diff --git a/examples/input_examples/search_reactome_demo.jsonl b/examples/input_examples/search_reactome_demo.jsonl
new file mode 100644
index 00000000..57ebf31f
--- /dev/null
+++ b/examples/input_examples/search_reactome_demo.jsonl
@@ -0,0 +1,4 @@
+{"content": "R-HSA-69278", "type": "reactome_id"}
+{"content": "apoptosis", "type": "keyword"}
+{"content": "TP53", "type": "gene_symbol"}
+{"content": "MAPK1\nERK2\nPTEN", "type": "gene_list"}
diff --git a/examples/search/search_reactome/search_reactome_config.yaml b/examples/search/search_reactome/search_reactome_config.yaml
new file mode 100644
index 00000000..b05ae91d
--- /dev/null
+++ b/examples/search/search_reactome/search_reactome_config.yaml
@@ -0,0 +1,29 @@
+global_params:
+  working_dir: cache
+  kv_backend: rocksdb # key-value store backend, support: rocksdb, json_kv
+  graph_backend: kuzu # graph database backend, support: kuzu, networkx
+
+nodes:
+  - id: read_step
+    op_name: read
+    type: source
+    dependencies: []
+    params:
+      input_path:
+        - examples/input_examples/search_reactome_demo.jsonl # input file path, support json, jsonl, txt, pdf. See examples/input_examples for examples
+
+  - id: search_step
+    op_name: search
+    type: map_batch
+    dependencies:
+      - read_step # search_step depends on read_step
+    execution_params:
+      replicas: 1
+      batch_size: 10
+    save_output: true
+    params:
+      data_source: reactome # data source for searcher, support: uniprot, ncbi, rnacentral, reactome
+      reactome_params:
+        species: "Homo sapiens" # species name, support: Homo sapiens, Mus musculus, Rattus norvegicus, etc.
+        timeout: 30 # request timeout in seconds
+        max_retries: 3 # maximum number of retries for failed requests
diff --git a/graphgen/models/__init__.py b/graphgen/models/__init__.py
index 6b75587c..1cab54d7 100644
--- a/graphgen/models/__init__.py
+++ b/graphgen/models/__init__.py
@@ -43,6 +43,7 @@
     )
     from .rephraser import StyleControlledRephraser
     from .searcher.db.ncbi_searcher import NCBISearch
+    from .searcher.db.reactome_searcher import ReactomeSearcher
     from .searcher.db.rnacentral_searcher import RNACentralSearch
     from .searcher.db.uniprot_searcher import UniProtSearch
     from .searcher.kg.wiki_search import WikiSearch
@@ -97,6 +98,7 @@
     # Searcher
     "NCBISearch": ".searcher.db.ncbi_searcher",
     "RNACentralSearch": ".searcher.db.rnacentral_searcher",
+    "ReactomeSearcher": ".searcher.db.reactome_searcher",
     "UniProtSearch": ".searcher.db.uniprot_searcher",
     "WikiSearch": ".searcher.kg.wiki_search",
     "BingSearch": ".searcher.web.bing_search",
diff --git a/graphgen/models/searcher/db/reactome_searcher.py b/graphgen/models/searcher/db/reactome_searcher.py
new file mode 100644
index 00000000..71db324a
--- /dev/null
+++ b/graphgen/models/searcher/db/reactome_searcher.py
@@ -0,0 +1,302 @@
+import re
+import time
+from typing import Any, Dict, Optional
+
+import requests
+from requests.exceptions import RequestException
+
+from graphgen.utils import logger
+
+
+class ReactomeSearcher:
+    """
+    Reactome Pathway Search client for retrieving biological pathways by UniProt ID.
+
+    Supports:
+    1) Search pathways associated with a protein by UniProt accession.
+    2) Rank pathways by relevance (curated vs inferred, diagram availability).
+    3) Fetch detailed annotations for top-ranked pathways.
+
+    API Documentation: https://reactome.org/ContentService
+    """
+
+    CONTENT_URL = "https://reactome.org/ContentService"
+
+    # UniProt accession pattern (e.g., P04637, Q96KN2, O14763)
+    UNIPROT_PATTERN = re.compile(
+        r"^[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}$"
+    )
+
+    def __init__(
+        self,
+        species: str = "Homo sapiens",
+        timeout: int = 30,
+        top_n_details: int = 5,
+    ):
+        """
+        Initialize the Reactome Pathway Search client.
+
+        Args:
+            species: Species name (e.g., "Homo sapiens", "Mus musculus") or code ("HSA").
