Parameters Reference

Name: SHIPIT Agent
Author: SHIPIT

Every constructor parameter for every agent type, RAG, memory, and storage class — with types, defaults, and when-to-use notes.

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A single-page reference to every public constructor parameter in shipit_agent. Use this as the cheat sheet you keep open in another tab while you build.

Conventions:

Type — Python type hint
Default — value when omitted (or required for required args)
What it does — one-line description
Use it when — concrete trigger for setting it

`Agent`

The core class. Every other agent type wraps an Agent internally.

python

from shipit_agent import Agent

agent = Agent(
    llm=llm,
    rag=my_rag,
    max_iterations=8,
    parallel_tool_execution=True,
)

Parameter	Type	Default	What it does	Use it when
`llm`	`LLM`	required	The LLM client used for every completion.	Always.
`prompt`	`str`	`DEFAULT_AGENT_PROMPT`	The system prompt.	Override to give the agent a persona or domain framing.
`tools`	`list[Tool]`	`[]`	Tools the agent can call.	Pass your own tool list. Use `with_builtins()` for the full catalogue.
`mcps`	`list[MCPServer]`	`[]`	MCP servers attached at run time.	Connect to remote / local MCP tool servers.
`name`	`str`	`"shipit"`	Agent identifier surfaced in events and traces.	Multi-agent setups — give each one a name.
`description`	`str`	`""`	Free-form description (used in traces and supervisor delegation).	Multi-worker supervisors.
`metadata`	`dict[str, Any]`	`{}`	Arbitrary metadata attached to every event.	Tag runs with request id, user id, etc.
`history`	`list[Message]`	`[]`	Pre-existing conversation messages to seed the run.	Resume a chat from previous context.
`memory_store`	`MemoryStore \| None`	`None`	Long-term memory store.	Cross-conversation knowledge persistence.
`session_store`	`SessionStore \| None`	`None`	Conversation history store.	Multi-turn chat with persistence.
`credential_store`	`CredentialStore \| None`	`None`	OAuth tokens / API keys.	Tools that need credentials (Slack, Gmail, …).
`trace_store`	`TraceStore \| None`	`None`	Audit log of every event.	Production observability.
`session_id`	`str \| None`	`None`	Logical session identifier.	Multi-turn chat with `session_store`.
`trace_id`	`str \| None`	`None`	Logical trace identifier.	Distributed tracing.
`max_iterations`	`int`	`4`	Hard cap on LLM iterations per run.	Bump higher for deep reasoning, lower to fail fast.
`retry_policy`	`RetryPolicy`	`RetryPolicy()`	LLM/tool retry behaviour.	Production with flaky upstreams.
`router_policy`	`RouterPolicy`	`RouterPolicy()`	Auto-planning + tool routing.	Disable auto-plan with `RouterPolicy(auto_plan=False)`.
`parallel_tool_execution`	`bool`	`False`	Run independent tool calls in parallel.	Latency-sensitive runs with multiple parallel calls.
`hooks`	`AgentHooks \| None`	`None`	Pre/post-LLM and tool middleware.	Custom logging, redaction, instrumentation.
`context_window_tokens`	`int`	`0`	Auto-compact messages above this token count (0 = off).	Long runs against models with finite context.
`replan_interval`	`int`	`0`	Re-run the planner every N iterations (0 = off).	Long-horizon tasks where the plan should evolve.
`rag`	`RAG \| None`	`None`	Auto-wires RAG tools + system prompt + source tracker.	Grounded answers with citations.

Class methods:

Agent.with_builtins(llm=..., **kwargs) — also wires the full builtin tool catalogue (web search, code exec, file workspace, integrations, …).

