synap-git 1.2.1

Persistent structural context infrastructure for AI coding agents.

Synap 🧠

The Deterministic Context Injector for AI Coding Agents.

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Synap is NOT a RAG system. It is a strict, deterministic background daemon that mirrors your Git state and proactively pushes exact contextual boundaries to AI coding agents via the Model Context Protocol (MCP). Agents do not query Synap—Synap injects reality into Agents.

🏛️ Philosophy & Vision

1. DETERMINISTIC FIRST: If the agent sees it, it exists exactly as shown in the local filesystem.
2. PUSH, NOT PULL: Agents are terrible at querying for what they don't know exists. Synap bounds their context upfront.
3. NOTHING HAPPENS SILENTLY: Every action, decision, and LLM call is tracked, costed, and logged.
4. LOCAL ONLY: Your code never leaves your machine unless you explicitly configure a remote LLM. All state is kept in .synap/synap.db.
5. MIRROR GIT EXACTLY: Synap's state machine is perfectly synced to HEAD. If you switch branches, your agent's memory switches branches instantly.

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⚡ Performance Architecture (v1.1.0)

Synap is built on the Git-Snapshot Paradigm to guarantee sub-100ms response times for everyday agent workflows:

• Two separate code paths: First-run indexing uses process-pool parallel parsing (ProcessPoolExecutor) to build the initial codebase structure, while subsequent indexing uses a Git delta change detector (git diff-tree) to process only changed files. (Reason: Avoids full filesystem scans on every run).
• No filesystem scan / file hashing on incremental runs: Synap uses Git blob OIDs to detect changed files. (Reason: Git already hashes everything, making filesystem reads redundant).
• Decoupled non-deterministic LLM pipeline: Structural parsing (deterministic, fast) completes immediately and enqueues wiki generation (non-deterministic, slow) to a background worker, while lazy caching fallback handles CLI/API wiki requests. (Reason: Never block structural indexing or CLI commands on LLM API response latency).
• SQLite optimizations: SQLite writes are grouped into a single transaction per batch (Reason: Avoids disk sync overhead per row). SQLite FTS5 index matches symbols in sub-milliseconds (Reason: Eliminates slow wildcard LIKE table scans). Pre-computed dot-separated module_key columns allow O(1) module resolution (Reason: Prevents suffix-matching scans).

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🏗️ High-Level Architecture (HLD)

Synap bridges the gap between your local file system, Git history, and the LLM via a 3-layer indexing strategy.

graph TD
    subgraph Local Repository
        FS[File System]
        Git[Git History]
    end

    subgraph Synap Daemon
        I[Indexer Engine]
        DB[(SQLite synap.db)]
        W[Wiki Engine .synap/wiki/]
        M[Context Injector memory.py]
    end

    subgraph IDE / AI
        MCP[MCP Server]
        Agent[AI Coding Agent]
    end

    FS -->|Change Events| I
    Git -->|Branch/Revert Events| I
    I -->|L1 Structural| DB
    I -->|L2 Semantic| W
    I -->|L3 Behavioral| DB

    DB --> M
    W --> M
    M -->|Injection Payload| MCP
    MCP <-->|Read/Write Tools| Agent

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🧩 The 3 Layers of Context

Synap provides three distinct layers of context to completely ground the agent:

1. L1: Structural (The Truth)

Deterministic mapping of code architecture using Tree-sitter.

• Fast, 100% accurate symbol extraction.
• Graph edges mapping Imports, Inherits, Calls, and References.
• Primary Key: sha256(path + content_hash) to completely eliminate edge-case collisions.

2. L2: Semantic (The "Why")

Hierarchical generated documentation stored locally as Markdown files (.synap/wiki/).

• File Level: What does this file do?
• Module Level: How do these files relate?
• Project Level: overview.md and architecture.md.

3. L3: Behavioral (Agent Memory)

Synap tracks the history of agent actions so the AI doesn't repeat past mistakes.