+            timeout: Request timeout in seconds.
+            top_n_details: Number of top pathways to fetch detailed annotations for.
+        """
+        self.timeout = timeout
+        self.species = self._normalize_species(species)
+        self.top_n_details = top_n_details
+        self.session = requests.Session()
+        self.session.headers.update({"Accept": "application/json"})
+
+    @staticmethod
+    def _normalize_species(species: str) -> str:
+        """Convert species code to full name."""
+        species_map = {
+            "HSA": "Homo sapiens",
+            "MMU": "Mus musculus",
+            "RNO": "Rattus norvegicus",
+            "GGA": "Gallus gallus",
+            "CEL": "Caenorhabditis elegans",
+            "DME": "Drosophila melanogaster",
+        }
+        return species_map.get(species.upper(), species)
+
+    @staticmethod
+    def _is_uniprot_accession(text: str) -> bool:
+        """Check if text is a valid UniProt accession number."""
+        if not text or not isinstance(text, str):
+            return False
+        return bool(ReactomeSearcher.UNIPROT_PATTERN.match(text.strip()))
+
+    def _calculate_relevance_score(self, pathway: Dict[str, Any]) -> int:
+        """
+        Calculate relevance score for pathway ranking.
+        Higher score indicates higher biological significance.
+
+        Scoring criteria:
+        - Manual curation (not inferred): +10
+        - Has pathway diagram: +5
+        - Disease-related: +3
+        - Specific biological terms in name: +2
+        """
+        score = 0
+
+        # Prioritize manually curated over computational predictions
+        if not pathway.get("isInferred", True):
+            score += 10
+
+        # Visual representations indicate well-characterized pathways
+        if pathway.get("hasDiagram", False):
+            score += 5
+
+        # Disease pathways often have higher clinical relevance
+        if pathway.get("isInDisease", False):
+            score += 3
+
+        # Prefer specific pathway types over generic classifications
+        name = pathway.get("displayName", "").lower()
+        specific_terms = [
+            "signaling",
+            "regulation",
+            "activation",
+            "pathway",
+            "synthesis",
+            "degradation",
+            "repair",
+            "apoptosis",
+        ]
+        if any(term in name for term in specific_terms):
+            score += 2
+
+        return score
+
+    def _fetch_pathway_details(self, pathway_stid: str) -> Optional[Dict[str, Any]]:
+        """
+        Fetch detailed information for a specific pathway.
+
+        Args:
+            pathway_stid: Reactome stable ID (e.g., "R-HSA-111288").
+
+        Returns:
+            Dictionary with detailed annotations or None if fetch fails.
+        """
+        url = f"{self.CONTENT_URL}/data/query/{pathway_stid}"
+
+        try:
+            response = self.session.get(url, timeout=self.timeout)
+            if response.status_code == 404:
+                logger.warning("Pathway %s not found in Reactome", pathway_stid)
+                return None
+
+            response.raise_for_status()
+            data = response.json()
+
+            # Extract key annotations
+            details = {
+                "schemaClass": data.get("schemaClass"),
+                "summation": data.get("summation", [None])[0]
+                if data.get("summation")
+                else None,
+                "compartment": [
+                    c.get("displayName") for c in data.get("compartment", [])
+                ],
+                "disease": [d.get("displayName") for d in data.get("disease", [])],
+                "sub_pathways": [
+                    {"stId": e.get("stId"), "name": e.get("displayName")}
+                    for e in data.get("hasEvent", [])[:5]  # First 5 sub-events
+                ],
+                "literature_references": [
+                    {
+                        "pubMedId": ref.get("pubMedIdentifier"),
+                        "title": ref.get("displayName"),
+                    }
+                    for ref in data.get("literatureReference", [])[:3]  # Top 3 refs
+                ],
+            }
+
+            return details
+
+        except RequestException as e:
+            logger.error("Failed to fetch details for pathway %s: %s", pathway_stid, e)
+            return None
+
+    def search_by_uniprot_id(self, accession: str) -> Optional[Dict]:
+        """
+        Search Reactome pathways by UniProt accession number.
+
+        Retrieves all pathways associated with the protein, ranks them by relevance,
+        and fetches detailed annotations for the top N pathways.
+
+        Args:
+            accession: UniProt accession number (e.g., "P04637" for TP53).
