Metadata-Version: 2.4
Name: toolrecall
Version: 0.8.1
Summary: The Deterministic Tool Cache for LLM Agents — no LLM decides what to cache. SQLite FTS5, zero deps, MCP multiplexer, zero-trust WAF.
Author-email: Robin Schultka <robin@nousresearch.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/whiskybeer/toolrecall
Project-URL: Repository, https://github.com/whiskybeer/toolrecall
Project-URL: Documentation, https://github.com/whiskybeer/toolrecall#readme
Keywords: tool-cache,llm,agent,token-savings,sqlite,fts5
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: mcp-fastmcp
Requires-Dist: mcp>=1.0.0; extra == "mcp-fastmcp"
Provides-Extra: yaml
Requires-Dist: pyyaml>=6.0; extra == "yaml"
Provides-Extra: toml-write
Requires-Dist: tomli-w>=1.0; extra == "toml-write"
Provides-Extra: dev
Requires-Dist: pytest>=9.0; extra == "dev"
Requires-Dist: ruff>=0.1; extra == "dev"
Dynamic: license-file

# ToolRecall — Deterministic Tool Cache for LLM Agents

ToolRecall sits between your agent and the OS (or your API provider). On repeat calls it serves cached results from local SQLite instead of re-executing system commands or re-sending requests to the LLM. Caching is deterministic — byte-identical until mtime/TTL expiry — which qualifies every API call for provider prefix-caching discounts (up to 90% at Anthropic/OpenAI).

**1 tick instead of 4:** A file read normally needs `stat → open → read → close`. ToolRecall needs only `stat` (mtime check) — on cache hit the bytes come from memory, bypassing disk entirely.

**Zero pip dependencies. Python 3.11+ stdlib only.** 76 KB install. Everything starts automatically.

```bash
pip install toolrecall
toolrecall setup          # One-shot: config → systemd → shim → daemon start
```

> **Zero config mode:** After `toolrecall setup`, every command like `toolrecall status`, `toolrecall mcp`, or `toolrecall serve` auto-starts the daemon if it isn't running. You never need to think about it.

**Three layers of caching (all active by default):**

| Path | What it does | How to connect | Default |
|------|-------------|---------------|---------|
| **OS-level Shim** | Patches every Python process — `open()` and `subprocess.run()` are transparently cached. **Zero imports needed. Works with any agent.** | Installed via `toolrecall setup` or auto-installed on first command. | ✅ Installed via `.pth` in site-packages |
| **Forward proxy** | Intercepts HTTP requests to API providers (OpenAI, Anthropic, etc.) — caches full responses by body hash. **Zero tokens consumed on cache hit.** | `export OPENAI_BASE_URL=http://localhost:8569` — or set any SDK's base URL | ✅ On (`:8569`) |
| **MCP bridge** | Caches tool output (file reads, terminal commands) — agent connects as an MCP client. Server names auto-resolve from registry. | Add to `~/.claude/.mcp.json` or run `toolrecall mcp` | ✅ On (stdio) |

**Requirements:** Python 3.11+ (`sqlite3`, `tomllib`, `json`, `http.server`, `urllib` from stdlib).

---

## What It Does

ToolRecall intercepts tool calls at the daemon level and returns cached results when inputs haven't changed:

| Mechanism | What gets cached | Invalidation | Token saving |
|-----------|----------------|-------------|-----------|
| **File cache** | First disk read per file | `mtime` changes → fresh read | Smaller context → provider prefix-cache discounts |
| **Terminal cache** | Static commands (hostname, whoami, pwd, uname, uptime, df, free, crontab) | TTL-based (default 300s) | Same output never re-sent to LLM |
| **MCP cache** | External MCP server responses (GitHub, time, fetch…) | TTL-based (default 60s, per-server override) | Repeated tool results served from local cache |
| **Script/Code cache** | `cached_run`, `cached_exec` output | `ttl=0` disables caching | Same as file cache |
| **Forward proxy** | Full API responses (chat completions to OpenAI, Anthropic, DeepSeek…) | Body hash — same request → same response | **Zero tokens consumed** — cache hit never reaches the provider |
| **Context Tracker** | Tracks dirty/clean files via checkpoints | In-memory (resets on daemon restart) | **93.8% O(n²) reduction** — drop clean files from context |

Dynamic commands (`git`, `ls`, `curl`) and state-changing operations always execute live.

