reporecall
Health Gecti
- License — License: MIT
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Community trust — 18 GitHub stars
Code Gecti
- Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
- Permissions — No dangerous permissions requested
This tool acts as a local memory and retrieval system for codebases. It indexes your local repository using AST parsing and call graphs to provide focused code context to AI assistants like Claude, aiming to reduce token usage and eliminate the need for the AI to make repeated tool calls.
Security Assessment
Overall Risk: Low. The tool runs a local daemon and utilizes a local SQLite database for indexing, meaning your code is processed entirely on your machine. A light code scan of 12 files found no dangerous patterns, hardcoded secrets, or requests for dangerous system permissions. Because it analyzes your local files, it inherently accesses your source code, but there is no indication that it executes arbitrary shell commands or transmits your data over the internet.
Quality Assessment
The project appears healthy and actively maintained. It was updated very recently (0 days ago), uses the permissive MIT license, and has clear, detailed documentation. It has a small but growing community footprint with 18 GitHub stars, which is typical for a niche developer utility. The straightforward npm installation and well-defined intent-based retrieval architecture suggest a high level of care in its development.
Verdict
Safe to use.
Local codebase memory for Claude Code and MCP - AST indexing, call graphs, hybrid search. 0 tool calls, 3-8x fewer tokens.
Reporecall
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Local codebase memory and retrieval for Claude Code and MCP.
Reporecall indexes your repository locally, classifies each query by intent, and injects focused code context or a bounded summary before Claude answers.
Quick Start
npm install -g @proofofwork-agency/reporecall
reporecall init
reporecall index
reporecall serve
v0.4.0 — Intent-Based Retrieval Overhaul
This release replaces the old R0 / R1 / R2 routing model with intent-based query modes. The old model described retrieval shape (exact, trace, broad) — the new model describes what the user actually wants:
| Mode | Purpose |
|---|---|
lookup |
Exact symbol, file, endpoint, or module lookup |
trace |
Implementation path — "how does X work", "what calls Y" |
bug |
Causal debugging — symptom descriptions, "why does this fail" |
architecture |
Broad inventory — "which files implement…", "full flow from A to B" |
change |
Cross-cutting edits — "add logging across the auth flow" |
skip |
Meta/chat/non-code prompts |
Other changes in this release: streaming windowed indexing, adaptive embedding batches, semantic feature extraction, summary_only delivery for low-confidence bundles, PreToolUse hook guidance, and SQLite ABI self-repair.
Features
- Intent-based retrieval — query mode selected by local rule-based classification, no LLM
- Multi-signal search — FTS keywords, vector similarity, AST metadata, semantic features, imports, call graphs
- Bug localization — dedicated pipeline with subject profiling, contradiction pruning, and graph expansion
- Delivery modes —
code_context(focused chunks) orsummary_only(structured summary when confidence is low) - Hook guidance — context strength, execution surface, missing evidence, and recommended next reads
- Local memory — persistent rules, facts, episodes, and working context across sessions
- Streaming indexer — bounded file windows, adaptive embedding batches, lower peak heap
- SQLite ABI self-repair — detects native module mismatch and attempts automatic rebuild
- MCP server —
search_code,find_callers,get_symbol,explain_flow, memory tools, and more
Architecture
flowchart TB
User["User Prompt"]
Hook["Prompt Hook"]
Daemon["Local Daemon"]
Intent["Intent Classifier"]
subgraph Retrieval["Retrieval Pipeline"]
Decompose["Query Decomposition"]
Resolve["Target Resolution"]
FTS["FTS Search"]
Vector["Vector Search"]
Semantic["Semantic Feature Search"]
Graph["Caller / Neighbor Expansion"]
Prune["Contradiction Pruning"]
Select["Bundle Selection"]
end
subgraph Storage["Local Storage"]
Index["Chunk / Target / Feature Indexes"]
Memory["Memory Store"]
end
User --> Hook
Hook --> Daemon
Daemon --> Intent
Intent --> Decompose
Decompose --> Resolve
Resolve --> FTS
Resolve --> Vector
Resolve --> Semantic
FTS --> Graph
Vector --> Graph
Semantic --> Graph
Graph --> Prune
Prune --> Select
Select --> Hook
Index --> Resolve
Index --> FTS
Index --> Vector
Index --> Semantic
Memory --> Daemon
flowchart LR
Old["Old: R0 / R1 / R2"] --> Problem1["Prompt shape chosen too early"]
Old --> Problem2["Broad prompts over-injected"]
Old --> Problem3["Bug reports drifted into lexical noise"]
Problem1 --> New["New: Intent-Based Retrieval"]
Problem2 --> New
Problem3 --> New
New --> Lookup["lookup"]
New --> Trace["trace"]
New --> Bug["bug"]
New --> Architecture["architecture"]
New --> Change["change"]
Bug --> Evidence["Evidence-chain scoring"]
Architecture --> Summary["summary_only when weak"]
Trace --> Path["seed + graph path reconstruction"]
CLI
reporecall init # Create .memory/, hooks, MCP config
reporecall index # Index the codebase
reporecall serve # Start daemon + file watcher
reporecall explain # Inspect retrieval for a query
reporecall mcp # Run as MCP server (stdio)
reporecall doctor # Health checks
reporecall search # Direct search
reporecall stats # Index statistics
reporecall graph # Call graph queries
reporecall conventions # Detected conventions
MCP Tools
search_code, find_callers, find_callees, get_symbol, get_imports, explain_flow, build_stack_tree, resolve_seed, index_codebase, get_stats, clear_index, recall_memories, store_memory, forget_memory, list_memories, explain_memory, compact_memories, clear_working_memory
Development
npm install
npm run build
npm test
Key source files: src/search/intent.ts, src/search/hybrid.ts, src/search/context-assembler.ts, src/indexer/pipeline.ts, src/daemon/server.ts, src/memory/
License
MIT
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