`DeepAgent`

Power-user create_deep_agent factory. Strict superset of LangChain's create_deep_agent. See DeepAgent docs.

python

from shipit_agent.deep import DeepAgent

agent = DeepAgent.with_builtins(
    llm=llm,
    rag=rag,
    verify=True,
    reflect=True,
    max_iterations=20,
)

Parameter	Type	Default	What it does	Use it when
`llm`	`LLM`	required	Backing LLM client.	Always.
`name`	`str`	`"shipit-deep-agent"`	Agent identifier.	Multi-agent setups.
`description`	`str`	`"A deep agent that plans, verifies, and uses a workspace."`	Free-form description.	Supervisor delegation.
`prompt`	`str`	`DEEP_AGENT_PROMPT`	Opinionated deep-agent system prompt.	Override only if you know what you're doing.
`extra_tools`	`list[Tool]`	`[]`	User tools to add on top of the deep tool set.	Add domain-specific tools.
`mcps`	`list[MCPServer]`	`[]`	MCP servers.	Tool servers.
`workspace_root`	`str`	`".shipit_workspace"`	Filesystem root for `workspace_files`.	Customise where the agent's notes live.
Power features
`rag`	`RAG \| None`	`None`	Auto-wired RAG with source citations.	Grounded answers.
`memory`	`AgentMemory \| None`	`None`	Long-term cross-session memory.	Multi-session knowledge retention.
`memory_store`	`MemoryStore \| None`	`None`	Backing memory store.	Custom persistence.
`session_store`	`SessionStore \| None`	`None`	Session backing for `chat()`.	Persistent chat.
`verify`	`bool`	`False`	Run `verify_output` after every answer; verdict on `result.metadata["verification"]`.	High-stakes answers that must be checked.
`reflect`	`bool`	`False`	Wrap in `ReflectiveAgent` — generate, critique, revise loop.	Quality matters more than latency.
`reflect_threshold`	`float`	`0.8`	Quality target (0..1) for reflection.	Tune how strict reflection is.
`reflect_max_iterations`	`int`	`3`	Max self-critique iterations.	Cap reflection cost.
`goal`	`Goal \| None`	`None`	Switch to goal-driven mode (delegates to `GoalAgent`).	You have explicit success criteria.
Runtime tuning
`max_iterations`	`int`	`8`	Max LLM iterations.	Deeper reasoning vs faster fails.
`parallel_tool_execution`	`bool`	`True`	Parallelise independent tool calls.	Default on — disable for debugging.
`context_window_tokens`	`int`	`0`	Auto-compact above N tokens.	Very long runs against finite-context models.
`retry_policy`	`RetryPolicy \| None`	`None`	Override retry behaviour.	Flaky upstreams.
`router_policy`	`RouterPolicy \| None`	`None`	Override routing.	Disable auto-plan.
`hooks`	`AgentHooks \| None`	`None`	Middleware.	Custom logging / redaction.
`trace_store`	`TraceStore \| None`	`None`	Audit log.	Production observability.
`credential_store`	`CredentialStore \| None`	`None`	OAuth tokens.	Connector tools.
Builtins
`use_builtins`	`bool`	`False`	Bundle the regular built-in tool catalogue.	Default on for `with_builtins()`.
`web_search_provider`	`str`	`"duckduckgo"`	Web search backend.	`"brave"`, `"tavily"`, `"serper"`, `"playwright"`.
`web_search_api_key`	`str \| None`	`None`	API key for the web search provider.	Required for everything except DDG.

Methods: run, stream, chat, add_tool, add_mcp. See DeepAgent docs.

`create_deep_agent` (functional helper)

LangChain-compatible spelling.

python

from shipit_agent.deep import create_deep_agent

agent = create_deep_agent(
    llm=llm,
    tools=[my_tool, plain_python_function],
    system_prompt="...",
    rag=rag,
    verify=True,
)

Parameter	Type	Default	What it does
`llm`	`LLM`	required	LLM client.
`tools`	`list[Tool \| Callable]`	`None`	Tools or plain Python functions. Functions are auto-wrapped as `FunctionTool`.
`system_prompt`	`str`	`DEEP_AGENT_PROMPT`	System prompt.
`rag`	`RAG \| None`	`None`	RAG instance.
`use_builtins`	`bool`	`False`	Bundle the builtin tool catalogue.
`verify`	`bool`	`False`	Verification mode.
`reflect`	`bool`	`False`	Reflective mode.
`goal`	`Goal \| None`	`None`	Goal-driven mode.
`memory`	`AgentMemory \| None`	`None`	Long-term memory.
`max_iterations`	`int`	`8`	Iteration cap.
`**kwargs`	—	—	Forwarded to `DeepAgent.__init__`.

