cave-agent 0.7.4

CaveAgent is a tool-augmented agent framework that enables function-calling through LLM code generation and provides runtime state management. Unlike traditional JSON-schema approaches, it natively handles complex Python objects like DataFrames and ndarrays within a persistent runtime, enabling lossless data flow across multi-turn interactions.

CaveAgent: Transforming LLMs into Stateful Runtime Operators

"From text-in-text-out to (text&object)-in-(text&object)-out"

---

Most LLM agents operate under a text-in-text-out paradigm, with tool interactions constrained to JSON primitives. CaveAgent breaks this with Stateful Runtime Management—a persistent Python runtime with direct variable injection and retrieval:

• Inject any Python object into the runtime—DataFrames, models, database connections, custom class instances—as first-class variables the LLM can manipulate
• Persist state across turns without serialization; objects live in the runtime, not in the context window
• Retrieve manipulated objects back as native Python types for downstream

Table of Contents

• Installation
• Hello World
• Runtimes
• Examples
  • Data Visualization
  • Function Calling
  • Stateful Object Interactions
  • Multi-Agent Coordination
  • Real-time Streaming
  • Security Rules
• Agent Skills
  • Creating a Skill
  • How Skills Load
  • Using Skills
  • Injection Module
• Context Compaction
• API Resilience
• Features
• Configuration
• LLM Provider Support

Installation

pip install 'cave-agent[all]'

Choose your installation:

# OpenAI support
pip install 'cave-agent[openai]'

# 100+ LLM providers via LiteLLM
pip install 'cave-agent[litellm]'

# Process-isolated kernel runtime (IPyKernelRuntime)
pip install 'cave-agent[ipykernel]'

Hello World

import asyncio
from cave_agent import CaveAgent
from cave_agent.runtime import IPythonRuntime, Variable, Function
from cave_agent.models import LiteLLMModel

model = LiteLLMModel(model_id="model-id", api_key="your-api-key", custom_llm_provider="openai")

async def main():
    def reverse(s: str) -> str:
        """Reverse a string"""
        return s[::-1]

    runtime = IPythonRuntime(
        variables=[
            Variable("secret", "!dlrow ,olleH", "A reversed message"),
            Variable("greeting", "", "Store the reversed message"),
        ],
        functions=[Function(reverse)],
    )
    agent = CaveAgent(model, runtime=runtime)
    response = await agent.run("Reverse the secret")
    print(await runtime.retrieve("secret"))  # Hello, world!
    print(response.content)              # Agent's text response

asyncio.run(main())

Runtimes

CaveAgent provides two runtime backends. Both share the same API for injecting functions, variables, and types — choose based on your trust and isolation requirements.

IPythonRuntime (default)

Code runs in the same process via an embedded IPython shell. Injected objects (DataFrames, DB connections, custom classes) are accessed directly — no serialization overhead.

from cave_agent.runtime import IPythonRuntime, Function, Variable

runtime = IPythonRuntime(
    functions=[Function(my_func)],
    variables=[Variable("data", my_dataframe, "Input data")],
)
agent = CaveAgent(model, runtime=runtime)

Best for: trusted environments, internal tools, when you need zero-overhead access to complex Python objects.

IPyKernelRuntime (process-isolated)

Code runs in a separate IPython kernel process. If the code crashes (segfault, OOM, infinite loop), the host process stays alive — just reset the kernel and continue.

pip install 'cave-agent[ipykernel]'

from cave_agent.runtime import IPyKernelRuntime, Function, Variable

async with IPyKernelRuntime(
    functions=[Function(my_func)],
    variables=[Variable("data", [1, 2, 3], "Input data")],
) as runtime:
    agent = CaveAgent(model, runtime=runtime)
    response = await agent.run("Analyze the data")

Injected objects are serialized via dill, which supports local functions, closures, lambdas, and most Python objects.

Best for: untrusted code execution, multi-tenant environments, sandboxed agent workflows.

Comparison

	IPythonRuntime	IPyKernelRuntime
Isolation	Same process	Separate process
Crash impact	Host process dies	Kernel restarts, host survives
Object injection	Direct reference, zero-copy	Serialized via dill
Startup	Instant	~1s (kernel launch)
Local functions / closures	Always works	Works (via dill)
Requires	(included)	pip install 'cave-agent[ipykernel]'

Examples

Data Visualization

from cave_agent import CaveAgent
from cave_agent.runtime import IPythonRuntime, Variable
from cave_agent.models import LiteLLMModel

model = LiteLLMModel(model_id="model-id", api_key="your-api-key", custom_llm_provider="openai")

# 1. Inject — real DB connection & chart config manager
runtime = IPythonRuntime(
    variables=[
        Variable("engine", database_engine),             # SQLAlchemy Engine
        Variable("echarts_config_manager", EChartsConfigManager()),  # Chart collector
    ]
)
agent = CaveAgent(model, runtime=runtime)