### Measured effect

In a 13-hour session (Hermes + Gemini 3.1 Pro, 386 messages, 13 project files):

- **89% hit rate** (91% file cache): 827 tool calls served from SQLite instead of OS
- **73% fewer file-read tokens** at 3× re-read (~204K → ~55K unique)
- **~81% fewer** at 10× re-read (~630K → ~55K unique)
- **~20 min less wait time** — each cache hit avoids ~1.5s subprocess fork
- **Provider prefix-caching** becomes reliable: byte-identical payloads qualify for Anthropic/OpenAI's up-to-90% discount on every call

Source: [Benchmark](docs/BENCHMARK.md)

---

## One-Time Setup

ToolRecall should be installed once per machine, then it works transparently for all agents.

```bash
pip install toolrecall         # installs CLI + Shim (.pth file activates on next Python start)
toolrecall setup               # config → systemd service → shim → daemon start
```

That's it. Now **every** Python process on this machine transparently caches file reads and terminal commands through ToolRecall.

### What `toolrecall setup` does

| Step | Details |
|------|---------|
| **Config** | Creates `~/.config/toolrecall/toolrecall.toml` with default-deny security |
| **Systemd** | Generates `~/.config/systemd/user/toolrecall-daemon.service` (enables auto-restart) |
| **Shim** | Installs `tr_shim.pth` in your site-packages — every Python process auto-caches |
| **Daemon** | Starts the cache daemon (background process with LRU + SQLite) |

### What happens on every CLI command

Every `toolrecall` command that needs the daemon (`status`, `mcp`, `serve`, `stats`, etc.) automatically:

1. **Checks if the shim is installed** — auto-installs it if missing
2. **Checks if the daemon is running** — auto-starts it if not

This means you can run `toolrecall status` on a fresh install and it "just works" — no extra steps.

### Daemon auto-start (fallback chain)

| Try | Method | When |
|-----|--------|------|
| 1 | `systemctl --user start toolrecall-daemon` | Linux with systemd |
| 2 | `os.fork()` + `run_daemon()` | Docker, macOS, Codespaces |
| 3 | `subprocess.DETACHED_PROCESS` | Windows |

---

## Architecture

```
[ Any Python process ]     [ Claude Code ]   [ Cursor IDE ]   [ Hermes Agent ]
       │ (shim .pth)              │                │               │
       ▼                          │                │               │
+─────────────────────+            │                │               │
│  Shim (transparent) │            │                │               │
│  open() → cached    │            │                │               │
│  subprocess → cache │            │                │               │
+─────────┬───────────┘            │                │               │
          │                        │                │               │
          │              +─────────┴────────┬───────┴───────────────┘
          │              │  Standard stdio MCP   OR   HTTP (:8569)
          │              +────────────────────┬──────────────────────+
          │                                  │
+─────────▼──────────────────────────────────▼────────────────────+
│                  ToolRecall Daemon                                 │
│  ┌─────────────────────────────┐                                  │
│  │   In-Memory LRU (Cache)     │                                  │
│  └──────────────┬──────────────┘                                  │
│  ┌──────────────▼──────────────┐                                  │
│  │   SQLite WAL (Persistent)   │                                  │
│  └─────────────────────────────┘                                  │
│  ┌─────────────────────────────┐                                  │
│  │   MCP Server Multiplexer    │                                  │
│  └──────────────┬──────────────┘                                  │
+─────────────────┼────────────────+
                  │ Lazy-Loaded stdio Subprocesses
+─────────────────▼────────────────+
│ [ Downstream MCP: GitHub / Time ]  │
+────────────────────────────────────+
```

**Shim layer (at the OS level):** When `tr_shim.pth` is in `site-packages`, every Python process on the machine auto-patches `builtins.open()` and `subprocess.run()` — no imports needed. This is the truly agent-agnostic path: any Python agent (Hermes, Claude Code, Cursor, Aider, Cline) transparently benefits without any configuration.

**Daemon layer (process level):** Holds the hybrid in-memory LRU + SQLite WAL cache, the MCP Multiplexer (manages subprocesses for external MCP servers), the Forward Proxy (caches full API responses via body hash), and the Security Gate (path allowlist, sensitive file blocklist, cognitive scan).

**How they work together:**

1. **Python process** calls `open("file.py")` → Shim intercepts → `cached_read()` via Daemon UDS → returns cached bytes or reads from disk
2. **Agent** calls `cached_read()` via MCP → Daemon → same cache (shared with Shim)
3. **Any SDK** sends API request to `localhost:8569` → Forward Proxy hashes body → checks same SQLite cache

---

## MCP Multiplexer

When running multiple agents on the same machine (5 Claude Code sessions + 3 Cursor instances), each one normally spawns its own subprocess for every MCP server (GitHub, Postgres, time…). That's 10× the RAM for the same tool.

The daemon's multiplexer shares one subprocess per server across **all** agents:

- **Lazy loading:** servers boot on first call, not at daemon start (~0.01s vs ~1.7s per server)
- **Idle timeout:** inactive subprocesses killed after 15 min (configurable)
- **Failure isolation:** one server crash doesn't affect others (auto-reconnect, max 3 attempts)
- **Secrets:** API tokens loaded from `~/.toolrecall/.env`, never exposed to the LLM
- **Auto-resolution:** Server names auto-resolve from the built-in registry — no `command`/`args` needed for common servers

All agents connect to **one** MCP server in their config: `toolrecall mcp`.