`GoalAgent`

Decompose → execute → self-evaluate.

python

from shipit_agent.deep import Goal, GoalAgent

goal_agent = GoalAgent.with_builtins(
    llm=llm,
    goal=Goal(
        objective="Build a calculator CLI",
        success_criteria=["Handles +,-,*,/", "Has tests", "Has error handling"],
    ),
    rag=rag,
)
result = goal_agent.run()  # GoalResult

Parameter	Type	Default	What it does
`llm`	`LLM`	required	LLM client.
`goal`	`Goal`	required	Objective + success criteria + max_steps.
`tools`	`list[Tool]`	`[]`	Tools the inner agent can use.
`mcps`	`list[MCPServer]`	`[]`	MCP servers.
`use_builtins`	`bool`	`False`	Bundle the builtin tool catalogue.
`prompt`	`str`	`"You are a helpful assistant. Complete the task thoroughly."`	System prompt.
`memory`	`AgentMemory \| None`	`None`	Long-term memory.
`rag`	`RAG \| None`	`None`	RAG instance — forwarded to every inner Agent build.
`**agent_kwargs`	—	—	Forwarded to the inner `Agent`.

Goal fields:

Field	Type	Default
`objective`	`str`	required
`success_criteria`	`list[str]`	`[]`
`max_steps`	`int`	`20`

`ReflectiveAgent`

Generate → critique → revise.

python

from shipit_agent.deep import ReflectiveAgent

agent = ReflectiveAgent.with_builtins(
    llm=llm,
    quality_threshold=0.8,
    max_reflections=3,
    rag=rag,
)

Parameter	Type	Default
`llm`	`LLM`	required
`tools`	`list[Tool]`	`[]`
`mcps`	`list[MCPServer]`	`[]`
`reflection_prompt`	`str`	`"Check for accuracy, completeness, and clarity."`
`max_reflections`	`int`	`3`
`quality_threshold`	`float`	`0.8`
`use_builtins`	`bool`	`False`
`prompt`	`str`	`"You are a helpful assistant."`
`memory`	`AgentMemory \| None`	`None`
`**agent_kwargs`	—	Forwarded to inner `Agent` (so `rag=` works).

`AdaptiveAgent`

Writes new tools at runtime.

python

from shipit_agent.deep import AdaptiveAgent

agent = AdaptiveAgent.with_builtins(
    llm=llm,
    can_create_tools=True,
    sandbox=True,
)

Parameter	Type	Default
`llm`	`LLM`	required
`tools`	`list[Tool]`	`[]`
`mcps`	`list[MCPServer]`	`[]`
`can_create_tools`	`bool`	`True`
`sandbox`	`bool`	`True`
`use_builtins`	`bool`	`False`
`prompt`	`str`	`"You are a helpful assistant."`
`**agent_kwargs`	—	Forwarded to inner `Agent`.

`Supervisor`

Coordinates multiple worker agents.

python

from shipit_agent.deep import Supervisor

supervisor = Supervisor.with_builtins(
    llm=llm,
    worker_configs=[{"name": "researcher", "prompt": "You research."},
        {"name": "writer", "prompt": "You write."},],
    rag=rag,
    max_delegations=15,
)

Parameter	Type	Default
`llm`	`LLM`	required
`workers`	`list[Worker]`	required (use `with_builtins(worker_configs=...)` for the easy path)
`strategy`	`str`	`"plan_and_delegate"`
`allow_parallel`	`bool`	`False`
`max_delegations`	`int`	`15`
`rag`	`RAG \| None`	`None` — wired into every `with_builtins` worker
`**agent_kwargs`	—	Forwarded to every worker `Agent` built via `with_builtins`.

Worker fields:

Field	Type	Default
`name`	`str`	required
`agent`	`Any`	required
`capabilities`	`list[str]`	`[]`

`PersistentAgent`

Long-running checkpointed task.

python

from shipit_agent.deep import PersistentAgent

agent = PersistentAgent(
    llm=llm,
    checkpoint_dir="./checkpoints",
    checkpoint_interval=5,
    max_steps=50,
    rag=rag,
)
agent.run("long task", agent_id="task-1")
agent.resume(agent_id="task-1")  # after a crash

Parameter	Type	Default
`llm`	`LLM`	required
`tools`	`list[Tool]`	`[]`
`checkpoint_dir`	`str`	`".shipit_checkpoints"`
`checkpoint_interval`	`int`	`5`
`max_steps`	`int`	`50`
`rag`	`RAG \| None`	`None`
`**agent_kwargs`	—	Forwarded to inner `Agent`.