# 2. Query — LLM sees object types, not data
await agent.run("Show me the air quality trend for the past week")

# LLM generates & executes:
#   df = pd.read_sql("SELECT * FROM air_quality WHERE ...", engine)
#   echarts_config_manager.add_config({
#       "title": {"text": "Air Quality - Past Week"},
#       "xAxis": {"data": dates},
#       "series": [{"name": "PM2.5", "type": "line", "data": ...}]
#   })

# 3. Retrieve — get real chart configs for rendering
mgr = await runtime.retrieve("echarts_config_manager")  # Real Python object
configs = mgr.get_configs()

for config in configs:
    render_echarts(config)  # Render directly in web UI

Function Calling

# Inject functions and variables into runtime
runtime = IPythonRuntime(
    variables=[Variable("tasks", [], "User's task list")],
    functions=[Function(add_task), Function(complete_task)],
)
agent = CaveAgent(model, runtime=runtime)

await agent.run("Add 'buy groceries' to my tasks")
print(await runtime.retrieve("tasks"))  # [{'name': 'buy groceries', 'done': False}]

See examples/basic_usage.py for a complete example.

Stateful Object Interactions

# Inject objects with methods - LLM can call them directly
runtime = IPythonRuntime(
    types=[Type(Light), Type(Thermostat)],
    variables=[
        Variable("light", Light("Living Room"), "Smart light"),
        Variable("thermostat", Thermostat(), "Home thermostat"),
    ],
)
agent = CaveAgent(model, runtime=runtime)

await agent.run("Dim the light to 20% and set thermostat to 22°C")
light = await runtime.retrieve("light")  # Object with updated state

See examples/object_methods.py for a complete example.

Multi-Agent Coordination

# Sub-agents with their own runtimes
cleaner_agent = CaveAgent(model, runtime=IPythonRuntime(variables=[
    Variable("data", [], "Input"), Variable("cleaned_data", [], "Output"),
]))

analyzer_agent = CaveAgent(model, runtime=IPythonRuntime(variables=[
    Variable("data", [], "Input"), Variable("insights", {}, "Output"),
]))

# Orchestrator controls sub-agents as first-class objects
orchestrator = CaveAgent(model, runtime=IPythonRuntime(variables=[
    Variable("raw_data", raw_data, "Raw dataset"),
    Variable("cleaner", cleaner_agent, "Cleaner agent"),
    Variable("analyzer", analyzer_agent, "Analyzer agent"),
]))

# Inject → trigger → retrieve
await orchestrator.run("Clean raw_data using cleaner, then analyze using analyzer")
insights = await analyzer.runtime.retrieve("insights")

See examples/multi_agent.py for a complete example.

Real-time Streaming

from cave_agent.types import EventType

async for event in agent.stream_events("Analyze this data"):
    if event.type == EventType.THINKING_CHUNK:    # reasoning streams live (o1/o3, DeepSeek-R1, …)
        print(event.content, end="")
    elif event.type == EventType.TEXT:
        print(event.content, end="")
    elif event.type == EventType.CODE:
        print(f"\nExecuting: {event.content}")
    elif event.type == EventType.EXECUTION_OUTPUT:
        print(f"Result: {event.content}")

Reasoning models. When the model exposes a reasoning trace (OpenAI o1/o3, DeepSeek-R1, Qwen/Moonshot reasoning, …), CaveAgent streams it live as THINKING_CHUNK events (before the answer), then seals the segment with one THINKING event carrying the full trace. Reasoning is captured, kept out of the code-block parser, and never sent back on the wire.

See examples/stream.py for a complete example.

Security Rules

# Block dangerous operations with AST-based validation
rules = [
    ImportRule({"os", "subprocess", "sys"}),      # also blocks os.path, subprocess.run, ...
    FunctionRule({"eval", "exec", "open"}),        # also catches aliasing: f = open; f(...)
    AttributeRule({"__globals__", "__builtins__"}),
    RegexRule(r"rm\s+-rf|sudo\s+", "Block shell escapes"),
]
runtime = IPythonRuntime(security_checker=SecurityChecker(rules))

⚠️ SecurityChecker is advisory hardening, not a sandbox. Static AST analysis cannot catch every obfuscation (dynamic reflection, C-extension escapes, etc.). For genuinely untrusted code, run inside a real isolation boundary — a container with seccomp/gVisor and OS resource limits — and use the process-isolated IPyKernelRuntime. Treat these rules as defense-in-depth.

