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Deep Research Agent 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. Deep Research Agent 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 Deep Research Agent, the system includes a Deep Research Agent style tool-calling agent that can dynamically instantiate/retrieve/refine resources and improve during execution.

At a high level, Deep Research Agent 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.

• 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.

• 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.

DeepResearchAgent/
  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.)

Prerequisites:

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

Example:

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

Override model/workdir:

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