+
+        Returns:
+            Dictionary with pathway information or None if search fails:
+            {
+                "molecule_type": "protein",
+                "database": "Reactome",
+                "id": accession,
+                "content": {
+                    "total_found": int,
+                    "pathways": List[Dict]  # Top pathways with details
+                },
+                "url": str  # Link to Reactome search
+            }
+        """
+        if not self._is_uniprot_accession(accession):
+            logger.error("Invalid UniProt accession format: %s", accession)
+            return None
+
+        accession = accession.strip().upper()
+        logger.debug("Searching Reactome pathways for %s", accession)
+
+        # Step 1: Search for all pathways
+        url = f"{self.CONTENT_URL}/search/query"
+        params = {
+            "query": accession,
+            "species": self.species,
+            "rows": 100,
+            "type": "Pathway",
+        }
+
+        try:
+            response = self.session.get(url, params=params, timeout=self.timeout)
+            response.raise_for_status()
+            data = response.json()
+
+            hits = data.get("searchHits", [])
+            if not hits:
+                logger.info("No pathways found for %s in %s", accession, self.species)
+                return None
+
+            # Step 2: Extract basic pathway info
+            pathways = []
+            for hit in hits:
+                if hit.get("type") == "Pathway":
+                    pathways.append(
+                        {
+                            "stId": hit.get("stId"),
+                            "displayName": hit.get("displayName"),
+                            "dbId": hit.get("dbId"),
+                            "species": hit.get("species"),
+                            "isInDisease": hit.get("isInDisease", False),
+                            "isInferred": hit.get("isInferred", False),
+                            "hasDiagram": hit.get("hasDiagram", False),
+                            "url": f"https://reactome.org/PathwayBrowser/#{hit.get('stId')}",
+                        }
+                    )
+
+            logger.info("Found %d pathways for %s", len(pathways), accession)
+
+            # Step 3: Rank by relevance score
+            scored = [(self._calculate_relevance_score(pw), pw) for pw in pathways]
+            scored.sort(key=lambda x: x[0], reverse=True)
+            sorted_pathways = [pw for _, pw in scored]
+
+            # Step 4: Fetch details for top N pathways
+            top_pathways = []
+            for i, pw in enumerate(sorted_pathways[: self.top_n_details]):
+                details = self._fetch_pathway_details(pw["stId"])
+                if details:
+                    pw["details"] = details
+                    top_pathways.append(pw)
+
+                    # Small delay to avoid overwhelming API
+                    if i < self.top_n_details - 1:
+                        time.sleep(0.1)
+                else:
+                    # Include pathway even if details fetch fails
+                    pw["details"] = None
+                    top_pathways.append(pw)
+
+            # Construct result in standard format
+            result = {
+                "molecule_type": "protein",
+                "database": "Reactome",
+                "id": accession,
+                "content": {
+                    "total_found": len(pathways),
+                    "pathways": top_pathways,
+                },
+                "url": f"https://reactome.org/content/query?q={accession}",
+            }
+
+            return result
+
+        except RequestException as e:
+            logger.error("Failed to search Reactome for %s: %s", accession, e)
+            return None
+
+    def search(self, query: str, **kwargs) -> Optional[Dict]:
+        """
+        Search Reactome for pathway information.
+
+        Automatically detects query type (currently supports UniProt accession only).
+
+        Args:
+            query: Search query (UniProt accession number).
+            **kwargs: Additional arguments (unused).
+
+        Returns:
+            Dictionary with pathway information or None if not found.
+        """
+        if not query or not isinstance(query, str):
+            logger.error("Empty or invalid input for Reactome search")
+            return None
+
+        query = query.strip()
+        logger.debug("Reactome search query: %s", query)
+
+        result = None
+
+        if self._is_uniprot_accession(query):
+            logger.debug("Detected UniProt accession: %s", query)
+            result = self.search_by_uniprot_id(query)
+        else:
+            logger.warning("Query %s not recognized as UniProt accession", query)
+            # Try anyway as it might be a non-standard format
+            result = self.search_by_uniprot_id(query)
+
+        if result:
+            result["_search_query"] = query
+
+        return result
diff --git a/graphgen/operators/search/search_service.py b/graphgen/operators/search/search_service.py
index 1a599e25..5ad4deb2 100644
--- a/graphgen/operators/search/search_service.py
+++ b/graphgen/operators/search/search_service.py
@@ -58,6 +58,11 @@ def _init_searcher(self):
 
             params = self.kwargs.get("rnacentral_params", {})
             self.searcher = RNACentralSearch(**params)
+        elif self.data_source == "reactome":
+            from graphgen.models import ReactomeSearcher
+
+            params = self.kwargs.get("reactome_params", {})
+            self.searcher = ReactomeSearcher(**params)
         else:
             logger.error(f"Unknown data source: {self.data_source}")