory-call relaxation — benchmark feedback (Moby tests) flagged that forcing context_pack before every file read adds latency under memory pressure. A --fast mode that skips the tool-first gate for files under 50 lines is not yet implemented.

Medium-term

• Kubernetes / 2M-LOC scale validation — K8-1 through K8-5 test suite not yet completed. Goal: full scheduling-decision trace at < 8 000 tokens with CogniRepo vs. > 50 000 without.
• Plugin-registry pattern detection — done (COGNIREPO-203): a static, annotation-only heuristic pass tags symbols reachable via dynamic dispatch — celery-style @task/ @shared_task decorators, register(...)-style plugin-registration calls, Python __init_subclass__ hooks, and packaging entry-points (pyproject.toml [project.entry-points.*] / setup.cfg [options.entry_points]) — with dispatch:"dynamic" plus a RELATES_TO edge to a dynamic_dispatch CONCEPT node (_detect_dynamic_dispatch, _apply_entry_points_dispatch in intelligence/indexer/ast_indexer.py). Annotation-only by design — never fabricates a CALLS edge. Live-verified against cognirepo_test_repo/medium/celery's real @shared_task functions.
• BM25 over symbol names — partially done: core/_bm25.py is used by intelligence/retrieval/hybrid.py as the circuit-breaker fallback ranker (and by episodic search) when embeddings are unavailable, but it isn't yet the primary ranking signal for symbol-name partial-match recall (e.g. HttpClient matching http_client) in the normal (embeddings-available) retrieval path.
• Cross-session memory warm-up — Ansible benchmark noted episodic/memory retrieval is low-value on fresh sessions. cognirepo prime exists but is not run automatically on init; will make it opt-in default.

Longer-term

• cognirepo ask streaming REPL — full interactive session with tier routing, session persistence, and sub-agent delegation.
• Ruby, PHP, C#, Swift grammar support — tree-sitter grammars exist; need _TS_FUNCTION_TYPES/_TS_CLASS_TYPES mappings and call-extraction rules per language.
• Similarity edges in knowledge graph — done (COGNIREPO-202): post-index FAISS k-NN pass over already-embedded FUNCTION/CLASS symbol vectors adds a SIMILAR_TO edge (cosine ≥ 0.80, max 5/node, cross-file only) between near-duplicate symbols, both directions. Gated via config.json → indexing.similarity_edges (default on below 20k candidate symbols). Weighted (discounted) into intelligence/retrieval/hybrid.py::_graph_score.
• VS Code / JetBrains extension — surface lookup_symbol, context_pack, and who_calls directly in the editor sidebar without requiring an MCP-capable host.

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Documentation

Document	Description
docs/ARCHITECTURE.md	System design, component responsibilities, data flow
docs/architecture/SPECIFICATION.md	Technical spec, complexity signals, storage layout
docs/USAGE.md	Complete CLI, MCP, and Docker reference
docs/METRICS.md	Quantitative benchmarks: token reduction, lookup speedup, recall
CONTRIBUTING.md	How to add adapters, tools, and language support
SECURITY.md	Vulnerability reporting, data handling, trust model
docs/LANGUAGES.md	Language support details and roadmap

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Community

Questions, feedback, or just want to see what others are building? Join the CogniRepo Discord.

Want to contribute? See CONTRIBUTING.md — issues labeled good first issue are scoped for a first PR.

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License

CogniRepo is licensed under the MIT License.

• Free to use, study, modify, and distribute
• Use in proprietary products and commercial services — no restrictions
• No requirement to open-source your application

See LICENSE for full details.