More Examples

• Basic Usage: Function calling and object processing
• Runtime State: State management across interactions
• Object Methods: Class methods and complex objects
• Multi-Turn: Conversations with state persistence
• Multi-Agent: Data pipeline with multiple agents
• Stream: Streaming responses and events

Agent Skills

CaveAgent implements the Agent Skills open standard—a portable format for packaging instructions that agents can discover and use. Originally developed by Anthropic and now supported across the AI ecosystem (Claude, Gemini CLI, Cursor, VS Code, and more), Skills enable agents to acquire domain expertise on-demand.

Creating a Skill

A Skill is a directory containing a SKILL.md file with YAML frontmatter:

my-skill/
├── SKILL.md           # Required: Skill definition and instructions
└── injection.py       # Optional: Functions/variables/types to inject (CaveAgent extension)

SKILL.md structure:

---
name: data-processor
description: Process and analyze datasets with statistical methods. Use when working with data analysis tasks.
---

# Data Processing Instructions

## Quick Start
Use the injected functions to analyze datasets...

## Workflows
1. Activate the skill to load injected functions
2. Apply statistical analysis using the provided functions
3. Return structured results

Required fields: name (max 64 chars, lowercase with hyphens) and description (max 1024 chars)

Optional fields: license, compatibility, metadata

How Skills Load (Progressive Disclosure)

Skills use progressive disclosure to minimize context usage:

Level	When Loaded	Content
Metadata	At startup	name and description from YAML frontmatter (~100 tokens)
Instructions	When activated	SKILL.md body with guidance (loaded on-demand)

Using Skills

Skills are owned by the runtime — pass them into IPythonRuntime (or IPyKernelRuntime) alongside functions, variables, and types. The agent stays skill-agnostic and gains access to the injected activate_skill(name) builtin through the runtime.

from pathlib import Path
from cave_agent import CaveAgent
from cave_agent.runtime import IPythonRuntime, Function, Variable
from cave_agent.skills import Skill, SkillDiscovery

# Create skills directly
skill = Skill(
    name="my-skill",
    description="A custom skill",
    body_content="# Instructions\nFollow these steps...",
    functions=[Function(my_func)],
    variables=[Variable("config", value={})],
)
runtime = IPythonRuntime(skills=[skill])
agent = CaveAgent(model=model, runtime=runtime)

# Or load from a single SKILL.md file
skill = SkillDiscovery.from_file(Path("./my-skill/SKILL.md"))
runtime = IPythonRuntime(skills=[skill])
agent = CaveAgent(model=model, runtime=runtime)

# Or load an entire directory of skills
skills = SkillDiscovery.from_directory(Path("./skills"))
runtime = IPythonRuntime(skills=skills)
agent = CaveAgent(model=model, runtime=runtime)

When skills are registered on the runtime, the activate_skill(skill_name) builtin is automatically available to LLM-generated code. Calling it returns the skill's instructions and injects its exports (functions, variables, types) into the runtime namespace.

Because skills live on the runtime, multiple agents sharing a runtime naturally share its skill set — no duplicate injection, no hidden per-agent state.

Injection Module (CaveAgent Extension)

CaveAgent extends the Agent Skills standard with injection.py—allowing skills to inject functions, variables, and types directly into the runtime when activated:

from cave_agent.runtime import Function, Variable, Type
from dataclasses import dataclass

def analyze_data(data: list) -> dict:
    """Analyze data and return statistics."""
    return {"mean": sum(data) / len(data), "count": len(data)}

@dataclass
class AnalysisResult:
    mean: float
    count: int
    status: str

CONFIG = {"threshold": 0.5, "max_items": 1000}

__exports__ = [
    Function(analyze_data, description="Analyze data statistically"),
    Variable("CONFIG", value=CONFIG, description="Analysis configuration"),
    Type(AnalysisResult, description="Result structure"),
]

When activate_skill() is called, these exports are automatically injected into the runtime namespace.

See examples/skill_data_processor.py for a complete example.

Context Compaction

Long conversations inevitably fill up the model's context window. CaveAgent implements a multi-tier compaction strategy inspired by Claude Code's context management system.

How it works:

Token usage exceeds threshold?
        |
        v
  Tier 1: Microcompact (no LLM, instant)
  Clear old execution results, keep recent 6.
  Tokens under threshold? → done
        |
        v
  Tier 2: Full Compact (LLM summarization)
  Summarize older messages, keep recent half.
  Uses dual-phase prompt: <analysis> (discarded) + <summary> (kept).
        |
        v
  Tier 3: Trim Fallback (last resort)
  Keep recent half of messages, drop the rest.

The system message (index 0) is always preserved. A circuit breaker stops attempting LLM summarization after 3 consecutive failures, falling back to trim to avoid wasting API calls.

