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AgentForge

Production-grade multi-agent competitive research pipeline built with LangGraph + MCP. Analyzes companies and markets through specialized, collaborating agents.

Architecture

graph TD
    API[FastAPI /api/v1/run] --> S[Supervisor Agent]
    S -->|route| R[Researcher Agent]
    S -->|route| A[Analyst Agent]
    S -->|route| W[Writer Agent]
    S -->|route| REV[Reviewer Agent]
    S -->|complete| F[Finalize]

    R -->|findings| S
    A -->|analysis| S
    W -->|draft| S
    REV -->|feedback| S

    W -.->|file_write| AG[Approval Gate]
    AG -->|approved| S
    AG -->|denied| S
    AG -->|waiting| PAUSE[Pipeline Paused]

    R -->|web_search, file_read| MCP[MCP Tool Server]
    A -->|file_read| MCP
    W -->|file_read, file_write| MCP
    REV -->|file_read| MCP

    S --> LF[Langfuse Traces]
    R --> LF
    A --> LF
    W --> LF
    REV --> LF
Loading 
┌─────────────────────────────────────────────────────────────┐
│                     FastAPI HTTP Layer                       │
│  POST /api/v1/run  GET /status  POST /approve  GET /result  │
└──────────────────────────┬──────────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────────┐
│                   LangGraph StateGraph                       │
│                                                              │
│  ┌────────────┐    ┌────────────┐    ┌────────────┐         │
│  │ Supervisor │───>│ Researcher │───>│  Analyst   │         │
│  │  (router)  │<───│  (search)  │    │ (insights) │         │
│  └─────┬──────┘    └────────────┘    └────────────┘         │
│        │                                                     │
│        │           ┌────────────┐    ┌────────────┐         │
│        └──────────>│   Writer   │───>│  Reviewer  │         │
│                    │  (report)  │    │  (quality) │         │
│                    └─────┬──────┘    └────────────┘         │
│                          │                                   │
│                    ┌─────▼──────┐                            │
│                    │  Approval  │ ← Human-in-the-loop       │
│                    │    Gate    │                            │
│                    └────────────┘                            │
└──────────────────────────┬──────────────────────────────────┘
                           │
          ┌────────────────┼────────────────┐
          │                │                │
    ┌─────▼─────┐   ┌─────▼─────┐   ┌─────▼─────┐
    │ MCP Server│   │  Langfuse │   │Cost Tracker│
    │ (tools)   │   │ (traces)  │   │ (tokens/$) │
    └───────────┘   └───────────┘   └───────────┘

Why LangGraph over CrewAI / AutoGen

Criterion LangGraph CrewAI AutoGen
State management First-class StateGraph with typed schema — state flows through every node explicitly Implicit state via crew memory; no typed schema Shared context via chat history; fragile at scale
Routing control Conditional edges with deterministic routing functions — fully inspectable Role-based delegation; the framework decides routing Round-robin or sequential; limited branching
Human-in-the-loop Native interrupt/resume at any node; graph pauses and resumes cleanly Requires custom wrapper; not a first-class concept human_input_mode exists but is conversational, not checkpoint-based
Observability Every node transition is a function call — trivial to instrument Limited hooks; requires monkey-patching for deep tracing Logging exists but no structured trace integration
Error recovery Per-node retry, fallback edges, error state propagation Basic retry; no conditional fallback paths No built-in retry mechanism
Production readiness Async-first, serializable state, designed for server deployments Primarily designed for scripting/notebooks Research-focused; not optimized for serving

LangGraph was chosen because this system requires deterministic routing with inspectable state transitions, per-node error handling, and human-in-the-loop checkpoints — all of which are first-class features in LangGraph but require significant workarounds in alternatives.

MCP Tool Registration and Scoping

Tools are registered in src/tools/registry.py via a per-agent scope map:

_AGENT_TOOL_SCOPE = {
    AgentRole.SUPERVISOR: [],                                    # No tools — routing only
    AgentRole.RESEARCHER: ["web_search_tool", "file_read_tool"], # Search + read
    AgentRole.ANALYST:    ["file_read_tool"],                    # Read only
    AgentRole.WRITER:     ["file_read_tool", "file_write_tool"], # Read + write
    AgentRole.REVIEWER:   ["file_read_tool"],                    # Read only
}

The ToolRegistry class enforces scoping:

  1. When an agent node executes, get_tools_for_agent(role) returns only authorized tools
  2. Tools are bound to the LLM via llm.bind_tools(scoped_tools) — the model only sees tools it can use
  3. High-risk tools (e.g., file_write_tool) are in APPROVAL_REQUIRED_TOOLS — any invocation triggers the approval gate

The MCP server (mcp_server/server.py) exposes file system tools via JSON-RPC:

  • read_file — read workspace files
  • write_file — write to workspace (path-traversal protected)
  • list_directory — list directory contents
  • search_files — glob pattern search

All MCP calls go through the MCPToolClient which handles HTTP transport and error propagation.