• Checkpoints: The active task the agent is performing (synap checkpoint).
• Decisions: Architecture decisions logged by the agent.
• Lessons: Generated automatically when a developer runs git revert on an agent's commit!

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🔄 Low-Level Data Flows (LLD)

Git Mirroring Flow

Synap's daemon watches the Git repository. The state of the repository completely drives the context.

sequenceDiagram
    participant Dev as Developer
    participant Git as Git Repo
    participant Daemon as Synap Daemon
    participant DB as SQLite DB

    Dev->>Git: git checkout feature-branch
    Git-->>Daemon: Branch Switch Event
    Daemon->>DB: Swap active context immediately

    Dev->>Git: git revert <agent-commit>
    Git-->>Daemon: Revert Detected (20-ancestor check)
    Daemon->>DB: Log Pending Lesson (What failed?)
    Daemon->>Dev: Prompt interactive review of failure

Context Injection Flow

When an Agent starts a task, it receives a strict, pre-packaged header injection.

sequenceDiagram
    participant Agent
    participant MCP as MCP Server
    participant Memory as Context Injector
    participant DB as SQLite DB

    Agent->>MCP: Trigger Tool / Start Task
    MCP->>Memory: build_injection_context()
    Memory->>DB: Fetch Active Branch & Checkpoint
    Memory->>DB: Fetch Approved Lessons
    Memory->>DB: Check for Dirty Git Tree (Uncommitted changes)
    Memory-->>MCP: Formatted Context Header
    MCP-->>Agent: Proactive Context Grounding

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🚀 Quick Start

1. Installation

Install the Synap CLI via pip or uv:

pip install synap-git
# or using uv:
uv tool install synap-git

2. Setup & Initialize

Interactive onboarding to set up your LLM providers (keys stored securely in your OS keyring).

synap setup .

Initialize the repo (Use --skip-llm to run in pure structural Mode A):

synap init .

3. Start the Daemon

Run the background watcher to keep Synap perfectly synced with Git:

synap start .

4. Connect to IDE

Start the MCP server to expose Synap to your AI Agent (Cursor, Windsurf, etc.):

synap mcp start .

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💻 CLI Command Reference

Synap uses a powerful, strict CLI interface. Every destructive action prompts for approval.

Core Lifecycle

• synap init . : Perform Pass 1 and Pass 2 indexing.
• synap start . : Launch the background polling daemon.
• synap status . : View active branch, indexed symbols, daemon state, and memory metrics.
• synap rollback . : Rollback active state to a previous commit.
• synap recover . : Recover from a corrupted database state.
• synap run . : Run all Synap services (Daemon, MCP, UI) concurrently.

L3 Memory Management

• synap memory status . : View counts of approved, pending, and expired lessons.
• synap memory prune . : Prune expired rules and cleanup memory.
• synap memory verify . : Detect dangling file references in active memory.
• synap lessons review . : Interactively review and Approve/Edit/Reject pending lessons.
• synap lessons approve <id> . : Approve a pending revert lesson to activate it.
• synap lessons reject <id> . : Reject and discard a pending lesson.
• synap checkpoint create . --doing "..." : Save the current context state.
• synap checkpoint list . : List all checkpoints for the active branch in a table.
• synap checkpoint restore <id> . : Show details of a checkpoint (or "latest").

Developer Tools

• synap wiki list . : List all generated wiki documentation files.
• synap wiki show <filepath> . : Render a specific wiki markdown page to the console.
• synap usage show . : Display detailed aggregated LLM token usage.
• synap usage clear . : Purge all LLM call history.
• synap doctor . : Validate SQLite integrity, Tree-sitter, tokenizers, LLM providers, and daemon heartbeat.
• synap mcp verify . : Verify MCP protocol, tool schemas, and contract stability.

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🤝 Contributing

Synap is built on the philosophy that AI tools must be transparent and controllable. If you're contributing:

• Do not introduce implicit RAG features.
• Adhere to the SYNAP_BUILD_SPEC.md strictly.
• Ensure all states are stored exclusively in synap.db or .synap/wiki/.

License: Apache 2.0 ](LICENSE.md)