Two refinements keep it accurate before an API token count is available:

• CJK-aware estimation. Chinese / Japanese / Korean text tokenizes ~2× denser than the chars/4 heuristic, so estimation switches to a per-class split once CJK density crosses a threshold — compaction then triggers at the right time for non-English conversations, not too late.
• Emergency recovery. If a request still overflows the window despite proactive compaction, an aggressive path (keep the most recent quarter, summarize the rest) runs and the call is retried once — see API Resilience.

agent = CaveAgent(
    model,
    runtime=runtime,
    context_window=128_000,  # triggers compaction at ~77% usage
)

API Resilience

CaveAgent is built to survive transient failures, provider quirks, and context overflow without dropping the run.

Typed error handling. Provider SDK errors are classified into a provider-agnostic hierarchy so each is handled correctly instead of blindly retried:

• Transient (429 / 5xx / 408 / connection errors) → retried up to 5 times with exponential backoff + jitter (0.5s, 1s, 2s, 4s, 8s); Retry-After is honored when present.
• Billing exhausted (insufficient_quota, credit balance, HTTP 402, …) → never retried — a top-up, not time, is what fixes it.
• Context too long → routed to compaction, not retry (below).

Reactive context-overflow recovery. If the model still rejects a prompt as too long (the local estimate under-counted), the agent aggressively compacts — keeps the most recent quarter, summarizes the rest — and retries the call once, instead of failing.

Streaming liveness. A sliding idle-timeout watchdog (stream_idle_timeout, default 120s) abandons a stalled stream — catching stalls transport timeouts miss (proxy keep-alives / provider pings keep the socket readable while no token arrives). A first-chunk connect failure is retried before any content is emitted; a mid-stream drop salvages what streamed instead of crashing the run.

Output truncation recovery. When the model's response is cut off (finish_reason="length"), the agent appends the partial response and asks the model to continue from the cutoff — up to 3 times.

Resource lifecycle. Every model implements aclose() (and works as an async with context manager) to release its HTTP connection pool cleanly.

Features

• Code-based function calling — the LLM writes and runs Python against injected objects, not rigid JSON tool schemas
• Stateful runtime — inject/retrieve real Python objects (DataFrames, connections, class instances) with state persisting across turns; expose class schemas for type-aware code generation
• Two runtimes — in-process IPythonRuntime or crash-isolated IPyKernelRuntime
• Reasoning models — streams o1 / DeepSeek-R1 reasoning live as THINKING_CHUNK events
• Execution control — step limits, a wall-clock run budget, and per-execution / stream-idle timeouts

Skills, compaction, resilience, security, multi-agent, and provider support each have a dedicated section above.

Configuration

Parameter	Type	Default	Description
model	Model	required	LLM model instance (OpenAIModel or LiteLLMModel)
runtime	Runtime	None	IPythonRuntime (default) or IPyKernelRuntime (process-isolated). Skills are now passed to the runtime — e.g. IPythonRuntime(skills=[...])
max_steps	int	10	Maximum execution steps per run
max_run_time	float | None	None	Wall-clock budget (seconds) for the whole run, checked at turn boundaries. None disables
context_window	int	128000	Model context window size in tokens. Controls when context compaction triggers
max_exec_output	int	5000	Max characters in execution output
max_exec_timeout	float | None	None	Max seconds per code execution. LLM is guided to use timeouts in network/DB calls
stream_idle_timeout	float | None	120.0	Max seconds between two stream chunks before the model call is abandoned (catches stalls transport timeouts miss). None disables
display	bool	True	Render events to terminal via Rich (Claude Code-style UI)
instructions	str	default	User instructions defining agent role and behavior
system_instructions	str	default	System-level execution rules and examples
system_prompt_template	str	default	Custom system prompt template
python_block_identifier	str	python	Code block language identifier
messages	List[Message]	None	Initial message history

LLM Provider Support

CaveAgent supports multiple LLM providers:

OpenAI-Compatible Models

from cave_agent.models import OpenAIModel

model = OpenAIModel(
    model_id="gpt-4",
    api_key="your-api-key",
    base_url="https://api.openai.com/v1"  # or your custom endpoint
)

# Release the HTTP connection pool when tearing down a long-lived model,
# or use it as an async context manager:
await model.aclose()
# async with OpenAIModel(model_id="gpt-4", api_key="...") as model:
#     ...

LiteLLM Models (Recommended)

LiteLLM provides unified access to hundreds of LLM providers:

from cave_agent.models import LiteLLMModel

# OpenAI
model = LiteLLMModel(
    model_id="gpt-4",
    api_key="your-api-key",
    custom_llm_provider='openai'
)

# Anthropic Claude
model = LiteLLMModel(
    model_id="claude-3-sonnet-20240229",
    api_key="your-api-key",
    custom_llm_provider='anthropic' 
)

# Google Gemini
model = LiteLLMModel(
    model_id="gemini/gemini-pro",
    api_key="your-api-key"
)

Contributing

Contributions are welcome! Please feel free to submit a PR. For more details, see CONTRIBUTING.md.

License

MIT License