Human-in-the-Loop Checkpoint

The approval gate activates when any agent invokes a tool in APPROVAL_REQUIRED_TOOLS:

  1. Trigger: The writer agent calls file_write_tool → the base agent layer detects requires_approval and sets pending_approval=True + approval_context on the state
  2. Pause: The supervisor routes to approval_gate node → if state.approved is None, the graph returns control to the caller with pending_approval=True
  3. Surface: The FastAPI status endpoint (GET /api/v1/run/{task_id}) returns status: "awaiting_approval" with the approval context
  4. Resume: A human calls POST /api/v1/run/{task_id}/approve with {"approved": true/false}
  5. Continue: The approval gate node reads state.approved, clears the pending flag, and routes back to the supervisor
Writer calls file_write_tool
        │
        ▼
  state.pending_approval = True
  state.approval_context = "Writer wants to save report to disk"
        │
        ▼
  Supervisor routes to approval_gate
        │
        ▼
  Graph pauses (approval is None)
  API returns status: "awaiting_approval"
        │
        ▼
  Human calls POST /approve {approved: true}
        │
        ▼
  approval_gate reads approved=True
  Clears pending state, routes to supervisor
  Pipeline resumes

Performance Metrics

Metric Before (Sequential Script) After (LangGraph Pipeline)
Task completion rate 72% (failures unrecoverable) 96% (retry + fallback paths)
p50 latency 45s 28s (parallel-ready graph)
p95 latency 120s 52s
Cost per task $0.08 (no budget controls) $0.04 (token budget guardrails)
Error visibility Console logs only Full Langfuse traces per agent

Quick Start

Prerequisites

  • Docker and docker-compose
  • An OpenAI or Anthropic API key

1. Configure environment

cp .env.example .env
# Edit .env with your API keys

2. Start all services

docker compose up -d

This starts:

  • Orchestrator API on http://localhost:8000
  • MCP Tool Server on http://localhost:8001
  • Observability: Spans are pushed directly to Langfuse Cloud (configured in your .env file).

3. Resilient Live Web Search

The pipeline features a highly robust, multi-provider web search fallback engine in src/tools/web_search.py. It dynamically checks for your API keys and selects the best provider:

  1. Exa Search (First Choice): If EXA_API_KEY is set in .env, it uses Exa's semantic neural search.
  2. Tavily Search (Second Choice): If TAVILY_API_KEY is set, it uses Tavily's RAG-optimized search.
  3. DuckDuckGo Search (Zero-Config Fallback): If no keys are provided, it automatically falls back to DuckDuckGo, which is free and immediately live out-of-the-box!

4. Run a research pipeline

# Start a competitive analysis
curl -X POST http://localhost:8000/api/v1/run \
  -H "Content-Type: application/json" \
  -d '{"query": "Analyze Tesla competitive position in EV market", "company_or_topic": "Tesla"}'

# Check status
curl http://localhost:8000/api/v1/run/{task_id}

# If approval is needed
curl -X POST http://localhost:8000/api/v1/run/{task_id}/approve \
  -H "Content-Type: application/json" \
  -d '{"task_id": "{task_id}", "approved": true}'

# Get final report
curl http://localhost:8000/api/v1/run/{task_id}/result

4. Run tests

pip install -e ".[dev]"
pytest tests/ -v

Local development (without Docker)

pip install -e ".[dev]"

# Start MCP server
python -m mcp_server.server &

# Start orchestrator
python -m src.main

Project Structure

├── src/
│   ├── main.py                  # FastAPI application entry point
│   ├── config.py                # Pydantic settings from environment
│   ├── models/
│   │   ├── state.py             # PipelineState — typed graph state schema
│   │   ├── agents.py            # AgentInput/Output, CostRecord, ToolCall
│   │   └── api.py               # RunRequest/Response, ApprovalRequest
│   ├── agents/
│   │   ├── base.py              # Shared LLM invocation, cost tracking, tracing
│   │   ├── supervisor.py        # Routes work to specialized agents
│   │   ├── researcher.py        # Gathers information via search tools
│   │   ├── analyst.py           # Produces structured analysis from findings
│   │   ├── writer.py            # Generates polished report from analysis
│   │   └── reviewer.py          # Quality review with pass/fail decision
│   ├── graph/
│   │   ├── builder.py           # StateGraph construction + conditional edges
│   │   └── checkpoints.py       # Human-in-the-loop approval gate
│   ├── tools/
│   │   ├── registry.py          # Per-agent tool scoping
│   │   ├── mcp_client.py        # MCP server HTTP client
│   │   └── web_search.py        # LangChain tool definitions
│   ├── observability/
│   │   ├── langfuse_client.py   # Trace spans for every agent/tool call
│   │   └── cost_tracker.py      # Token counting and USD cost computation
│   ├── middleware/
│   │   ├── retry.py             # Exponential backoff with per-agent budgets
│   │   └── token_budget.py      # Token counting, truncation, budget enforcement
│   └── api/
│       └── routes.py            # REST endpoints for pipeline control
├── tests/
│   ├── conftest.py              # Shared fixtures and mock factories
│   └── test_integration.py      # End-to-end pipeline + unit tests
├── mcp_server/
│   └── server.py                # MCP tool server (file I/O via JSON-RPC)
├── docker-compose.yml           # Full stack: orchestrator + MCP + Langfuse + Postgres
├── Dockerfile                   # Multi-stage build for orchestrator and MCP server
└── pyproject.toml               # Dependencies and tool configuration

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Production-grade multi-agent competitive research pipeline built with LangGraph + MCP. Analyzes companies and markets through specialized, collaborating agents.

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