`RAG`

The Super RAG facade. See Super RAG docs.

python

from shipit_agent.rag import RAG, HashingEmbedder

rag = RAG.default(embedder=HashingEmbedder(dimension=512))
rag.index_file("docs/manual.pdf")
ctx = rag.search("python version", top_k=5, enable_reranking=True)

Parameter	Type	Default	What it does
`vector_store`	`VectorStore`	required	Cosine vector index.
`embedder`	`Embedder \| Callable`	required	Anything coercible to an `Embedder`.
`keyword_store`	`KeywordStore \| None`	`None`	Optional BM25 index for hybrid search.
`reranker`	`Reranker \| None`	`None`	LLM / Cohere / cross-encoder reranker.
`chunker`	`DocumentChunker \| None`	`DocumentChunker()`	Chunking strategy.
`auto_embed_on_add`	`bool`	`True`	Set `False` when reloading chunks that already have embeddings.

RAG.search keyword arguments:

Parameter	Type	Default	What it does
`query`	`str`	required	Natural-language query.
`top_k`	`int`	`5`	Max chunks returned.
`filters`	`IndexFilters \| None`	`None`	Scope by source, document_id, metadata, time.
`hybrid_alpha`	`float`	`0.5`	1.0 = pure vector, 0.0 = pure BM25.
`enable_reranking`	`bool`	`False`	Run the reranker over top candidates.
`enable_recency_bias`	`bool`	`False`	Exponential decay over `created_at`.
`chunks_above`	`int`	`0`	Number of preceding neighbouring chunks to expand.
`chunks_below`	`int`	`0`	Number of following neighbouring chunks to expand.

`DocumentChunker`

Parameter	Type	Default	Description
`target_tokens`	`int`	`512`	Preferred chunk size.
`overlap_tokens`	`int`	`64`	Character-overlap budget between consecutive chunks.
`title_prefix_chars`	`int`	`64`	Title characters prepended to `text_for_embedding`.

`HashingEmbedder` / `CallableEmbedder`

python

from shipit_agent.rag import HashingEmbedder, CallableEmbedder

# Stdlib-only deterministic embedder (great for tests / demos)
hash_emb = HashingEmbedder(dimension=384, seed="shipit-rag")

# Wrap any callable
def embed(texts: list[str]) -> list[list[float]]:
    return my_provider.embed(texts)

custom_emb = CallableEmbedder(fn=embed, dimension=1536)

Class	Parameter	Default
`HashingEmbedder`	`dimension`	`384`
`HashingEmbedder`	`seed`	`"shipit-rag"`
`CallableEmbedder`	`fn`	required
`CallableEmbedder`	`dimension`	required

`LLMReranker`

python

from shipit_agent.rag import LLMReranker

reranker = LLMReranker(llm=my_llm, batch_size=10)

Parameter	Type	Default
`llm`	`LLM`	required
`batch_size`	`int`	`10`

`AgentMemory`

python

from shipit_agent import AgentMemory

memory = AgentMemory.default(llm=llm, embedding_fn=embed)

AgentMemory is the unified facade over ConversationMemory, SemanticMemory, and EntityMemory. See the Advanced Memory guide for the full parameter set on each component class.

`RetryPolicy`

python

from shipit_agent.policies import RetryPolicy

policy = RetryPolicy(
    max_retries=3,
    base_delay=1.0,
    max_delay=30.0,
    backoff_factor=2.0,
    retry_on_tool_failure=True,
)

Parameter	Type	Default	Description
`max_retries`	`int`	`3`	Max retry attempts.
`base_delay`	`float`	`1.0`	Initial delay in seconds.
`max_delay`	`float`	`30.0`	Cap on delay between retries.
`backoff_factor`	`float`	`2.0`	Exponential backoff multiplier.
`retry_on_tool_failure`	`bool`	`True`	Retry tools on transient failures.

