Metadata-Version: 2.4
Name: delegent
Version: 0.1.1
Summary: Async-first framework for tool-using agents with optional multi-agent delegation.
Author: Delegent Framework
License:                                  Apache License
                                   Version 2.0, January 2004
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Project-URL: Homepage, https://github.com/sauravsinghal/delegent
Keywords: agents,tools,delegation,aiohttp,mcp
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: aiohttp>=3.9.0
Requires-Dist: jsonschema>=4.21.1
Requires-Dist: protobuf<7.0.0,>=6.31.1
Requires-Dist: websockets>=12.0
Provides-Extra: mcp
Requires-Dist: mcp; extra == "mcp"
Requires-Dist: httpx; extra == "mcp"
Requires-Dist: httpx-sse; extra == "mcp"
Provides-Extra: nuncio
Requires-Dist: nuncio; extra == "nuncio"
Dynamic: license-file

# Delegent – Async‑First Agentic Framework 🚀

[![License: Apache-2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)
[![Python Versions](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-indigo.svg)](#)

**Delegent** is a lightweight, async‑first framework for building tool‑using agents. It supports:

- Simple single‑step agents (`SimpleAgentBuilder`) and advanced multi‑agent delegation (`AgentBuilder`).
- Seamless integration with **any** LLM client (Ollama, OpenAI, Anthropic, Gemini).
- Structured “thinking” streams, observability hooks and metrics.
- Built‑in support for **Nuncio** multi‑agent delegation, **MCP** remote tools, and approval‑required tool calls.
- A clean public API that can be imported directly from the top‑level `delegent` package.

---

## 📦 Installation

Install the official package via pip:

```bash
pip install delegent
```

Or using `uv`:

```bash
uv pip install delegent
```

---

## ✨ Quick Start – Simple Runtime

```python
import asyncio
from delegent import SimpleAgentBuilder, tool, OllamaClient

@tool(
    name="local.greet",
    description="Say hello",
    args_schema={"type": "object", "properties": {"name": {"type": "string"}}},
    keywords=["greet", "hello", "welcome"],
)
async def greet(args):
    return f"hello, {args['name']}"

async def main():
    llm = OllamaClient(model="llama3.1")
    agent = (
        SimpleAgentBuilder()
        .system("You are a helpful planner.")
        .llm(llm, format="json")
        .tools([greet])
        .build()
    )
    result = await agent.run("greet the user", {"name": "world"})
    print(result)

if __name__ == "__main__":
    asyncio.run(main())
```

---

## 🧭 Streaming "Thinking" – Structured Events

```python
from delegent import AgentBuilder, OllamaClient, Event

class PrintHandler:
    async def handle(self, event: Event):
        print(event.type, event.data)

agent = (
    AgentBuilder()
    .system("You are a planner.")
    .llm(OllamaClient(model="llama3.1"), format="json")
    .stream(PrintHandler())
    .build()
)
```

Available event types include `planner.started`, `tool.call`, `tool.result`, `final`, and, when delegating, `delegate.call`, `delegate.result`, etc. Use `stream_mode("thinking")` for a user‑friendly summary or `stream_mode("both")` for raw + thinking.

---

## 🔧 Advanced Features

### Multi‑Agent Delegation (Nuncio)

```python
from delegent import AgentBuilder, OllamaClient

agent = (
    AgentBuilder()
    .system("You are a Trading Agent.")
    .llm(OllamaClient(model="llama3.1"), format="json")
    .multi_agent(hub_uri="ws://localhost:8080/nuncio", token="dev-token", role="TRADING_AGENT")
    .delegate_capabilities(["RESEARCH_AGENT"])  # expose sub‑role(s)
    .build()
)
await agent.connect()
```

### Tool Approval (e.g., Payments)

```python
from delegent import AgentBuilder, tool, ApprovalHandler

@tool(
    name="payments.charge",
    description="Charge a customer",
    args_schema={"type": "object", "properties": {"amount": {"type": "number"}}},
    requires_approval=True,
    approval_prompt="Charge the customer?",
    cost_estimate="$100",
)
async def charge(args):
    return {"charged": True, "amount": args["amount"]}

class MyApproval(ApprovalHandler):
    async def approve(self, tool, args):
        # Hook into your UI / consent flow
        return True

agent = AgentBuilder().approval(MyApproval()).tools([charge]).build()
```

### MCP Remote Tools

```python
from delegent import AgentBuilder, MCPClientConfig, MCPTool

async def call_tool(name, args):
    # Call your remote service here
    return {"ok": True}

config = MCPClientConfig(
    tools=[MCPTool(name="mcp.search", description="Search", args_schema={"type": "object"})],
    call_tool=call_tool,
)
agent = AgentBuilder().mcp(config).llm(OllamaClient(model="llama3.1"), format="json").build()
```

---

## 📚 Documentation

- **Specification** – [`docs/FRAMEWORK.md`](./docs/FRAMEWORK.md)
- **Full User Guide** – [`docs/USER_GUIDE.md`](./docs/USER_GUIDE.md)
- **Cookbook (Examples)** – [`docs/COOKBOOK.md`](./docs/COOKBOOK.md)

All documentation is rendered on the GitHub repository and is also displayed on PyPI via the `long_description` field.

