# DeepResearchAgent

**Repository Path**: mirrors_trending/DeepResearchAgent

## Basic Information

- **Project Name**: DeepResearchAgent
- **Description**: DeepResearchAgent is a hierarchical multi-agent system designed not only for deep research tasks but also for general-purpose task solving. The framework leverages a top-level planning agent to coordinate multiple specialized lower-level agents, enabling automated task decomposition and efficient execution across diverse and complex domains.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2025-06-07
- **Last Updated**: 2026-03-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Autogenesis

English | [中文说明](README_zh.md)

Autogenesis is a self-evolution protocol and runtime for LLM-based agent systems.

Recent agent protocols often under-specify cross-entity **lifecycle/context management**, **version tracking**, and **safe evolution update interfaces**, which encourages monolithic compositions and brittle glue code. Autogenesis addresses this by decoupling **what evolves** from **how evolution occurs**:

- **RSPL (Resource Substrate Protocol Layer)**: models *prompts, agents, tools, environments, and memory* as protocol-registered resources with explicit **state**, **lifecycle**, and **versioned** interfaces.
- **SEPL (Self Evolution Protocol Layer)**: specifies a closed-loop operator interface to **propose**, **assess**, and **commit** improvements with auditable lineage and **rollback**.

Built on Autogenesis, the system includes an **Autogenesis-Agent** style tool-calling agent that can dynamically instantiate/retrieve/refine resources and improve during execution.

## Architecture

![Autogenesis architecture](docs/architecture.png)

## Self-evolution at a glance

At a high level, Autogenesis supports an iterative loop:

- **Act**: an agent produces actions/outputs using an LLM and the available tools/environments.
- **Observe**: capture outcomes, traces, intermediate reasoning, and environment feedback.
- **Optimize**: update prompts/solutions/variables using an optimizer (e.g., reflection or RL-style methods).
- **Remember**: persist summaries/insights/records to memory for later steps and sessions.

## Core building blocks

- **Agents (`src/agent/`)**: runtime logic that decides *what to do next* (planning, tool-calling, domain agents, etc.).
- **Tools (`src/tool/`)**: callable capabilities exposed to agents (workflow tools + default tools).
- **Environments (`src/environment/`)**: stateful interfaces that tools/agents can interact with (filesystem, trading backtest envs, browser/mobile envs, etc.).
- **Memory (`src/memory/`)**: session/event memory systems for summarization, insights, and long-term state.
- **Optimizers (`src/optimizer/`)**: self-improvement algorithms that turn feedback into updated prompts/solutions/variables (reflection, GRPO, Reinforce++, etc.).
- **Tracing & versioning (`src/tracer/`, `src/version/`)**: record trajectories and manage iterative artifacts across runs.
- **Config system (`configs/`, `src/config/`)**: MMEngine-style configs to compose agents/tools/envs/memory/models consistently.

## Design goals

- **Composable**: add/replace agents, tools, environments, memory systems, and optimizers without rewriting the whole stack.
- **Inspectable**: structured traces and memory events make it easier to analyze failures and improvement steps.
- **Evolvable**: explicit optimizers + persistent memory enable iterative refinement rather than one-shot inference.

## Repository layout

```
Autogenesis/
  configs/                 # config composition (agents/tools/envs/memory/models)
  src/
    agent/                 # agents
    environment/           # environments
    tool/                  # tools
    memory/                # memory systems
    optimizer/             # self-evolution optimizers
    model/                 # model manager + provider backends
    prompt/                # prompt templates / prompt manager
    tracer/                # tracing
    version/               # versioning
  libs/                    # vendored libraries
  workdir/                 # runtime artifacts (logs, traces, results, etc.)
```

## Empirical studies

See empirical results and benchmark protocols in `docs/empirical_studies.md`.

## Optional: run a Tool-Calling Agent

Prerequisites:
- Install dependencies in your environment
- Copy `.env.template` to `.env` and set a model API key (e.g. `OPENROUTER_API_KEY=...`)

Example:

```bash
python examples/run_tool_calling_agent.py --config configs/tool_calling_agent.py
```

Override model/workdir:

```bash
python examples/run_tool_calling_agent.py \
  --config configs/tool_calling_agent.py \
  --cfg-options model_name=openrouter/gpt-4o workdir=workdir/demo tag=demo
```