`RouterPolicy`

python

from shipit_agent.policies import RouterPolicy

policy = RouterPolicy(
    auto_plan=True,
    use_tool_search=True,
    tool_search_top_k=5,
)

Parameter	Type	Default	Description
`auto_plan`	`bool`	`True`	Auto-invoke `plan_task` before the first LLM call.
`use_tool_search`	`bool`	`False`	Use `tool_search` to short-list tools.
`tool_search_top_k`	`int`	`5`	Number of tools surfaced by `tool_search`.

`AgentChatSession` (`Agent.chat_session()`, `DeepAgent.chat()`)

python

session = agent.chat_session(session_id="user-42", trace_id="req-1")
for event in session.stream("Hi"):
    print(event.message)

Parameter	Type	Default	Description
`agent`	`Agent`	required	The underlying agent.
`session_id`	`str`	required	Logical session identifier.
`trace_id`	`str \| None`	`None`	Distributed-trace id.
`session_store`	`SessionStore \| None`	`None`	Backing store. Defaults to `agent.session_store` or `InMemorySessionStore`.

Storage backends

Class	Module	Constructor
`InMemorySessionStore`	`shipit_agent.stores`	`InMemorySessionStore()`
`FileSessionStore`	`shipit_agent.stores`	`FileSessionStore(root="path/")`
`InMemoryMemoryStore`	`shipit_agent.stores`	`InMemoryMemoryStore()`
`FileMemoryStore`	`shipit_agent.stores`	`FileMemoryStore(root="path/")`
`InMemoryTraceStore`	`shipit_agent`	`InMemoryTraceStore()`
`FileTraceStore`	`shipit_agent`	`FileTraceStore(root="path/")`

All four protocols (SessionStore, MemoryStore, TraceStore, CredentialStore) are documented in Architecture.

CLI — `shipit chat`

bash

shipit chat [--agent TYPE] [--provider NAME] [--session-id ID]
            [--session-dir PATH] [--workspace PATH]
            [--rag-file PATH ...] [--rag-dim INT]
            [--reflect] [--verify]
            [--goal TEXT] [--criteria TEXT ...]
            [--no-builtins] [--quiet]

Flag	Type	Default	Description
`--agent`	choice	`deep`	One of `agent`, `deep`, `goal`, `reflective`, `adaptive`, `supervisor`, `persistent`.
`--provider`	str	`$SHIPIT_LLM_PROVIDER`	LLM provider override.
`--session-id`	str	random	Resume a specific session id.
`--session-dir`	path	(in-memory)	Persist sessions to disk.
`--workspace`	path	`.shipit_workspace`	Workspace root for `workspace_files`.
`--rag-file`	path (repeatable)	`[]`	Index a file before the session starts.
`--rag-dim`	int	`512`	`HashingEmbedder` dimension when `--rag-file` is used.
`--reflect`	flag	off	Enable reflective mode (`DeepAgent`).
`--verify`	flag	off	Enable verification mode (`DeepAgent`).
`--goal`	str	none	Goal objective (use with `--agent goal`).
`--criteria`	str (repeatable)	`[]`	Goal success criterion.
`--no-builtins`	flag	off	Skip the regular builtin tool catalogue.
`--quiet`	flag	off	Hide intermediate event stream.

Slash commands inside the REPL: /help, /agent, /agents, /tools, /sources, /index, /rag, /goal, /reflect, /verify, /history, /clear, /save, /load, /reset, /quiet, /info, /exit, /quit. See DeepAgent docs.

Agent

DeepAgent

create_deep_agent (functional helper)

GoalAgent

ReflectiveAgent

AdaptiveAgent

Supervisor

PersistentAgent

RAG

DocumentChunker

HashingEmbedder / CallableEmbedder

LLMReranker

AgentMemory

RetryPolicy

RouterPolicy

AgentChatSession (Agent.chat_session(), DeepAgent.chat())

Storage backends

CLI — shipit chat

See also

`Agent`

`DeepAgent`

`create_deep_agent` (functional helper)

`GoalAgent`

`ReflectiveAgent`

`AdaptiveAgent`

`Supervisor`

`PersistentAgent`

`RAG`

`DocumentChunker`

`HashingEmbedder` / `CallableEmbedder`

`LLMReranker`

`AgentMemory`

`RetryPolicy`

`RouterPolicy`

`AgentChatSession` (`Agent.chat_session()`, `DeepAgent.chat()`)

CLI — `shipit chat`