Deep Worker CLI

deep-worker is a Python package that runs LangChain agents as background daemon processes and exposes them to the interactive dwcli client.

This is a fork of LangChain's deepagents-cli, renamed to deep-worker and extended with additional providers, tools, and infrastructure (see the root README for the component map).

Quick Install

There are two supported ways to use deep-worker:

1. From a source checkout (recommended for development) — no pip install:

# Point the launchers into your PATH (project policy: no virtual environments)
ln -sf /path/to/checkout/deep_worker/deep_worker_cli/deep-worker ~/.local/bin/deep-worker
ln -sf /path/to/checkout/deep_worker/deep_worker_cli/dwcli ~/.local/bin/dwcli

2. From a built wheel (pip install deep-worker) — for consuming the package without the source tree. See docs/BUILD_INSTALL_GUIDE.md for build, check, and install instructions. Note the wheel's limitations (--import-claude is unavailable; deepagents is vendored, so the official PyPI deepagents must not be co-installed).

See deep_worker/README.md for the no-venv runtime model.

Providers & Capabilities (installed wheel)

The wheel ships its five provider packages inside the deep_worker namespace — never as top-level packages (avoids collisions with real PyPI distributions such as claude-code-provider / semantic-search):

from deep_worker.providers.claude_code_provider import ClaudeCodeChatModel
from deep_worker.providers.semantic_search import SemanticSearchIndexer

Optional capabilities are gated behind extras and reported with actionable CapabilityError remedies when missing:

The universal web_search delegation tool works with any LLM provider (tri-state policy): default auto skips it with a single warning when the SDK fork is absent; --enable-web-search fails loudly; --disable-web-search never touches the SDK. DEEP_WORKER_SOURCE_ROOT / DEEP_WORKER_PROVIDER_SOURCE force source-checkout provider resolution (development). Full details: docs/BUILD_INSTALL_GUIDE.md.

Key Features

• 🚀 Background Agent Processes: Run agents as daemon processes with deep-worker
• 💬 Interactive CLI: Connect to running agents with dwcli
• 🧹 Clean History: Start agents with fresh state using --clean option
• 🔧 Customizable: Support for custom prompts, skills, and configurations
• 📊 Session Management: Conversation history and thread management with -rm, --history, --remove-before
• 💾 Flexible Storage: Choose between persistent SQLite or in-memory storage

🔒 Security Notice

⚠️ IMPORTANT: deep-worker-cli is designed for LOCAL TRUSTED ENVIRONMENTS ONLY

• All network services bind to localhost (127.0.0.1) only
• NO external network access - ports are not exposed outside your machine
• No authentication/authorization - relies on localhost isolation
• Suitable for personal development, NOT for production servers
• See SECURITY.md for detailed security requirements

Never expose deep-worker ports to external networks!

🤔 What is this?

Using an LLM to call tools in a loop is the simplest form of an agent. This architecture, however, can yield agents that are "shallow" and fail to plan and act over longer, more complex tasks.

Applications like "Deep Research", "Manus", and "Claude Code" have gotten around this limitation by implementing a combination of four things: a planning tool, sub agents, access to a file system, and a detailed prompt.

deep-worker implements these in a general purpose way so that you can easily create a Deep Agent for your application.

Acknowledgements: This project was primarily inspired by Claude Code, and initially was largely an attempt to see what made Claude Code general purpose, and make it even more so.

💾 Storage Options

By default, agents persist conversation history to SQLite database. You can control storage behavior:

# Default: SQLite persistence (production)
deep-worker my_agent

# Temporary in-memory storage (no persistence)
deep-worker --memory-only temp_agent

# Force SQLite even in test environment
deep-worker --use-sqlite test_agent

List all conversation threads:

# List all threads
deep-worker --list

# List with message counts (slower)
deep-worker --list --with-counts

# Filter by agent name
deep-worker --list --agent my_agent

# JSON output
deep-worker --list --format json

Thread management commands:

# Remove a specific thread
deep-worker -rm thread_id

# Dry run (preview without deleting)
deep-worker -rm thread_id --dry-run

# View thread history (works offline)
deep-worker --history thread_id

# View history as JSON
deep-worker --history thread_id --format json

# ⚠️ DANGER: --remove-before is GLOBAL and DESTRUCTIVE.
# It sweeps every sessions_*.db under ~/.deepagents/ AND every
# ~/.deep_worker/<agent>/summaries/<thread>.md across ALL agents for the
# current $HOME. NEVER run this command from an ad-hoc shell / integration
# test without first isolating $HOME to a temp directory (see the
# isolated_home / fake_home fixtures under tests/).
#
# Safety guard: if DATE resolves to a future timestamp, the command
# refuses by default (exit code 2) and requires --force to proceed.
# Future cutoffs match every row and are almost always bugs.
#
# Always start with --dry-run.

# Remove old conversation data (threads AND offloaded summary sections)
# before a specific date. See the Summarization storage section below
# for what "summary sections" means.
deep-worker --remove-before 2026-01-01

# Remove data older than 30 days
deep-worker --remove-before 30d

# Other relative formats: 7d, 2w, 3m, 1y
deep-worker --remove-before 1y --dry-run

Summarization storage — find and manage offloaded conversation history:

When the model's context fills up, SummarizationMiddleware evicts older messages and writes them to an on-disk file so the agent can later refer back to them. Files live at:

~/.deep_worker/<agent_name>/summaries/<thread_id>.md

Each file is an append-only log with one ## Summarized at <ISO-timestamp> section per summarization event. The path quoted in the synthetic summary message injected into the agent's history is exactly this absolute path — you can cat/grep/less it directly. See deep_worker/docs/DESIGN_SUMMARIZATION_PERSISTENT_STORAGE.md.

# List all offloaded summaries (text table)
deep-worker --list-summaries

# Filter by agent
deep-worker --list-summaries --agent my_agent

# Machine-readable JSON output
deep-worker --list-summaries --format json

The --remove-before DATE command also prunes summary files:

• If every section in a summary file is older than DATE → the file is deleted.
• If some are older and some are newer → the file is atomically rewritten (via <file>.tmp + os.replace) keeping only the newer sections.
• If all are newer → the file is untouched.

Note: Tests automatically use in-memory storage by default for faster execution and test isolation.

🏁 Turn-ending tools

Some tools are the agent's answer for a turn: once they succeed there is nothing useful left for the model to say, and calling it again only produces filler text. send_message_to_agent works this way out of the box — an outgoing inter-agent message is the agent's response for that turn.

Any tool can be given the same behavior by declaring it explicitly:

from deep_worker_cli.agent import create_cli_agent
from deep_worker_cli.middleware import TurnEndingTool

agent, backend = create_cli_agent(
    model="anthropic:claude-sonnet-4-5-20250929",
    assistant_id="extractor-42",
    project_memory_dir=work_dir,
    tools=[submit_fragments_tool],
    turn_ending_tools=[
        TurnEndingTool(
            tool_name="submit_fragments",
            is_success=lambda result: result.get("document_closed") is True,
            echo_field=None,
        ),
    ],
)

TurnEndingTool fields:

Notes:

• Registration is explicit — a tool never becomes turn-ending because of its name. There is no naming convention and no prefix matching, so an MCP server cannot alter an agent's control flow by choosing a suggestive tool name.
• send_message_to_agent is registered automatically; re-declaring it (or any name twice) raises DuplicateTurnEndingToolError at startup rather than being silently ignored.
• With echo_field=None the turn ends on the tool result itself and no assistant message is created — appropriate when the tool's output is not conversational. Use echo_field when the caller should see an answer.
• If the model made parallel tool calls, the turn is not ended, so it can see every result — including any sibling failure.
• A failing is_success predicate is treated as "not successful" and logged; it never stalls the agent.

See docs/DESIGN_TOOL_COMPLETION_MIDDLEWARE.md for the full contract.

📖 Resources

• Root README — component map and quick start
• docs/ — documentation index
• SECURITY.md — security model
• Upstream — the deepagents-cli project this is forked from