### Quick Config Example

```toml
# ~/.config/toolrecall/toolrecall.toml
[mcp_multiplex]
servers = ["time", "github", "fetch"]
```

### Built-in Servers (zero deps)

| Server | What it does |
|--------|-------------|
| `time` | Current time in any timezone — stdlib only |
| `github` | GitHub API (create repo, push files, list commits) — `urllib` only |
| `sequential-thinking` | Reasoning validation, contradiction detection — no network |
| `fetch` | Fetch URLs — stdlib only (`urllib.request`), 500KB configurable limit via `TOOLRECALL_FETCH_MAX_BYTES` |

### External Servers (needs `uvx`)

| Server | Package |
|--------|---------|
| `filesystem` | `mcp-server-filesystem` — safe file access |
| `git` | `mcp-server-git` — Git operations |
| `memory` | `mcp-server-memory` — knowledge graph |
| `brave-search` | `@anthropic/mcp-server-brave-search` — web search |
| `playwright` | `@playwright/mcp` — browser automation |
| `slack` | `mcp-server-slack` — Slack workspace |

See [MCP Multiplexer](docs/MCP_MULTIPLEXER.md) for full configuration details.

---

## Security

ToolRecall doesn't prevent prompt injection — it cages the consequences:

- **Default-deny path allowlist:** Without config, NO paths are readable. `toolrecall init` prompts for paths interactively.
- **Sensitive file blocklist:** `.env`, `.ssh/`, `.pem`, `.aws/`, etc. are blocked even inside allowed paths.
- **`allow_terminal=false`** (default): drops all `cached_terminal` calls into a void.
- **`os.path.realpath()`:** catches `../../../etc/shadow` traversal before OS is touched.
- **Cognitive Pre-Fight:** Deterministic regex scan on MCP tool arguments for override instructions, jailbreak tags, exfiltration URLs. Zero LLM, ~0.001ms hot path.
- **AST injection check:** Parses tool arguments as Python AST — blocks `exec()`, `eval()`, `__import__()` calls.
- **Daemon IPC via UDS:** No open ports, immune to SSRF.

See [Security Architecture](SECURITY.md) for the full trust boundary.

---

## Quick Reference — CLI

```
toolrecall setup          One-shot: config + systemd service + shim + daemon start  [required once]
toolrecall init           Create default config.toml and .env
toolrecall status         Cache status and stats               [auto-starts daemon]
toolrecall stats          Detailed cache statistics (JSON)     [auto-starts daemon]
toolrecall invalidate     Clear all caches                     [auto-starts daemon]
toolrecall restart        Health check + clean daemon restart  [auto-starts daemon]
toolrecall mcp            Start MCP Bridge                     [auto-starts daemon]
toolrecall serve          Forward proxy (cache API responses)  [auto-starts daemon]
toolrecall debug          Start debug/demo server              [auto-starts daemon]
toolrecall index          Build/update FTS5 knowledge database [auto-starts daemon]
toolrecall config-set     Set a config value
toolrecall daemon         Start/stop/manage cache daemon
toolrecall shim           Install/uninstall OS-level cache shim (.pth file)
toolrecall nginx          Generate nginx config
```

## Agent Integration — zero-config for any agent

ToolRecall's daemon provides two agent-agnostic caching layers. Neither requires per-agent configuration:

### Layer 1: Python Shim (transparent, any Python agent)

After `toolrecall setup`, every Python process on this machine auto-caches `open()` and `subprocess.run()` through ToolRecall. Hermes, Claude Code (Python), Cursor, Aider — all benefit without any config change.

```bash
pip install toolrecall
toolrecall setup              # One-shot: shim + daemon + opencode config
# Done — every Python process now transparently caches
```

### Layer 2: MCP Bridge (any MCP-compatible agent)

Connect **any MCP agent** by registering one server. The same config works for all agents.

**opencode (v1.17+):**
`toolrecall setup` writes this automatically to `~/.opencode/opencode.jsonc`. Or add manually:

```jsonc
// ~/.opencode/opencode.jsonc
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "toolrecall": {
      "type": "local",
      "command": "toolrecall",
      "args": ["mcp"],
      "enabled": true
    }
  }
}
```

**Claude Code / Cursor / Cline / Windsurf / Continue:**

```json
// ~/.claude/settings.json  or  ~/.cursor/mcp.json  or  ~/.config/cline/mcp_settings.json
{
  "mcpServers": {
    "toolrecall": {
      "command": "toolrecall",
      "args": ["mcp"]
    }
  }
}
```

**Hermes Agent:**
Hermes already ships with ToolRecall built in — the tools `cached_read`, `cached_terminal`, `mcp_call`, etc. are available directly in your toolset.