---

## 🤝 Contributing

We welcome contributions! Please:

1. Fork the repository.
2. Create a feature branch (`git checkout -b my‑feature`).
3. Run the test suite locally (`uv run python -m pytest`).
4. Submit a Pull Request.

For detailed guidelines, see [`CONTRIBUTING.md`](./CONTRIBUTING.md).

---

## 📄 License

Released under the **Apache License 2.0**. See the bundled `LICENSE` file for details.

---

## 📬 Contact & Support

- GitHub Issues: <https://github.com/nuncio-labs/delegent/issues>
- Email: `nunciolabs@gmail.com`

---

_Enjoy building next‑generation agents with Delegent!_

This framework is intentionally **simple by default** and modular:

- Create agents with a **system prompt**
- Attach local tools easily
- Mirror MCP tools into the same registry
- Stream structured “thinking” events
- Use any LLM client (Ollama, OpenAI, Anthropic, Gemini)
- Use Nuncio for multi‑agent delegation (with streaming)

Simple runtime uses `SimpleAgentBuilder` and only supports `action` + `final` plans.
Advanced features (delegate/approval) use `AgentBuilder`.

---

## Documentation Map

- **Specification**: [`docs/FRAMEWORK.md`](https://github.com/nuncio-labs/delegent/blob/main/docs/FRAMEWORK.md)
- **Full Guide**: [`docs/USER_GUIDE.md`](https://github.com/nuncio-labs/delegent/blob/main/docs/USER_GUIDE.md)
- **Practical Examples (Cookbook)**: [`docs/COOKBOOK.md`](https://github.com/nuncio-labs/delegent/blob/main/docs/COOKBOOK.md)

---

## Quick Start (Simple Runtime)

```python
import asyncio
from delegent import SimpleAgentBuilder, tool, OllamaClient


@tool(
    name="local.greet",
    description="Say hello",
    args_schema={"type": "object", "properties": {"name": {"type": "string"}}},
    keywords=["greet", "hello", "welcome"],
)
async def greet(args):
    return f"hello, {args['name']}"


async def main():
    ollama = OllamaClient(model="llama3.1")
    agent = (
        SimpleAgentBuilder()
        .system("You are a helpful planner.")
        .llm(ollama, format="json")
        .tools([greet])
        .build()
    )
    result = await agent.run("greet the user", {"name": "world"})
    print(result)


if __name__ == "__main__":
    asyncio.run(main())
```

---

## Streaming “Thinking” (Structured Events)

```python
from delegent import AgentBuilder, OllamaClient, Event


class PrintHandler:
    async def handle(self, event: Event):
        print(event.type, event.data)


agent = (
    SimpleAgentBuilder()
    .system("You are a planner.")
    .llm(OllamaClient(model="llama3.1"), format="json")
    .stream(PrintHandler())
    .build()
)
```

Events emitted:
`planner.started`, `planner.plan`, `tool.call`, `tool.result`, `final`
In delegate mode, you also get:
`peer.dialog.started`, `peer.dialog.ended`, `tool.stream`, `delegate.call`, `delegate.result`

New lifecycle/correlation events include:
`run.started`, `planner.call`, `planner.error`
All runtime events include `run_id` (auto-generated if absent). If provided in context, `session_id` is propagated in event payloads.

### Stream Modes

Stream output can be selected with:

- `raw` (default): existing technical events unchanged.
- `thinking`: user-friendly thinking summaries.
- `both`: thinking summaries plus prefixed raw events (`raw.<event_type>`).

```python
agent = (
    AgentBuilder()
    .system("You are a planner.")
    .llm(OllamaClient(model="llama3.1"), format="json")
    .stream_mode("thinking")
    .build()
)
```

You can also attach a custom thinking sink:

```python
class MyThinkingHandler:
    async def handle(self, event):
        print("THINK:", event.type, event.data)

agent = AgentBuilder().stream_mode("thinking").thinking_handler(MyThinkingHandler()).build()
```

Thinking mode currently includes planner `thought` text and links parent `run_id` with delegated child run IDs when available.
Friendly stream is optimized for readability; raw stream remains the source of truth for debugging.