**Aider:**
```bash
aider --mcp-toolrecall
# or add to ~/.aider.mcp.json with the same format as above
```

All agents share **one daemon** and **one cache** — no duplication, no conflict.

---

## Forward Proxy (API-level caching)

Cache API responses before they leave your machine. The forward proxy starts **automatically** with the daemon — no extra command needed.

```bash
export OPENAI_BASE_URL=http://localhost:8569/v1   # Any OpenAI-compatible SDK
# or override the base URL in your provider config / client init
```

| Provider SDK | How to connect | Token savings |
|-------------|---------------|---------------|
| **Any OpenAI-compatible client** | `export OPENAI_BASE_URL=http://localhost:8569/v1` | **Zero tokens consumed** — cache hit never reaches the provider |
| **Custom port** | `toolrecall serve --port 9090` | Same |

### MCP Bridge (tool-level caching)

Connect **any MCP agent** by adding one server:

```json
{
  "mcpServers": {
    "toolrecall": {
      "command": "toolrecall",
      "args": ["mcp"]
    }
  }
}
```

Works for **Claude Desktop, Claude Code, Cursor, Cline, Windsurf, Continue, and any MCP-compatible agent** with zero per-agent variations.

### OS-level Shim (zero-config caching)

Once `toolrecall setup` is run (or any CLI command auto-installs it), the **shim .pth file** lives in `site-packages/tr_shim.pth`. Every Python process on the machine automatically caches `open()` and `subprocess.run()` through the ToolRecall daemon — **no imports, no agent configuration**.

| Agent | How to connect | Token savings |
|-------|---------------|---------------|
| **Any Python binary** | Just `pip install toolrecall` — the `.pth` in site-packages auto-patches `open()` / `subprocess.run()` | ✅ Transparent, agent-agnostic |
| **Any MCP agent** | Add the `toolrecall` server to your MCP config | ✅ Universal |
| **Forward proxy** | `export OPENAI_BASE_URL=http://localhost:8569` | ✅ Zero-token cache hits |

---

## Configuration

TOML (stdlib `tomllib`) or YAML (optional, requires `pyyaml`).

```toml
# ~/.config/toolrecall/toolrecall.toml (created by toolrecall init)
[mcp]
allowed_paths = ["/home/user/projects"]  # Add your project dirs — default-deny!
allow_terminal = false
allow_invalidate = false
default_ttl = 60

[mcp_multiplex]
enabled = true
servers = ["time", "sequential-thinking"]

[forward_proxy]
# Forward proxy starts on :8569 automatically with the daemon
```

`TOOLRECALL_*` environment variables override TOML.

---

## Uninstall

```bash
toolrecall shim --uninstall          # Remove .pth from site-packages
systemctl --user stop toolrecall-daemon
systemctl --user disable toolrecall-daemon
pip uninstall toolrecall
rm -rf ~/.toolrecall ~/.config/toolrecall
```

---

## Platform Support

| Platform | Transport | Status |
|----------|-----------|--------|
| **Linux** | Unix Domain Sockets | ✅ Tested in CI |
| **macOS** | Unix Domain Sockets | ✅ Should work (POSIX). Not in CI. |
| **Windows** | TCP localhost:8568 fallback | ⚠️ Core + transport tested. CLI works. |

---

## Documentation

- [Architecture](docs/ARCHITECTURE.md) — daemon design, layers, IPC
- [Architecture Diagram](docs/ARCHITECTURE_DIAGRAM.md) — system and sequence diagrams, token costs, Context Tracker
- [CLI Reference](docs/CLI.md) — all subcommands explained
- [Configuration Reference](docs/CONFIG_REFERENCE.md) — config.toml, config.py, all env vars
- [Context Tracker](docs/CONTEXT_TRACKER.md) — checkpoint-based dirty-file tracking, O(n²) breakdown
- [How It Works](docs/HOW_IT_WORKS.md) — quick technical overview
- [MCP Multiplexer](docs/MCP_MULTIPLEXER.md) — single-daemon MCP management, server registry
- [Testing Guide](docs/TESTING.md) — test philosophy, organization, per-file coverage
- [Benchmark](docs/BENCHMARK.md) — measured performance, token savings
- [Knowledge DB](docs/KNOWLEDGE_DB.md) — FTS5 indexing guide
- [Docker Deployment](docs/DOCKER.md) — containerized stack
- [Security Architecture](SECURITY.md) — WAF details, trust boundary
- [Troubleshooting](docs/TROUBLESHOOTING.md) — common fixes
- [Appendix](docs/APPENDIX.md) — comparison tables, OSI model, ROI, vision, audit
- [Hermes Transparent Cache](docs/HERMES_TRANSPARENT_CACHE.md) — auto-patching for Hermes Agent