Delegation is not a tool. The planner should return a `delegate` plan:

```json
{
  "type": "delegate",
  "thought": "Ask the research agent for a signal.",
  "agent_role": "RESEARCH_AGENT",
  "query": "Provide a BUY/NO_BUY signal for AAPL",
  "context": { "symbol": "AAPL" }
}
```

---

## Preferred Tools (Soft Bias)

The planner prefers tools when relevant. You can also override explicitly:

```python
result = await agent.run(
    "greet the user",
    {"name": "world", "preferred_tools": ["local.greet"]},
)
```

---

## Planning History

The planner receives step history and a summary (when history is long).

```python
agent = AgentBuilder().history_limit(5).build()
```

Planner context includes:

- `history`: recent steps (plan + results)
- `history_summary`: compact summary of older steps

---

## Observability (Opt-In)

### Metrics

```python
from delegent import AgentBuilder, FrameworkMetricsCollector

collector = FrameworkMetricsCollector()
agent = AgentBuilder().llm(ollama, format="json").metrics(collector).build()
```

### Structured Logging (JSON stdout)

```python
agent = AgentBuilder().llm(ollama, format="json").logging().build()
```

Or provide config:

```python
agent = AgentBuilder().logging({"level": "INFO", "redact_keys": ["email"]}).build()
```

### Internal Tracing (In-Memory Spans)

```python
from delegent import AgentBuilder, TraceCollector

trace_collector = TraceCollector()
agent = AgentBuilder().llm(ollama, format="json").tracing(trace_collector).build()
```

No observability handler is auto-enabled by default.

---

## Tool Approval (Payments / Sensitive Actions)

Mark tools as requiring approval and provide a handler:

```python
from delegent import AgentBuilder, tool, ApprovalHandler

@tool(
    name="payments.charge",
    description="Charge a customer",
    args_schema={"type": "object", "properties": {"amount": {"type": "number"}}},
    requires_approval=True,
    approval_prompt="Charge the customer?",
    cost_estimate="$100",
)
async def charge(args):
    return {"charged": True, "amount": args["amount"]}

class MyApproval(ApprovalHandler):
    async def approve(self, tool, args):
        return True  # integrate your UI/consent flow here

agent = AgentBuilder().approval(MyApproval()).tools([charge]).build()
```

---

## MCP Tools (Registry‑Backed)

```python
from delegent import AgentBuilder, MCPClientConfig, MCPTool

async def call_tool(name, args):
    # call your MCP server here
    return {"ok": True}

config = MCPClientConfig(
    tools=[MCPTool(name="mcp.search", description="Search", args_schema={"type": "object"})],
    call_tool=call_tool,
)

agent = AgentBuilder().mcp(config).llm(ollama, format="json").build()
```

---

## Nuncio Multi‑Agent (Delegate Mode)

```python
from delegent import AgentBuilder, OllamaClient

agent = (
    AgentBuilder()
    .system("You are a Trading Agent.")
    .llm(OllamaClient(model="llama3.1"), format="json")
    .multi_agent(hub_uri="ws://localhost:8080/nuncio", token="dev-token", role="TRADING_AGENT")
    .delegate_capabilities(["RESEARCH_AGENT"])
    .build()
)

await agent.connect()
```

---

## Memory and Sessions

- Memory APIs support explicit session scoping:
  - `add(text, metadata=None, session_id=None)`
  - `retrieve(query, k=5, session_id=None)`
- The agent passes `context.session_id` explicitly into memory calls.
- Legacy store behavior without explicit `session_id` remains supported for compatibility.

For parallel calls on the same `Agent` instance:

- `Agent.run` is asyncio non-blocking and can run concurrently.
- Session-scoped memory isolation depends on passing distinct `session_id` values in context.
- Blocking tool handlers can still block the event loop; tools should be async-safe.

---

## LLM Clients

Built‑in:

- `OllamaClient`
- `OpenAIClient`
- `AnthropicClient`
- `GeminiClient`

All implement: `complete(prompt, **kwargs)`

---

## Public API

- `SimpleAgentBuilder` (simple runtime)
  - `.system(prompt)`
  - `.llm(client, **kwargs)`
  - `.tools([...])`
  - `.mcp(config)`
  - `.stream(handler)`
  - `.build() -> Agent`

- `AgentBuilder`
  - `.system(prompt)`
  - `.llm(client, **kwargs)`
  - `.tools([...])`
  - `.mcp(config)`
  - `.nuncio(config)` (legacy remote tool adapter)
  - `.remote_tool(name, role)` (legacy)
  - `.multi_agent(hub_uri, token, role, capabilities)`
  - `.delegate_capabilities(roles)`
  - `.stream(handler)`
  - `.stream_mode("raw" | "thinking" | "both")`
  - `.thinking_handler(handler)`
  - `.metrics(collector)`
  - `.logging(handler_or_config=None)`
  - `.tracing(handler_or_collector=None)`
  - `.allow_delegate(enabled)`
  - `.delegate_timeout(seconds)`
  - `.build() -> Agent`

- `Agent`
  - `.run(query, context=None)`
  - `.stream(query, context=None)` → async generator of events
  - `.connect()` / `.close()` for delegate mode
